From f9b8dbf9db6c5b6cdd4ec9b5e35fbc1d9939a45e Mon Sep 17 00:00:00 2001 From: Guo Date: Tue, 4 Jun 2019 16:34:28 +0800 Subject: [PATCH 001/193] Correct the wrong iterative DFS implementation (#867) * Update DFS.py * Update DFS.py --- graphs/DFS.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/graphs/DFS.py b/graphs/DFS.py index d3c34fabb7b3..c9843ca25382 100644 --- a/graphs/DFS.py +++ b/graphs/DFS.py @@ -18,10 +18,15 @@ def dfs(graph, start): explored, stack = set(), [start] explored.add(start) while stack: - v = stack.pop() # the only difference from BFS is to pop last element here instead of first one + v = stack.pop() # one difference from BFS is to pop last element here instead of first one + + if v in explored: + continue + + explored.add(v) + for w in graph[v]: if w not in explored: - explored.add(w) stack.append(w) return explored From 0f229e0870050478a6b3fa1ecf60d73b694b98da Mon Sep 17 00:00:00 2001 From: cclauss Date: Wed, 5 Jun 2019 03:09:04 +0200 Subject: [PATCH 002/193] Atbash.py: Both raw_input() and unichr() were removed in Python 3 (#855) * Atbash.py: Both raw_input() and unichr() were removed in Python 3 @sateslayer and @AnupKumarPanwar your reviews please. * Remove any leading / trailing whitespace from user input --- ciphers/Atbash.py | 25 ++++++++++++++++--------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/ciphers/Atbash.py b/ciphers/Atbash.py index 4920e3049756..162614c727ee 100644 --- a/ciphers/Atbash.py +++ b/ciphers/Atbash.py @@ -1,14 +1,21 @@ +try: # Python 2 + raw_input + unichr +except NameError: # Python 3 + raw_input = input + unichr = chr + + def Atbash(): - inp=raw_input("Enter the sentence to be encrypted ") output="" - for i in inp: - extract=ord(i) - if extract>=65 and extract<=90: - output+=(unichr(155-extract)) - elif extract>=97 and extract<=122: - output+=(unichr(219-extract)) + for i in raw_input("Enter the sentence to be encrypted ").strip(): + extract = ord(i) + if 65 <= extract <= 90: + output += unichr(155-extract) + elif 97 <= extract <= 122: + output += unichr(219-extract) else: output+=i - print (output) + print(output) -Atbash() ; +Atbash() From ebe227c38646fc6720124eae5aedc68b61a4b664 Mon Sep 17 00:00:00 2001 From: Daniel Xu Date: Tue, 4 Jun 2019 21:37:05 -0400 Subject: [PATCH 003/193] Removed Graphs and move prim.py to graphs (#872) * Move prim.py from Graphs to graphs * Removed prim.py from Graphs * Update prim.py --- {Graphs => graphs}/prim.py | 3 --- 1 file changed, 3 deletions(-) rename {Graphs => graphs}/prim.py (99%) diff --git a/Graphs/prim.py b/graphs/prim.py similarity index 99% rename from Graphs/prim.py rename to graphs/prim.py index c9f91d4b0700..f7e08278966d 100644 --- a/Graphs/prim.py +++ b/graphs/prim.py @@ -28,7 +28,6 @@ def __init__(self, id): """ Arguments: id - input an id to identify the vertex - Attributes: neighbors - a list of the vertices it is linked to edges - a dict to store the edges's weight @@ -59,9 +58,7 @@ def addEdge(self, vertex, weight): def prim(graph, root): """ Prim's Algorithm. - Return a list with the edges of a Minimum Spanning Tree - prim(graph, graph[0]) """ A = [] From 6e894ba3e8bab4e9e0515b91e685904ec828cf43 Mon Sep 17 00:00:00 2001 From: CharlesRitter Date: Fri, 7 Jun 2019 11:38:43 -0400 Subject: [PATCH 004/193] Odd-Even Transposition Sort (#769) * -Added a single-threaded implementation of odd-even transposition sort. This is a modified bubble sort meant to work with multiple processors. Since this is running on a single thread, it has the same running time as bubble sort. * -Added a parallel implementation of Odd-Even Transposition sort This implementation uses multiprocessing to perform the swaps at each step of the algorithm simultaneously. --- sorts/Odd-Even_transposition_parallel.py | 127 ++++++++++++++++++ .../Odd-Even_transposition_single-threaded.py | 32 +++++ 2 files changed, 159 insertions(+) create mode 100644 sorts/Odd-Even_transposition_parallel.py create mode 100644 sorts/Odd-Even_transposition_single-threaded.py diff --git a/sorts/Odd-Even_transposition_parallel.py b/sorts/Odd-Even_transposition_parallel.py new file mode 100644 index 000000000000..d7f983fc0469 --- /dev/null +++ b/sorts/Odd-Even_transposition_parallel.py @@ -0,0 +1,127 @@ +""" +This is an implementation of odd-even transposition sort. + +It works by performing a series of parallel swaps between odd and even pairs of +variables in the list. + +This implementation represents each variable in the list with a process and +each process communicates with its neighboring processes in the list to perform +comparisons. +They are synchronized with locks and message passing but other forms of +synchronization could be used. +""" +from multiprocessing import Process, Pipe, Lock + +#lock used to ensure that two processes do not access a pipe at the same time +processLock = Lock() + +""" +The function run by the processes that sorts the list + +position = the position in the list the prcoess represents, used to know which + neighbor we pass our value to +value = the initial value at list[position] +LSend, RSend = the pipes we use to send to our left and right neighbors +LRcv, RRcv = the pipes we use to receive from our left and right neighbors +resultPipe = the pipe used to send results back to main +""" +def oeProcess(position, value, LSend, RSend, LRcv, RRcv, resultPipe): + global processLock + + #we perform n swaps since after n swaps we know we are sorted + #we *could* stop early if we are sorted already, but it takes as long to + #find out we are sorted as it does to sort the list with this algorithm + for i in range(0, 10): + + if( (i + position) % 2 == 0 and RSend != None): + #send your value to your right neighbor + processLock.acquire() + RSend[1].send(value) + processLock.release() + + #receive your right neighbor's value + processLock.acquire() + temp = RRcv[0].recv() + processLock.release() + + #take the lower value since you are on the left + value = min(value, temp) + elif( (i + position) % 2 != 0 and LSend != None): + #send your value to your left neighbor + processLock.acquire() + LSend[1].send(value) + processLock.release() + + #receive your left neighbor's value + processLock.acquire() + temp = LRcv[0].recv() + processLock.release() + + #take the higher value since you are on the right + value = max(value, temp) + #after all swaps are performed, send the values back to main + resultPipe[1].send(value) + +""" +the function which creates the processes that perform the parallel swaps + +arr = the list to be sorted +""" +def OddEvenTransposition(arr): + + processArray = [] + tempRrcv = None + tempLrcv = None + + resultPipe = [] + + #initialize the list of pipes where the values will be retrieved + for a in arr: + resultPipe.append(Pipe()) + + #creates the processes + #the first and last process only have one neighbor so they are made outside + #of the loop + tempRs = Pipe() + tempRr = Pipe() + processArray.append(Process(target = oeProcess, args = (0, arr[0], None, tempRs, None, tempRr, resultPipe[0]))) + tempLr = tempRs + tempLs = tempRr + + for i in range(1, len(arr) - 1): + tempRs = Pipe() + tempRr = Pipe() + processArray.append(Process(target = oeProcess, args = (i, arr[i], tempLs, tempRs, tempLr, tempRr, resultPipe[i]))) + tempLr = tempRs + tempLs = tempRr + + processArray.append(Process(target = oeProcess, args = (len(arr) - 1, arr[len(arr) - 1], tempLs, None, tempLr, None, resultPipe[len(arr) - 1]))) + + #start the processes + for p in processArray: + p.start() + + #wait for the processes to end and write their values to the list + for p in range(0, len(resultPipe)): + arr[p] = resultPipe[p][0].recv() + processArray[p].join() + + return(arr) + + +#creates a reverse sorted list and sorts it +def main(): + arr = [] + + for i in range(10, 0, -1): + arr.append(i) + print("Initial List") + print(*arr) + + list = OddEvenTransposition(arr) + + print("Sorted List\n") + print(*arr) + +if __name__ == "__main__": + main() diff --git a/sorts/Odd-Even_transposition_single-threaded.py b/sorts/Odd-Even_transposition_single-threaded.py new file mode 100644 index 000000000000..ec5f3cf14e55 --- /dev/null +++ b/sorts/Odd-Even_transposition_single-threaded.py @@ -0,0 +1,32 @@ +""" +This is a non-parallelized implementation of odd-even transpostiion sort. + +Normally the swaps in each set happen simultaneously, without that the algorithm +is no better than bubble sort. +""" + +def OddEvenTransposition(arr): + for i in range(0, len(arr)): + for i in range(i % 2, len(arr) - 1, 2): + if arr[i + 1] < arr[i]: + arr[i], arr[i + 1] = arr[i + 1], arr[i] + print(*arr) + + return arr + +#creates a list and sorts it +def main(): + list = [] + + for i in range(10, 0, -1): + list.append(i) + print("Initial List") + print(*list) + + list = OddEvenTransposition(list) + + print("Sorted List\n") + print(*list) + +if __name__ == "__main__": + main() From 9b945cb2b4ad77418e6576b7960fda8228214de9 Mon Sep 17 00:00:00 2001 From: StephenGemin <45926479+StephenGemin@users.noreply.github.com> Date: Sat, 8 Jun 2019 08:25:34 -0400 Subject: [PATCH 005/193] Iterative fibonacci with unittests from slash (#882) * iterative and formula fibonacci methods Added two ways to calculate the fibonacci sequence: (1) iterative (2) formula. I've also added a timer decorator so someone can see the difference in computation time between these two methods. Added two unittests using the slash framework. * Update test_fibonacci.py * remove inline comments per Contributing Guidelines * Update sol5.py * Create placeholder.py * Update and rename maths/test_fibonacci.py to maths/tests/test_fibonacci.py * Delete placeholder.py * Create __init__.py * Update test_fibonacci.py * Rename Maths/lucasSeries.py to maths/lucasSeries.py * Update and rename Project Euler/Problem 01/sol5.py to project_euler/problem_01/sol6.py --- Project Euler/Problem 01/sol5.py | 8 --- maths/fibonacci.py | 120 +++++++++++++++++++++++++++++++ {Maths => maths}/lucasSeries.py | 0 maths/tests/__init__.py | 1 + maths/tests/test_fibonacci.py | 34 +++++++++ project_euler/problem_01/sol6.py | 9 +++ 6 files changed, 164 insertions(+), 8 deletions(-) delete mode 100644 Project Euler/Problem 01/sol5.py create mode 100644 maths/fibonacci.py rename {Maths => maths}/lucasSeries.py (100%) create mode 100644 maths/tests/__init__.py create mode 100644 maths/tests/test_fibonacci.py create mode 100644 project_euler/problem_01/sol6.py diff --git a/Project Euler/Problem 01/sol5.py b/Project Euler/Problem 01/sol5.py deleted file mode 100644 index 2cb67d2524e2..000000000000 --- a/Project Euler/Problem 01/sol5.py +++ /dev/null @@ -1,8 +0,0 @@ -a=3 -result=0 -while a=<1000: - if(a%3==0 and a%5==0): - result+=a - elif(a%15==0): - result-=a -print(result) diff --git a/maths/fibonacci.py b/maths/fibonacci.py new file mode 100644 index 000000000000..0a0611f21379 --- /dev/null +++ b/maths/fibonacci.py @@ -0,0 +1,120 @@ +# fibonacci.py +""" +1. Calculates the iterative fibonacci sequence + +2. Calculates the fibonacci sequence with a formula + an = [ Phin - (phi)n ]/Sqrt[5] + reference-->Su, Francis E., et al. "Fibonacci Number Formula." Math Fun Facts. +""" +import math +import functools +import time +from decimal import getcontext, Decimal + +getcontext().prec = 100 + + +def timer_decorator(func): + @functools.wraps(func) + def timer_wrapper(*args, **kwargs): + start = time.time() + func(*args, **kwargs) + end = time.time() + if int(end - start) > 0: + print(f'Run time for {func.__name__}: {(end - start):0.2f}s') + else: + print(f'Run time for {func.__name__}: {(end - start)*1000:0.2f}ms') + return func(*args, **kwargs) + return timer_wrapper + + +# define Python user-defined exceptions +class Error(Exception): + """Base class for other exceptions""" + + +class ValueTooLargeError(Error): + """Raised when the input value is too large""" + + +class ValueTooSmallError(Error): + """Raised when the input value is not greater than one""" + + +class ValueLessThanZero(Error): + """Raised when the input value is less than zero""" + + +def _check_number_input(n, min_thresh, max_thresh=None): + """ + :param n: single integer + :type n: int + :param min_thresh: min threshold, single integer + :type min_thresh: int + :param max_thresh: max threshold, single integer + :type max_thresh: int + :return: boolean + """ + try: + if n >= min_thresh and max_thresh is None: + return True + elif min_thresh <= n <= max_thresh: + return True + elif n < 0: + raise ValueLessThanZero + elif n < min_thresh: + raise ValueTooSmallError + elif n > max_thresh: + raise ValueTooLargeError + except ValueLessThanZero: + print("Incorrect Input: number must not be less than 0") + except ValueTooSmallError: + print(f'Incorrect Input: input number must be > {min_thresh} for the recursive calculation') + except ValueTooLargeError: + print(f'Incorrect Input: input number must be < {max_thresh} for the recursive calculation') + return False + + +@timer_decorator +def fib_iterative(n): + """ + :param n: calculate Fibonacci to the nth integer + :type n:int + :return: Fibonacci sequence as a list + """ + n = int(n) + if _check_number_input(n, 2): + seq_out = [0, 1] + a, b = 0, 1 + for _ in range(n-len(seq_out)): + a, b = b, a+b + seq_out.append(b) + return seq_out + + +@timer_decorator +def fib_formula(n): + """ + :param n: calculate Fibonacci to the nth integer + :type n:int + :return: Fibonacci sequence as a list + """ + seq_out = [0, 1] + n = int(n) + if _check_number_input(n, 2, 1000000): + sqrt = Decimal(math.sqrt(5)) + phi_1 = Decimal(1 + sqrt) / Decimal(2) + phi_2 = Decimal(1 - sqrt) / Decimal(2) + for i in range(2, n): + temp_out = ((phi_1**Decimal(i)) - (phi_2**Decimal(i))) * (Decimal(sqrt) ** Decimal(-1)) + seq_out.append(int(temp_out)) + return seq_out + + +if __name__ == '__main__': + num = 20 + # print(f'{fib_recursive(num)}\n') + # print(f'{fib_iterative(num)}\n') + # print(f'{fib_formula(num)}\n') + fib_iterative(num) + fib_formula(num) diff --git a/Maths/lucasSeries.py b/maths/lucasSeries.py similarity index 100% rename from Maths/lucasSeries.py rename to maths/lucasSeries.py diff --git a/maths/tests/__init__.py b/maths/tests/__init__.py new file mode 100644 index 000000000000..2c4a6048556c --- /dev/null +++ b/maths/tests/__init__.py @@ -0,0 +1 @@ +from .. import fibonacci diff --git a/maths/tests/test_fibonacci.py b/maths/tests/test_fibonacci.py new file mode 100644 index 000000000000..7d36c755e346 --- /dev/null +++ b/maths/tests/test_fibonacci.py @@ -0,0 +1,34 @@ +""" +To run with slash: +1. run pip install slash (may need to install C++ builds from Visual Studio website) +2. In the command prompt navigate to your project folder +3. then type--> slash run -vv -k tags:fibonacci .. + -vv indicates the level of verbosity (how much stuff you want the test to spit out after running) + -k is a way to select the tests you want to run. This becomes much more important in large scale projects. +""" + +import slash +from .. import fibonacci + +default_fib = [0, 1, 1, 2, 3, 5, 8] + + +@slash.tag('fibonacci') +@slash.parametrize(('n', 'seq'), [(2, [0, 1]), (3, [0, 1, 1]), (9, [0, 1, 1, 2, 3, 5, 8, 13, 21])]) +def test_different_sequence_lengths(n, seq): + """Test output of varying fibonacci sequence lengths""" + iterative = fibonacci.fib_iterative(n) + formula = fibonacci.fib_formula(n) + assert iterative == seq + assert formula == seq + + +@slash.tag('fibonacci') +@slash.parametrize('n', [7.3, 7.8, 7.0]) +def test_float_input_iterative(n): + """Test when user enters a float value""" + iterative = fibonacci.fib_iterative(n) + formula = fibonacci.fib_formula(n) + assert iterative == default_fib + assert formula == default_fib + diff --git a/project_euler/problem_01/sol6.py b/project_euler/problem_01/sol6.py new file mode 100644 index 000000000000..54c3073f3897 --- /dev/null +++ b/project_euler/problem_01/sol6.py @@ -0,0 +1,9 @@ +a = 3 +result = 0 +while a < 1000: + if(a % 3 == 0 or a % 5 == 0): + result += a + elif(a % 15 == 0): + result -= a + a += 1 +print(result) From 066f37402d87e31b35a7d9439b394445a1404461 Mon Sep 17 00:00:00 2001 From: guij15 <43374716+guij15@users.noreply.github.com> Date: Mon, 10 Jun 2019 14:46:36 +0800 Subject: [PATCH 006/193] Update newton_raphson.py (#891) --- maths/newton_raphson.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/maths/newton_raphson.py b/maths/newton_raphson.py index c08bcedc9a4d..cc6c92734fd4 100644 --- a/maths/newton_raphson.py +++ b/maths/newton_raphson.py @@ -33,7 +33,7 @@ def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6,logsteps=Fal if error < maxerror: break else: - raise ValueError("Itheration limit reached, no converging solution found") + raise ValueError("Iteration limit reached, no converging solution found") if logsteps: #If logstep is true, then log intermediate steps return a, error, steps @@ -47,4 +47,4 @@ def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6,logsteps=Fal plt.xlabel("step") plt.ylabel("error") plt.show() - print("solution = {%f}, error = {%f}" % (solution, error)) \ No newline at end of file + print("solution = {%f}, error = {%f}" % (solution, error)) From 05e5172093dbd0633ce83044603073dd2be675c4 Mon Sep 17 00:00:00 2001 From: Hector S Date: Tue, 11 Jun 2019 07:24:53 -0400 Subject: [PATCH 007/193] .vs/ directory, matrix_multiplication_addition file and binary tree directory (#894) * Added print function into matrix_multiplication_addition.py and removed blank space in data_structures/binary tree directory * Removed .vs/ folder per #893 * Rename matrix_multiplication_addition.py to matrix_operation.py --- .vs/Python/v15/.suo | Bin 16896 -> 0 bytes .vs/slnx.sqlite | Bin 176128 -> 0 bytes .../{binary tree => binary_tree}/AVL_tree.py | 0 .../binary_search_tree.py | 0 .../fenwick_tree.py | 0 .../lazy_segment_tree.py | 0 .../segment_tree.py | 0 .../{binary tree => binary_tree}/treap.py | 0 ...ication_addition.py => matrix_operation.py} | 15 ++++++++------- 9 files changed, 8 insertions(+), 7 deletions(-) delete mode 100644 .vs/Python/v15/.suo delete mode 100644 .vs/slnx.sqlite rename data_structures/{binary tree => binary_tree}/AVL_tree.py (100%) rename data_structures/{binary tree => binary_tree}/binary_search_tree.py (100%) rename data_structures/{binary tree => binary_tree}/fenwick_tree.py (100%) rename data_structures/{binary tree => binary_tree}/lazy_segment_tree.py (100%) rename data_structures/{binary tree => binary_tree}/segment_tree.py (100%) rename data_structures/{binary tree => binary_tree}/treap.py (100%) rename matrix/{matrix_multiplication_addition.py => matrix_operation.py} (80%) diff --git a/.vs/Python/v15/.suo b/.vs/Python/v15/.suo deleted file mode 100644 index 0e3f4807567d1e684e2302bf9cfb6cc11e1c136d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 16896 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z+U{0KVWc{^kgu~C`uBL@7w*VtX+6j&jkWG(BC__?auEw z^D*6lMwpC-NnwhK{%C51Ne0fg04AwtTPj^3IrD~chK3CDwTLfK8wypD!f~!+N+^c`*EwVwZ+uHyM~pCu*uCOs!9>6NvUeq=Tear0BYJjq2F1_B zCi;O$lN#N|LIEXthF;Deg<}?Tawl7lRd|O#A6rD z@J^nNUFdf*8yz&VdpVI2J)8P^#Am`r%Qw^z8mqDBh#pyXzj!t_&{_xRKCUr?NNon2 p4Z&tvDVtGOyR5Mjl3%qMY}OLRY6dpb#R~drtPqyW!K|_UzW}A;oi+df diff --git a/data_structures/binary tree/AVL_tree.py b/data_structures/binary_tree/AVL_tree.py similarity index 100% rename from data_structures/binary tree/AVL_tree.py rename to data_structures/binary_tree/AVL_tree.py diff --git a/data_structures/binary tree/binary_search_tree.py b/data_structures/binary_tree/binary_search_tree.py similarity index 100% rename from data_structures/binary tree/binary_search_tree.py rename to data_structures/binary_tree/binary_search_tree.py diff --git a/data_structures/binary tree/fenwick_tree.py b/data_structures/binary_tree/fenwick_tree.py similarity index 100% rename from data_structures/binary tree/fenwick_tree.py rename to data_structures/binary_tree/fenwick_tree.py diff --git a/data_structures/binary tree/lazy_segment_tree.py b/data_structures/binary_tree/lazy_segment_tree.py similarity index 100% rename from data_structures/binary tree/lazy_segment_tree.py rename to data_structures/binary_tree/lazy_segment_tree.py diff --git a/data_structures/binary tree/segment_tree.py b/data_structures/binary_tree/segment_tree.py similarity index 100% rename from data_structures/binary tree/segment_tree.py rename to data_structures/binary_tree/segment_tree.py diff --git a/data_structures/binary tree/treap.py b/data_structures/binary_tree/treap.py similarity index 100% rename from data_structures/binary tree/treap.py rename to data_structures/binary_tree/treap.py diff --git a/matrix/matrix_multiplication_addition.py b/matrix/matrix_operation.py similarity index 80% rename from matrix/matrix_multiplication_addition.py rename to matrix/matrix_operation.py index dd50db729e43..dd7c01582681 100644 --- a/matrix/matrix_multiplication_addition.py +++ b/matrix/matrix_operation.py @@ -1,3 +1,5 @@ +from __future__ import print_function + def add(matrix_a, matrix_b): rows = len(matrix_a) columns = len(matrix_a[0]) @@ -63,13 +65,12 @@ def main(): matrix_b = [[3, 4], [7, 4]] matrix_c = [[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]] matrix_d = [[3, 0, 2], [2, 0, -2], [0, 1, 1]] - - print(add(matrix_a, matrix_b)) - print(multiply(matrix_a, matrix_b)) - print(identity(5)) - print(minor(matrix_c , 1 , 2)) - print(determinant(matrix_b)) - print(inverse(matrix_d)) + print('Add Operation, %s + %s = %s \n' %(matrix_a, matrix_b, (add(matrix_a, matrix_b)))) + print('Multiply Operation, %s * %s = %s \n' %(matrix_a, matrix_b, multiply(matrix_a, matrix_b))) + print('Identity: %s \n' %identity(5)) + print('Minor of %s = %s \n' %(matrix_c, minor(matrix_c , 1 , 2))) + print('Determinant of %s = %s \n' %(matrix_b, determinant(matrix_b))) + print('Inverse of %s = %s\n'%(matrix_d, inverse(matrix_d))) if __name__ == '__main__': main() From af1925bcd9926c36a418acb5f5269455cc6c6f34 Mon Sep 17 00:00:00 2001 From: StephenGemin <45926479+StephenGemin@users.noreply.github.com> Date: Wed, 12 Jun 2019 10:54:30 -0400 Subject: [PATCH 008/193] Remove empty folder in analysis/compression_analysis (#897) * Add compression_analysis removing analysis/compression_analysis to just compression_analysis * Delete PSNR-example-base.png * Delete PSNR-example-comp-10.jpg * Delete compressed_image.png * Delete example_image.jpg * Delete example_wikipedia_image.jpg * Delete original_image.png * Delete psnr.py --- .../PSNR-example-base.png | Bin .../PSNR-example-comp-10.jpg | Bin .../compressed_image.png | Bin .../example_image.jpg | Bin .../example_wikipedia_image.jpg | Bin .../original_image.png | Bin .../psnr.py | 0 7 files changed, 0 insertions(+), 0 deletions(-) rename {analysis/compression_analysis => compression_analysis}/PSNR-example-base.png (100%) rename {analysis/compression_analysis => compression_analysis}/PSNR-example-comp-10.jpg (100%) rename {analysis/compression_analysis => compression_analysis}/compressed_image.png (100%) rename {analysis/compression_analysis => compression_analysis}/example_image.jpg (100%) rename {analysis/compression_analysis => compression_analysis}/example_wikipedia_image.jpg (100%) rename {analysis/compression_analysis => compression_analysis}/original_image.png (100%) rename {analysis/compression_analysis => compression_analysis}/psnr.py (100%) diff --git a/analysis/compression_analysis/PSNR-example-base.png b/compression_analysis/PSNR-example-base.png similarity index 100% rename from analysis/compression_analysis/PSNR-example-base.png rename to compression_analysis/PSNR-example-base.png diff --git a/analysis/compression_analysis/PSNR-example-comp-10.jpg b/compression_analysis/PSNR-example-comp-10.jpg similarity index 100% rename from analysis/compression_analysis/PSNR-example-comp-10.jpg rename to compression_analysis/PSNR-example-comp-10.jpg diff --git a/analysis/compression_analysis/compressed_image.png b/compression_analysis/compressed_image.png similarity index 100% rename from analysis/compression_analysis/compressed_image.png rename to compression_analysis/compressed_image.png diff --git a/analysis/compression_analysis/example_image.jpg b/compression_analysis/example_image.jpg similarity index 100% rename from analysis/compression_analysis/example_image.jpg rename to compression_analysis/example_image.jpg diff --git a/analysis/compression_analysis/example_wikipedia_image.jpg b/compression_analysis/example_wikipedia_image.jpg similarity index 100% rename from analysis/compression_analysis/example_wikipedia_image.jpg rename to compression_analysis/example_wikipedia_image.jpg diff --git a/analysis/compression_analysis/original_image.png b/compression_analysis/original_image.png similarity index 100% rename from analysis/compression_analysis/original_image.png rename to compression_analysis/original_image.png diff --git a/analysis/compression_analysis/psnr.py b/compression_analysis/psnr.py similarity index 100% rename from analysis/compression_analysis/psnr.py rename to compression_analysis/psnr.py From 1b3affc2eddc8288f8bb4a73167d99b3eb2c4fc8 Mon Sep 17 00:00:00 2001 From: Erfan Alimohammadi Date: Sun, 16 Jun 2019 01:07:23 +0430 Subject: [PATCH 009/193] fix typo (#902) --- machine_learning/linear_regression.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/machine_learning/linear_regression.py b/machine_learning/linear_regression.py index 8c23f1f77908..03f16629e451 100644 --- a/machine_learning/linear_regression.py +++ b/machine_learning/linear_regression.py @@ -1,6 +1,6 @@ """ Linear regression is the most basic type of regression commonly used for -predictive analysis. The idea is preety simple, we have a dataset and we have +predictive analysis. The idea is pretty simple, we have a dataset and we have a feature's associated with it. The Features should be choose very cautiously as they determine, how much our model will be able to make future predictions. We try to set these Feature weights, over many iterations, so that they best From 6e2fb22f5e9a821f226d87298b08d8da4b3b3efd Mon Sep 17 00:00:00 2001 From: archithadge <45902236+archithadge@users.noreply.github.com> Date: Sun, 16 Jun 2019 18:49:20 +0530 Subject: [PATCH 010/193] Problem 234 project Euler (#883) * Problem 234 project Euler * Update project_euler/problem_234 Co-Authored-By: Emmanuel Arias * Update project_euler/problem_234 Co-Authored-By: Emmanuel Arias * Update project_euler/problem_234 Co-Authored-By: Emmanuel Arias * Update project_euler/problem_234 Co-Authored-By: Emmanuel Arias * Update project_euler/problem_234 Co-Authored-By: Emmanuel Arias * Update project_euler/problem_234 Co-Authored-By: Emmanuel Arias * Update and rename problem_234 to problem_234.py * Made suggested changes else was not required temp declared afterwards suggested changes are correct.Thank u! * Rename project_euler/problem_234.py to project_euler/problem_234/sol1.py --- project_euler/problem_234/sol1.py | 32 +++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 project_euler/problem_234/sol1.py diff --git a/project_euler/problem_234/sol1.py b/project_euler/problem_234/sol1.py new file mode 100644 index 000000000000..c7a6bd97d66b --- /dev/null +++ b/project_euler/problem_234/sol1.py @@ -0,0 +1,32 @@ +# https://projecteuler.net/problem=234 +def fib(a, b, n): + + if n==1: + return a + elif n==2: + return b + elif n==3: + return str(a)+str(b) + + temp = 0 + for x in range(2,n): + c=str(a) + str(b) + temp = b + b = c + a = temp + return c + + +q=int(input()) +for x in range(q): + l=[i for i in input().split()] + c1=0 + c2=1 + while(1): + + if len(fib(l[0],l[1],c2)) Date: Mon, 17 Jun 2019 03:13:36 -0700 Subject: [PATCH 011/193] Corrected wrong DFS implementation (#903) --- graphs/DFS.py | 1 - 1 file changed, 1 deletion(-) diff --git a/graphs/DFS.py b/graphs/DFS.py index c9843ca25382..68bf60e3c298 100644 --- a/graphs/DFS.py +++ b/graphs/DFS.py @@ -16,7 +16,6 @@ def dfs(graph, start): to the node's children onto the iterator stack. When the iterator at the top of the stack terminates, we'll pop it off the stack.""" explored, stack = set(), [start] - explored.add(start) while stack: v = stack.pop() # one difference from BFS is to pop last element here instead of first one From ef147484ab456b6c55128d809bee05c1bf4638cf Mon Sep 17 00:00:00 2001 From: Daniel Xu Date: Mon, 17 Jun 2019 06:17:53 -0400 Subject: [PATCH 012/193] Added script for automatically generating DIRECTORY.md (#889) * Added script for automatically generating DIRECTORY.md * Sort and list files by alphabetical order * Rename script.py to ~script.py --- DIRECTORY.md | 374 +++++++++++++++++++++++++++++++++++++++++++++++++++ ~script.py | 70 ++++++++++ 2 files changed, 444 insertions(+) create mode 100644 DIRECTORY.md create mode 100644 ~script.py diff --git a/DIRECTORY.md b/DIRECTORY.md new file mode 100644 index 000000000000..ad25772b56b6 --- /dev/null +++ b/DIRECTORY.md @@ -0,0 +1,374 @@ +## Analysis + * Compression Analysis + * [psnr](https://github.com/TheAlgorithms/Python/blob/master/analysis/compression_analysis/psnr.py) +## Arithmetic Analysis + * [bisection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/bisection.py) + * [intersection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/intersection.py) + * [lu decomposition](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/lu_decomposition.py) + * [newton method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_method.py) + * [newton raphson method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_raphson_method.py) +## Binary Tree + * [basic binary tree](https://github.com/TheAlgorithms/Python/blob/master/binary_tree/basic_binary_tree.py) +## Boolean Algebra + * [quine mc cluskey](https://github.com/TheAlgorithms/Python/blob/master/boolean_algebra/quine_mc_cluskey.py) +## Ciphers + * [affine cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/affine_cipher.py) + * [Atbash](https://github.com/TheAlgorithms/Python/blob/master/ciphers/Atbash.py) + * [base16](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base16.py) + * [base32](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base32.py) + * [base64 cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base64_cipher.py) + * [base85](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base85.py) + * [brute force caesar cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/brute_force_caesar_cipher.py) + * [caesar cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/caesar_cipher.py) + * [cryptomath module](https://github.com/TheAlgorithms/Python/blob/master/ciphers/cryptomath_module.py) + * [elgamal key generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/elgamal_key_generator.py) + * [hill cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/hill_cipher.py) + * [morse Code implementation](https://github.com/TheAlgorithms/Python/blob/master/ciphers/morse_Code_implementation.py) + * [onepad cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/onepad_cipher.py) + * [playfair cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/playfair_cipher.py) + * [prehistoric men](https://github.com/TheAlgorithms/Python/blob/master/ciphers/prehistoric_men.txt) + * [rabin miller](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rabin_miller.py) + * [rot13](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rot13.py) + * [rsa cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_cipher.py) + * [rsa key generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_key_generator.py) + * [simple substitution cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/simple_substitution_cipher.py) + * [trafid cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/trafid_cipher.py) + * [transposition cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher.py) + * [transposition cipher encrypt decrypt file](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher_encrypt_decrypt_file.py) + * [vigenere cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/vigenere_cipher.py) + * [xor cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/xor_cipher.py) +## Compression + * [huffman](https://github.com/TheAlgorithms/Python/blob/master/compression/huffman.py) +## Data Structures + * [arrays](https://github.com/TheAlgorithms/Python/blob/master/data_structures/arrays.py) + * [avl](https://github.com/TheAlgorithms/Python/blob/master/data_structures/avl.py) + * [LCA](https://github.com/TheAlgorithms/Python/blob/master/data_structures/LCA.py) + * Binary Tree + * [AVL tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary%20tree/AVL_tree.py) + * [binary search tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary%20tree/binary_search_tree.py) + * [fenwick tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary%20tree/fenwick_tree.py) + * [lazy segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary%20tree/lazy_segment_tree.py) + * [segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary%20tree/segment_tree.py) + * [treap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary%20tree/treap.py) + * Hashing + * [double hash](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/double_hash.py) + * [hash table](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table.py) + * [hash table with linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table_with_linked_list.py) + * [quadratic probing](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/quadratic_probing.py) + * Number Theory + * [prime numbers](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/number_theory/prime_numbers.py) + * Heap + * [heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap.py) + * Linked List + * [doubly linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/doubly_linked_list.py) + * [is Palindrome](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/is_Palindrome.py) + * [singly linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/singly_linked_list.py) + * [swapNodes](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/swapNodes.py) + * Queue + * [double ended queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/double_ended_queue.py) + * [queue on list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_list.py) + * [queue on pseudo stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_pseudo_stack.py) + * Stacks + * [balanced parentheses](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/balanced_parentheses.py) + * [infix to postfix conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_postfix_conversion.py) + * [infix to prefix conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_prefix_conversion.py) + * [next](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/next.py) + * [postfix evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/postfix_evaluation.py) + * [stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack.py) + * [stock span problem](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stock_span_problem.py) + * Trie + * [trie](https://github.com/TheAlgorithms/Python/blob/master/data_structures/trie/trie.py) + * Union Find + * [tests union find](https://github.com/TheAlgorithms/Python/blob/master/data_structures/union_find/tests_union_find.py) + * [union find](https://github.com/TheAlgorithms/Python/blob/master/data_structures/union_find/union_find.py) +## Digital Image Processing + * Filters + * [median filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/median_filter.py) +## Dynamic Programming + * [abbreviation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/abbreviation.py) + * [bitmask](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/bitmask.py) + * [coin change](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/coin_change.py) + * [edit distance](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/edit_distance.py) + * [fast fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fast_fibonacci.py) + * [fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fibonacci.py) + * [floyd warshall](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/floyd_warshall.py) + * [Fractional Knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/Fractional_Knapsack.py) + * [integer partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/integer_partition.py) + * [k means clustering tensorflow](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/k_means_clustering_tensorflow.py) + * [knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/knapsack.py) + * [longest common subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_common_subsequence.py) + * [longest increasing subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence.py) + * [longest increasing subsequence O(nlogn)](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence_O(nlogn).py) + * [longest sub array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_sub_array.py) + * [matrix chain order](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/matrix_chain_order.py) + * [max sub array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sub_array.py) + * [minimum partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_partition.py) + * [rod cutting](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/rod_cutting.py) + * [subset generation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/subset_generation.py) +## File Transfer Protocol + * [ftp client server](https://github.com/TheAlgorithms/Python/blob/master/file_transfer_protocol/ftp_client_server.py) + * [ftp send receive](https://github.com/TheAlgorithms/Python/blob/master/file_transfer_protocol/ftp_send_receive.py) +## Graphs + * [a star](https://github.com/TheAlgorithms/Python/blob/master/graphs/a_star.py) + * [articulation points](https://github.com/TheAlgorithms/Python/blob/master/graphs/articulation_points.py) + * [basic graphs](https://github.com/TheAlgorithms/Python/blob/master/graphs/basic_graphs.py) + * [bellman ford](https://github.com/TheAlgorithms/Python/blob/master/graphs/bellman_ford.py) + * [BFS](https://github.com/TheAlgorithms/Python/blob/master/graphs/BFS.py) + * [bfs shortest path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_shortest_path.py) + * [breadth first search](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search.py) + * [check bipartite graph bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_bfs.py) + * [check bipartite graph dfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_dfs.py) + * [depth first search](https://github.com/TheAlgorithms/Python/blob/master/graphs/depth_first_search.py) + * [DFS](https://github.com/TheAlgorithms/Python/blob/master/graphs/DFS.py) + * [dijkstra](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra.py) + * [dijkstra 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_2.py) + * [dijkstra algorithm](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_algorithm.py) + * [Directed and Undirected (Weighted) Graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/Directed_and_Undirected_(Weighted)_Graph.py) + * [edmonds karp multiple source and sink](https://github.com/TheAlgorithms/Python/blob/master/graphs/edmonds_karp_multiple_source_and_sink.py) + * [Eulerian path and circuit for undirected graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/Eulerian_path_and_circuit_for_undirected_graph.py) + * [even tree](https://github.com/TheAlgorithms/Python/blob/master/graphs/even_tree.py) + * [finding bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py) + * [floyd warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/floyd_warshall.py) + * [graph list](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py) + * [graph matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py) + * [kahns algorithm long](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_long.py) + * [kahns algorithm topo](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_topo.py) + * [minimum spanning tree kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal.py) + * [minimum spanning tree prims](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims.py) + * [multi hueristic astar](https://github.com/TheAlgorithms/Python/blob/master/graphs/multi_hueristic_astar.py) + * [page rank](https://github.com/TheAlgorithms/Python/blob/master/graphs/page_rank.py) + * [prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/prim.py) + * [scc kosaraju](https://github.com/TheAlgorithms/Python/blob/master/graphs/scc_kosaraju.py) + * [tarjans scc](https://github.com/TheAlgorithms/Python/blob/master/graphs/tarjans_scc.py) +## Hashes + * [chaos machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/chaos_machine.py) + * [md5](https://github.com/TheAlgorithms/Python/blob/master/hashes/md5.py) + * [sha1](https://github.com/TheAlgorithms/Python/blob/master/hashes/sha1.py) +## Linear Algebra Python + * Src + * [lib](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra_python/src/lib.py) + * [tests](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra_python/src/tests.py) +## Machine Learning + * [decision tree](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/decision_tree.py) + * [gradient descent](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_descent.py) + * [k means clust](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_means_clust.py) + * [linear regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_regression.py) + * [logistic regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/logistic_regression.py) + * [perceptron](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/perceptron.py) + * [scoring functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/scoring_functions.py) + * Random Forest Classification + * [random forest classification](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/Random%20Forest%20Classification/random_forest_classification.py) + * [Social Network Ads](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/Random%20Forest%20Classification/Social_Network_Ads.csv) + * Random Forest Regression + * [Position Salaries](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/Random%20Forest%20Regression/Position_Salaries.csv) + * [random forest regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/Random%20Forest%20Regression/random_forest_regression.py) +## Maths + * [3n+1](https://github.com/TheAlgorithms/Python/blob/master/maths/3n+1.py) + * [abs](https://github.com/TheAlgorithms/Python/blob/master/maths/abs.py) + * [abs Max](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_Max.py) + * [abs Min](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_Min.py) + * [average](https://github.com/TheAlgorithms/Python/blob/master/maths/average.py) + * [basic maths](https://github.com/TheAlgorithms/Python/blob/master/maths/basic_maths.py) + * [Binary Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/Binary_Exponentiation.py) + * [extended euclidean algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/extended_euclidean_algorithm.py) + * [factorial python](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_python.py) + * [factorial recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_recursive.py) + * [fermat little theorem](https://github.com/TheAlgorithms/Python/blob/master/maths/fermat_little_theorem.py) + * [fibonacci](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci.py) + * [fibonacci sequence recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci_sequence_recursion.py) + * [find lcm](https://github.com/TheAlgorithms/Python/blob/master/maths/find_lcm.py) + * [Find Max](https://github.com/TheAlgorithms/Python/blob/master/maths/Find_Max.py) + * [Find Min](https://github.com/TheAlgorithms/Python/blob/master/maths/Find_Min.py) + * [greater common divisor](https://github.com/TheAlgorithms/Python/blob/master/maths/greater_common_divisor.py) + * [Hanoi](https://github.com/TheAlgorithms/Python/blob/master/maths/Hanoi.py) + * [lucasSeries](https://github.com/TheAlgorithms/Python/blob/master/maths/lucasSeries.py) + * [modular exponential](https://github.com/TheAlgorithms/Python/blob/master/maths/modular_exponential.py) + * [newton raphson](https://github.com/TheAlgorithms/Python/blob/master/maths/newton_raphson.py) + * [Prime Check](https://github.com/TheAlgorithms/Python/blob/master/maths/Prime_Check.py) + * [segmented sieve](https://github.com/TheAlgorithms/Python/blob/master/maths/segmented_sieve.py) + * [sieve of eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/sieve_of_eratosthenes.py) + * [simpson rule](https://github.com/TheAlgorithms/Python/blob/master/maths/simpson_rule.py) + * [trapezoidal rule](https://github.com/TheAlgorithms/Python/blob/master/maths/trapezoidal_rule.py) + * Tests + * [test fibonacci](https://github.com/TheAlgorithms/Python/blob/master/maths/tests/test_fibonacci.py) +## Matrix + * [matrix multiplication addition](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_multiplication_addition.py) + * [searching in sorted matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/searching_in_sorted_matrix.py) +## Networking Flow + * [ford fulkerson](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/ford_fulkerson.py) + * [minimum cut](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/minimum_cut.py) +## Neural Network + * [bpnn](https://github.com/TheAlgorithms/Python/blob/master/neural_network/bpnn.py) + * [convolution neural network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/convolution_neural_network.py) + * [perceptron](https://github.com/TheAlgorithms/Python/blob/master/neural_network/perceptron.py) +## Other + * [anagrams](https://github.com/TheAlgorithms/Python/blob/master/other/anagrams.py) + * [binary exponentiation](https://github.com/TheAlgorithms/Python/blob/master/other/binary_exponentiation.py) + * [binary exponentiation 2](https://github.com/TheAlgorithms/Python/blob/master/other/binary_exponentiation_2.py) + * [detecting english programmatically](https://github.com/TheAlgorithms/Python/blob/master/other/detecting_english_programmatically.py) + * [dictionary](https://github.com/TheAlgorithms/Python/blob/master/other/dictionary.txt) + * [euclidean gcd](https://github.com/TheAlgorithms/Python/blob/master/other/euclidean_gcd.py) + * [finding Primes](https://github.com/TheAlgorithms/Python/blob/master/other/finding_Primes.py) + * [fischer yates shuffle](https://github.com/TheAlgorithms/Python/blob/master/other/fischer_yates_shuffle.py) + * [frequency finder](https://github.com/TheAlgorithms/Python/blob/master/other/frequency_finder.py) + * [linear congruential generator](https://github.com/TheAlgorithms/Python/blob/master/other/linear_congruential_generator.py) + * [n queens](https://github.com/TheAlgorithms/Python/blob/master/other/n_queens.py) + * [nested brackets](https://github.com/TheAlgorithms/Python/blob/master/other/nested_brackets.py) + * [palindrome](https://github.com/TheAlgorithms/Python/blob/master/other/palindrome.py) + * [password generator](https://github.com/TheAlgorithms/Python/blob/master/other/password_generator.py) + * [primelib](https://github.com/TheAlgorithms/Python/blob/master/other/primelib.py) + * [sierpinski triangle](https://github.com/TheAlgorithms/Python/blob/master/other/sierpinski_triangle.py) + * [tower of hanoi](https://github.com/TheAlgorithms/Python/blob/master/other/tower_of_hanoi.py) + * [two sum](https://github.com/TheAlgorithms/Python/blob/master/other/two_sum.py) + * [word patterns](https://github.com/TheAlgorithms/Python/blob/master/other/word_patterns.py) + * [words](https://github.com/TheAlgorithms/Python/blob/master/other/words) + * Game Of Life + * [game o life](https://github.com/TheAlgorithms/Python/blob/master/other/game_of_life/game_o_life.py) + * [sample](https://github.com/TheAlgorithms/Python/blob/master/other/game_of_life/sample.gif) +## Project Euler + * Problem 01 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol3.py) + * [sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol4.py) + * [sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol5.py) + * [sol6](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol6.py) + * Problem 02 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol3.py) + * [sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol4.py) + * Problem 03 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_03/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_03/sol2.py) + * Problem 04 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_04/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_04/sol2.py) + * Problem 05 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_05/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_05/sol2.py) + * Problem 06 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_06/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_06/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_06/sol3.py) + * Problem 07 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_07/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_07/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_07/sol3.py) + * Problem 08 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_08/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_08/sol2.py) + * Problem 09 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_09/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_09/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_09/sol3.py) + * Problem 10 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_10/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_10/sol2.py) + * Problem 11 + * [grid](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_11/grid.txt) + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_11/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_11/sol2.py) + * Problem 12 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_12/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_12/sol2.py) + * Problem 13 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_13/sol1.py) + * Problem 14 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_14/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_14/sol2.py) + * Problem 15 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_15/sol1.py) + * Problem 16 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_16/sol1.py) + * Problem 17 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_17/sol1.py) + * Problem 19 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_19/sol1.py) + * Problem 20 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_20/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_20/sol2.py) + * Problem 21 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_21/sol1.py) + * Problem 22 + * [p022 names](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/p022_names.txt) + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/sol2.py) + * Problem 24 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_24/sol1.py) + * Problem 25 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_25/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_25/sol2.py) + * Problem 28 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_28/sol1.py) + * Problem 29 + * [solution](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_29/solution.py) + * Problem 31 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_31/sol1.py) + * Problem 36 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_36/sol1.py) + * Problem 40 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_40/sol1.py) + * Problem 48 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_48/sol1.py) + * Problem 52 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_52/sol1.py) + * Problem 53 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_53/sol1.py) + * Problem 76 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_76/sol1.py) +## Searches + * [binary search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py) + * [interpolation search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py) + * [jump search](https://github.com/TheAlgorithms/Python/blob/master/searches/jump_search.py) + * [linear search](https://github.com/TheAlgorithms/Python/blob/master/searches/linear_search.py) + * [quick select](https://github.com/TheAlgorithms/Python/blob/master/searches/quick_select.py) + * [sentinel linear search](https://github.com/TheAlgorithms/Python/blob/master/searches/sentinel_linear_search.py) + * [tabu search](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_search.py) + * [tabu test data](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_test_data.txt) + * [ternary search](https://github.com/TheAlgorithms/Python/blob/master/searches/ternary_search.py) + * [test interpolation search](https://github.com/TheAlgorithms/Python/blob/master/searches/test_interpolation_search.py) + * [test tabu search](https://github.com/TheAlgorithms/Python/blob/master/searches/test_tabu_search.py) +## Simple Client + * [client](https://github.com/TheAlgorithms/Python/blob/master/simple_client/client.py) + * [server](https://github.com/TheAlgorithms/Python/blob/master/simple_client/server.py) +## Sorts + * [Bitonic Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/Bitonic_Sort.py) + * [bogo sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bogo_sort.py) + * [bubble sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bubble_sort.py) + * [bucket sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bucket_sort.py) + * [cocktail shaker sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cocktail_shaker_sort.py) + * [comb sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/comb_sort.py) + * [counting sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/counting_sort.py) + * [cycle sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cycle_sort.py) + * [external sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/external_sort.py) + * [gnome sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/gnome_sort.py) + * [heap sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/heap_sort.py) + * [insertion sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/insertion_sort.py) + * [merge sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort.py) + * [merge sort fastest](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort_fastest.py) + * [Odd-Even transposition parallel](https://github.com/TheAlgorithms/Python/blob/master/sorts/Odd-Even_transposition_parallel.py) + * [Odd-Even transposition single-threaded](https://github.com/TheAlgorithms/Python/blob/master/sorts/Odd-Even_transposition_single-threaded.py) + * [pancake sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pancake_sort.py) + * [pigeon sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pigeon_sort.py) + * [quick sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort.py) + * [quick sort 3 partition](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort_3_partition.py) + * [radix sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/radix_sort.py) + * [random normal distribution quicksort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_normal_distribution_quicksort.py) + * [random pivot quick sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_pivot_quick_sort.py) + * [selection sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/selection_sort.py) + * [shell sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/shell_sort.py) + * [tests](https://github.com/TheAlgorithms/Python/blob/master/sorts/tests.py) + * [tim sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tim_sort.py) + * [topological sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/topological_sort.py) + * [tree sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tree_sort.py) + * [wiggle sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/wiggle_sort.py) +## Strings + * [knuth morris pratt](https://github.com/TheAlgorithms/Python/blob/master/strings/knuth_morris_pratt.py) + * [levenshtein distance](https://github.com/TheAlgorithms/Python/blob/master/strings/levenshtein_distance.py) + * [manacher](https://github.com/TheAlgorithms/Python/blob/master/strings/manacher.py) + * [min cost string conversion](https://github.com/TheAlgorithms/Python/blob/master/strings/min_cost_string_conversion.py) + * [naive String Search](https://github.com/TheAlgorithms/Python/blob/master/strings/naive_String_Search.py) + * [rabin karp](https://github.com/TheAlgorithms/Python/blob/master/strings/rabin_karp.py) +## Traversals + * [binary tree traversals](https://github.com/TheAlgorithms/Python/blob/master/traversals/binary_tree_traversals.py) diff --git a/~script.py b/~script.py new file mode 100644 index 000000000000..4a2c61c83563 --- /dev/null +++ b/~script.py @@ -0,0 +1,70 @@ +""" +This is a simple script that will scan through the current directory +and generate the corresponding DIRECTORY.md file, can also specify +files or folders to be ignored. +""" +import os + + +# Target URL (master) +URL = "https://github.com/TheAlgorithms/Python/blob/master/" + + +def tree(d, ignores, ignores_ext): + return _markdown(d, ignores, ignores_ext, 0) + + +def _markdown(parent, ignores, ignores_ext, depth): + out = "" + dirs, files = [], [] + for i in os.listdir(parent): + full = os.path.join(parent, i) + name, ext = os.path.splitext(i) + if i in ignores or ext in ignores_ext: + continue + if os.path.isfile(full): + # generate list + pre = parent.replace("./", "").replace(" ", "%20") + # replace all spaces to safe URL + child = i.replace(" ", "%20") + files.append((pre, child, name)) + else: + dirs.append(i) + # Sort files + files.sort(key=lambda e: e[2].lower()) + for f in files: + pre, child, name = f + out += " " * depth + "* [" + name.replace("_", " ") + "](" + URL + pre + "/" + child + ")\n" + # Sort directories + dirs.sort() + for i in dirs: + full = os.path.join(parent, i) + i = i.replace("_", " ").title() + if depth == 0: + out += "## " + i + "\n" + else: + out += " " * depth + "* " + i + "\n" + out += _markdown(full, ignores, ignores_ext, depth+1) + return out + + +# Specific files or folders with the given names will be ignored +ignores = [".vs", + ".gitignore", + ".git", + "script.py", + "__init__.py", +] +# Files with given entensions will be ignored +ignores_ext = [ + ".md", + ".ipynb", + ".png", + ".jpg", + ".yml" +] + + +if __name__ == "__main__": + with open("DIRECTORY.md", "w+") as f: + f.write(tree(".", ignores, ignores_ext)) From b8937364dc18a8ba59ef5a3cdbc0ffc85b604c86 Mon Sep 17 00:00:00 2001 From: Hector S Date: Tue, 18 Jun 2019 06:27:08 -0400 Subject: [PATCH 013/193] Fixed typo and capitalized some words (#900) File looks more elegant now ;-) --- CONTRIBUTING.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 9b2ac0025dca..19b928c187f9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -10,10 +10,10 @@ Welcome to [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python)! Befo We are very happy that you consider implementing algorithms and data structure for others! This repository is referenced and used by learners from all over the globe. Being one of our contributors, you agree and confirm that: -- your did your work - no plagiarism allowed +- You did your work - no plagiarism allowed - Any plagiarized work will not be merged. -- your work will be distributed under [MIT License](License) once your pull request is merged -- you submitted work fulfils or mostly fulfils our styles and standards +- Your work will be distributed under [MIT License](License) once your pull request is merged +- You submitted work fulfils or mostly fulfils our styles and standards **New implementation** is welcome! For example, new solutions for a problem, different representations for a graph data structure or algorithm designs with different complexity. @@ -115,8 +115,8 @@ We want your work to be readable by others; therefore, we encourage you to note - Most importantly, - - **be consistent with this guidelines while submitting.** - - **join** [Gitter](https://gitter.im/TheAlgorithms) **now!** + - **Be consistent with this guidelines while submitting.** + - **Join** [Gitter](https://gitter.im/TheAlgorithms) **now!** - Happy coding! From a99acae32da09285b1a87c9e3dc9bf956c9077f6 Mon Sep 17 00:00:00 2001 From: StephenGemin <45926479+StephenGemin@users.noreply.github.com> Date: Tue, 18 Jun 2019 06:28:01 -0400 Subject: [PATCH 014/193] Add docstring and comments per Issue #727 (#895) I've added comments to make understanding this method a little easier for those that are not familiar. This should close out #727 . Other changes: 1. added if __name__ == '__main__' rather than the "# MAIN" comment 2. put in return for distances and vertices. Previously everything was just printed out, but someone may find it useful to have the algorithm return something. 3. Other PEP8 changes 4. Added example input and expected output as a check to make sure any future changes will give the same output. --- graphs/floyd_warshall.py | 102 ++++++++++++++++++++++++++++++--------- 1 file changed, 78 insertions(+), 24 deletions(-) diff --git a/graphs/floyd_warshall.py b/graphs/floyd_warshall.py index fae8b19b351a..a1d12aac02b4 100644 --- a/graphs/floyd_warshall.py +++ b/graphs/floyd_warshall.py @@ -1,9 +1,16 @@ +# floyd_warshall.py +""" + The problem is to find the shortest distance between all pairs of vertices in a weighted directed graph that can + have negative edge weights. +""" + from __future__ import print_function -def printDist(dist, V): + +def _print_dist(dist, v): print("\nThe shortest path matrix using Floyd Warshall algorithm\n") - for i in range(V): - for j in range(V): + for i in range(v): + for j in range(v): if dist[i][j] != float('inf') : print(int(dist[i][j]),end = "\t") else: @@ -12,37 +19,84 @@ def printDist(dist, V): -def FloydWarshall(graph, V): - dist=[[float('inf') for i in range(V)] for j in range(V)] +def floyd_warshall(graph, v): + """ + :param graph: 2D array calculated from weight[edge[i, j]] + :type graph: List[List[float]] + :param v: number of vertices + :type v: int + :return: shortest distance between all vertex pairs + distance[u][v] will contain the shortest distance from vertex u to v. + + 1. For all edges from v to n, distance[i][j] = weight(edge(i, j)). + 3. The algorithm then performs distance[i][j] = min(distance[i][j], distance[i][k] + distance[k][j]) for each + possible pair i, j of vertices. + 4. The above is repeated for each vertex k in the graph. + 5. Whenever distance[i][j] is given a new minimum value, next vertex[i][j] is updated to the next vertex[i][k]. + """ + + dist=[[float('inf') for _ in range(v)] for _ in range(v)] - for i in range(V): - for j in range(V): + for i in range(v): + for j in range(v): dist[i][j] = graph[i][j] - for k in range(V): - for i in range(V): - for j in range(V): + # check vertex k against all other vertices (i, j) + for k in range(v): + # looping through rows of graph array + for i in range(v): + # looping through columns of graph array + for j in range(v): if dist[i][k]!=float('inf') and dist[k][j]!=float('inf') and dist[i][k]+dist[k][j] < dist[i][j]: dist[i][j] = dist[i][k] + dist[k][j] - printDist(dist, V) + _print_dist(dist, v) + return dist, v -#MAIN -V = int(input("Enter number of vertices: ")) -E = int(input("Enter number of edges: ")) +if __name__== '__main__': + v = int(input("Enter number of vertices: ")) + e = int(input("Enter number of edges: ")) + + graph = [[float('inf') for i in range(v)] for j in range(v)] + + for i in range(v): + graph[i][i] = 0.0 + + # src and dst are indices that must be within the array size graph[e][v] + # failure to follow this will result in an error + for i in range(e): + print("\nEdge ",i+1) + src = int(input("Enter source:")) + dst = int(input("Enter destination:")) + weight = float(input("Enter weight:")) + graph[src][dst] = weight + + floyd_warshall(graph, v) + + + # Example Input + # Enter number of vertices: 3 + # Enter number of edges: 2 -graph = [[float('inf') for i in range(V)] for j in range(V)] + # # generated graph from vertex and edge inputs + # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]] + # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]] -for i in range(V): - graph[i][i] = 0.0 + # specify source, destination and weight for edge #1 + # Edge 1 + # Enter source:1 + # Enter destination:2 + # Enter weight:2 -for i in range(E): - print("\nEdge ",i+1) - src = int(input("Enter source:")) - dst = int(input("Enter destination:")) - weight = float(input("Enter weight:")) - graph[src][dst] = weight + # specify source, destination and weight for edge #2 + # Edge 2 + # Enter source:2 + # Enter destination:1 + # Enter weight:1 -FloydWarshall(graph, V) + # # Expected Output from the vertice, edge and src, dst, weight inputs!! + # 0 INF INF + # INF 0 2 + # INF 1 0 From 12a16d63b7bdc1da0e0b215dfd5d8b938234f4ed Mon Sep 17 00:00:00 2001 From: Adeoti Ayodeji <33290249+Lord-sarcastic@users.noreply.github.com> Date: Sat, 22 Jun 2019 05:42:28 +0100 Subject: [PATCH 015/193] Update average.py (#908) Reduced lines of code and extra processing on the line: n += 1 --- maths/average.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/maths/average.py b/maths/average.py index dc70836b5e83..d15601dd64ca 100644 --- a/maths/average.py +++ b/maths/average.py @@ -1,11 +1,10 @@ def average(nums): sum = 0 - n = 0 for x in nums: sum += x - n += 1 - avg = sum / n + avg = sum / len(nums) print(avg) + return avg def main(): average([2, 4, 6, 8, 20, 50, 70]) From a212efee5b44312c8b4b626ae412bacc5f4117fd Mon Sep 17 00:00:00 2001 From: zachzhu2016 <48337051+zachzhu2016@users.noreply.github.com> Date: Sun, 23 Jun 2019 08:32:12 -0700 Subject: [PATCH 016/193] Corrected wrong Dijkstra priority queue implementation (#909) * Corrected wrong DFS implementation * changed list into hash set for dijkstra priority queue implementation. --- graphs/dijkstra.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/graphs/dijkstra.py b/graphs/dijkstra.py index 6b08b28fcfd3..4b6bc347b061 100644 --- a/graphs/dijkstra.py +++ b/graphs/dijkstra.py @@ -20,12 +20,12 @@ def dijkstra(graph, start, end): heap = [(0, start)] # cost from start node,end node - visited = [] + visited = set() while heap: (cost, u) = heapq.heappop(heap) if u in visited: continue - visited.append(u) + visited.add(u) if u == end: return cost for v, c in G[u]: From b7cff04574f5288c0483040c11be3bcc2b396a32 Mon Sep 17 00:00:00 2001 From: wuminbin Date: Mon, 24 Jun 2019 18:11:07 +0800 Subject: [PATCH 017/193] better implementation for midpoint (#914) --- arithmetic_analysis/bisection.py | 4 ++-- searches/binary_search.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/arithmetic_analysis/bisection.py b/arithmetic_analysis/bisection.py index c81fa84f81e1..8bf3f09782a3 100644 --- a/arithmetic_analysis/bisection.py +++ b/arithmetic_analysis/bisection.py @@ -14,7 +14,7 @@ def bisection(function, a, b): # finds where the function becomes 0 in [a,b] us print("couldn't find root in [a,b]") return else: - mid = (start + end) / 2 + mid = start + (end - start) / 2.0 while abs(start - mid) > 10**-7: # until we achieve precise equals to 10^-7 if function(mid) == 0: return mid @@ -22,7 +22,7 @@ def bisection(function, a, b): # finds where the function becomes 0 in [a,b] us end = mid else: start = mid - mid = (start + end) / 2 + mid = start + (end - start) / 2.0 return mid diff --git a/searches/binary_search.py b/searches/binary_search.py index 1d5da96586cd..e658dac2a3ef 100644 --- a/searches/binary_search.py +++ b/searches/binary_search.py @@ -45,7 +45,7 @@ def binary_search(sorted_collection, item): right = len(sorted_collection) - 1 while left <= right: - midpoint = (left + right) // 2 + midpoint = left + (right - left) // 2 current_item = sorted_collection[midpoint] if current_item == item: return midpoint From be4150c720187391488786053ac7a13eeb965446 Mon Sep 17 00:00:00 2001 From: brajesh-rit Date: Wed, 26 Jun 2019 10:57:08 -0500 Subject: [PATCH 018/193] Create spiralPrint.py (#844) * Create spiralPrint.py * Update spiralPrint.py --- matrix/spiralPrint.py | 66 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 matrix/spiralPrint.py diff --git a/matrix/spiralPrint.py b/matrix/spiralPrint.py new file mode 100644 index 000000000000..447881e508e7 --- /dev/null +++ b/matrix/spiralPrint.py @@ -0,0 +1,66 @@ +""" +This program print the matix in spiral form. +This problem has been solved through recursive way. + + Matrix must satisfy below conditions + i) matrix should be only one or two dimensional + ii)column of all the row should be equal +""" +def checkMatrix(a): + # must be + if type(a) == list and len(a) > 0: + if type(a[0]) == list: + prevLen = 0 + for i in a: + if prevLen == 0: + prevLen = len(i) + result = True + elif prevLen == len(i): + result = True + else: + result = False + else: + result = True + else: + result = False + return result + + +def spiralPrint(a): + + if checkMatrix(a) and len(a) > 0: + + matRow = len(a) + if type(a[0]) == list: + matCol = len(a[0]) + else: + for dat in a: + print(dat), + return + + # horizotal printing increasing + for i in range(0, matCol): + print(a[0][i]), + # vertical printing down + for i in range(1, matRow): + print(a[i][matCol - 1]), + # horizotal printing decreasing + if matRow > 1: + for i in range(matCol - 2, -1, -1): + print(a[matRow - 1][i]), + # vertical printing up + for i in range(matRow - 2, 0, -1): + print(a[i][0]), + remainMat = [row[1:matCol - 1] for row in a[1:matRow - 1]] + if len(remainMat) > 0: + spiralPrint(remainMat) + else: + return + else: + print("Not a valid matrix") + return + + +# driver code +a = [[1 , 2, 3, 4],[5, 6, 7, 8],[9, 10, 11, 12]] +spiralPrint(a) From 34889fc6d8d3ac1cf5af11039cc1ec40185f778e Mon Sep 17 00:00:00 2001 From: BruceLee569 <49506152+BruceLee569@users.noreply.github.com> Date: Fri, 28 Jun 2019 23:55:31 +0800 Subject: [PATCH 019/193] Update quick_sort.py (#928) Use the last element as the first pivot, for it's easy to pop, this saves one element space. Iterating with the original list saves half the space, instead of generate a new shallow copy list by slice method. --- sorts/quick_sort.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/sorts/quick_sort.py b/sorts/quick_sort.py index 223c26fde1fe..7e8c868ebb06 100644 --- a/sorts/quick_sort.py +++ b/sorts/quick_sort.py @@ -33,17 +33,16 @@ def quick_sort(collection): if length <= 1: return collection else: - pivot = collection[0] - # Modify the list comprehensions to reduce the number of judgments, the speed has increased by more than 50%. - greater = [] - lesser = [] - for element in collection[1:]: + # Use the last element as the first pivot + pivot = collection.pop() + # Put elements greater than pivot in greater list + # Put elements lesser than pivot in lesser list + greater, lesser = [], [] + for element in collection: if element > pivot: greater.append(element) else: lesser.append(element) - # greater = [element for element in collection[1:] if element > pivot] - # lesser = [element for element in collection[1:] if element <= pivot] return quick_sort(lesser) + [pivot] + quick_sort(greater) From 2333f933236fbd57f9ed8644b0f8f7a19d10a2e7 Mon Sep 17 00:00:00 2001 From: PatOnTheBack <51241310+PatOnTheBack@users.noreply.github.com> Date: Sun, 30 Jun 2019 00:41:26 -0400 Subject: [PATCH 020/193] Change Declaration of Var 'j' to None (#921) Since `j` is redefined before it is used, it makes more sense to declare it with the value `None` instead of `1`. This fixes a [warning from lgtm](https://lgtm.com/projects/g/TheAlgorithms/Python/snapshot/66c4afbd0f28f9989f35ddbeb5c9263390c5d192/files/other/primelib.py?sort=name&dir=ASC&mode=heatmap) --- other/primelib.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/other/primelib.py b/other/primelib.py index 19572f8611cb..c371bc1b9861 100644 --- a/other/primelib.py +++ b/other/primelib.py @@ -283,7 +283,7 @@ def goldbach(number): # run variable for while-loops. i = 0 - j = 1 + j = None # exit variable. for break up the loops loop = True From bd4017928ed3054016ea21b8464f36db5fa007dc Mon Sep 17 00:00:00 2001 From: PatOnTheBack <51241310+PatOnTheBack@users.noreply.github.com> Date: Mon, 1 Jul 2019 04:10:18 -0400 Subject: [PATCH 021/193] Added Whitespace and Docstring (#924) * Added Whitespace and Docstring I modified the file to make Pylint happier and make the code more readable. * Beautified Code and Added Docstring I modified the file to make Pylint happier and make the code more readable. * Added DOCSTRINGS, Wikipedia link, and whitespace I added DOCSTRINGS and whitespace to make the code more readable and understandable. * Improved Formatting * Wrapped comments * Fixed spelling error for `movement` variable * Added DOCSTRINGs * Improved Formatting * Corrected whitespace to improve readability. * Added docstrings. * Made comments fit inside an 80 column layout. --- arithmetic_analysis/lu_decomposition.py | 48 +++++++++++++------------ arithmetic_analysis/newton_method.py | 29 +++++++++------ maths/Hanoi.py | 21 ++++++----- maths/abs.py | 11 ++++-- maths/average.py | 13 +++++-- maths/find_lcm.py | 7 ++++ sorts/bucket_sort.py | 31 ++++++++++------ sorts/gnome_sort.py | 20 ++++++----- sorts/tests.py | 8 +++-- sorts/topological_sort.py | 3 ++ sorts/tree_sort.py | 33 ++++++++++------- sorts/wiggle_sort.py | 17 ++++++--- 12 files changed, 154 insertions(+), 87 deletions(-) diff --git a/arithmetic_analysis/lu_decomposition.py b/arithmetic_analysis/lu_decomposition.py index f291d2dfe003..19e259afb826 100644 --- a/arithmetic_analysis/lu_decomposition.py +++ b/arithmetic_analysis/lu_decomposition.py @@ -1,32 +1,36 @@ +"""Lower-Upper (LU) Decomposition.""" + # lower–upper (LU) decomposition - https://en.wikipedia.org/wiki/LU_decomposition import numpy -def LUDecompose (table): + +def LUDecompose(table): # Table that contains our data # Table has to be a square array so we need to check first - rows,columns=numpy.shape(table) - L=numpy.zeros((rows,columns)) - U=numpy.zeros((rows,columns)) - if rows!=columns: + rows, columns = numpy.shape(table) + L = numpy.zeros((rows, columns)) + U = numpy.zeros((rows, columns)) + if rows != columns: return [] - for i in range (columns): - for j in range(i-1): - sum=0 - for k in range (j-1): - sum+=L[i][k]*U[k][j] - L[i][j]=(table[i][j]-sum)/U[j][j] - L[i][i]=1 - for j in range(i-1,columns): - sum1=0 - for k in range(i-1): - sum1+=L[i][k]*U[k][j] - U[i][j]=table[i][j]-sum1 - return L,U + for i in range(columns): + for j in range(i - 1): + sum = 0 + for k in range(j - 1): + sum += L[i][k] * U[k][j] + L[i][j] = (table[i][j] - sum) / U[j][j] + L[i][i] = 1 + for j in range(i - 1, columns): + sum1 = 0 + for k in range(i - 1): + sum1 += L[i][k] * U[k][j] + U[i][j] = table[i][j] - sum1 + return L, U + if __name__ == "__main__": - matrix =numpy.array([[2,-2,1], - [0,1,2], - [5,3,1]]) - L,U = LUDecompose(matrix) + matrix = numpy.array([[2, -2, 1], + [0, 1, 2], + [5, 3, 1]]) + L, U = LUDecompose(matrix) print(L) print(U) diff --git a/arithmetic_analysis/newton_method.py b/arithmetic_analysis/newton_method.py index 2ed29502522e..cf5649ee3f3b 100644 --- a/arithmetic_analysis/newton_method.py +++ b/arithmetic_analysis/newton_method.py @@ -1,18 +1,25 @@ +"""Newton's Method.""" + # Newton's Method - https://en.wikipedia.org/wiki/Newton%27s_method -def newton(function,function1,startingInt): #function is the f(x) and function1 is the f'(x) - x_n=startingInt - while True: - x_n1=x_n-function(x_n)/function1(x_n) - if abs(x_n-x_n1) < 10**-5: - return x_n1 - x_n=x_n1 - + +# function is the f(x) and function1 is the f'(x) +def newton(function, function1, startingInt): + x_n = startingInt + while True: + x_n1 = x_n - function(x_n) / function1(x_n) + if abs(x_n - x_n1) < 10**-5: + return x_n1 + x_n = x_n1 + + def f(x): - return (x**3) - (2 * x) -5 + return (x**3) - (2 * x) - 5 + def f1(x): - return 3 * (x**2) -2 + return 3 * (x**2) - 2 + if __name__ == "__main__": - print(newton(f,f1,3)) + print(newton(f, f1, 3)) diff --git a/maths/Hanoi.py b/maths/Hanoi.py index dd04d0fa58d8..c7b435a8fe3e 100644 --- a/maths/Hanoi.py +++ b/maths/Hanoi.py @@ -1,5 +1,8 @@ +"""Tower of Hanoi.""" + # @author willx75 -# Tower of Hanoi recursion game algorithm is a game, it consists of three rods and a number of disks of different sizes, which can slide onto any rod +# Tower of Hanoi recursion game algorithm is a game, it consists of three rods +# and a number of disks of different sizes, which can slide onto any rod import logging @@ -7,18 +10,20 @@ logging.basicConfig(level=logging.DEBUG) -def Tower_Of_Hanoi(n, source, dest, by, mouvement): +def Tower_Of_Hanoi(n, source, dest, by, movement): + """Tower of Hanoi - Move plates to different rods.""" if n == 0: return n elif n == 1: - mouvement += 1 - # no print statement (you could make it an optional flag for printing logs) + movement += 1 + # no print statement + # (you could make it an optional flag for printing logs) logging.debug('Move the plate from', source, 'to', dest) - return mouvement + return movement else: - mouvement = mouvement + Tower_Of_Hanoi(n-1, source, by, dest, 0) + movement = movement + Tower_Of_Hanoi(n - 1, source, by, dest, 0) logging.debug('Move the plate from', source, 'to', dest) - mouvement = mouvement + 1 + Tower_Of_Hanoi(n-1, by, dest, source, 0) - return mouvement + movement = movement + 1 + Tower_Of_Hanoi(n - 1, by, dest, source, 0) + return movement diff --git a/maths/abs.py b/maths/abs.py index 6d0596478d5f..624823fc183e 100644 --- a/maths/abs.py +++ b/maths/abs.py @@ -1,6 +1,10 @@ +"""Absolute Value.""" + + def absVal(num): """ - Function to fins absolute value of numbers. + Find the absolute value of a number. + >>absVal(-5) 5 >>absVal(0) @@ -11,8 +15,11 @@ def absVal(num): else: return num + def main(): - print(absVal(-34)) # = 34 + """Print absolute value of -34.""" + print(absVal(-34)) # = 34 + if __name__ == '__main__': main() diff --git a/maths/average.py b/maths/average.py index d15601dd64ca..78387111022d 100644 --- a/maths/average.py +++ b/maths/average.py @@ -1,13 +1,20 @@ +"""Find mean of a list of numbers.""" + + def average(nums): + """Find mean of a list of numbers.""" sum = 0 for x in nums: - sum += x + sum += x avg = sum / len(nums) print(avg) return avg + def main(): - average([2, 4, 6, 8, 20, 50, 70]) + """Call average module to find mean of a specific list of numbers.""" + average([2, 4, 6, 8, 20, 50, 70]) + if __name__ == '__main__': - main() + main() diff --git a/maths/find_lcm.py b/maths/find_lcm.py index 126242699ab7..779cb128898e 100644 --- a/maths/find_lcm.py +++ b/maths/find_lcm.py @@ -1,4 +1,10 @@ +"""Find Least Common Multiple.""" + +# https://en.wikipedia.org/wiki/Least_common_multiple + + def find_lcm(num_1, num_2): + """Find the LCM of two numbers.""" max = num_1 if num_1 > num_2 else num_2 lcm = max while (True): @@ -9,6 +15,7 @@ def find_lcm(num_1, num_2): def main(): + """Use test numbers to run the find_lcm algorithm.""" num_1 = 12 num_2 = 76 print(find_lcm(num_1, num_2)) diff --git a/sorts/bucket_sort.py b/sorts/bucket_sort.py index c4d61874fc47..5c4a71513ed3 100644 --- a/sorts/bucket_sort.py +++ b/sorts/bucket_sort.py @@ -1,19 +1,26 @@ #!/usr/bin/env python + +"""Illustrate how to implement bucket sort algorithm.""" + # Author: OMKAR PATHAK # This program will illustrate how to implement bucket sort algorithm -# Wikipedia says: Bucket sort, or bin sort, is a sorting algorithm that works by distributing the -# elements of an array into a number of buckets. Each bucket is then sorted individually, either using -# a different sorting algorithm, or by recursively applying the bucket sorting algorithm. It is a -# distribution sort, and is a cousin of radix sort in the most to least significant digit flavour. -# Bucket sort is a generalization of pigeonhole sort. Bucket sort can be implemented with comparisons -# and therefore can also be considered a comparison sort algorithm. The computational complexity estimates -# involve the number of buckets. +# Wikipedia says: Bucket sort, or bin sort, is a sorting algorithm that works +# by distributing the elements of an array into a number of buckets. +# Each bucket is then sorted individually, either using a different sorting +# algorithm, or by recursively applying the bucket sorting algorithm. It is a +# distribution sort, and is a cousin of radix sort in the most to least +# significant digit flavour. +# Bucket sort is a generalization of pigeonhole sort. Bucket sort can be +# implemented with comparisons and therefore can also be considered a +# comparison sort algorithm. The computational complexity estimates involve the +# number of buckets. # Time Complexity of Solution: # Best Case O(n); Average Case O(n); Worst Case O(n) -DEFAULT_BUCKET_SIZE=5 +DEFAULT_BUCKET_SIZE = 5 + def bucket_sort(my_list, bucket_size=DEFAULT_BUCKET_SIZE): if len(my_list) == 0: @@ -24,12 +31,14 @@ def bucket_sort(my_list, bucket_size=DEFAULT_BUCKET_SIZE): buckets = [[] for _ in range(int(bucket_count))] for i in range(len(my_list)): - buckets[int((my_list[i] - min_value) // bucket_size)].append(my_list[i]) + buckets[int((my_list[i] - min_value) // bucket_size) + ].append(my_list[i]) return sorted([buckets[i][j] for i in range(len(buckets)) - for j in range(len(buckets[i]))]) + for j in range(len(buckets[i]))]) + if __name__ == "__main__": user_input = input('Enter numbers separated by a comma:').strip() unsorted = [float(n) for n in user_input.split(',') if len(user_input) > 0] - print(bucket_sort(unsorted)) \ No newline at end of file + print(bucket_sort(unsorted)) diff --git a/sorts/gnome_sort.py b/sorts/gnome_sort.py index 2927b097f11d..075749e37663 100644 --- a/sorts/gnome_sort.py +++ b/sorts/gnome_sort.py @@ -1,29 +1,31 @@ +"""Gnome Sort Algorithm.""" + from __future__ import print_function + def gnome_sort(unsorted): - """ - Pure implementation of the gnome sort algorithm in Python. - """ + """Pure implementation of the gnome sort algorithm in Python.""" if len(unsorted) <= 1: return unsorted - + i = 1 - + while i < len(unsorted): - if unsorted[i-1] <= unsorted[i]: + if unsorted[i - 1] <= unsorted[i]: i += 1 else: - unsorted[i-1], unsorted[i] = unsorted[i], unsorted[i-1] + unsorted[i - 1], unsorted[i] = unsorted[i], unsorted[i - 1] i -= 1 if (i == 0): i = 1 - + + if __name__ == '__main__': try: raw_input # Python 2 except NameError: raw_input = input # Python 3 - + user_input = raw_input('Enter numbers separated by a comma:\n').strip() unsorted = [int(item) for item in user_input.split(',')] gnome_sort(unsorted) diff --git a/sorts/tests.py b/sorts/tests.py index 225763625f51..ec8c8361912f 100644 --- a/sorts/tests.py +++ b/sorts/tests.py @@ -1,3 +1,5 @@ +"""Test Sort Algorithms for Errors.""" + from bogo_sort import bogo_sort from bubble_sort import bubble_sort from bucket_sort import bucket_sort @@ -36,8 +38,8 @@ TODO: - Fix some broken tests in particular cases (as [] for example), - Unify the input format: should always be function(input_collection) (no additional args) - - Unify the output format: should always be a collection instead of updating input elements - and returning None + - Unify the output format: should always be a collection instead of + updating input elements and returning None - Rewrite some algorithms in function format (in case there is no function definition) ''' @@ -71,4 +73,4 @@ for function in TEST_FUNCTIONS: for case in TEST_CASES: result = function(case['input']) - assert result == case['expected'], 'Executed function: {}, {} != {}'.format(function.__name__, result, case['expected']) + assert result == case['expected'], 'Executed function: {}, {} != {}'.format(function.__name__, result, case['expected']) diff --git a/sorts/topological_sort.py b/sorts/topological_sort.py index db4dd250a119..400cfb4ca270 100644 --- a/sorts/topological_sort.py +++ b/sorts/topological_sort.py @@ -1,3 +1,5 @@ +"""Topological Sort.""" + from __future__ import print_function # a # / \ @@ -28,6 +30,7 @@ def topological_sort(start, visited, sort): # return sort return sort + if __name__ == '__main__': sort = topological_sort('a', [], []) print(sort) diff --git a/sorts/tree_sort.py b/sorts/tree_sort.py index d06b0de28e56..baa4fc1acc20 100644 --- a/sorts/tree_sort.py +++ b/sorts/tree_sort.py @@ -1,14 +1,18 @@ -# Tree_sort algorithm -# Build a BST and in order traverse. +""" +Tree_sort algorithm. + +Build a BST and in order traverse. +""" + class node(): # BST data structure def __init__(self, val): self.val = val - self.left = None - self.right = None - - def insert(self,val): + self.left = None + self.right = None + + def insert(self, val): if self.val: if val < self.val: if self.left is None: @@ -23,24 +27,27 @@ def insert(self,val): else: self.val = val + def inorder(root, res): - # Recursive travesal + # Recursive travesal if root: - inorder(root.left,res) + inorder(root.left, res) res.append(root.val) - inorder(root.right,res) + inorder(root.right, res) + def tree_sort(arr): # Build BST if len(arr) == 0: return arr root = node(arr[0]) - for i in range(1,len(arr)): + for i in range(1, len(arr)): root.insert(arr[i]) - # Traverse BST in order. + # Traverse BST in order. res = [] - inorder(root,res) + inorder(root, res) return res + if __name__ == '__main__': - print(tree_sort([10,1,3,2,9,14,13])) + print(tree_sort([10, 1, 3, 2, 9, 14, 13])) diff --git a/sorts/wiggle_sort.py b/sorts/wiggle_sort.py index 0d4f20e3f96b..606feb4d3dd1 100644 --- a/sorts/wiggle_sort.py +++ b/sorts/wiggle_sort.py @@ -1,17 +1,24 @@ """ -Given an unsorted array nums, reorder it such that nums[0] < nums[1] > nums[2] < nums[3].... +Wiggle Sort. + +Given an unsorted array nums, reorder it such +that nums[0] < nums[1] > nums[2] < nums[3].... For example: -if input numbers = [3, 5, 2, 1, 6, 4] +if input numbers = [3, 5, 2, 1, 6, 4] one possible Wiggle Sorted answer is [3, 5, 1, 6, 2, 4]. """ + + def wiggle_sort(nums): + """Perform Wiggle Sort.""" for i in range(len(nums)): - if (i % 2 == 1) == (nums[i-1] > nums[i]): - nums[i-1], nums[i] = nums[i], nums[i-1] + if (i % 2 == 1) == (nums[i - 1] > nums[i]): + nums[i - 1], nums[i] = nums[i], nums[i - 1] + if __name__ == '__main__': print("Enter the array elements:\n") - array=list(map(int,input().split())) + array = list(map(int, input().split())) print("The unsorted array is:\n") print(array) wiggle_sort(array) From a2236cfb97ce96b13c03be9761a3d053997aace9 Mon Sep 17 00:00:00 2001 From: PatOnTheBack <51241310+PatOnTheBack@users.noreply.github.com> Date: Tue, 2 Jul 2019 00:05:43 -0400 Subject: [PATCH 022/193] Improve Formatting and Code Quality (#934) * Improved Formatting of basic_maths.py - Added docstrings. - Improved whitespace formatting. - Renamed functions to match snake_case. * Improved Formatting of factorial_python.py - Added docstrings. - Improved whitespace formatting. - Renamed constants to match UPPER_CASE. * Improved Formatting of factorial_recursive.py - Improved whitespace formatting to meet PyLint standards. * Improved Code to Conform to PyLint - Renamed `max` to `max_num` to avoid redefining built-in 'max' [pylint] - Removed unnecessary parens after 'while' keyword [pylint] * Improved Formatting of factorial_recursive.py - Added docstrings. - Improved whitespace formatting. --- maths/basic_maths.py | 66 +++++++++++++++++++++--------------- maths/factorial_python.py | 22 ++++++------ maths/factorial_recursive.py | 17 +++++----- maths/find_lcm.py | 8 ++--- sorts/pancake_sort.py | 15 ++++---- 5 files changed, 71 insertions(+), 57 deletions(-) diff --git a/maths/basic_maths.py b/maths/basic_maths.py index 6e8c919a001d..cd7bac0113b8 100644 --- a/maths/basic_maths.py +++ b/maths/basic_maths.py @@ -1,74 +1,84 @@ +"""Implementation of Basic Math in Python.""" import math -def primeFactors(n): + +def prime_factors(n): + """Find Prime Factors.""" pf = [] while n % 2 == 0: pf.append(2) n = int(n / 2) - - for i in range(3, int(math.sqrt(n))+1, 2): + + for i in range(3, int(math.sqrt(n)) + 1, 2): while n % i == 0: pf.append(i) n = int(n / i) - + if n > 2: pf.append(n) - + return pf -def numberOfDivisors(n): + +def number_of_divisors(n): + """Calculate Number of Divisors of an Integer.""" div = 1 - + temp = 1 while n % 2 == 0: temp += 1 n = int(n / 2) - div = div * (temp) - - for i in range(3, int(math.sqrt(n))+1, 2): + div = div * (temp) + + for i in range(3, int(math.sqrt(n)) + 1, 2): temp = 1 while n % i == 0: temp += 1 n = int(n / i) div = div * (temp) - + return div -def sumOfDivisors(n): + +def sum_of_divisors(n): + """Calculate Sum of Divisors.""" s = 1 - + temp = 1 while n % 2 == 0: temp += 1 n = int(n / 2) if temp > 1: - s *= (2**temp - 1) / (2 - 1) - - for i in range(3, int(math.sqrt(n))+1, 2): + s *= (2**temp - 1) / (2 - 1) + + for i in range(3, int(math.sqrt(n)) + 1, 2): temp = 1 while n % i == 0: temp += 1 n = int(n / i) if temp > 1: s *= (i**temp - 1) / (i - 1) - + return s -def eulerPhi(n): - l = primeFactors(n) + +def euler_phi(n): + """Calculte Euler's Phi Function.""" + l = prime_factors(n) l = set(l) s = n for x in l: - s *= (x - 1)/x - return s + s *= (x - 1) / x + return s + def main(): - print(primeFactors(100)) - print(numberOfDivisors(100)) - print(sumOfDivisors(100)) - print(eulerPhi(100)) - + """Print the Results of Basic Math Operations.""" + print(prime_factors(100)) + print(number_of_divisors(100)) + print(sum_of_divisors(100)) + print(euler_phi(100)) + + if __name__ == '__main__': main() - - \ No newline at end of file diff --git a/maths/factorial_python.py b/maths/factorial_python.py index 376983e08dab..6c1349fd5f4c 100644 --- a/maths/factorial_python.py +++ b/maths/factorial_python.py @@ -1,19 +1,19 @@ -# Python program to find the factorial of a number provided by the user. +"""Python program to find the factorial of a number provided by the user.""" # change the value for a different result -num = 10 +NUM = 10 # uncomment to take input from the user -#num = int(input("Enter a number: ")) +# num = int(input("Enter a number: ")) -factorial = 1 +FACTORIAL = 1 # check if the number is negative, positive or zero -if num < 0: - print("Sorry, factorial does not exist for negative numbers") -elif num == 0: - print("The factorial of 0 is 1") +if NUM < 0: + print("Sorry, factorial does not exist for negative numbers") +elif NUM == 0: + print("The factorial of 0 is 1") else: - for i in range(1,num + 1): - factorial = factorial*i - print("The factorial of",num,"is",factorial) + for i in range(1, NUM + 1): + FACTORIAL = FACTORIAL * i + print("The factorial of", NUM, "is", FACTORIAL) diff --git a/maths/factorial_recursive.py b/maths/factorial_recursive.py index 41391a2718f6..06173dcbcd7d 100644 --- a/maths/factorial_recursive.py +++ b/maths/factorial_recursive.py @@ -1,13 +1,14 @@ def fact(n): - """ - Return 1, if n is 1 or below, - otherwise, return n * fact(n-1). - """ - return 1 if n <= 1 else n * fact(n-1) + """ + Return 1, if n is 1 or below, + otherwise, return n * fact(n-1). + """ + return 1 if n <= 1 else n * fact(n - 1) + """ -Shown factorial for i, +Show factorial for i, where i ranges from 1 to 20. """ -for i in range(1,21): - print(i, ": ", fact(i), sep='') +for i in range(1, 21): + print(i, ": ", fact(i), sep='') diff --git a/maths/find_lcm.py b/maths/find_lcm.py index 779cb128898e..9062d462b8b3 100644 --- a/maths/find_lcm.py +++ b/maths/find_lcm.py @@ -5,12 +5,12 @@ def find_lcm(num_1, num_2): """Find the LCM of two numbers.""" - max = num_1 if num_1 > num_2 else num_2 - lcm = max - while (True): + max_num = num_1 if num_1 > num_2 else num_2 + lcm = max_num + while True: if ((lcm % num_1 == 0) and (lcm % num_2 == 0)): break - lcm += max + lcm += max_num return lcm diff --git a/sorts/pancake_sort.py b/sorts/pancake_sort.py index 478a9a967d27..3b48bc6e46d9 100644 --- a/sorts/pancake_sort.py +++ b/sorts/pancake_sort.py @@ -1,17 +1,20 @@ -# Pancake sort algorithm +"""Pancake Sort Algorithm.""" # Only can reverse array from 0 to i + def pancake_sort(arr): + """Sort Array with Pancake Sort.""" cur = len(arr) while cur > 1: # Find the maximum number in arr mi = arr.index(max(arr[0:cur])) - # Reverse from 0 to mi - arr = arr[mi::-1] + arr[mi+1:len(arr)] - # Reverse whole list - arr = arr[cur-1::-1] + arr[cur:len(arr)] + # Reverse from 0 to mi + arr = arr[mi::-1] + arr[mi + 1:len(arr)] + # Reverse whole list + arr = arr[cur - 1::-1] + arr[cur:len(arr)] cur -= 1 return arr + if __name__ == '__main__': - print(pancake_sort([0,10,15,3,2,9,14,13])) + print(pancake_sort([0, 10, 15, 3, 2, 9, 14, 13])) From 27a8184ccf871a3afa22839761bf507306c52d58 Mon Sep 17 00:00:00 2001 From: Dharni0607 <30770547+Dharni0607@users.noreply.github.com> Date: Tue, 2 Jul 2019 17:49:31 +0530 Subject: [PATCH 023/193] add ons in string directory - Bayer_Moore_Search (#933) --- strings/Boyer_Moore_Search.py | 88 +++++++++++++++++++++++++++++++++++ 1 file changed, 88 insertions(+) create mode 100644 strings/Boyer_Moore_Search.py diff --git a/strings/Boyer_Moore_Search.py b/strings/Boyer_Moore_Search.py new file mode 100644 index 000000000000..781ff0ca6106 --- /dev/null +++ b/strings/Boyer_Moore_Search.py @@ -0,0 +1,88 @@ +""" +The algorithm finds the pattern in given text using following rule. + +The bad-character rule considers the mismatched character in Text. +The next occurrence of that character to the left in Pattern is found, + +If the mismatched character occurs to the left in Pattern, +a shift is proposed that aligns text block and pattern. + +If the mismatched character does not occur to the left in Pattern, +a shift is proposed that moves the entirety of Pattern past +the point of mismatch in the text. + +If there no mismatch then the pattern matches with text block. + +Time Complexity : O(n/m) + n=length of main string + m=length of pattern string +""" + + +class BoyerMooreSearch: + + + def __init__(self, text, pattern): + self.text, self.pattern = text, pattern + self.textLen, self.patLen = len(text), len(pattern) + + + def match_in_pattern(self, char): + """ finds the index of char in pattern in reverse order + + Paremeters : + char (chr): character to be searched + + Returns : + i (int): index of char from last in pattern + -1 (int): if char is not found in pattern + """ + + for i in range(self.patLen-1, -1, -1): + if char == self.pattern[i]: + return i + return -1 + + + def mismatch_in_text(self, currentPos): + """ finds the index of mis-matched character in text when compared with pattern from last + + Paremeters : + currentPos (int): current index position of text + + Returns : + i (int): index of mismatched char from last in text + -1 (int): if there is no mis-match between pattern and text block + """ + + for i in range(self.patLen-1, -1, -1): + if self.pattern[i] != self.text[currentPos + i]: + return currentPos + i + return -1 + + + def bad_character_heuristic(self): + # searches pattern in text and returns index positions + positions = [] + for i in range(self.textLen - self.patLen + 1): + mismatch_index = self.mismatch_in_text(i) + if mismatch_index == -1: + positions.append(i) + else: + match_index = self.match_in_pattern(self.text[mismatch_index]) + i = mismatch_index - match_index #shifting index + return positions + + +text = "ABAABA" +pattern = "AB" +bms = BoyerMooreSearch(text, pattern) +positions = bms.bad_character_heuristic() + +if len(positions) == 0: + print("No match found") +else: + print("Pattern found in following positions: ") + print(positions) + + From 0f56ab5c3cfe50505d12e48493b0f043d1a6fc7a Mon Sep 17 00:00:00 2001 From: Dharni0607 <30770547+Dharni0607@users.noreply.github.com> Date: Tue, 2 Jul 2019 17:50:25 +0530 Subject: [PATCH 024/193] Divide and conquer Algorithms Issue#817 (#938) * add ons in string directory - Bayer_Moore_Search * created divide_and_conquer folder and added max_sub_array_sum.py under it (issue #817) --- divide_and_conquer/max_sub_array_sum.py | 72 +++++++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 divide_and_conquer/max_sub_array_sum.py diff --git a/divide_and_conquer/max_sub_array_sum.py b/divide_and_conquer/max_sub_array_sum.py new file mode 100644 index 000000000000..531a45abca6f --- /dev/null +++ b/divide_and_conquer/max_sub_array_sum.py @@ -0,0 +1,72 @@ +""" +Given a array of length n, max_sub_array_sum() finds the maximum of sum of contiguous sub-array using divide and conquer method. + +Time complexity : O(n log n) + +Ref : INTRODUCTION TO ALGORITHMS THIRD EDITION (section : 4, sub-section : 4.1, page : 70) + +""" + + +def max_sum_from_start(array): + """ This function finds the maximum contiguous sum of array from 0 index + + Parameters : + array (list[int]) : given array + + Returns : + max_sum (int) : maximum contiguous sum of array from 0 index + + """ + array_sum = 0 + max_sum = float("-inf") + for num in array: + array_sum += num + if array_sum > max_sum: + max_sum = array_sum + return max_sum + + +def max_cross_array_sum(array, left, mid, right): + """ This function finds the maximum contiguous sum of left and right arrays + + Parameters : + array, left, mid, right (list[int], int, int, int) + + Returns : + (int) : maximum of sum of contiguous sum of left and right arrays + + """ + + max_sum_of_left = max_sum_from_start(array[left:mid+1][::-1]) + max_sum_of_right = max_sum_from_start(array[mid+1: right+1]) + return max_sum_of_left + max_sum_of_right + + +def max_sub_array_sum(array, left, right): + """ This function finds the maximum of sum of contiguous sub-array using divide and conquer method + + Parameters : + array, left, right (list[int], int, int) : given array, current left index and current right index + + Returns : + int : maximum of sum of contiguous sub-array + + """ + + # base case: array has only one element + if left == right: + return array[right] + + # Recursion + mid = (left + right) // 2 + left_half_sum = max_sub_array_sum(array, left, mid) + right_half_sum = max_sub_array_sum(array, mid + 1, right) + cross_sum = max_cross_array_sum(array, left, mid, right) + return max(left_half_sum, right_half_sum, cross_sum) + + +array = [-2, -5, 6, -2, -3, 1, 5, -6] +array_length = len(array) +print("Maximum sum of contiguous subarray:", max_sub_array_sum(array, 0, array_length - 1)) + From 65a12fa317935e04ed0e747db234fe4601e96084 Mon Sep 17 00:00:00 2001 From: Jigyasa G <33327397+jpg-130@users.noreply.github.com> Date: Tue, 2 Jul 2019 20:53:35 +0530 Subject: [PATCH 025/193] Adding sum of subsets (#929) --- dynamic_programming/sum_of_subset.py | 34 ++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 dynamic_programming/sum_of_subset.py diff --git a/dynamic_programming/sum_of_subset.py b/dynamic_programming/sum_of_subset.py new file mode 100644 index 000000000000..f6509a259c5d --- /dev/null +++ b/dynamic_programming/sum_of_subset.py @@ -0,0 +1,34 @@ +def isSumSubset(arr, arrLen, requiredSum): + + # a subset value says 1 if that subset sum can be formed else 0 + #initially no subsets can be formed hence False/0 + subset = ([[False for i in range(requiredSum + 1)] for i in range(arrLen + 1)]) + + #for each arr value, a sum of zero(0) can be formed by not taking any element hence True/1 + for i in range(arrLen + 1): + subset[i][0] = True + + #sum is not zero and set is empty then false + for i in range(1, requiredSum + 1): + subset[0][i] = False + + for i in range(1, arrLen + 1): + for j in range(1, requiredSum + 1): + if arr[i-1]>j: + subset[i][j] = subset[i-1][j] + if arr[i-1]<=j: + subset[i][j] = (subset[i-1][j] or subset[i-1][j-arr[i-1]]) + + #uncomment to print the subset + # for i in range(arrLen+1): + # print(subset[i]) + + return subset[arrLen][requiredSum] + +arr = [2, 4, 6, 8] +requiredSum = 5 +arrLen = len(arr) +if isSumSubset(arr, arrLen, requiredSum): + print("Found a subset with required sum") +else: + print("No subset with required sum") \ No newline at end of file From 4fb4cb4fd1023e742ba7c06bce70e042294cd157 Mon Sep 17 00:00:00 2001 From: cclauss Date: Wed, 3 Jul 2019 09:21:03 +0200 Subject: [PATCH 026/193] Travis CI: Simplify the testing (#887) * Travis CI: Simplify the testing * flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics * python: 3.7 * dist: xenial for python: 3.7 * Delete .lgtm.yml These changes were created to get around the fact that Travis CI was not enabled on this repo. Now that Travis is enabled, we can remove these modifications. The problems are that: 1. [LGTM does not want us running flake8 on their infrstructure](https://discuss.lgtm.com/t/can-i-get-lgtm-to-run-flake8-tests/1445/6) and 2. when we do, it [does not work as expected](https://discuss.lgtm.com/t/tests-are-not-automatically-run-when-lgtm-yml-is-modified/1446/4). --- .lgtm.yml | 12 ------------ .travis.yml | 29 ++++------------------------- 2 files changed, 4 insertions(+), 37 deletions(-) delete mode 100644 .lgtm.yml diff --git a/.lgtm.yml b/.lgtm.yml deleted file mode 100644 index ec550ab72705..000000000000 --- a/.lgtm.yml +++ /dev/null @@ -1,12 +0,0 @@ -extraction: - python: - python_setup: - version: 3 - after_prepare: - - python3 -m pip install --upgrade --user flake8 - before_index: - - python3 -m flake8 --version # flake8 3.6.0 on CPython 3.6.5 on Linux - # stop the build if there are Python syntax errors or undefined names - - python3 -m flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - - python3 -m flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics diff --git a/.travis.yml b/.travis.yml index 2440899e4f25..8676e5127334 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,26 +1,5 @@ language: python -cache: pip -python: - - 2.7 - - 3.6 - #- nightly - #- pypy - #- pypy3 -matrix: - allow_failures: - - python: nightly - - python: pypy - - python: pypy3 -install: - #- pip install -r requirements.txt - - pip install flake8 # pytest # add another testing frameworks later -before_script: - # stop the build if there are Python syntax errors or undefined names - - flake8 . --count --select=E9,F63,F72,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics -script: - - true # pytest --capture=sys # add other tests here -notifications: - on_success: change - on_failure: change # `always` will be the setting once code changes slow down +dist: xenial # required for Python >= 3.7 +python: 3.7 +install: pip install flake8 +script: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics From 03f994077557e67493f41ba4c3f7aca08da000d4 Mon Sep 17 00:00:00 2001 From: Ashok Bakthavathsalam Date: Wed, 3 Jul 2019 21:01:10 +0530 Subject: [PATCH 027/193] Refactored to one pop() (#917) --- sorts/merge_sort.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sorts/merge_sort.py b/sorts/merge_sort.py index ecbad7075119..714861e72642 100644 --- a/sorts/merge_sort.py +++ b/sorts/merge_sort.py @@ -37,7 +37,7 @@ def merge(left, right): ''' result = [] while left and right: - result.append(left.pop(0) if left[0] <= right[0] else right.pop(0)) + result.append((left if left[0] <= right[0] else right).pop(0)) return result + left + right if len(collection) <= 1: return collection @@ -53,4 +53,4 @@ def merge(left, right): user_input = raw_input('Enter numbers separated by a comma:\n').strip() unsorted = [int(item) for item in user_input.split(',')] - print(*merge_sort(unsorted), sep=',') \ No newline at end of file + print(*merge_sort(unsorted), sep=',') From 035457f569dc79e8b7b6d3a493895c67182d3539 Mon Sep 17 00:00:00 2001 From: Dharni0607 <30770547+Dharni0607@users.noreply.github.com> Date: Thu, 4 Jul 2019 13:19:14 +0530 Subject: [PATCH 028/193] closest pair of points algo (#943) * created divide_and_conquer folder and added max_sub_array_sum.py under it (issue #817) * additional file in divide_and_conqure (closest pair of points) --- divide_and_conquer/closest_pair_of_points.py | 113 +++++++++++++++++++ divide_and_conquer/max_subarray_sum.py | 75 ++++++++++++ 2 files changed, 188 insertions(+) create mode 100644 divide_and_conquer/closest_pair_of_points.py create mode 100644 divide_and_conquer/max_subarray_sum.py diff --git a/divide_and_conquer/closest_pair_of_points.py b/divide_and_conquer/closest_pair_of_points.py new file mode 100644 index 000000000000..cc5be428db79 --- /dev/null +++ b/divide_and_conquer/closest_pair_of_points.py @@ -0,0 +1,113 @@ +""" +The algorithm finds distance btw closest pair of points in the given n points. +Approach used -> Divide and conquer +The points are sorted based on Xco-ords +& by applying divide and conquer approach, +minimum distance is obtained recursively. + +>> closest points lie on different sides of partition +This case handled by forming a strip of points +whose Xco-ords distance is less than closest_pair_dis +from mid-point's Xco-ords. +Closest pair distance is found in the strip of points. (closest_in_strip) + +min(closest_pair_dis, closest_in_strip) would be the final answer. + +Time complexity: O(n * (logn)^2) +""" + + +import math + + +def euclidean_distance_sqr(point1, point2): + return pow(point1[0] - point2[0], 2) + pow(point1[1] - point2[1], 2) + + +def column_based_sort(array, column = 0): + return sorted(array, key = lambda x: x[column]) + + +def dis_between_closest_pair(points, points_counts, min_dis = float("inf")): + """ brute force approach to find distance between closest pair points + + Parameters : + points, points_count, min_dis (list(tuple(int, int)), int, int) + + Returns : + min_dis (float): distance between closest pair of points + + """ + + for i in range(points_counts - 1): + for j in range(i+1, points_counts): + current_dis = euclidean_distance_sqr(points[i], points[j]) + if current_dis < min_dis: + min_dis = current_dis + return min_dis + + +def dis_between_closest_in_strip(points, points_counts, min_dis = float("inf")): + """ closest pair of points in strip + + Parameters : + points, points_count, min_dis (list(tuple(int, int)), int, int) + + Returns : + min_dis (float): distance btw closest pair of points in the strip (< min_dis) + + """ + + for i in range(min(6, points_counts - 1), points_counts): + for j in range(max(0, i-6), i): + current_dis = euclidean_distance_sqr(points[i], points[j]) + if current_dis < min_dis: + min_dis = current_dis + return min_dis + + +def closest_pair_of_points_sqr(points, points_counts): + """ divide and conquer approach + + Parameters : + points, points_count (list(tuple(int, int)), int) + + Returns : + (float): distance btw closest pair of points + + """ + + # base case + if points_counts <= 3: + return dis_between_closest_pair(points, points_counts) + + # recursion + mid = points_counts//2 + closest_in_left = closest_pair_of_points(points[:mid], mid) + closest_in_right = closest_pair_of_points(points[mid:], points_counts - mid) + closest_pair_dis = min(closest_in_left, closest_in_right) + + """ cross_strip contains the points, whose Xcoords are at a + distance(< closest_pair_dis) from mid's Xcoord + """ + + cross_strip = [] + for point in points: + if abs(point[0] - points[mid][0]) < closest_pair_dis: + cross_strip.append(point) + + cross_strip = column_based_sort(cross_strip, 1) + closest_in_strip = dis_between_closest_in_strip(cross_strip, + len(cross_strip), closest_pair_dis) + return min(closest_pair_dis, closest_in_strip) + + +def closest_pair_of_points(points, points_counts): + return math.sqrt(closest_pair_of_points_sqr(points, points_counts)) + + +points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (0, 2), (5, 6), (1, 2)] +points = column_based_sort(points) +print("Distance:", closest_pair_of_points(points, len(points))) + + diff --git a/divide_and_conquer/max_subarray_sum.py b/divide_and_conquer/max_subarray_sum.py new file mode 100644 index 000000000000..0428f4e13768 --- /dev/null +++ b/divide_and_conquer/max_subarray_sum.py @@ -0,0 +1,75 @@ +""" +Given a array of length n, max_subarray_sum() finds +the maximum of sum of contiguous sub-array using divide and conquer method. + +Time complexity : O(n log n) + +Ref : INTRODUCTION TO ALGORITHMS THIRD EDITION +(section : 4, sub-section : 4.1, page : 70) + +""" + + +def max_sum_from_start(array): + """ This function finds the maximum contiguous sum of array from 0 index + + Parameters : + array (list[int]) : given array + + Returns : + max_sum (int) : maximum contiguous sum of array from 0 index + + """ + array_sum = 0 + max_sum = float("-inf") + for num in array: + array_sum += num + if array_sum > max_sum: + max_sum = array_sum + return max_sum + + +def max_cross_array_sum(array, left, mid, right): + """ This function finds the maximum contiguous sum of left and right arrays + + Parameters : + array, left, mid, right (list[int], int, int, int) + + Returns : + (int) : maximum of sum of contiguous sum of left and right arrays + + """ + + max_sum_of_left = max_sum_from_start(array[left:mid+1][::-1]) + max_sum_of_right = max_sum_from_start(array[mid+1: right+1]) + return max_sum_of_left + max_sum_of_right + + +def max_subarray_sum(array, left, right): + """ Maximum contiguous sub-array sum, using divide and conquer method + + Parameters : + array, left, right (list[int], int, int) : + given array, current left index and current right index + + Returns : + int : maximum of sum of contiguous sub-array + + """ + + # base case: array has only one element + if left == right: + return array[right] + + # Recursion + mid = (left + right) // 2 + left_half_sum = max_subarray_sum(array, left, mid) + right_half_sum = max_subarray_sum(array, mid + 1, right) + cross_sum = max_cross_array_sum(array, left, mid, right) + return max(left_half_sum, right_half_sum, cross_sum) + + +array = [-2, -5, 6, -2, -3, 1, 5, -6] +array_length = len(array) +print("Maximum sum of contiguous subarray:", max_subarray_sum(array, 0, array_length - 1)) + From 05fc7f8a33df7d08dba1f162d7618f4e353b534f Mon Sep 17 00:00:00 2001 From: Hector S Date: Thu, 4 Jul 2019 11:18:57 -0400 Subject: [PATCH 029/193] Added '~script.py' to ignore files and updated DIRECTORY.md (#926) * Added print function into matrix_multiplication_addition.py and removed blank space in data_structures/binary tree directory * Removed .vs/ folder per #893 * Rename matrix_multiplication_addition.py to matrix_operation.py * Added '~script.py' to ignore files and updated DIRECTORY.md --- DIRECTORY.md | 22 ++++++++++++---------- ~script.py | 2 +- 2 files changed, 13 insertions(+), 11 deletions(-) diff --git a/DIRECTORY.md b/DIRECTORY.md index ad25772b56b6..befd634c1eb0 100644 --- a/DIRECTORY.md +++ b/DIRECTORY.md @@ -1,6 +1,3 @@ -## Analysis - * Compression Analysis - * [psnr](https://github.com/TheAlgorithms/Python/blob/master/analysis/compression_analysis/psnr.py) ## Arithmetic Analysis * [bisection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/bisection.py) * [intersection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/intersection.py) @@ -39,17 +36,19 @@ * [xor cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/xor_cipher.py) ## Compression * [huffman](https://github.com/TheAlgorithms/Python/blob/master/compression/huffman.py) +## Compression Analysis + * [psnr](https://github.com/TheAlgorithms/Python/blob/master/compression_analysis/psnr.py) ## Data Structures * [arrays](https://github.com/TheAlgorithms/Python/blob/master/data_structures/arrays.py) * [avl](https://github.com/TheAlgorithms/Python/blob/master/data_structures/avl.py) * [LCA](https://github.com/TheAlgorithms/Python/blob/master/data_structures/LCA.py) * Binary Tree - * [AVL tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary%20tree/AVL_tree.py) - * [binary search tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary%20tree/binary_search_tree.py) - * [fenwick tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary%20tree/fenwick_tree.py) - * [lazy segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary%20tree/lazy_segment_tree.py) - * [segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary%20tree/segment_tree.py) - * [treap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary%20tree/treap.py) + * [AVL tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/AVL_tree.py) + * [binary search tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_search_tree.py) + * [fenwick tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/fenwick_tree.py) + * [lazy segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lazy_segment_tree.py) + * [segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/segment_tree.py) + * [treap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/treap.py) * Hashing * [double hash](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/double_hash.py) * [hash table](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table.py) @@ -192,8 +191,9 @@ * Tests * [test fibonacci](https://github.com/TheAlgorithms/Python/blob/master/maths/tests/test_fibonacci.py) ## Matrix - * [matrix multiplication addition](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_multiplication_addition.py) + * [matrix operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_operation.py) * [searching in sorted matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/searching_in_sorted_matrix.py) + * [spiralPrint](https://github.com/TheAlgorithms/Python/blob/master/matrix/spiralPrint.py) ## Networking Flow * [ford fulkerson](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/ford_fulkerson.py) * [minimum cut](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/minimum_cut.py) @@ -294,6 +294,8 @@ * [p022 names](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/p022_names.txt) * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/sol1.py) * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/sol2.py) + * Problem 234 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_234/sol1.py) * Problem 24 * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_24/sol1.py) * Problem 25 diff --git a/~script.py b/~script.py index 4a2c61c83563..c44f3436fcec 100644 --- a/~script.py +++ b/~script.py @@ -52,7 +52,7 @@ def _markdown(parent, ignores, ignores_ext, depth): ignores = [".vs", ".gitignore", ".git", - "script.py", + "~script.py", "__init__.py", ] # Files with given entensions will be ignored From 408c5deb3adb0f128f51d3d72e7664f5b864e9b7 Mon Sep 17 00:00:00 2001 From: Shoujue Xu Date: Fri, 5 Jul 2019 16:20:11 +0800 Subject: [PATCH 030/193] add gaussian filter algorithm and lena.jpg (#955) --- .../filters/gaussian_filter.py | 53 ++++++++++++++++++ .../filters/median_filter.py | 2 +- digital_image_processing/image_data/lena.jpg | Bin 0 -> 104428 bytes 3 files changed, 54 insertions(+), 1 deletion(-) create mode 100644 digital_image_processing/filters/gaussian_filter.py create mode 100644 digital_image_processing/image_data/lena.jpg diff --git a/digital_image_processing/filters/gaussian_filter.py b/digital_image_processing/filters/gaussian_filter.py new file mode 100644 index 000000000000..ff85ce047220 --- /dev/null +++ b/digital_image_processing/filters/gaussian_filter.py @@ -0,0 +1,53 @@ +""" +Implementation of gaussian filter algorithm +""" +from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey +from numpy import pi, mgrid, exp, square, zeros, ravel, dot, uint8 + + +def gen_gaussian_kernel(k_size, sigma): + center = k_size // 2 + x, y = mgrid[0-center:k_size-center, 0-center:k_size-center] + g = 1/(2*pi*sigma) * exp(-(square(x) + square(y))/(2*square(sigma))) + return g + + +def gaussian_filter(image, k_size, sigma): + height, width = image.shape[0], image.shape[1] + # dst image height and width + dst_height = height-k_size+1 + dst_width = width-k_size+1 + + # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows + image_array = zeros((dst_height*dst_width, k_size*k_size)) + row = 0 + for i in range(0, dst_height): + for j in range(0, dst_width): + window = ravel(image[i:i + k_size, j:j + k_size]) + image_array[row, :] = window + row += 1 + + # turn the kernel into shape(k*k, 1) + gaussian_kernel = gen_gaussian_kernel(k_size, sigma) + filter_array = ravel(gaussian_kernel) + + # reshape and get the dst image + dst = dot(image_array, filter_array).reshape(dst_height, dst_width).astype(uint8) + + return dst + + +if __name__ == '__main__': + # read original image + img = imread(r'../image_data/lena.jpg') + # turn image in gray scale value + gray = cvtColor(img, COLOR_BGR2GRAY) + + # get values with two different mask size + gaussian3x3 = gaussian_filter(gray, 3, sigma=1) + gaussian5x5 = gaussian_filter(gray, 5, sigma=0.8) + + # show result images + imshow('gaussian filter with 3x3 mask', gaussian3x3) + imshow('gaussian filter with 5x5 mask', gaussian5x5) + waitKey() diff --git a/digital_image_processing/filters/median_filter.py b/digital_image_processing/filters/median_filter.py index eea4295632a1..ed20b1ab7f78 100644 --- a/digital_image_processing/filters/median_filter.py +++ b/digital_image_processing/filters/median_filter.py @@ -28,7 +28,7 @@ def median_filter(gray_img, mask=3): if __name__ == '__main__': # read original image - img = imread('lena.jpg') + img = imread('../image_data/lena.jpg') # turn image in gray scale value gray = cvtColor(img, COLOR_BGR2GRAY) diff --git a/digital_image_processing/image_data/lena.jpg b/digital_image_processing/image_data/lena.jpg new file mode 100644 index 0000000000000000000000000000000000000000..15c4d9764effe5ad9d84dc0e4af553788f80dc73 GIT binary patch literal 104428 zcmb5V_gfR+7cCq*gdU0lqLfe)ib|Ct(j}pXfE1~MbdX*IrB`Vop?4A>G!a6TCZa+> zzz{&XpmYIgB7%7ReBbx}0e8}rm0F)H}*?*q$KclAl&(ly-Q&H2>(9-^Ir(>X}rDLF@rKM-2X8`_Z|Bjd#flU9M z{I|&eHR+GZ6%5mEU^0kH%BR=Y+4prraQ+W!p(pr)au zqNSq%(El4%W&aoJUv+5z-6Rz?)&ELJNkz?0^DmT!>5u;+MQ72Lzk2hDQ)We2^MFeh z0e13_p_4;3|D6Lc{hL6^PQ?zm2beG-%Uw~5W*}#V$J~1=7-k_CkB3v!w3Ue}y=ieZ zS+(Aq4}kCvZ*A@Pjw=;wqa5mo#kSRgzZ&DEk3K!?_mbkK3B%WVDSVFoA{xBz`m-5o 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zN&=u{B6d4Z>G!R^+}#f&uASY=Dk70$2E&$-{;W-N732j4LpJ0Me)O~pbft=fx}>BW zrCuE-NguRYcIGxcp z!c~kqlis#M2szwsqDJ~{teYO(^_0e$E;X2fVG@ECCqtsPAKumV)p1{#LG;sB)Tx!g zQSKHBOzQ`z@;Yr|965YFRaVfbMo91n5@hDhsQ_)u=Vw#KW0Z%BS!^{R*;toV(@f(c zp-3S|P^q->fv zsx=&a;$LMXMF?M<^ziR0nbLfT5pRJCkgu29_8PAG&QFuR87hL1Y zUP58qvO$3ZeF;2|Q)ZMGP4$VZ5lMGGU0c$UbTV|(bpv?Yot)rnrnd8}XwWo3Leik6 yIl0^)cx&|dTJ_=g3hA!ftp!xbFQr7MI#PT?eYDnXD$1cp9n&fbS5{0Jy8qc;#+b|i literal 0 HcmV?d00001 From 217615abf68592e69725bbb4ef8d86713e9be1ce Mon Sep 17 00:00:00 2001 From: PatOnTheBack <51241310+PatOnTheBack@users.noreply.github.com> Date: Fri, 5 Jul 2019 04:34:46 -0400 Subject: [PATCH 031/193] Removed Unused Variables (#949) - Removed two unused variables. - Changed `a` to `_` since the `a` variable is never used. This addresses [3 alerts from lgtm](https://lgtm.com/projects/g/TheAlgorithms/Python/snapshot/d55bbcd204dfbd436914a5f9031a6a8fdf22f6f4/files/sorts/Odd-Even_transposition_parallel.py?sort=name&dir=ASC&mode=heatmap). --- sorts/Odd-Even_transposition_parallel.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/sorts/Odd-Even_transposition_parallel.py b/sorts/Odd-Even_transposition_parallel.py index d7f983fc0469..9bf81a39e27a 100644 --- a/sorts/Odd-Even_transposition_parallel.py +++ b/sorts/Odd-Even_transposition_parallel.py @@ -70,13 +70,11 @@ def oeProcess(position, value, LSend, RSend, LRcv, RRcv, resultPipe): def OddEvenTransposition(arr): processArray = [] - tempRrcv = None - tempLrcv = None resultPipe = [] #initialize the list of pipes where the values will be retrieved - for a in arr: + for _ in arr: resultPipe.append(Pipe()) #creates the processes From 1c9d995b9eb05f439fee5892210af3ab659f9760 Mon Sep 17 00:00:00 2001 From: PatOnTheBack <51241310+PatOnTheBack@users.noreply.github.com> Date: Fri, 5 Jul 2019 04:36:48 -0400 Subject: [PATCH 032/193] Implement Three New Algorithms (#948) * Create average_median.py I created a program to calculate the median of a list of numbers. * Changed Odd to Even in String * Create decimal_to_binary.py - Added 'conversions' folder. - Created a decimal to binary converter. * Implemented Decimal to Octal Algorithm - I created a decimal to octal converter based on the converter in the TheAlgorithms/Python project. - I added two newlines to make the output of decimal_to_binary.py better. --- conversions/decimal_to_binary.py | 25 +++++++++++++++++++ conversions/decimal_to_octal.py | 38 +++++++++++++++++++++++++++++ maths/average_median.py | 41 ++++++++++++++++++++++++++++++++ 3 files changed, 104 insertions(+) create mode 100644 conversions/decimal_to_binary.py create mode 100644 conversions/decimal_to_octal.py create mode 100644 maths/average_median.py diff --git a/conversions/decimal_to_binary.py b/conversions/decimal_to_binary.py new file mode 100644 index 000000000000..43ceee61a388 --- /dev/null +++ b/conversions/decimal_to_binary.py @@ -0,0 +1,25 @@ +"""Convert a Decimal Number to a Binary Number.""" + + +def decimal_to_binary(num): + """Convert a Decimal Number to a Binary Number.""" + binary = [] + while num > 0: + binary.insert(0, num % 2) + num >>= 1 + return "".join(str(e) for e in binary) + + +def main(): + """Print binary equivelents of decimal numbers.""" + print("\n2 in binary is:") + print(decimal_to_binary(2)) # = 10 + print("\n7 in binary is:") + print(decimal_to_binary(7)) # = 111 + print("\n35 in binary is:") + print(decimal_to_binary(35)) # = 100011 + print("\n") + + +if __name__ == '__main__': + main() diff --git a/conversions/decimal_to_octal.py b/conversions/decimal_to_octal.py new file mode 100644 index 000000000000..187a0300e33a --- /dev/null +++ b/conversions/decimal_to_octal.py @@ -0,0 +1,38 @@ +"""Convert a Decimal Number to an Octal Number.""" + +import math + +# Modified from: +# https://github.com/TheAlgorithms/Javascript/blob/master/Conversions/DecimalToOctal.js + + +def decimal_to_octal(num): + """Convert a Decimal Number to an Octal Number.""" + octal = 0 + counter = 0 + while num > 0: + remainder = num % 8 + octal = octal + (remainder * math.pow(10, counter)) + counter += 1 + num = math.floor(num / 8) # basically /= 8 without remainder if any + # This formatting removes trailing '.0' from `octal`. + return'{0:g}'.format(float(octal)) + + +def main(): + """Print octal equivelents of decimal numbers.""" + print("\n2 in octal is:") + print(decimal_to_octal(2)) # = 2 + print("\n8 in octal is:") + print(decimal_to_octal(8)) # = 10 + print("\n65 in octal is:") + print(decimal_to_octal(65)) # = 101 + print("\n216 in octal is:") + print(decimal_to_octal(216)) # = 330 + print("\n512 in octal is:") + print(decimal_to_octal(512)) # = 1000 + print("\n") + + +if __name__ == '__main__': + main() diff --git a/maths/average_median.py b/maths/average_median.py new file mode 100644 index 000000000000..565bb4afd112 --- /dev/null +++ b/maths/average_median.py @@ -0,0 +1,41 @@ +""" +Find median of a list of numbers. + +Read more about medians: + https://en.wikipedia.org/wiki/Median +""" + + +def median(nums): + """Find median of a list of numbers.""" + # Sort list + sorted_list = sorted(nums) + print("List of numbers:") + print(sorted_list) + + # Is number of items in list even? + if len(sorted_list) % 2 == 0: + # Find index for first middle value. + mid_index_1 = len(sorted_list) / 2 + # Find index for second middle value. + mid_index_2 = -(len(sorted_list) / 2) - 1 + # Divide middle values by 2 to get average (mean). + med = (sorted_list[mid_index_1] + sorted_list[mid_index_2]) / float(2) + return med # Return makes `else:` unnecessary. + # Number of items is odd. + mid_index = (len(sorted_list) - 1) / 2 + # Middle index is median. + med = sorted_list[mid_index] + return med + + +def main(): + """Call average module to find median of a specific list of numbers.""" + print("Odd number of numbers:") + print(median([2, 4, 6, 8, 20, 50, 70])) + print("Even number of numbers:") + print(median([2, 4, 6, 8, 20, 50])) + + +if __name__ == '__main__': + main() From 506bb5ccfe97fe5b37faa2bcd9df0fd0fab07ac0 Mon Sep 17 00:00:00 2001 From: Jarred Allen Date: Fri, 5 Jul 2019 01:43:16 -0700 Subject: [PATCH 033/193] Add Red-Black Binary Search Trees (#954) * Wrote most of an rbt, missing just removal * Added some convenience methods. * Added a color method * Wrote code to delete, but has issues :( * Fixed a bug in Red-Black trees * Fixed bug in tree color validation and delete repairing * Clean up == comparison to None --- data_structures/binary_tree/red_black_tree.py | 665 ++++++++++++++++++ 1 file changed, 665 insertions(+) create mode 100644 data_structures/binary_tree/red_black_tree.py diff --git a/data_structures/binary_tree/red_black_tree.py b/data_structures/binary_tree/red_black_tree.py new file mode 100644 index 000000000000..4ca1301dd8fe --- /dev/null +++ b/data_structures/binary_tree/red_black_tree.py @@ -0,0 +1,665 @@ +class RedBlackTree: + """ + A Red-Black tree, which is a self-balancing BST (binary search + tree). + + This tree has similar performance to AVL trees, but the balancing is + less strict, so it will perform faster for writing/deleting nodes + and slower for reading in the average case, though, because they're + both balanced binary search trees, both will get the same asymptotic + perfomance. + + To read more about them, https://en.wikipedia.org/wiki/Red–black_tree + + Unless otherwise specified, all asymptotic runtimes are specified in + terms of the size of the tree. + """ + def __init__(self, label=None, color=0, parent=None, left=None, right=None): + """Initialize a new Red-Black Tree node with the given values: + label: The value associated with this node + color: 0 if black, 1 if red + parent: The parent to this node + left: This node's left child + right: This node's right child + """ + self.label = label + self.parent = parent + self.left = left + self.right = right + self.color = color + + # Here are functions which are specific to red-black trees + + def rotate_left(self): + """Rotate the subtree rooted at this node to the left and + returns the new root to this subtree. + + Perfoming one rotation can be done in O(1). + """ + parent = self.parent + right = self.right + self.right = right.left + if self.right: + self.right.parent = self + self.parent = right + right.left = self + if parent is not None: + if parent.left is self: + parent.left = right + else: + parent.right = right + right.parent = parent + return right + + def rotate_right(self): + """Rotate the subtree rooted at this node to the right and + returns the new root to this subtree. + + Performing one rotation can be done in O(1). + """ + parent = self.parent + left = self.left + self.left = left.right + if self.left: + self.left.parent = self + self.parent = left + left.right = self + if parent is not None: + if parent.right is self: + parent.right = left + else: + parent.left = left + left.parent = parent + return left + + def insert(self, label): + """Inserts label into the subtree rooted at self, performs any + rotations necessary to maintain balance, and then returns the + new root to this subtree (likely self). + + This is guaranteed to run in O(log(n)) time. + """ + if self.label is None: + # Only possible with an empty tree + self.label = label + return self + if self.label == label: + return self + elif self.label > label: + if self.left: + self.left.insert(label) + else: + self.left = RedBlackTree(label, 1, self) + self.left._insert_repair() + else: + if self.right: + self.right.insert(label) + else: + self.right = RedBlackTree(label, 1, self) + self.right._insert_repair() + return self.parent or self + + def _insert_repair(self): + """Repair the coloring from inserting into a tree.""" + if self.parent is None: + # This node is the root, so it just needs to be black + self.color = 0 + elif color(self.parent) == 0: + # If the parent is black, then it just needs to be red + self.color = 1 + else: + uncle = self.parent.sibling + if color(uncle) == 0: + if self.is_left() and self.parent.is_right(): + self.parent.rotate_right() + self.right._insert_repair() + elif self.is_right() and self.parent.is_left(): + self.parent.rotate_left() + self.left._insert_repair() + elif self.is_left(): + self.grandparent.rotate_right() + self.parent.color = 0 + self.parent.right.color = 1 + else: + self.grandparent.rotate_left() + self.parent.color = 0 + self.parent.left.color = 1 + else: + self.parent.color = 0 + uncle.color = 0 + self.grandparent.color = 1 + self.grandparent._insert_repair() + + def remove(self, label): + """Remove label from this tree.""" + if self.label == label: + if self.left and self.right: + # It's easier to balance a node with at most one child, + # so we replace this node with the greatest one less than + # it and remove that. + value = self.left.get_max() + self.label = value + self.left.remove(value) + else: + # This node has at most one non-None child, so we don't + # need to replace + child = self.left or self.right + if self.color == 1: + # This node is red, and its child is black + # The only way this happens to a node with one child + # is if both children are None leaves. + # We can just remove this node and call it a day. + if self.is_left(): + self.parent.left = None + else: + self.parent.right = None + else: + # The node is black + if child is None: + # This node and its child are black + if self.parent is None: + # The tree is now empty + return RedBlackTree(None) + else: + self._remove_repair() + if self.is_left(): + self.parent.left = None + else: + self.parent.right = None + self.parent = None + else: + # This node is black and its child is red + # Move the child node here and make it black + self.label = child.label + self.left = child.left + self.right = child.right + if self.left: + self.left.parent = self + if self.right: + self.right.parent = self + elif self.label > label: + if self.left: + self.left.remove(label) + else: + if self.right: + self.right.remove(label) + return self.parent or self + + def _remove_repair(self): + """Repair the coloring of the tree that may have been messed up.""" + if color(self.sibling) == 1: + self.sibling.color = 0 + self.parent.color = 1 + if self.is_left(): + self.parent.rotate_left() + else: + self.parent.rotate_right() + if color(self.parent) == 0 and color(self.sibling) == 0 \ + and color(self.sibling.left) == 0 \ + and color(self.sibling.right) == 0: + self.sibling.color = 1 + self.parent._remove_repair() + return + if color(self.parent) == 1 and color(self.sibling) == 0 \ + and color(self.sibling.left) == 0 \ + and color(self.sibling.right) == 0: + self.sibling.color = 1 + self.parent.color = 0 + return + if (self.is_left() + and color(self.sibling) == 0 + and color(self.sibling.right) == 0 + and color(self.sibling.left) == 1): + self.sibling.rotate_right() + self.sibling.color = 0 + self.sibling.right.color = 1 + if (self.is_right() + and color(self.sibling) == 0 + and color(self.sibling.right) == 1 + and color(self.sibling.left) == 0): + self.sibling.rotate_left() + self.sibling.color = 0 + self.sibling.left.color = 1 + if (self.is_left() + and color(self.sibling) == 0 + and color(self.sibling.right) == 1): + self.parent.rotate_left() + self.grandparent.color = self.parent.color + self.parent.color = 0 + self.parent.sibling.color = 0 + if (self.is_right() + and color(self.sibling) == 0 + and color(self.sibling.left) == 1): + self.parent.rotate_right() + self.grandparent.color = self.parent.color + self.parent.color = 0 + self.parent.sibling.color = 0 + + def check_color_properties(self): + """Check the coloring of the tree, and return True iff the tree + is colored in a way which matches these five properties: + (wording stolen from wikipedia article) + 1. Each node is either red or black. + 2. The root node is black. + 3. All leaves are black. + 4. If a node is red, then both its children are black. + 5. Every path from any node to all of its descendent NIL nodes + has the same number of black nodes. + + This function runs in O(n) time, because properties 4 and 5 take + that long to check. + """ + # I assume property 1 to hold because there is nothing that can + # make the color be anything other than 0 or 1. + + # Property 2 + if self.color: + # The root was red + print('Property 2') + return False; + + # Property 3 does not need to be checked, because None is assumed + # to be black and is all the leaves. + + # Property 4 + if not self.check_coloring(): + print('Property 4') + return False + + # Property 5 + if self.black_height() is None: + print('Property 5') + return False + # All properties were met + return True + + def check_coloring(self): + """A helper function to recursively check Property 4 of a + Red-Black Tree. See check_color_properties for more info. + """ + if self.color == 1: + if color(self.left) == 1 or color(self.right) == 1: + return False + if self.left and not self.left.check_coloring(): + return False + if self.right and not self.right.check_coloring(): + return False + return True + + def black_height(self): + """Returns the number of black nodes from this node to the + leaves of the tree, or None if there isn't one such value (the + tree is color incorrectly). + """ + if self is None: + # If we're already at a leaf, there is no path + return 1 + left = RedBlackTree.black_height(self.left) + right = RedBlackTree.black_height(self.right) + if left is None or right is None: + # There are issues with coloring below children nodes + return None + if left != right: + # The two children have unequal depths + return None + # Return the black depth of children, plus one if this node is + # black + return left + (1-self.color) + + # Here are functions which are general to all binary search trees + + def __contains__(self, label): + """Search through the tree for label, returning True iff it is + found somewhere in the tree. + + Guaranteed to run in O(log(n)) time. + """ + return self.search(label) is not None + + def search(self, label): + """Search through the tree for label, returning its node if + it's found, and None otherwise. + + This method is guaranteed to run in O(log(n)) time. + """ + if self.label == label: + return self + elif label > self.label: + if self.right is None: + return None + else: + return self.right.search(label) + else: + if self.left is None: + return None + else: + return self.left.search(label) + + def floor(self, label): + """Returns the largest element in this tree which is at most label. + + This method is guaranteed to run in O(log(n)) time.""" + if self.label == label: + return self.label + elif self.label > label: + if self.left: + return self.left.floor(label) + else: + return None + else: + if self.right: + attempt = self.right.floor(label) + if attempt is not None: + return attempt + return self.label + + def ceil(self, label): + """Returns the smallest element in this tree which is at least label. + + This method is guaranteed to run in O(log(n)) time. + """ + if self.label == label: + return self.label + elif self.label < label: + if self.right: + return self.right.ceil(label) + else: + return None + else: + if self.left: + attempt = self.left.ceil(label) + if attempt is not None: + return attempt + return self.label + + def get_max(self): + """Returns the largest element in this tree. + + This method is guaranteed to run in O(log(n)) time. + """ + if self.right: + # Go as far right as possible + return self.right.get_max() + else: + return self.label + + def get_min(self): + """Returns the smallest element in this tree. + + This method is guaranteed to run in O(log(n)) time. + """ + if self.left: + # Go as far left as possible + return self.left.get_min() + else: + return self.label + + @property + def grandparent(self): + """Get the current node's grandparent, or None if it doesn't exist.""" + if self.parent is None: + return None + else: + return self.parent.parent + + @property + def sibling(self): + """Get the current node's sibling, or None if it doesn't exist.""" + if self.parent is None: + return None + elif self.parent.left is self: + return self.parent.right + else: + return self.parent.left + + def is_left(self): + """Returns true iff this node is the left child of its parent.""" + return self.parent and self.parent.left is self + + def is_right(self): + """Returns true iff this node is the right child of its parent.""" + return self.parent and self.parent.right is self + + def __bool__(self): + return True + + def __len__(self): + """ + Return the number of nodes in this tree. + """ + ln = 1 + if self.left: + ln += len(self.left) + if self.right: + ln += len(self.right) + return ln + + def preorder_traverse(self): + yield self.label + if self.left: + yield from self.left.preorder_traverse() + if self.right: + yield from self.right.preorder_traverse() + + def inorder_traverse(self): + if self.left: + yield from self.left.inorder_traverse() + yield self.label + if self.right: + yield from self.right.inorder_traverse() + + + def postorder_traverse(self): + if self.left: + yield from self.left.postorder_traverse() + if self.right: + yield from self.right.postorder_traverse() + yield self.label + + def __repr__(self): + from pprint import pformat + if self.left is None and self.right is None: + return "'%s %s'" % (self.label, (self.color and 'red') or 'blk') + return pformat({'%s %s' % (self.label, (self.color and 'red') or 'blk'): + (self.left, self.right)}, + indent=1) + + def __eq__(self, other): + """Test if two trees are equal.""" + if self.label == other.label: + return self.left == other.left and self.right == other.right + else: + return False + +def color(node): + """Returns the color of a node, allowing for None leaves.""" + if node is None: + return 0 + else: + return node.color + +""" +Code for testing the various functions of the red-black tree. +""" + +def test_rotations(): + """Test that the rotate_left and rotate_right functions work.""" + # Make a tree to test on + tree = RedBlackTree(0) + tree.left = RedBlackTree(-10, parent=tree) + tree.right = RedBlackTree(10, parent=tree) + tree.left.left = RedBlackTree(-20, parent=tree.left) + tree.left.right = RedBlackTree(-5, parent=tree.left) + tree.right.left = RedBlackTree(5, parent=tree.right) + tree.right.right = RedBlackTree(20, parent=tree.right) + # Make the right rotation + left_rot = RedBlackTree(10) + left_rot.left = RedBlackTree(0, parent=left_rot) + left_rot.left.left = RedBlackTree(-10, parent=left_rot.left) + left_rot.left.right = RedBlackTree(5, parent=left_rot.left) + left_rot.left.left.left = RedBlackTree(-20, parent=left_rot.left.left) + left_rot.left.left.right = RedBlackTree(-5, parent=left_rot.left.left) + left_rot.right = RedBlackTree(20, parent=left_rot) + tree = tree.rotate_left() + if tree != left_rot: + return False + tree = tree.rotate_right() + tree = tree.rotate_right() + # Make the left rotation + right_rot = RedBlackTree(-10) + right_rot.left = RedBlackTree(-20, parent=right_rot) + right_rot.right = RedBlackTree(0, parent=right_rot) + right_rot.right.left = RedBlackTree(-5, parent=right_rot.right) + right_rot.right.right = RedBlackTree(10, parent=right_rot.right) + right_rot.right.right.left = RedBlackTree(5, parent=right_rot.right.right) + right_rot.right.right.right = RedBlackTree(20, parent=right_rot.right.right) + if tree != right_rot: + return False + return True + +def test_insertion_speed(): + """Test that the tree balances inserts to O(log(n)) by doing a lot + of them. + """ + tree = RedBlackTree(-1) + for i in range(300000): + tree = tree.insert(i) + return True + +def test_insert(): + """Test the insert() method of the tree correctly balances, colors, + and inserts. + """ + tree = RedBlackTree(0) + tree.insert(8) + tree.insert(-8) + tree.insert(4) + tree.insert(12) + tree.insert(10) + tree.insert(11) + ans = RedBlackTree(0, 0) + ans.left = RedBlackTree(-8, 0, ans) + ans.right = RedBlackTree(8, 1, ans) + ans.right.left = RedBlackTree(4, 0, ans.right) + ans.right.right = RedBlackTree(11, 0, ans.right) + ans.right.right.left = RedBlackTree(10, 1, ans.right.right) + ans.right.right.right = RedBlackTree(12, 1, ans.right.right) + return tree == ans + +def test_insert_and_search(): + """Tests searching through the tree for values.""" + tree = RedBlackTree(0) + tree.insert(8) + tree.insert(-8) + tree.insert(4) + tree.insert(12) + tree.insert(10) + tree.insert(11) + if 5 in tree or -6 in tree or -10 in tree or 13 in tree: + # Found something not in there + return False + if not (11 in tree and 12 in tree and -8 in tree and 0 in tree): + # Didn't find something in there + return False + return True + +def test_insert_delete(): + """Test the insert() and delete() method of the tree, verifying the + insertion and removal of elements, and the balancing of the tree. + """ + tree = RedBlackTree(0) + tree = tree.insert(-12) + tree = tree.insert(8) + tree = tree.insert(-8) + tree = tree.insert(15) + tree = tree.insert(4) + tree = tree.insert(12) + tree = tree.insert(10) + tree = tree.insert(9) + tree = tree.insert(11) + tree = tree.remove(15) + tree = tree.remove(-12) + tree = tree.remove(9) + if not tree.check_color_properties(): + return False + if list(tree.inorder_traverse()) != [-8, 0, 4, 8, 10, 11, 12]: + return False + return True + +def test_floor_ceil(): + """Tests the floor and ceiling functions in the tree.""" + tree = RedBlackTree(0) + tree.insert(-16) + tree.insert(16) + tree.insert(8) + tree.insert(24) + tree.insert(20) + tree.insert(22) + tuples = [(-20, None, -16), (-10, -16, 0), (8, 8, 8), (50, 24, None)] + for val, floor, ceil in tuples: + if tree.floor(val) != floor or tree.ceil(val) != ceil: + return False + return True + +def test_min_max(): + """Tests the min and max functions in the tree.""" + tree = RedBlackTree(0) + tree.insert(-16) + tree.insert(16) + tree.insert(8) + tree.insert(24) + tree.insert(20) + tree.insert(22) + if tree.get_max() != 22 or tree.get_min() != -16: + return False + return True + +def test_tree_traversal(): + """Tests the three different tree traversal functions.""" + tree = RedBlackTree(0) + tree.insert(-16) + tree.insert(16) + tree.insert(8) + tree.insert(24) + tree.insert(20) + tree.insert(22) + if list(tree.inorder_traverse()) != [-16, 0, 8, 16, 20, 22, 24]: + return False + if list(tree.preorder_traverse()) != [0, -16, 16, 8, 22, 20, 24]: + return False + if list(tree.postorder_traverse()) != [-16, 8, 20, 24, 22, 16, 0]: + return False + return True + +def main(): + if test_rotations(): + print('Rotating right and left works!') + else: + print('Rotating right and left doesn\'t work. :(') + if test_insert(): + print('Inserting works!') + else: + print('Inserting doesn\'t work :(') + if test_insert_and_search(): + print('Searching works!') + else: + print('Searching doesn\'t work :(') + if test_insert_delete(): + print('Deleting works!') + else: + print('Deleting doesn\'t work :(') + if test_floor_ceil(): + print('Floor and ceil work!') + else: + print('Floor and ceil don\'t work :(') + if test_tree_traversal(): + print('Tree traversal works!') + else: + print('Tree traversal doesn\'t work :(') + print('Testing tree balancing...') + print('This should only be a few seconds.') + test_insertion_speed() + print('Done!') + +if __name__ == '__main__': + main() From afb98e6c232dac73fb895f9e06a645a82410fd9e Mon Sep 17 00:00:00 2001 From: Dhandarah Date: Fri, 5 Jul 2019 05:47:18 -0300 Subject: [PATCH 034/193] KNN (#944) Creates an example of KNN algorithm using sklearn library. --- machine_learning/knn_sklearn.py | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 machine_learning/knn_sklearn.py diff --git a/machine_learning/knn_sklearn.py b/machine_learning/knn_sklearn.py new file mode 100644 index 000000000000..64582564304f --- /dev/null +++ b/machine_learning/knn_sklearn.py @@ -0,0 +1,28 @@ +from sklearn.model_selection import train_test_split +from sklearn.datasets import load_iris +from sklearn.neighbors import KNeighborsClassifier + +#Load iris file +iris = load_iris() +iris.keys() + + +print('Target names: \n {} '.format(iris.target_names)) +print('\n Features: \n {}'.format(iris.feature_names)) + +#Train set e Test set +X_train, X_test, y_train, y_test = train_test_split(iris['data'],iris['target'], random_state=4) + +#KNN + +knn = KNeighborsClassifier (n_neighbors = 1) +knn.fit(X_train, y_train) + +#new array to test +X_new = [[1,2,1,4], + [2,3,4,5]] + +prediction = knn.predict(X_new) + +print('\nNew array: \n {}' + '\n\nTarget Names Prediction: \n {}'.format(X_new, iris['target_names'][prediction])) From 831558d38dd00c7b4d64743ca4f3fe62d16e71d1 Mon Sep 17 00:00:00 2001 From: Hetal Kuvadia Date: Fri, 5 Jul 2019 14:18:36 +0530 Subject: [PATCH 035/193] #945 Backtracking Algorithms (#953) * Adding nqueens.py for backtracking * Adding sum_of_subsets.py for backtracking * Update nqueens.py * Rename nqueens.py to n_queens.py * Deleting /other/n_queens.py --- backtracking/n_queens.py | 84 ++++++++++++++++++++++++++++++++++ backtracking/sum_of_subsets.py | 45 ++++++++++++++++++ other/n_queens.py | 77 ------------------------------- 3 files changed, 129 insertions(+), 77 deletions(-) create mode 100644 backtracking/n_queens.py create mode 100644 backtracking/sum_of_subsets.py delete mode 100644 other/n_queens.py diff --git a/backtracking/n_queens.py b/backtracking/n_queens.py new file mode 100644 index 000000000000..dfd4498b166b --- /dev/null +++ b/backtracking/n_queens.py @@ -0,0 +1,84 @@ +''' + + The nqueens problem is of placing N queens on a N * N + chess board such that no queen can attack any other queens placed + on that chess board. + This means that one queen cannot have any other queen on its horizontal, vertical and + diagonal lines. + +''' +solution = [] + +def isSafe(board, row, column): + ''' + This function returns a boolean value True if it is safe to place a queen there considering + the current state of the board. + + Parameters : + board(2D matrix) : board + row ,column : coordinates of the cell on a board + + Returns : + Boolean Value + + ''' + for i in range(len(board)): + if board[row][i] == 1: + return False + for i in range(len(board)): + if board[i][column] == 1: + return False + for i,j in zip(range(row,-1,-1),range(column,-1,-1)): + if board[i][j] == 1: + return False + for i,j in zip(range(row,-1,-1),range(column,len(board))): + if board[i][j] == 1: + return False + return True + +def solve(board, row): + ''' + It creates a state space tree and calls the safe function untill it receives a + False Boolean and terminates that brach and backtracks to the next + poosible solution branch. + ''' + if row >= len(board): + ''' + If the row number exceeds N we have board with a successful combination + and that combination is appended to the solution list and the board is printed. + + ''' + solution.append(board) + printboard(board) + print() + return + for i in range(len(board)): + ''' + For every row it iterates through each column to check if it is feesible to place a + queen there. + If all the combinations for that particaular branch are successfull the board is + reinitialized for the next possible combination. + ''' + if isSafe(board,row,i): + board[row][i] = 1 + solve(board,row+1) + board[row][i] = 0 + return False + +def printboard(board): + ''' + Prints the boards that have a successfull combination. + ''' + for i in range(len(board)): + for j in range(len(board)): + if board[i][j] == 1: + print("Q", end = " ") + else : + print(".", end = " ") + print() + +#n=int(input("The no. of queens")) +n = 8 +board = [[0 for i in range(n)]for j in range(n)] +solve(board, 0) +print("The total no. of solutions are :", len(solution)) diff --git a/backtracking/sum_of_subsets.py b/backtracking/sum_of_subsets.py new file mode 100644 index 000000000000..b01bffbb651d --- /dev/null +++ b/backtracking/sum_of_subsets.py @@ -0,0 +1,45 @@ +''' + The sum-of-subsetsproblem states that a set of non-negative integers, and a value M, + determine all possible subsets of the given set whose summation sum equal to given M. + + Summation of the chosen numbers must be equal to given number M and one number can + be used only once. +''' + +def generate_sum_of_subsets_soln(nums, max_sum): + result = [] + path = [] + num_index = 0 + remaining_nums_sum = sum(nums) + create_state_space_tree(nums, max_sum, num_index, path,result, remaining_nums_sum) + return result + +def create_state_space_tree(nums,max_sum,num_index,path,result, remaining_nums_sum): + ''' + Creates a state space tree to iterate through each branch using DFS. + It terminates the branching of a node when any of the two conditions + given below satisfy. + This algorithm follows depth-fist-search and backtracks when the node is not branchable. + + ''' + if sum(path) > max_sum or (remaining_nums_sum + sum(path)) < max_sum: + return + if sum(path) == max_sum: + result.append(path) + return + for num_index in range(num_index,len(nums)): + create_state_space_tree(nums, max_sum, num_index + 1, path + [nums[num_index]], result, remaining_nums_sum - nums[num_index]) + +''' +remove the comment to take an input from the user + +print("Enter the elements") +nums = list(map(int, input().split())) +print("Enter max_sum sum") +max_sum = int(input()) + +''' +nums = [3, 34, 4, 12, 5, 2] +max_sum = 9 +result = generate_sum_of_subsets_soln(nums,max_sum) +print(*result) \ No newline at end of file diff --git a/other/n_queens.py b/other/n_queens.py deleted file mode 100644 index 0e80a0cff5e9..000000000000 --- a/other/n_queens.py +++ /dev/null @@ -1,77 +0,0 @@ -#! /usr/bin/python3 -import sys - -def nqueens(board_width): - board = [0] - current_row = 0 - while True: - conflict = False - - for review_index in range(0, current_row): - left = board[review_index] - (current_row - review_index) - right = board[review_index] + (current_row - review_index); - if (board[current_row] == board[review_index] or (left >= 0 and left == board[current_row]) or (right < board_width and right == board[current_row])): - conflict = True; - break - - if (current_row == 0 and conflict == False): - board.append(0) - current_row = 1 - continue - - if (conflict == True): - board[current_row] += 1 - - if (current_row == 0 and board[current_row] == board_width): - print("No solution exists for specificed board size.") - return None - - while True: - if (board[current_row] == board_width): - board[current_row] = 0 - if (current_row == 0): - print("No solution exists for specificed board size.") - return None - - board.pop() - current_row -= 1 - board[current_row] += 1 - - if board[current_row] != board_width: - break - else: - current_row += 1 - if (current_row == board_width): - break - - board.append(0) - return board - -def print_board(board): - if (board == None): - return - - board_width = len(board) - for row in range(board_width): - line_print = [] - for column in range(board_width): - if column == board[row]: - line_print.append("Q") - else: - line_print.append(".") - print(line_print) - - -if __name__ == '__main__': - default_width = 8 - for arg in sys.argv: - if (arg.isdecimal() and int(arg) > 3): - default_width = int(arg) - break - - if (default_width == 8): - print("Running algorithm with board size of 8. Specify an alternative Chess board size for N-Queens as a command line argument.") - - board = nqueens(default_width) - print(board) - print_board(board) From 4e413c018342e71e064c3bdd4a692121bda14fcb Mon Sep 17 00:00:00 2001 From: Anup Kumar Panwar <1anuppanwar@gmail.com> Date: Sat, 6 Jul 2019 11:11:20 +0530 Subject: [PATCH 036/193] Updated README --- CONTRIBUTING.md | 12 +- DIRECTORY.py | 50 +++ README.md | 346 ++++++++++++++++++ .../image_data}/PSNR-example-base.png | Bin .../image_data}/PSNR-example-comp-10.jpg | Bin .../image_data}/compressed_image.png | Bin .../image_data}/example_image.jpg | Bin .../image_data}/example_wikipedia_image.jpg | Bin .../image_data}/original_image.png | Bin .../peak_signal_to_noise_ratio.py | 8 +- data_structures/__init__.py | 0 data_structures/arrays.py | 3 - data_structures/avl.py | 181 --------- data_structures/{ => binary_tree}/LCA.py | 0 .../binary_tree}/basic_binary_tree.py | 0 data_structures/hashing/__init__.py | 6 - .../hashing/number_theory/__init__.py | 0 .../{swapNodes.py => swap_nodes.py} | 0 data_structures/queue/__init__.py | 0 .../{next.py => next_greater_element.py} | 0 data_structures/union_find/__init__.py | 0 .../union_find/tests_union_find.py | 78 ---- data_structures/union_find/union_find.py | 87 ----- .../Social_Network_Ads.csv | 0 .../random_forest_classification.py | 0 .../random_forest_classifier.ipynb} | 0 .../Position_Salaries.csv | 0 .../random_forest_regression.ipynb} | 0 .../random_forest_regression.py | 0 maths/Hanoi.py | 29 -- maths/{lucasSeries.py => lucas series.py} | 0 maths/tests/__init__.py | 1 - maths/tests/test_fibonacci.py | 34 -- matrix/{spiralPrint.py => spiral_print.py} | 0 ....py => back_propagation_neural_network.py} | 0 ...b => fully_connected_neural_network.ipynb} | 0 .../game_o_life.py => game_of_life.py} | 0 other/game_of_life/sample.gif | Bin 228847 -> 0 bytes searches/test_interpolation_search.py | 93 ----- searches/test_tabu_search.py | 46 --- simple_client/README.md | 6 - simple_client/client.py | 29 -- simple_client/server.py | 21 -- sorts/sorting_graphs.png | Bin 10362 -> 0 bytes sorts/tests.py | 76 ---- 45 files changed, 404 insertions(+), 702 deletions(-) create mode 100644 DIRECTORY.py rename {compression_analysis => compression/image_data}/PSNR-example-base.png (100%) rename {compression_analysis => compression/image_data}/PSNR-example-comp-10.jpg (100%) rename {compression_analysis => compression/image_data}/compressed_image.png (100%) rename {compression_analysis => compression/image_data}/example_image.jpg (100%) rename {compression_analysis => compression/image_data}/example_wikipedia_image.jpg (100%) rename {compression_analysis => compression/image_data}/original_image.png (100%) rename compression_analysis/psnr.py => compression/peak_signal_to_noise_ratio.py (71%) delete mode 100644 data_structures/__init__.py delete mode 100644 data_structures/arrays.py delete mode 100644 data_structures/avl.py rename data_structures/{ => binary_tree}/LCA.py (100%) rename {binary_tree => data_structures/binary_tree}/basic_binary_tree.py (100%) delete mode 100644 data_structures/hashing/__init__.py delete mode 100644 data_structures/hashing/number_theory/__init__.py rename data_structures/linked_list/{swapNodes.py => swap_nodes.py} (100%) delete mode 100644 data_structures/queue/__init__.py rename data_structures/stacks/{next.py => next_greater_element.py} (100%) delete mode 100644 data_structures/union_find/__init__.py delete mode 100644 data_structures/union_find/tests_union_find.py delete mode 100644 data_structures/union_find/union_find.py rename machine_learning/{Random Forest Classification => random_forest_classification}/Social_Network_Ads.csv (100%) rename machine_learning/{Random Forest Classification => random_forest_classification}/random_forest_classification.py (100%) rename machine_learning/{Random Forest Classification/Random Forest Classifier.ipynb => random_forest_classification/random_forest_classifier.ipynb} (100%) rename machine_learning/{Random Forest Regression => random_forest_regression}/Position_Salaries.csv (100%) rename machine_learning/{Random Forest Regression/Random Forest Regression.ipynb => random_forest_regression/random_forest_regression.ipynb} (100%) rename machine_learning/{Random Forest Regression => random_forest_regression}/random_forest_regression.py (100%) delete mode 100644 maths/Hanoi.py rename maths/{lucasSeries.py => lucas series.py} (100%) delete mode 100644 maths/tests/__init__.py delete mode 100644 maths/tests/test_fibonacci.py rename matrix/{spiralPrint.py => spiral_print.py} (100%) rename neural_network/{bpnn.py => back_propagation_neural_network.py} (100%) rename neural_network/{fcn.ipynb => fully_connected_neural_network.ipynb} (100%) rename other/{game_of_life/game_o_life.py => game_of_life.py} (100%) delete mode 100644 other/game_of_life/sample.gif delete mode 100644 searches/test_interpolation_search.py delete mode 100644 searches/test_tabu_search.py delete mode 100644 simple_client/README.md delete mode 100644 simple_client/client.py delete mode 100644 simple_client/server.py delete mode 100644 sorts/sorting_graphs.png delete mode 100644 sorts/tests.py diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 19b928c187f9..ac632574e870 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -72,9 +72,9 @@ We want your work to be readable by others; therefore, we encourage you to note - Write tests to illustrate your work. - The following "testing" approaches are not encouraged: + The following "testing" approaches are **not** encouraged: - ```python + ```python* input('Enter your input:') # Or even worse... input = eval(raw_input("Enter your input: ")) @@ -97,13 +97,9 @@ We want your work to be readable by others; therefore, we encourage you to note #### Other Standard While Submitting Your Work -- File extension for code should be `.py`. - -- Please file your work to let others use it in the future. Here are the examples that are acceptable: +- File extension for code should be `.py`. Jupiter notebook files are acceptable in machine learning algorithms. - - Camel cases - - `-` Hyphenated names - - `_` Underscore-separated names +- Strictly use snake case (underscore separated) in your file name, as it will be easy to parse in future using scripts. If possible, follow the standard *within* the folder you are submitting to. diff --git a/DIRECTORY.py b/DIRECTORY.py new file mode 100644 index 000000000000..434b2a3dd3ed --- /dev/null +++ b/DIRECTORY.py @@ -0,0 +1,50 @@ +import os + +def getListOfFiles(dirName): + # create a list of file and sub directories + # names in the given directory + listOfFile = os.listdir(dirName) + allFiles = list() + # Iterate over all the entries + for entry in listOfFile: + # if entry == listOfFile[len(listOfFile)-1]: + # continue + if entry=='.git': + continue + # Create full path + fullPath = os.path.join(dirName, entry) + entryName = entry.split('_') + # print(entryName) + ffname = '' + try: + for word in entryName: + temp = word[0].upper() + word[1:] + ffname = ffname + ' ' + temp + # print(temp) + final_fn = ffname.replace('.py', '') + final_fn = final_fn.strip() + print('* ['+final_fn+']('+fullPath+')') + # pass + except: + pass + # If entry is a directory then get the list of files in this directory + if os.path.isdir(fullPath): + print ('\n## '+entry) + filesInCurrDir = getListOfFiles(fullPath) + for file in filesInCurrDir: + fileName = file.split('/') + fileName = fileName[len(fileName)-1] + + # print (fileName) + allFiles = allFiles + filesInCurrDir + else: + allFiles.append(fullPath) + + return allFiles + + +dirName = './'; + +# Get the list of all files in directory tree at given path +listOfFiles = getListOfFiles(dirName) +# print (listOfFiles) \ No newline at end of file diff --git a/README.md b/README.md index 527b80269fdc..9edddb60552a 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,5 @@ # The Algorithms - Python + [![Donate](https://img.shields.io/badge/Donate-PayPal-green.svg)](https://www.paypal.me/TheAlgorithms/100)   [![Gitter chat](https://badges.gitter.im/gitterHQ/gitter.png)](https://gitter.im/TheAlgorithms)   [![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg)](https://gitpod.io/#https://github.com/TheAlgorithms/Python) @@ -7,6 +8,17 @@ These implementations are for learning purposes. They may be less efficient than the implementations in the Python standard library. +## Owners + +Anup Kumar Panwar +  [[Gmail](mailto:1anuppanwar@gmail.com?Subject=The%20Algorithms%20-%20Python) +  [Gihub](https://github.com/anupkumarpanwar) +  [LinkedIn](https://www.linkedin.com/in/anupkumarpanwar/)] + +Chetan Kaushik +  [[Gmail](mailto:dynamitechetan@gmail.com?Subject=The%20Algorithms%20-%20Python) +  [Gihub](https://github.com/dynamitechetan) +  [LinkedIn](https://www.linkedin.com/in/chetankaushik/)] ## Contribution Guidelines @@ -15,3 +27,337 @@ Read our [Contribution Guidelines](CONTRIBUTING.md) before you contribute. ## Community Channel We're on [Gitter](https://gitter.im/TheAlgorithms)! Please join us. + +# Algorithms + +## Hashes + +- [Md5](./hashes/md5.py) +- [Chaos Machine](./hashes/chaos_machine.py) +- [Sha1](./hashes/sha1.py) + +## File Transfer Protocol + +- [Ftp Client Server](./file_transfer_protocol/ftp_client_server.py) +- [Ftp Send Receive](./file_transfer_protocol/ftp_send_receive.py) + +## Backtracking + +- [N Queens](./backtracking/n_queens.py) +- [Sum Of Subsets](./backtracking/sum_of_subsets.py) + +## Ciphers + +- [Transposition Cipher](./ciphers/transposition_cipher.py) +- [Atbash](./ciphers/Atbash.py) +- [Rot13](./ciphers/rot13.py) +- [Rabin Miller](./ciphers/rabin_miller.py) +- [Transposition Cipher Encrypt Decrypt File](./ciphers/transposition_cipher_encrypt_decrypt_file.py) +- [Affine Cipher](./ciphers/affine_cipher.py) +- [Trafid Cipher](./ciphers/trafid_cipher.py) +- [Base16](./ciphers/base16.py) +- [Elgamal Key Generator](./ciphers/elgamal_key_generator.py) +- [Rsa Cipher](./ciphers/rsa_cipher.py) +- [Prehistoric Men.txt](./ciphers/prehistoric_men.txt) +- [Vigenere Cipher](./ciphers/vigenere_cipher.py) +- [Xor Cipher](./ciphers/xor_cipher.py) +- [Brute Force Caesar Cipher](./ciphers/brute_force_caesar_cipher.py) +- [Rsa Key Generator](./ciphers/rsa_key_generator.py) +- [Simple Substitution Cipher](./ciphers/simple_substitution_cipher.py) +- [Playfair Cipher](./ciphers/playfair_cipher.py) +- [Morse Code Implementation](./ciphers/morse_Code_implementation.py) +- [Base32](./ciphers/base32.py) +- [Base85](./ciphers/base85.py) +- [Base64 Cipher](./ciphers/base64_cipher.py) +- [Onepad Cipher](./ciphers/onepad_cipher.py) +- [Caesar Cipher](./ciphers/caesar_cipher.py) +- [Hill Cipher](./ciphers/hill_cipher.py) +- [Cryptomath Module](./ciphers/cryptomath_module.py) + +## Arithmetic Analysis + +- [Bisection](./arithmetic_analysis/bisection.py) +- [Newton Method](./arithmetic_analysis/newton_method.py) +- [Newton Raphson Method](./arithmetic_analysis/newton_raphson_method.py) +- [Intersection](./arithmetic_analysis/intersection.py) +- [Lu Decomposition](./arithmetic_analysis/lu_decomposition.py) + +## Boolean Algebra + +- [Quine Mc Cluskey](./boolean_algebra/quine_mc_cluskey.py) + +## Traversals + +- [Binary Tree Traversals](./traversals/binary_tree_traversals.py) + +## Maths + +- [Average](./maths/average.py) +- [Abs Max](./maths/abs_Max.py) +- [Average Median](./maths/average_median.py) +- [Trapezoidal Rule](./maths/trapezoidal_rule.py) +- [Prime Check](./maths/Prime_Check.py) +- [Modular Exponential](./maths/modular_exponential.py) +- [Newton Raphson](./maths/newton_raphson.py) +- [Factorial Recursive](./maths/factorial_recursive.py) +- [Extended Euclidean Algorithm](./maths/extended_euclidean_algorithm.py) +- [Greater Common Divisor](./maths/greater_common_divisor.py) +- [Fibonacci](./maths/fibonacci.py) +- [Find Lcm](./maths/find_lcm.py) +- [Find Max](./maths/Find_Max.py) +- [Fermat Little Theorem](./maths/fermat_little_theorem.py) +- [Factorial Python](./maths/factorial_python.py) +- [Fibonacci Sequence Recursion](./maths/fibonacci_sequence_recursion.py) +- [Sieve Of Eratosthenes](./maths/sieve_of_eratosthenes.py) +- [Abs Min](./maths/abs_Min.py) +- [Lucas Series](./maths/lucasSeries.py) +- [Segmented Sieve](./maths/segmented_sieve.py) +- [Find Min](./maths/Find_Min.py) +- [Abs](./maths/abs.py) +- [Simpson Rule](./maths/simpson_rule.py) +- [Basic Maths](./maths/basic_maths.py) +- [3n+1](./maths/3n+1.py) +- [Binary Exponentiation](./maths/Binary_Exponentiation.py) + +## Digital Image Processing + +- ## Filters + + - [Median Filter](./digital_image_processing/filters/median_filter.py) + - [Gaussian Filter](./digital_image_processing/filters/gaussian_filter.py) + + +## Compression + +- [Peak Signal To Noise Ratio](./compression_analysis/peak_signal_to_noise_ratio.py) +- [Huffman](./compression/huffman.py) + +## Graphs + +- [BFS Shortest Path](./graphs/bfs_shortest_path.py) +- [Directed And Undirected (Weighted) Graph](<./graphs/Directed_and_Undirected_(Weighted)_Graph.py>) +- [Minimum Spanning Tree Prims](./graphs/minimum_spanning_tree_prims.py) +- [Graph Matrix](./graphs/graph_matrix.py) +- [Basic Graphs](./graphs/basic_graphs.py) +- [Dijkstra 2](./graphs/dijkstra_2.py) +- [Tarjans Strongly Connected Components](./graphs/tarjans_scc.py) +- [Check Bipartite Graph BFS](./graphs/check_bipartite_graph_bfs.py) +- [Depth First Search](./graphs/depth_first_search.py) +- [Kahns Algorithm Long](./graphs/kahns_algorithm_long.py) +- [Breadth First Search](./graphs/breadth_first_search.py) +- [Dijkstra](./graphs/dijkstra.py) +- [Articulation Points](./graphs/articulation_points.py) +- [Bellman Ford](./graphs/bellman_ford.py) +- [Check Bipartite Graph Dfs](./graphs/check_bipartite_graph_dfs.py) +- [Strongly Connected Components Kosaraju](./graphs/scc_kosaraju.py) +- [Multi Hueristic Astar](./graphs/multi_hueristic_astar.py) +- [Page Rank](./graphs/page_rank.py) +- [Eulerian Path And Circuit For Undirected Graph](./graphs/Eulerian_path_and_circuit_for_undirected_graph.py) +- [Edmonds Karp Multiple Source And Sink](./graphs/edmonds_karp_multiple_source_and_sink.py) +- [Floyd Warshall](./graphs/floyd_warshall.py) +- [Minimum Spanning Tree Kruskal](./graphs/minimum_spanning_tree_kruskal.py) +- [Prim](./graphs/prim.py) +- [Kahns Algorithm Topo](./graphs/kahns_algorithm_topo.py) +- [BFS](./graphs/BFS.py) +- [Finding Bridges](./graphs/finding_bridges.py) +- [Graph List](./graphs/graph_list.py) +- [Dijkstra Algorithm](./graphs/dijkstra_algorithm.py) +- [A Star](./graphs/a_star.py) +- [Even Tree](./graphs/even_tree.py) +- [DFS](./graphs/DFS.py) + +## Networking Flow + +- [Minimum Cut](./networking_flow/minimum_cut.py) +- [Ford Fulkerson](./networking_flow/ford_fulkerson.py) + +## Matrix + +- [Matrix Operation](./matrix/matrix_operation.py) +- [Searching In Sorted Matrix](./matrix/searching_in_sorted_matrix.py) +- [Spiral Print](./matrix/spiral_print.py) + +## Searches + +- [Quick Select](./searches/quick_select.py) +- [Binary Search](./searches/binary_search.py) +- [Interpolation Search](./searches/interpolation_search.py) +- [Jump Search](./searches/jump_search.py) +- [Linear Search](./searches/linear_search.py) +- [Ternary Search](./searches/ternary_search.py) +- [Tabu Search](./searches/tabu_search.py) +- [Sentinel Linear Search](./searches/sentinel_linear_search.py) + +## Conversions + +- [Decimal To Binary](./conversions/decimal_to_binary.py) +- [Decimal To Octal](./conversions/decimal_to_octal.py) + +## Dynamic Programming + +- [Fractional Knapsack](./dynamic_programming/Fractional_Knapsack.py) +- [Sum Of Subset](./dynamic_programming/sum_of_subset.py) +- [Fast Fibonacci](./dynamic_programming/fast_fibonacci.py) +- [Bitmask](./dynamic_programming/bitmask.py) +- [Abbreviation](./dynamic_programming/abbreviation.py) +- [Rod Cutting](./dynamic_programming/rod_cutting.py) +- [Knapsack](./dynamic_programming/knapsack.py) +- [Max Sub Array](./dynamic_programming/max_sub_array.py) +- [Fibonacci](./dynamic_programming/fibonacci.py) +- [Minimum Partition](./dynamic_programming/minimum_partition.py) +- [K Means Clustering Tensorflow](./dynamic_programming/k_means_clustering_tensorflow.py) +- [Coin Change](./dynamic_programming/coin_change.py) +- [Subset Generation](./dynamic_programming/subset_generation.py) +- [Floyd Warshall](./dynamic_programming/floyd_warshall.py) +- [Longest Sub Array](./dynamic_programming/longest_sub_array.py) +- [Integer Partition](./dynamic_programming/integer_partition.py) +- [Matrix Chain Order](./dynamic_programming/matrix_chain_order.py) +- [Edit Distance](./dynamic_programming/edit_distance.py) +- [Longest Common Subsequence](./dynamic_programming/longest_common_subsequence.py) +- [Longest Increasing Subsequence O(nlogn)](<./dynamic_programming/longest_increasing_subsequence_O(nlogn).py>) +- [Longest Increasing Subsequence](./dynamic_programming/longest_increasing_subsequence.py) + +## Divide And Conquer + +- [Max Subarray Sum](./divide_and_conquer/max_subarray_sum.py) +- [Max Sub Array Sum](./divide_and_conquer/max_sub_array_sum.py) +- [Closest Pair Of Points](./divide_and_conquer/closest_pair_of_points.py) + +## Strings + +- [Knuth Morris Pratt](./strings/knuth_morris_pratt.py) +- [Rabin Karp](./strings/rabin_karp.py) +- [Naive String Search](./strings/naive_String_Search.py) +- [Levenshtein Distance](./strings/levenshtein_distance.py) +- [Min Cost String Conversion](./strings/min_cost_string_conversion.py) +- [Boyer Moore Search](./strings/Boyer_Moore_Search.py) +- [Manacher](./strings/manacher.py) + +## Sorts + +- [Quick Sort](./sorts/quick_sort.py) +- [Selection Sort](./sorts/selection_sort.py) +- [Bitonic Sort](./sorts/Bitonic_Sort.py) +- [Cycle Sort](./sorts/cycle_sort.py) +- [Comb Sort](./sorts/comb_sort.py) +- [Topological Sort](./sorts/topological_sort.py) +- [Merge Sort Fastest](./sorts/merge_sort_fastest.py) +- [Random Pivot Quick Sort](./sorts/random_pivot_quick_sort.py) +- [Heap Sort](./sorts/heap_sort.py) +- [Insertion Sort](./sorts/insertion_sort.py) +- [Counting Sort](./sorts/counting_sort.py) +- [Bucket Sort](./sorts/bucket_sort.py) +- [Quick Sort 3 Partition](./sorts/quick_sort_3_partition.py) +- [Bogo Sort](./sorts/bogo_sort.py) +- [Shell Sort](./sorts/shell_sort.py) +- [Pigeon Sort](./sorts/pigeon_sort.py) +- [Odd-Even Transposition Parallel](./sorts/Odd-Even_transposition_parallel.py) +- [Tree Sort](./sorts/tree_sort.py) +- [Cocktail Shaker Sort](./sorts/cocktail_shaker_sort.py) +- [Random Normal Distribution Quicksort](./sorts/random_normal_distribution_quicksort.py) +- [Wiggle Sort](./sorts/wiggle_sort.py) +- [Pancake Sort](./sorts/pancake_sort.py) +- [External Sort](./sorts/external_sort.py) +- [Tim Sort](./sorts/tim_sort.py) +- [Sorting Graphs.png](./sorts/sorting_graphs.png) +- [Radix Sort](./sorts/radix_sort.py) +- [Odd-Even Transposition Single-threaded](./sorts/Odd-Even_transposition_single-threaded.py) +- [Bubble Sort](./sorts/bubble_sort.py) +- [Gnome Sort](./sorts/gnome_sort.py) +- [Merge Sort](./sorts/merge_sort.py) + +## Machine Learning + +- [Perceptron](./machine_learning/perceptron.py) +- [Random Forest Classifier](./machine_learning/random_forest_classification/random_forest_classifier.ipynb) +- [NaiveBayes.ipynb](./machine_learning/NaiveBayes.ipynb) +- [Scoring Functions](./machine_learning/scoring_functions.py) +- [Logistic Regression](./machine_learning/logistic_regression.py) +- [Gradient Descent](./machine_learning/gradient_descent.py) +- [Linear Regression](./machine_learning/linear_regression.py) +- [Random Forest Regression](./machine_learning/random_forest_regression/random_forest_regression.py) +- [Random Forest Regression](./machine_learning/random_forest_regression/random_forest_regression.ipynb) +- [Reuters One Vs Rest Classifier.ipynb](./machine_learning/reuters_one_vs_rest_classifier.ipynb) +- [Decision Tree](./machine_learning/decision_tree.py) +- [Knn Sklearn](./machine_learning/knn_sklearn.py) +- [K Means Clust](./machine_learning/k_means_clust.py) + +## Neural Network + +- [Perceptron](./neural_network/perceptron.py) +- [Fully Connected Neural Network](./neural_network/fully_connected_neural_network.ipynb) +- [Convolution Neural Network](./neural_network/convolution_neural_network.py) +- [Back Propagation Neural Network](./neural_network/back_propagation_neural_network.py) + +## Data Structures + +- ## Binary Tree + + - [Basic Binary Tree](./data_structures/binary_tree/basic_binary_tree.py) + - [Red Black Tree](./data_structures/binary_tree/red_black_tree.py) + - [Fenwick Tree](./data_structures/binary_tree/fenwick_tree.py) + - [Treap](./data_structures/binary_tree/treap.py) + - [AVL Tree](./data_structures/binary_tree/AVL_tree.py) + - [Segment Tree](./data_structures/binary_tree/segment_tree.py) + - [Lazy Segment Tree](./data_structures/binary_tree/lazy_segment_tree.py) + - [Binary Search Tree](./data_structures/binary_tree/binary_search_tree.py) + +- ## Trie + + - [Trie](./data_structures/trie/trie.py) + +- ## Linked List + + - [Swap Nodes](./data_structures/linked_list/swap_nodes.py) + - [Doubly Linked List](./data_structures/linked_list/doubly_linked_list.py) + - [Singly Linked List](./data_structures/linked_list/singly_linked_list.py) + - [Is Palindrome](./data_structures/linked_list/is_Palindrome.py) + +- ## Stacks + + - [Postfix Evaluation](./data_structures/stacks/postfix_evaluation.py) + - [Balanced Parentheses](./data_structures/stacks/balanced_parentheses.py) + - [Infix To Prefix Conversion](./data_structures/stacks/infix_to_prefix_conversion.py) + - [Stack](./data_structures/stacks/stack.py) + - [Infix To Postfix Conversion](./data_structures/stacks/infix_to_postfix_conversion.py) + - [Next Greater Element](./data_structures/stacks/next_greater_element.py) + - [Stock Span Problem](./data_structures/stacks/stock_span_problem.py) + +- ## Queue + + - [Queue On Pseudo Stack](./data_structures/queue/queue_on_pseudo_stack.py) + - [Double Ended Queue](./data_structures/queue/double_ended_queue.py) + - [Queue On List](./data_structures/queue/queue_on_list.py) + +- ## Heap + + - [Heap](./data_structures/heap/heap.py) + +- ## Hashing + + - [Hash Table With Linked List](./data_structures/hashing/hash_table_with_linked_list.py) + - [Quadratic Probing](./data_structures/hashing/quadratic_probing.py) + - [Hash Table](./data_structures/hashing/hash_table.py) + - [Double Hash](./data_structures/hashing/double_hash.py) + + +## Other + +- [Detecting English Programmatically](./other/detecting_english_programmatically.py) +- [Fischer Yates Shuffle](./other/fischer_yates_shuffle.py) +- [Primelib](./other/primelib.py) +- [Binary Exponentiation 2](./other/binary_exponentiation_2.py) +- [Anagrams](./other/anagrams.py) +- [Palindrome](./other/palindrome.py) +- [Finding Primes](./other/finding_Primes.py) +- [Two Sum](./other/two_sum.py) +- [Password Generator](./other/password_generator.py) +- [Linear Congruential Generator](./other/linear_congruential_generator.py) +- [Frequency Finder](./other/frequency_finder.py) +- [Euclidean Gcd](./other/euclidean_gcd.py) +- [Word Patterns](./other/word_patterns.py) +- [Nested Brackets](./other/nested_brackets.py) +- [Binary Exponentiation](./other/binary_exponentiation.py) +- [Sierpinski Triangle](./other/sierpinski_triangle.py) +- [Game Of Life](./other/game_of_life.py) +- [Tower Of Hanoi](./other/tower_of_hanoi.py) diff --git a/compression_analysis/PSNR-example-base.png b/compression/image_data/PSNR-example-base.png similarity index 100% rename from compression_analysis/PSNR-example-base.png rename to compression/image_data/PSNR-example-base.png diff --git a/compression_analysis/PSNR-example-comp-10.jpg b/compression/image_data/PSNR-example-comp-10.jpg similarity index 100% rename from compression_analysis/PSNR-example-comp-10.jpg rename to compression/image_data/PSNR-example-comp-10.jpg diff --git a/compression_analysis/compressed_image.png b/compression/image_data/compressed_image.png similarity index 100% rename from compression_analysis/compressed_image.png rename to compression/image_data/compressed_image.png diff --git a/compression_analysis/example_image.jpg b/compression/image_data/example_image.jpg similarity index 100% rename from compression_analysis/example_image.jpg rename to compression/image_data/example_image.jpg diff --git a/compression_analysis/example_wikipedia_image.jpg b/compression/image_data/example_wikipedia_image.jpg similarity index 100% rename from compression_analysis/example_wikipedia_image.jpg rename to compression/image_data/example_wikipedia_image.jpg diff --git a/compression_analysis/original_image.png b/compression/image_data/original_image.png similarity index 100% rename from compression_analysis/original_image.png rename to compression/image_data/original_image.png diff --git a/compression_analysis/psnr.py b/compression/peak_signal_to_noise_ratio.py similarity index 71% rename from compression_analysis/psnr.py rename to compression/peak_signal_to_noise_ratio.py index 0f21aac07d34..b0efb1462dcc 100644 --- a/compression_analysis/psnr.py +++ b/compression/peak_signal_to_noise_ratio.py @@ -21,11 +21,11 @@ def psnr(original, contrast): def main(): dir_path = os.path.dirname(os.path.realpath(__file__)) # Loading images (original image and compressed image) - original = cv2.imread(os.path.join(dir_path, 'original_image.png')) - contrast = cv2.imread(os.path.join(dir_path, 'compressed_image.png'), 1) + original = cv2.imread(os.path.join(dir_path, 'image_data/original_image.png')) + contrast = cv2.imread(os.path.join(dir_path, 'image_data/compressed_image.png'), 1) - original2 = cv2.imread(os.path.join(dir_path, 'PSNR-example-base.png')) - contrast2 = cv2.imread(os.path.join(dir_path, 'PSNR-example-comp-10.jpg'), 1) + original2 = cv2.imread(os.path.join(dir_path, 'image_data/PSNR-example-base.png')) + contrast2 = cv2.imread(os.path.join(dir_path, 'image_data/PSNR-example-comp-10.jpg'), 1) # Value expected: 29.73dB print("-- First Test --") diff --git a/data_structures/__init__.py b/data_structures/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/data_structures/arrays.py b/data_structures/arrays.py deleted file mode 100644 index feb061013556..000000000000 --- a/data_structures/arrays.py +++ /dev/null @@ -1,3 +0,0 @@ -arr = [10, 20, 30, 40] -arr[1] = 30 # set element 1 (20) of array to 30 -print(arr) diff --git a/data_structures/avl.py b/data_structures/avl.py deleted file mode 100644 index d01e8f825368..000000000000 --- a/data_structures/avl.py +++ /dev/null @@ -1,181 +0,0 @@ -""" -An AVL tree -""" -from __future__ import print_function - - -class Node: - - def __init__(self, label): - self.label = label - self._parent = None - self._left = None - self._right = None - self.height = 0 - - @property - def right(self): - return self._right - - @right.setter - def right(self, node): - if node is not None: - node._parent = self - self._right = node - - @property - def left(self): - return self._left - - @left.setter - def left(self, node): - if node is not None: - node._parent = self - self._left = node - - @property - def parent(self): - return self._parent - - @parent.setter - def parent(self, node): - if node is not None: - self._parent = node - self.height = self.parent.height + 1 - else: - self.height = 0 - - -class AVL: - - def __init__(self): - self.root = None - self.size = 0 - - def insert(self, value): - node = Node(value) - - if self.root is None: - self.root = node - self.root.height = 0 - self.size = 1 - else: - # Same as Binary Tree - dad_node = None - curr_node = self.root - - while True: - if curr_node is not None: - - dad_node = curr_node - - if node.label < curr_node.label: - curr_node = curr_node.left - else: - curr_node = curr_node.right - else: - node.height = dad_node.height - dad_node.height += 1 - if node.label < dad_node.label: - dad_node.left = node - else: - dad_node.right = node - self.rebalance(node) - self.size += 1 - break - - def rebalance(self, node): - n = node - - while n is not None: - height_right = n.height - height_left = n.height - - if n.right is not None: - height_right = n.right.height - - if n.left is not None: - height_left = n.left.height - - if abs(height_left - height_right) > 1: - if height_left > height_right: - left_child = n.left - if left_child is not None: - h_right = (left_child.right.height - if (left_child.right is not None) else 0) - h_left = (left_child.left.height - if (left_child.left is not None) else 0) - if (h_left > h_right): - self.rotate_left(n) - break - else: - self.double_rotate_right(n) - break - else: - right_child = n.right - if right_child is not None: - h_right = (right_child.right.height - if (right_child.right is not None) else 0) - h_left = (right_child.left.height - if (right_child.left is not None) else 0) - if (h_left > h_right): - self.double_rotate_left(n) - break - else: - self.rotate_right(n) - break - n = n.parent - - def rotate_left(self, node): - aux = node.parent.label - node.parent.label = node.label - node.parent.right = Node(aux) - node.parent.right.height = node.parent.height + 1 - node.parent.left = node.right - - - def rotate_right(self, node): - aux = node.parent.label - node.parent.label = node.label - node.parent.left = Node(aux) - node.parent.left.height = node.parent.height + 1 - node.parent.right = node.right - - def double_rotate_left(self, node): - self.rotate_right(node.getRight().getRight()) - self.rotate_left(node) - - def double_rotate_right(self, node): - self.rotate_left(node.getLeft().getLeft()) - self.rotate_right(node) - - def empty(self): - if self.root is None: - return True - return False - - def preShow(self, curr_node): - if curr_node is not None: - self.preShow(curr_node.left) - print(curr_node.label, end=" ") - self.preShow(curr_node.right) - - def preorder(self, curr_node): - if curr_node is not None: - self.preShow(curr_node.left) - self.preShow(curr_node.right) - print(curr_node.label, end=" ") - - def getRoot(self): - return self.root - -t = AVL() -t.insert(1) -t.insert(2) -t.insert(3) -# t.preShow(t.root) -# print("\n") -# t.insert(4) -# t.insert(5) -# t.preShow(t.root) -# t.preorden(t.root) diff --git a/data_structures/LCA.py b/data_structures/binary_tree/LCA.py similarity index 100% rename from data_structures/LCA.py rename to data_structures/binary_tree/LCA.py diff --git a/binary_tree/basic_binary_tree.py b/data_structures/binary_tree/basic_binary_tree.py similarity index 100% rename from binary_tree/basic_binary_tree.py rename to data_structures/binary_tree/basic_binary_tree.py diff --git a/data_structures/hashing/__init__.py b/data_structures/hashing/__init__.py deleted file mode 100644 index b96ddd478458..000000000000 --- a/data_structures/hashing/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .hash_table import HashTable - -class QuadraticProbing(HashTable): - - def __init__(self): - super(self.__class__, self).__init__() diff --git a/data_structures/hashing/number_theory/__init__.py b/data_structures/hashing/number_theory/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/data_structures/linked_list/swapNodes.py b/data_structures/linked_list/swap_nodes.py similarity index 100% rename from data_structures/linked_list/swapNodes.py rename to data_structures/linked_list/swap_nodes.py diff --git a/data_structures/queue/__init__.py b/data_structures/queue/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/data_structures/stacks/next.py b/data_structures/stacks/next_greater_element.py similarity index 100% rename from data_structures/stacks/next.py rename to data_structures/stacks/next_greater_element.py diff --git a/data_structures/union_find/__init__.py b/data_structures/union_find/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/data_structures/union_find/tests_union_find.py b/data_structures/union_find/tests_union_find.py deleted file mode 100644 index b0708778ddbd..000000000000 --- a/data_structures/union_find/tests_union_find.py +++ /dev/null @@ -1,78 +0,0 @@ -from __future__ import absolute_import -from .union_find import UnionFind -import unittest - - -class TestUnionFind(unittest.TestCase): - def test_init_with_valid_size(self): - uf = UnionFind(5) - self.assertEqual(uf.size, 5) - - def test_init_with_invalid_size(self): - with self.assertRaises(ValueError): - uf = UnionFind(0) - - with self.assertRaises(ValueError): - uf = UnionFind(-5) - - def test_union_with_valid_values(self): - uf = UnionFind(10) - - for i in range(11): - for j in range(11): - uf.union(i, j) - - def test_union_with_invalid_values(self): - uf = UnionFind(10) - - with self.assertRaises(ValueError): - uf.union(-1, 1) - - with self.assertRaises(ValueError): - uf.union(11, 1) - - def test_same_set_with_valid_values(self): - uf = UnionFind(10) - - for i in range(11): - for j in range(11): - if i == j: - self.assertTrue(uf.same_set(i, j)) - else: - self.assertFalse(uf.same_set(i, j)) - - uf.union(1, 2) - self.assertTrue(uf.same_set(1, 2)) - - uf.union(3, 4) - self.assertTrue(uf.same_set(3, 4)) - - self.assertFalse(uf.same_set(1, 3)) - self.assertFalse(uf.same_set(1, 4)) - self.assertFalse(uf.same_set(2, 3)) - self.assertFalse(uf.same_set(2, 4)) - - uf.union(1, 3) - self.assertTrue(uf.same_set(1, 3)) - self.assertTrue(uf.same_set(1, 4)) - self.assertTrue(uf.same_set(2, 3)) - self.assertTrue(uf.same_set(2, 4)) - - uf.union(4, 10) - self.assertTrue(uf.same_set(1, 10)) - self.assertTrue(uf.same_set(2, 10)) - self.assertTrue(uf.same_set(3, 10)) - self.assertTrue(uf.same_set(4, 10)) - - def test_same_set_with_invalid_values(self): - uf = UnionFind(10) - - with self.assertRaises(ValueError): - uf.same_set(-1, 1) - - with self.assertRaises(ValueError): - uf.same_set(11, 0) - - -if __name__ == '__main__': - unittest.main() diff --git a/data_structures/union_find/union_find.py b/data_structures/union_find/union_find.py deleted file mode 100644 index 40eea67ac944..000000000000 --- a/data_structures/union_find/union_find.py +++ /dev/null @@ -1,87 +0,0 @@ -class UnionFind(): - """ - https://en.wikipedia.org/wiki/Disjoint-set_data_structure - - The union-find is a disjoint-set data structure - - You can merge two sets and tell if one set belongs to - another one. - - It's used on the Kruskal Algorithm - (https://en.wikipedia.org/wiki/Kruskal%27s_algorithm) - - The elements are in range [0, size] - """ - def __init__(self, size): - if size <= 0: - raise ValueError("size should be greater than 0") - - self.size = size - - # The below plus 1 is because we are using elements - # in range [0, size]. It makes more sense. - - # Every set begins with only itself - self.root = [i for i in range(size+1)] - - # This is used for heuristic union by rank - self.weight = [0 for i in range(size+1)] - - def union(self, u, v): - """ - Union of the sets u and v. - Complexity: log(n). - Amortized complexity: < 5 (it's very fast). - """ - - self._validate_element_range(u, "u") - self._validate_element_range(v, "v") - - if u == v: - return - - # Using union by rank will guarantee the - # log(n) complexity - rootu = self._root(u) - rootv = self._root(v) - weight_u = self.weight[rootu] - weight_v = self.weight[rootv] - if weight_u >= weight_v: - self.root[rootv] = rootu - if weight_u == weight_v: - self.weight[rootu] += 1 - else: - self.root[rootu] = rootv - - def same_set(self, u, v): - """ - Return true if the elements u and v belongs to - the same set - """ - - self._validate_element_range(u, "u") - self._validate_element_range(v, "v") - - return self._root(u) == self._root(v) - - def _root(self, u): - """ - Get the element set root. - This uses the heuristic path compression - See wikipedia article for more details. - """ - - if u != self.root[u]: - self.root[u] = self._root(self.root[u]) - - return self.root[u] - - def _validate_element_range(self, u, element_name): - """ - Raises ValueError if element is not in range - """ - if u < 0 or u > self.size: - msg = ("element {0} with value {1} " - "should be in range [0~{2}]")\ - .format(element_name, u, self.size) - raise ValueError(msg) diff --git a/machine_learning/Random Forest Classification/Social_Network_Ads.csv b/machine_learning/random_forest_classification/Social_Network_Ads.csv similarity index 100% rename from machine_learning/Random Forest Classification/Social_Network_Ads.csv rename to machine_learning/random_forest_classification/Social_Network_Ads.csv diff --git a/machine_learning/Random Forest Classification/random_forest_classification.py b/machine_learning/random_forest_classification/random_forest_classification.py similarity index 100% rename from machine_learning/Random Forest Classification/random_forest_classification.py rename to machine_learning/random_forest_classification/random_forest_classification.py diff --git a/machine_learning/Random Forest Classification/Random Forest Classifier.ipynb b/machine_learning/random_forest_classification/random_forest_classifier.ipynb similarity index 100% rename from machine_learning/Random Forest Classification/Random Forest Classifier.ipynb rename to machine_learning/random_forest_classification/random_forest_classifier.ipynb diff --git a/machine_learning/Random Forest Regression/Position_Salaries.csv b/machine_learning/random_forest_regression/Position_Salaries.csv similarity index 100% rename from machine_learning/Random Forest Regression/Position_Salaries.csv rename to machine_learning/random_forest_regression/Position_Salaries.csv diff --git a/machine_learning/Random Forest Regression/Random Forest Regression.ipynb b/machine_learning/random_forest_regression/random_forest_regression.ipynb similarity index 100% rename from machine_learning/Random Forest Regression/Random Forest Regression.ipynb rename to machine_learning/random_forest_regression/random_forest_regression.ipynb diff --git a/machine_learning/Random Forest Regression/random_forest_regression.py b/machine_learning/random_forest_regression/random_forest_regression.py similarity index 100% rename from machine_learning/Random Forest Regression/random_forest_regression.py rename to machine_learning/random_forest_regression/random_forest_regression.py diff --git a/maths/Hanoi.py b/maths/Hanoi.py deleted file mode 100644 index c7b435a8fe3e..000000000000 --- a/maths/Hanoi.py +++ /dev/null @@ -1,29 +0,0 @@ -"""Tower of Hanoi.""" - -# @author willx75 -# Tower of Hanoi recursion game algorithm is a game, it consists of three rods -# and a number of disks of different sizes, which can slide onto any rod - -import logging - -log = logging.getLogger() -logging.basicConfig(level=logging.DEBUG) - - -def Tower_Of_Hanoi(n, source, dest, by, movement): - """Tower of Hanoi - Move plates to different rods.""" - if n == 0: - return n - elif n == 1: - movement += 1 - # no print statement - # (you could make it an optional flag for printing logs) - logging.debug('Move the plate from', source, 'to', dest) - return movement - else: - - movement = movement + Tower_Of_Hanoi(n - 1, source, by, dest, 0) - logging.debug('Move the plate from', source, 'to', dest) - - movement = movement + 1 + Tower_Of_Hanoi(n - 1, by, dest, source, 0) - return movement diff --git a/maths/lucasSeries.py b/maths/lucas series.py similarity index 100% rename from maths/lucasSeries.py rename to maths/lucas series.py diff --git a/maths/tests/__init__.py b/maths/tests/__init__.py deleted file mode 100644 index 2c4a6048556c..000000000000 --- a/maths/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .. import fibonacci diff --git a/maths/tests/test_fibonacci.py b/maths/tests/test_fibonacci.py deleted file mode 100644 index 7d36c755e346..000000000000 --- a/maths/tests/test_fibonacci.py +++ /dev/null @@ -1,34 +0,0 @@ -""" -To run with slash: -1. run pip install slash (may need to install C++ builds from Visual Studio website) -2. In the command prompt navigate to your project folder -3. then type--> slash run -vv -k tags:fibonacci .. - -vv indicates the level of verbosity (how much stuff you want the test to spit out after running) - -k is a way to select the tests you want to run. This becomes much more important in large scale projects. -""" - -import slash -from .. import fibonacci - -default_fib = [0, 1, 1, 2, 3, 5, 8] - - -@slash.tag('fibonacci') -@slash.parametrize(('n', 'seq'), [(2, [0, 1]), (3, [0, 1, 1]), (9, [0, 1, 1, 2, 3, 5, 8, 13, 21])]) -def test_different_sequence_lengths(n, seq): - """Test output of varying fibonacci sequence lengths""" - iterative = fibonacci.fib_iterative(n) - formula = fibonacci.fib_formula(n) - assert iterative == seq - assert formula == seq - - -@slash.tag('fibonacci') -@slash.parametrize('n', [7.3, 7.8, 7.0]) -def test_float_input_iterative(n): - """Test when user enters a float value""" - iterative = fibonacci.fib_iterative(n) - formula = fibonacci.fib_formula(n) - assert iterative == default_fib - assert formula == default_fib - diff --git a/matrix/spiralPrint.py b/matrix/spiral_print.py similarity index 100% rename from matrix/spiralPrint.py rename to matrix/spiral_print.py diff --git a/neural_network/bpnn.py 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a/searches/test_interpolation_search.py +++ /dev/null @@ -1,93 +0,0 @@ -import unittest -from interpolation_search import interpolation_search, interpolation_search_by_recursion - -class Test_interpolation_search(unittest.TestCase): - def setUp(self): - # un-sorted case - self.collection1 = [5,3,4,6,7] - self.item1 = 4 - # sorted case, result exists - self.collection2 = [10,30,40,45,50,66,77,93] - self.item2 = 66 - # sorted case, result doesn't exist - self.collection3 = [10,30,40,45,50,66,77,93] - self.item3 = 67 - # equal elements case, result exists - self.collection4 = [10,10,10,10,10] - self.item4 = 10 - # equal elements case, result doesn't exist - self.collection5 = [10,10,10,10,10] - self.item5 = 3 - # 1 element case, result exists - self.collection6 = [10] - self.item6 = 10 - # 1 element case, result doesn't exists - self.collection7 = [10] - self.item7 = 1 - - def tearDown(self): - pass - - def test_interpolation_search(self): - self.assertEqual(interpolation_search(self.collection1, self.item1), None) - - self.assertEqual(interpolation_search(self.collection2, self.item2), self.collection2.index(self.item2)) - - self.assertEqual(interpolation_search(self.collection3, self.item3), None) - - self.assertEqual(interpolation_search(self.collection4, self.item4), self.collection4.index(self.item4)) - - self.assertEqual(interpolation_search(self.collection5, self.item5), None) - - self.assertEqual(interpolation_search(self.collection6, self.item6), self.collection6.index(self.item6)) - - self.assertEqual(interpolation_search(self.collection7, self.item7), None) - - - -class Test_interpolation_search_by_recursion(unittest.TestCase): - def setUp(self): - # un-sorted case - self.collection1 = [5,3,4,6,7] - self.item1 = 4 - # sorted case, result exists - self.collection2 = [10,30,40,45,50,66,77,93] - self.item2 = 66 - # sorted case, result doesn't exist - self.collection3 = [10,30,40,45,50,66,77,93] - self.item3 = 67 - # equal elements case, result exists - self.collection4 = [10,10,10,10,10] - self.item4 = 10 - # equal elements case, result doesn't exist - self.collection5 = [10,10,10,10,10] - self.item5 = 3 - # 1 element case, result exists - self.collection6 = [10] - self.item6 = 10 - # 1 element case, result doesn't exists - self.collection7 = [10] - self.item7 = 1 - - def tearDown(self): - pass - - def test_interpolation_search_by_recursion(self): - self.assertEqual(interpolation_search_by_recursion(self.collection1, self.item1, 0, len(self.collection1)-1), None) - - self.assertEqual(interpolation_search_by_recursion(self.collection2, self.item2, 0, len(self.collection2)-1), self.collection2.index(self.item2)) - - self.assertEqual(interpolation_search_by_recursion(self.collection3, self.item3, 0, len(self.collection3)-1), None) - - self.assertEqual(interpolation_search_by_recursion(self.collection4, self.item4, 0, len(self.collection4)-1), self.collection4.index(self.item4)) - - self.assertEqual(interpolation_search_by_recursion(self.collection5, self.item5, 0, len(self.collection5)-1), None) - - self.assertEqual(interpolation_search_by_recursion(self.collection6, self.item6, 0, len(self.collection6)-1), self.collection6.index(self.item6)) - - self.assertEqual(interpolation_search_by_recursion(self.collection7, self.item7, 0, len(self.collection7)-1), None) - - - -if __name__ == '__main__': - unittest.main() diff --git a/searches/test_tabu_search.py b/searches/test_tabu_search.py deleted file mode 100644 index e6f73e6a9002..000000000000 --- a/searches/test_tabu_search.py +++ /dev/null @@ -1,46 +0,0 @@ -import unittest -import os -from tabu_search import generate_neighbours, generate_first_solution, find_neighborhood, tabu_search - -TEST_FILE = os.path.join(os.path.dirname(__file__), './tabu_test_data.txt') - -NEIGHBOURS_DICT = {'a': [['b', '20'], ['c', '18'], ['d', '22'], ['e', '26']], - 'c': [['a', '18'], ['b', '10'], ['d', '23'], ['e', '24']], - 'b': [['a', '20'], ['c', '10'], ['d', '11'], ['e', '12']], - 'e': [['a', '26'], ['b', '12'], ['c', '24'], ['d', '40']], - 'd': [['a', '22'], ['b', '11'], ['c', '23'], ['e', '40']]} - -FIRST_SOLUTION = ['a', 'c', 'b', 'd', 'e', 'a'] - -DISTANCE = 105 - -NEIGHBOURHOOD_OF_SOLUTIONS = [['a', 'e', 'b', 'd', 'c', 'a', 90], - ['a', 'c', 'd', 'b', 'e', 'a', 90], - ['a', 'd', 'b', 'c', 'e', 'a', 93], - ['a', 'c', 'b', 'e', 'd', 'a', 102], - ['a', 'c', 'e', 'd', 'b', 'a', 113], - ['a', 'b', 'c', 'd', 'e', 'a', 119]] - - -class TestClass(unittest.TestCase): - def test_generate_neighbours(self): - neighbours = generate_neighbours(TEST_FILE) - - self.assertEqual(NEIGHBOURS_DICT, neighbours) - - def test_generate_first_solutions(self): - first_solution, distance = generate_first_solution(TEST_FILE, NEIGHBOURS_DICT) - - self.assertEqual(FIRST_SOLUTION, first_solution) - self.assertEqual(DISTANCE, distance) - - def test_find_neighbours(self): - neighbour_of_solutions = find_neighborhood(FIRST_SOLUTION, NEIGHBOURS_DICT) - - self.assertEqual(NEIGHBOURHOOD_OF_SOLUTIONS, neighbour_of_solutions) - - def test_tabu_search(self): - best_sol, best_cost = tabu_search(FIRST_SOLUTION, DISTANCE, NEIGHBOURS_DICT, 4, 3) - - self.assertEqual(['a', 'd', 'b', 'e', 'c', 'a'], best_sol) - self.assertEqual(87, best_cost) diff --git a/simple_client/README.md b/simple_client/README.md deleted file mode 100644 index f51947f2105a..000000000000 --- a/simple_client/README.md +++ /dev/null @@ -1,6 +0,0 @@ -# simple client server - -#### Note: -- Run **`server.py`** first. -- Now, run **`client.py`**. -- verify the output. diff --git a/simple_client/client.py b/simple_client/client.py deleted file mode 100644 index db162f43c78a..000000000000 --- a/simple_client/client.py +++ /dev/null @@ -1,29 +0,0 @@ -# client.py - -import socket - -HOST, PORT = '127.0.0.1', 1400 - -s = socket.socket( - - socket.AF_INET, # ADDRESS FAMILIES - #Name Purpose - #AF_UNIX, AF_LOCAL Local communication - #AF_INET IPv4 Internet protocols - #AF_INET6 IPv6 Internet protocols - #AF_APPLETALK Appletalk - #AF_BLUETOOTH Bluetooth - - - socket.SOCK_STREAM # SOCKET TYPES - #Name Way of Interaction - #SOCK_STREAM TCP - #SOCK_DGRAM UDP -) -s.connect((HOST, PORT)) - -s.send('Hello World'.encode('ascii'))#in UDP use sendto() -data = s.recv(1024)#in UDP use recvfrom() - -s.close()#end the connection -print(repr(data.decode('ascii'))) diff --git a/simple_client/server.py b/simple_client/server.py deleted file mode 100644 index c23075608a90..000000000000 --- a/simple_client/server.py +++ /dev/null @@ -1,21 +0,0 @@ -# server.py - -import socket - -HOST, PORT = '127.0.0.1', 1400 - -s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)#refer to client.py -s.bind((HOST, PORT)) -s.listen(1)#listen for 1 connection - -conn, addr = s.accept()#start the actual data flow - -print('connected to:', addr) - -while 1: - data = conn.recv(1024).decode('ascii')#receive 1024 bytes and decode using ascii - if not data: - break - 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-from cocktail_shaker_sort import cocktail_shaker_sort -from comb_sort import comb_sort -from counting_sort import counting_sort -from cycle_sort import cycle_sort -from gnome_sort import gnome_sort -from heap_sort import heap_sort -from insertion_sort import insertion_sort -from merge_sort_fastest import merge_sort as merge_sort_fastest -from merge_sort import merge_sort -from pancake_sort import pancake_sort -from quick_sort_3_partition import quick_sort_3partition -from quick_sort import quick_sort -from radix_sort import radix_sort -from random_pivot_quick_sort import quick_sort_random -from selection_sort import selection_sort -from shell_sort import shell_sort -from tim_sort import tim_sort -from topological_sort import topological_sort -from tree_sort import tree_sort -from wiggle_sort import wiggle_sort - - -TEST_CASES = [ - {'input': [8, 7, 6, 5, 4, 3, -2, -5], 'expected': [-5, -2, 3, 4, 5, 6, 7, 8]}, - {'input': [-5, -2, 3, 4, 5, 6, 7, 8], 'expected': [-5, -2, 3, 4, 5, 6, 7, 8]}, - {'input': [5, 6, 1, 4, 0, 1, -2, -5, 3, 7], 'expected': [-5, -2, 0, 1, 1, 3, 4, 5, 6, 7]}, - {'input': [2, -2], 'expected': [-2, 2]}, - {'input': [1], 'expected': [1]}, - {'input': [], 'expected': []}, -] - -''' - TODO: - - Fix some broken tests in particular cases (as [] for example), - - Unify the input format: should always be function(input_collection) (no additional args) - - Unify the output format: should always be a collection instead of - updating input elements and returning None - - Rewrite some algorithms in function format (in case there is no function definition) -''' - -TEST_FUNCTIONS = [ - bogo_sort, - bubble_sort, - bucket_sort, - cocktail_shaker_sort, - comb_sort, - counting_sort, - cycle_sort, - gnome_sort, - heap_sort, - insertion_sort, - merge_sort_fastest, - merge_sort, - pancake_sort, - quick_sort_3partition, - quick_sort, - radix_sort, - quick_sort_random, - selection_sort, - shell_sort, - tim_sort, - topological_sort, - tree_sort, - wiggle_sort, -] - - -for function in TEST_FUNCTIONS: - for case in TEST_CASES: - result = function(case['input']) - assert result == case['expected'], 'Executed function: {}, {} != {}'.format(function.__name__, result, case['expected']) From f30f8493e6ef91f9d95ae88b0d2b59ef1f102681 Mon Sep 17 00:00:00 2001 From: Anup Kumar Panwar <1anuppanwar@gmail.com> Date: Sat, 6 Jul 2019 11:12:26 +0530 Subject: [PATCH 037/193] Updated README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9edddb60552a..a609dc077a77 100644 --- a/README.md +++ b/README.md @@ -129,7 +129,7 @@ We're on [Gitter](https://gitter.im/TheAlgorithms)! Please join us. ## Compression -- [Peak Signal To Noise Ratio](./compression_analysis/peak_signal_to_noise_ratio.py) +- [Peak Signal To Noise Ratio](./compression/peak_signal_to_noise_ratio.py) - [Huffman](./compression/huffman.py) ## Graphs From b23834062c5a78e2aeda70336eb66c07c7209181 Mon Sep 17 00:00:00 2001 From: Anup Kumar Panwar <1anuppanwar@gmail.com> Date: Sat, 6 Jul 2019 11:14:55 +0530 Subject: [PATCH 038/193] refactor --- DIRECTORY.py | 50 -------------------------------------------------- 1 file changed, 50 deletions(-) delete mode 100644 DIRECTORY.py diff --git a/DIRECTORY.py b/DIRECTORY.py deleted file mode 100644 index 434b2a3dd3ed..000000000000 --- a/DIRECTORY.py +++ /dev/null @@ -1,50 +0,0 @@ -import os - -def getListOfFiles(dirName): - # create a list of file and sub directories - # names in the given directory - listOfFile = os.listdir(dirName) - allFiles = list() - # Iterate over all the entries - for entry in listOfFile: - # if entry == listOfFile[len(listOfFile)-1]: - # continue - if entry=='.git': - continue - # Create full path - fullPath = os.path.join(dirName, entry) - entryName = entry.split('_') - # print(entryName) - ffname = '' - try: - for word in entryName: - temp = word[0].upper() + word[1:] - ffname = ffname + ' ' + temp - # print(temp) - final_fn = ffname.replace('.py', '') - final_fn = final_fn.strip() - print('* ['+final_fn+']('+fullPath+')') - # pass - except: - pass - # If entry is a directory then get the list of files in this directory - if os.path.isdir(fullPath): - print ('\n## '+entry) - filesInCurrDir = getListOfFiles(fullPath) - for file in filesInCurrDir: - fileName = file.split('/') - fileName = fileName[len(fileName)-1] - - # print (fileName) - allFiles = allFiles + filesInCurrDir - else: - allFiles.append(fullPath) - - return allFiles - - -dirName = './'; - -# Get the list of all files in directory tree at given path -listOfFiles = getListOfFiles(dirName) -# print (listOfFiles) \ No newline at end of file From 1161393b39516f97b359a14dc9043d298bc897be Mon Sep 17 00:00:00 2001 From: Anup Kumar Panwar <1anuppanwar@gmail.com> Date: Sat, 6 Jul 2019 11:21:08 +0530 Subject: [PATCH 039/193] updated CONTRIBUTING.md --- CONTRIBUTING.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index ac632574e870..03de387a8acd 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -107,6 +107,8 @@ We want your work to be readable by others; therefore, we encourage you to note - If you have modified/added documentation work, make sure your language is concise and contains no grammar mistake. +- Update the README file if you have added any new algorithm. Only entry corresponding to the algorithm is to be made, not need to add sample data, test files or solutions to problems like Project Euler, in the README. + - Add a corresponding explanation to [Algorithms-Explanation](https://github.com/TheAlgorithms/Algorithms-Explanation) (Optional but recommended). - Most importantly, From aa663037f6f5d09b9cb47baa34c61aa1d7d2cbe1 Mon Sep 17 00:00:00 2001 From: Anup Kumar Panwar <1anuppanwar@gmail.com> Date: Sat, 6 Jul 2019 13:47:56 +0530 Subject: [PATCH 040/193] Create FUNDING.yml --- .github/FUNDING.yml | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 .github/FUNDING.yml diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml new file mode 100644 index 000000000000..f07cea8a90f8 --- /dev/null +++ b/.github/FUNDING.yml @@ -0,0 +1,12 @@ +# These are supported funding model platforms + +github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] +patreon: # Replace with a single Patreon username +open_collective: # Replace with a single Open Collective username +ko_fi: # Replace with a single Ko-fi username +tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel +community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry +liberapay: TheAlgorithms +issuehunt: # Replace with a single IssueHunt username +otechie: # Replace with a single Otechie username +custom: ['http://paypal.me/TheAlgorithms/1000'] From 1951b4ca79665ea34253cd845540a34cd656d2f1 Mon Sep 17 00:00:00 2001 From: Anup Kumar Panwar <1anuppanwar@gmail.com> Date: Sat, 6 Jul 2019 14:04:27 +0530 Subject: [PATCH 041/193] Update FUNDING.yml --- .github/FUNDING.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml index f07cea8a90f8..514c9327e231 100644 --- a/.github/FUNDING.yml +++ b/.github/FUNDING.yml @@ -9,4 +9,4 @@ community_bridge: # Replace with a single Community Bridge project-name e.g., cl liberapay: TheAlgorithms issuehunt: # Replace with a single IssueHunt username otechie: # Replace with a single Otechie username -custom: ['http://paypal.me/TheAlgorithms/1000'] +custom: ['http://paypal.me/TheAlgorithms/1000', 'https://donorbox.org/thealgorithms'] From cc4cf3ece7e10f15435365ee331c3530f6c777f5 Mon Sep 17 00:00:00 2001 From: Erfan Alimohammadi Date: Sat, 6 Jul 2019 17:43:50 +0430 Subject: [PATCH 042/193] Generate all subsequences using backtracking (#961) * Add all_subsequences to backtracking directory --- backtracking/all_subsequences.py | 42 ++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 backtracking/all_subsequences.py diff --git a/backtracking/all_subsequences.py b/backtracking/all_subsequences.py new file mode 100644 index 000000000000..d868377234a8 --- /dev/null +++ b/backtracking/all_subsequences.py @@ -0,0 +1,42 @@ +''' + In this problem, we want to determine all possible subsequences + of the given sequence. We use backtracking to solve this problem. + + Time complexity: O(2^n), + where n denotes the length of the given sequence. +''' + + +def generate_all_subsequences(sequence): + create_state_space_tree(sequence, [], 0) + + +def create_state_space_tree(sequence, current_subsequence, index): + ''' + Creates a state space tree to iterate through each branch using DFS. + We know that each state has exactly two children. + It terminates when it reaches the end of the given sequence. + ''' + + if index == len(sequence): + print(current_subsequence) + return + + create_state_space_tree(sequence, current_subsequence, index + 1) + current_subsequence.append(sequence[index]) + create_state_space_tree(sequence, current_subsequence, index + 1) + current_subsequence.pop() + + +''' +remove the comment to take an input from the user + +print("Enter the elements") +sequence = list(map(int, input().split())) +''' + +sequence = [3, 1, 2, 4] +generate_all_subsequences(sequence) + +sequence = ["A", "B", "C"] +generate_all_subsequences(sequence) From 839160f83a46413b94ba9817edc2ec37bcad36fc Mon Sep 17 00:00:00 2001 From: Erfan Alimohammadi Date: Sat, 6 Jul 2019 17:49:36 +0430 Subject: [PATCH 043/193] Generate all permutations of a sequence, using backtracking (#962) * Fix typo * Add all_permutations algorithm to backtracking directory --- backtracking/all_permutations.py | 45 ++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 backtracking/all_permutations.py diff --git a/backtracking/all_permutations.py b/backtracking/all_permutations.py new file mode 100644 index 000000000000..8c332970ba53 --- /dev/null +++ b/backtracking/all_permutations.py @@ -0,0 +1,45 @@ +''' + In this problem, we want to determine all possible permutations + of the given sequence. We use backtracking to solve this problem. + + Time complexity: O(n!), + where n denotes the length of the given sequence. +''' + + +def generate_all_permutations(sequence): + create_state_space_tree(sequence, [], 0, [0 for i in range(len(sequence))]) + + +def create_state_space_tree(sequence, current_sequence, index, index_used): + ''' + Creates a state space tree to iterate through each branch using DFS. + We know that each state has exactly len(sequence) - index children. + It terminates when it reaches the end of the given sequence. + ''' + + if index == len(sequence): + print(current_sequence) + return + + for i in range(len(sequence)): + if not index_used[i]: + current_sequence.append(sequence[i]) + index_used[i] = True + create_state_space_tree(sequence, current_sequence, index + 1, index_used) + current_sequence.pop() + index_used[i] = False + + +''' +remove the comment to take an input from the user + +print("Enter the elements") +sequence = list(map(int, input().split())) +''' + +sequence = [3, 1, 2, 4] +generate_all_permutations(sequence) + +sequence = ["A", "B", "C"] +generate_all_permutations(sequence) From 781b7f86e720b9c5164047e46f2dedd807b9165b Mon Sep 17 00:00:00 2001 From: Erfan Alimohammadi Date: Sat, 6 Jul 2019 19:02:06 +0430 Subject: [PATCH 044/193] Fix readme and duplicate (#967) * Fix typo * Add all_permutations algorithm to backtracking directory * Update backtracking and D&C algorithms in README Update backtracking and divide_and_conquer algorithms in README * Remove the duplicated file --- README.md | 3 +- divide_and_conquer/max_sub_array_sum.py | 72 ------------------------- 2 files changed, 2 insertions(+), 73 deletions(-) delete mode 100644 divide_and_conquer/max_sub_array_sum.py diff --git a/README.md b/README.md index a609dc077a77..a28475791432 100644 --- a/README.md +++ b/README.md @@ -45,6 +45,8 @@ We're on [Gitter](https://gitter.im/TheAlgorithms)! Please join us. - [N Queens](./backtracking/n_queens.py) - [Sum Of Subsets](./backtracking/sum_of_subsets.py) +- [All Subsequences](./backtracking/all_subsequences.py) +- [All Permutations](./backtracking/all_permutations.py) ## Ciphers @@ -220,7 +222,6 @@ We're on [Gitter](https://gitter.im/TheAlgorithms)! Please join us. ## Divide And Conquer - [Max Subarray Sum](./divide_and_conquer/max_subarray_sum.py) -- [Max Sub Array Sum](./divide_and_conquer/max_sub_array_sum.py) - [Closest Pair Of Points](./divide_and_conquer/closest_pair_of_points.py) ## Strings diff --git a/divide_and_conquer/max_sub_array_sum.py b/divide_and_conquer/max_sub_array_sum.py deleted file mode 100644 index 531a45abca6f..000000000000 --- a/divide_and_conquer/max_sub_array_sum.py +++ /dev/null @@ -1,72 +0,0 @@ -""" -Given a array of length n, max_sub_array_sum() finds the maximum of sum of contiguous sub-array using divide and conquer method. - -Time complexity : O(n log n) - -Ref : INTRODUCTION TO ALGORITHMS THIRD EDITION (section : 4, sub-section : 4.1, page : 70) - -""" - - -def max_sum_from_start(array): - """ This function finds the maximum contiguous sum of array from 0 index - - Parameters : - array (list[int]) : given array - - Returns : - max_sum (int) : maximum contiguous sum of array from 0 index - - """ - array_sum = 0 - max_sum = float("-inf") - for num in array: - array_sum += num - if array_sum > max_sum: - max_sum = array_sum - return max_sum - - -def max_cross_array_sum(array, left, mid, right): - """ This function finds the maximum contiguous sum of left and right arrays - - Parameters : - array, left, mid, right (list[int], int, int, int) - - Returns : - (int) : maximum of sum of contiguous sum of left and right arrays - - """ - - max_sum_of_left = max_sum_from_start(array[left:mid+1][::-1]) - max_sum_of_right = max_sum_from_start(array[mid+1: right+1]) - return max_sum_of_left + max_sum_of_right - - -def max_sub_array_sum(array, left, right): - """ This function finds the maximum of sum of contiguous sub-array using divide and conquer method - - Parameters : - array, left, right (list[int], int, int) : given array, current left index and current right index - - Returns : - int : maximum of sum of contiguous sub-array - - """ - - # base case: array has only one element - if left == right: - return array[right] - - # Recursion - mid = (left + right) // 2 - left_half_sum = max_sub_array_sum(array, left, mid) - right_half_sum = max_sub_array_sum(array, mid + 1, right) - cross_sum = max_cross_array_sum(array, left, mid, right) - return max(left_half_sum, right_half_sum, cross_sum) - - -array = [-2, -5, 6, -2, -3, 1, 5, -6] -array_length = len(array) -print("Maximum sum of contiguous subarray:", max_sub_array_sum(array, 0, array_length - 1)) - From 69bed590368a10479a9ad225402aa540628a0457 Mon Sep 17 00:00:00 2001 From: Erfan Alimohammadi Date: Sat, 6 Jul 2019 20:01:52 +0430 Subject: [PATCH 045/193] Fix backtrack time complexity (#965) * Update backtracking/all_permutations.py --- backtracking/all_permutations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/backtracking/all_permutations.py b/backtracking/all_permutations.py index 8c332970ba53..299b708fef4e 100644 --- a/backtracking/all_permutations.py +++ b/backtracking/all_permutations.py @@ -2,7 +2,7 @@ In this problem, we want to determine all possible permutations of the given sequence. We use backtracking to solve this problem. - Time complexity: O(n!), + Time complexity: O(n! * n), where n denotes the length of the given sequence. ''' From 26df2aab1ee79de5a4b288a64ad9c118bfccab73 Mon Sep 17 00:00:00 2001 From: PatOnTheBack <51241310+PatOnTheBack@users.noreply.github.com> Date: Sat, 6 Jul 2019 11:35:12 -0400 Subject: [PATCH 046/193] Removed Unused `import sys` (#922) I removed `import sys` because it is not used in the program. This addresses a [recommendation from lgtm](https://lgtm.com/projects/g/TheAlgorithms/Python/snapshot/66c4afbd0f28f9989f35ddbeb5c9263390c5d192/files/ciphers/caesar_cipher.py?sort=name&dir=ASC&mode=heatmap) --- ciphers/caesar_cipher.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ciphers/caesar_cipher.py b/ciphers/caesar_cipher.py index 39c069c95a7c..e22f19b4851d 100644 --- a/ciphers/caesar_cipher.py +++ b/ciphers/caesar_cipher.py @@ -1,4 +1,3 @@ -import sys def encrypt(strng, key): encrypted = '' for x in strng: From 4ff2a9dd4e1a517cb0526ff51233bb6f1fc3fc8d Mon Sep 17 00:00:00 2001 From: Aditi Agarwal <31546143+aditiagarwal34550@users.noreply.github.com> Date: Sat, 6 Jul 2019 21:59:58 -0700 Subject: [PATCH 047/193] minimax (#947) * minimax.py minimax algorithm is used for game like tic tac toe. It traces the path and selects the optimal move. * minimax.py Minimax is used in decision making and game theory to find the optimal move for a player, when your opponent also plays optimally. It is widely used in games like Tic-Tac-Toe, Chess. * Delete minimax.py * Update minimax.py * Minimax is a backtracking algorithm that is used in game theory to find the optimal move for a player, assuming that your opponent also plays optimally --- backtracking/minimax.py | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 backtracking/minimax.py diff --git a/backtracking/minimax.py b/backtracking/minimax.py new file mode 100644 index 000000000000..5168306e71fc --- /dev/null +++ b/backtracking/minimax.py @@ -0,0 +1,28 @@ +import math + +''' Minimax helps to achieve maximum score in a game by checking all possible moves + depth is current depth in game tree. + nodeIndex is index of current node in scores[]. + if move is of maximizer return true else false + leaves of game tree is stored in scores[] + height is maximum height of Game tree +''' + +def minimax (Depth, nodeIndex, isMax, scores, height): + + if Depth == height: + return scores[nodeIndex] + + if isMax: + return (max(minimax(Depth + 1, nodeIndex * 2, False, scores, height), + minimax(Depth + 1, nodeIndex * 2 + 1, False, scores, height))) + return (min(minimax(Depth + 1, nodeIndex * 2, True, scores, height), + minimax(Depth + 1, nodeIndex * 2 + 1, True, scores, height))) + +if __name__ == "__main__": + + scores = [90, 23, 6, 33, 21, 65, 123, 34423] + height = math.log(len(scores), 2) + + print("Optimal value : ", end = "") + print(minimax(0, 0, True, scores, height)) From 95324927288135c740688d16db34381298139d66 Mon Sep 17 00:00:00 2001 From: Shahabaldin Mohammadi <45038855+stevelex-elex@users.noreply.github.com> Date: Sun, 7 Jul 2019 11:19:15 +0430 Subject: [PATCH 048/193] added enigma machine algorithm (#932) --- hashes/enigma_machine.py | 61 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) create mode 100644 hashes/enigma_machine.py diff --git a/hashes/enigma_machine.py b/hashes/enigma_machine.py new file mode 100644 index 000000000000..bd410c5cb21d --- /dev/null +++ b/hashes/enigma_machine.py @@ -0,0 +1,61 @@ +from __future__ import print_function + +alphabets = [chr(i) for i in range(32, 126)] +gear_one = [i for i in range(len(alphabets))] +gear_two = [i for i in range(len(alphabets))] +gear_three = [i for i in range(len(alphabets))] +reflector = [i for i in reversed(range(len(alphabets)))] +code = [] +gear_one_pos = gear_two_pos = gear_three_pos = 0 + + +def rotator(): + global gear_one_pos + global gear_two_pos + global gear_three_pos + i = gear_one[0] + gear_one.append(i) + del gear_one[0] + gear_one_pos += 1 + if gear_one_pos % int(len(alphabets)) == 0: + i = gear_two[0] + gear_two.append(i) + del gear_two[0] + gear_two_pos += 1 + if gear_two_pos % int(len(alphabets)) == 0: + i = gear_three[0] + gear_three.append(i) + del gear_three[0] + gear_three_pos += 1 + + +def engine(input_character): + target = alphabets.index(input_character) + target = gear_one[target] + target = gear_two[target] + target = gear_three[target] + target = reflector[target] + target = gear_three.index(target) + target = gear_two.index(target) + target = gear_one.index(target) + code.append(alphabets[target]) + rotator() + + +if __name__ == '__main__': + decode = input("Type your message:\n") + decode = list(decode) + while True: + try: + token = int(input("Please set token:(must be only digits)\n")) + break + except Exception as error: + print(error) + for i in range(token): + rotator() + for i in decode: + engine(i) + print("\n" + "".join(code)) + print( + f"\nYour Token is {token} please write it down.\nIf you want to decode " + f"this message again you should input same digits as token!") From 234b0a77c4d6186c9f0326233af84a8f75b35b6e Mon Sep 17 00:00:00 2001 From: Hector S Date: Sun, 7 Jul 2019 11:17:38 -0400 Subject: [PATCH 049/193] Simplied password_generator.py (#968) * Added print function into matrix_multiplication_addition.py and removed blank space in data_structures/binary tree directory * Removed .vs/ folder per #893 * Rename matrix_multiplication_addition.py to matrix_operation.py * Added main() function and simplified password generation. * Modified password_generator.py file according to suggestions in #968 --- other/password_generator.py | 66 +++++++++++++++++++++++-------------- 1 file changed, 42 insertions(+), 24 deletions(-) diff --git a/other/password_generator.py b/other/password_generator.py index 8916079fc758..fd0701041240 100644 --- a/other/password_generator.py +++ b/other/password_generator.py @@ -1,35 +1,53 @@ +"""Password generator allows you to generate a random password of length N.""" from __future__ import print_function -import string -import random - -letters = [letter for letter in string.ascii_letters] -digits = [digit for digit in string.digits] -symbols = [symbol for symbol in string.punctuation] -chars = letters + digits + symbols -random.shuffle(chars) - -min_length = 8 -max_length = 16 -password = ''.join(random.choice(chars) for x in range(random.randint(min_length, max_length))) -print('Password: ' + password) -print('[ If you are thinking of using this passsword, You better save it. ]') - - -# ALTERNATIVE METHODS +from random import choice +from string import ascii_letters, digits, punctuation + + +def password_generator(length=8): + """ + >>> len(password_generator()) + 8 + >>> len(password_generator(length=16)) + 16 + >>> len(password_generator(257)) + 257 + >>> len(password_generator(length=0)) + 0 + >>> len(password_generator(-1)) + 0 + """ + chars = tuple(ascii_letters) + tuple(digits) + tuple(punctuation) + return ''.join(choice(chars) for x in range(length)) + + +# ALTERNATIVE METHODS # ctbi= characters that must be in password -# i= how many letters or characters the password length will be -def password_generator(ctbi, i): - # Password generator = full boot with random_number, random_letters, and random_character FUNCTIONS - pass # Put your code here... +# i= how many letters or characters the password length will be +def alternative_password_generator(ctbi, i): + # Password generator = full boot with random_number, random_letters, and + # random_character FUNCTIONS + pass # Put your code here... def random_number(ctbi, i): - pass # Put your code here... + pass # Put your code here... def random_letters(ctbi, i): - pass # Put your code here... + pass # Put your code here... def random_characters(ctbi, i): - pass # Put your code here... + pass # Put your code here... + + +def main(): + length = int( + input('Please indicate the max length of your password: ').strip()) + print('Password generated:', password_generator(length)) + print('[If you are thinking of using this passsword, You better save it.]') + + +if __name__ == '__main__': + main() From 2b365284c80bbc2c7e5676481ed56308a5b1d888 Mon Sep 17 00:00:00 2001 From: PatOnTheBack <51241310+PatOnTheBack@users.noreply.github.com> Date: Sun, 7 Jul 2019 11:45:42 -0400 Subject: [PATCH 050/193] Removed Unnecessary Assignment for 'error' Var (#920) `error = abs(f(a))` was declared on line 24 and line 32. It is unnecessary to have in both places. I removed the second instance since it wastes resources to keep redefining the variable inside the for loop. This fixes an [issue found by lgtm](https://lgtm.com/projects/g/TheAlgorithms/Python/snapshot/66c4afbd0f28f9989f35ddbeb5c9263390c5d192/files/maths/newton_raphson.py?sort=name&dir=ASC&mode=heatmap) --- maths/newton_raphson.py | 1 - 1 file changed, 1 deletion(-) diff --git a/maths/newton_raphson.py b/maths/newton_raphson.py index cc6c92734fd4..d89f264acdd8 100644 --- a/maths/newton_raphson.py +++ b/maths/newton_raphson.py @@ -29,7 +29,6 @@ def newton_raphson(f, x0=0, maxiter=100, step=0.0001, maxerror=1e-6,logsteps=Fal a = a - f(a)/f1(a) #Calculate the next estimate if logsteps: steps.append(a) - error = abs(f(a)) if error < maxerror: break else: From b7f13d991cccd370d8ff5b27c1bdc36237a13473 Mon Sep 17 00:00:00 2001 From: cclauss Date: Mon, 8 Jul 2019 17:27:51 +0200 Subject: [PATCH 051/193] Travis CI: Run black, doctest, flake8, mypy, and pytest (#964) * Travis CI: Add type checking with mypy * Create requirements.txt * script: mypy --ignore-missing-stubs=cv2,numpy . * Delete requirements.txt * script: mypy --ignore-missing-imports . * Run doctests * Disable doctest -v other/detecting_english_programmatically.py * Pytest * No | * pytest || true * Run black doctest flake8 mypy pytest * after_success: Build Directory.md * Typo in filename: Dictionary.txt --> dictionary.txt' Discovered via doctest run in #964 * python -m doctest -v * pip install black flake8 mypy pytest * pytest --doctest-glob='*.py' * pytest --doctest-modules * pytest --doctest-modules ./sorts * pytest --doctest-modules ./ciphers ./other ./searches ./sorts ./strings || true * if __name__ == "__main__": * if __name__ == "__main__": * if __name__ == '__main__': * if __name__ == '__main__': * if __name__ == '__main__': * Create requirements.txt * Update requirements.txt * if __name__ == "__main__": * Lose the doctests * if __name__ == '__main__': * Remove print-a-tuple * doctest: Added missing spaces * Update tabu_search.py * The >>> are not doctests so change to >>) * Travis CI: Run black, doctest, flake8, mypy, and pytest * Link to the separate DIRECTORY.md file * Update README.md --- .travis.yml | 13 +- README.md | 339 +------------------- ciphers/Atbash.py | 4 +- ciphers/caesar_cipher.py | 5 +- other/detecting_english_programmatically.py | 2 +- other/sierpinski_triangle.py | 17 +- other/tower_of_hanoi.py | 4 +- requirements.txt | 6 + searches/tabu_search.py | 4 +- sorts/Bitonic_Sort.py | 25 +- sorts/bubble_sort.py | 2 +- 11 files changed, 56 insertions(+), 365 deletions(-) create mode 100644 requirements.txt diff --git a/.travis.yml b/.travis.yml index 8676e5127334..e67bd431a7c1 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,5 +1,14 @@ language: python dist: xenial # required for Python >= 3.7 python: 3.7 -install: pip install flake8 -script: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics +cache: pip +install: pip install -r requirements.txt +before_script: + - black --check . || true + - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics +script: + - mypy --ignore-missing-imports . + - pytest --doctest-modules ./ciphers ./other ./searches ./sorts ./strings +after_success: + - python ./~script.py + - cat DIRECTORY.md diff --git a/README.md b/README.md index a28475791432..30eccd361673 100644 --- a/README.md +++ b/README.md @@ -12,12 +12,12 @@ These implementations are for learning purposes. They may be less efficient than Anup Kumar Panwar   [[Gmail](mailto:1anuppanwar@gmail.com?Subject=The%20Algorithms%20-%20Python) -  [Gihub](https://github.com/anupkumarpanwar) +  [GitHub](https://github.com/anupkumarpanwar)   [LinkedIn](https://www.linkedin.com/in/anupkumarpanwar/)] Chetan Kaushik   [[Gmail](mailto:dynamitechetan@gmail.com?Subject=The%20Algorithms%20-%20Python) -  [Gihub](https://github.com/dynamitechetan) +  [GitHub](https://github.com/dynamitechetan)   [LinkedIn](https://www.linkedin.com/in/chetankaushik/)] ## Contribution Guidelines @@ -28,337 +28,6 @@ Read our [Contribution Guidelines](CONTRIBUTING.md) before you contribute. We're on [Gitter](https://gitter.im/TheAlgorithms)! Please join us. -# Algorithms +## Algorithms -## Hashes - -- [Md5](./hashes/md5.py) -- [Chaos Machine](./hashes/chaos_machine.py) -- [Sha1](./hashes/sha1.py) - -## File Transfer Protocol - -- [Ftp Client Server](./file_transfer_protocol/ftp_client_server.py) -- [Ftp Send Receive](./file_transfer_protocol/ftp_send_receive.py) - -## Backtracking - -- [N Queens](./backtracking/n_queens.py) -- [Sum Of Subsets](./backtracking/sum_of_subsets.py) -- [All Subsequences](./backtracking/all_subsequences.py) -- [All Permutations](./backtracking/all_permutations.py) - -## Ciphers - -- [Transposition Cipher](./ciphers/transposition_cipher.py) -- [Atbash](./ciphers/Atbash.py) -- [Rot13](./ciphers/rot13.py) -- [Rabin Miller](./ciphers/rabin_miller.py) -- [Transposition Cipher Encrypt Decrypt File](./ciphers/transposition_cipher_encrypt_decrypt_file.py) -- [Affine Cipher](./ciphers/affine_cipher.py) -- [Trafid Cipher](./ciphers/trafid_cipher.py) -- [Base16](./ciphers/base16.py) -- [Elgamal Key Generator](./ciphers/elgamal_key_generator.py) -- [Rsa Cipher](./ciphers/rsa_cipher.py) -- [Prehistoric Men.txt](./ciphers/prehistoric_men.txt) -- [Vigenere Cipher](./ciphers/vigenere_cipher.py) -- [Xor Cipher](./ciphers/xor_cipher.py) -- [Brute Force Caesar Cipher](./ciphers/brute_force_caesar_cipher.py) -- [Rsa Key Generator](./ciphers/rsa_key_generator.py) -- [Simple Substitution Cipher](./ciphers/simple_substitution_cipher.py) -- [Playfair Cipher](./ciphers/playfair_cipher.py) -- [Morse Code Implementation](./ciphers/morse_Code_implementation.py) -- [Base32](./ciphers/base32.py) -- [Base85](./ciphers/base85.py) -- [Base64 Cipher](./ciphers/base64_cipher.py) -- [Onepad Cipher](./ciphers/onepad_cipher.py) -- [Caesar Cipher](./ciphers/caesar_cipher.py) -- [Hill Cipher](./ciphers/hill_cipher.py) -- [Cryptomath Module](./ciphers/cryptomath_module.py) - -## Arithmetic Analysis - -- [Bisection](./arithmetic_analysis/bisection.py) -- [Newton Method](./arithmetic_analysis/newton_method.py) -- [Newton Raphson Method](./arithmetic_analysis/newton_raphson_method.py) -- [Intersection](./arithmetic_analysis/intersection.py) -- [Lu Decomposition](./arithmetic_analysis/lu_decomposition.py) - -## Boolean Algebra - -- [Quine Mc Cluskey](./boolean_algebra/quine_mc_cluskey.py) - -## Traversals - -- [Binary Tree Traversals](./traversals/binary_tree_traversals.py) - -## Maths - -- [Average](./maths/average.py) -- [Abs Max](./maths/abs_Max.py) -- [Average Median](./maths/average_median.py) -- [Trapezoidal Rule](./maths/trapezoidal_rule.py) -- [Prime Check](./maths/Prime_Check.py) -- [Modular Exponential](./maths/modular_exponential.py) -- [Newton Raphson](./maths/newton_raphson.py) -- [Factorial Recursive](./maths/factorial_recursive.py) -- [Extended Euclidean Algorithm](./maths/extended_euclidean_algorithm.py) -- [Greater Common Divisor](./maths/greater_common_divisor.py) -- [Fibonacci](./maths/fibonacci.py) -- [Find Lcm](./maths/find_lcm.py) -- [Find Max](./maths/Find_Max.py) -- [Fermat Little Theorem](./maths/fermat_little_theorem.py) -- [Factorial Python](./maths/factorial_python.py) -- [Fibonacci Sequence Recursion](./maths/fibonacci_sequence_recursion.py) -- [Sieve Of Eratosthenes](./maths/sieve_of_eratosthenes.py) -- [Abs Min](./maths/abs_Min.py) -- [Lucas Series](./maths/lucasSeries.py) -- [Segmented Sieve](./maths/segmented_sieve.py) -- [Find Min](./maths/Find_Min.py) -- [Abs](./maths/abs.py) -- [Simpson Rule](./maths/simpson_rule.py) -- [Basic Maths](./maths/basic_maths.py) -- [3n+1](./maths/3n+1.py) -- [Binary Exponentiation](./maths/Binary_Exponentiation.py) - -## Digital Image Processing - -- ## Filters - - - [Median Filter](./digital_image_processing/filters/median_filter.py) - - [Gaussian Filter](./digital_image_processing/filters/gaussian_filter.py) - - -## Compression - -- [Peak Signal To Noise Ratio](./compression/peak_signal_to_noise_ratio.py) -- [Huffman](./compression/huffman.py) - -## Graphs - -- [BFS Shortest Path](./graphs/bfs_shortest_path.py) -- [Directed And Undirected (Weighted) Graph](<./graphs/Directed_and_Undirected_(Weighted)_Graph.py>) -- [Minimum Spanning Tree Prims](./graphs/minimum_spanning_tree_prims.py) -- [Graph Matrix](./graphs/graph_matrix.py) -- [Basic Graphs](./graphs/basic_graphs.py) -- [Dijkstra 2](./graphs/dijkstra_2.py) -- [Tarjans Strongly Connected Components](./graphs/tarjans_scc.py) -- [Check Bipartite Graph BFS](./graphs/check_bipartite_graph_bfs.py) -- [Depth First Search](./graphs/depth_first_search.py) -- [Kahns Algorithm Long](./graphs/kahns_algorithm_long.py) -- [Breadth First Search](./graphs/breadth_first_search.py) -- [Dijkstra](./graphs/dijkstra.py) -- [Articulation Points](./graphs/articulation_points.py) -- [Bellman Ford](./graphs/bellman_ford.py) -- [Check Bipartite Graph Dfs](./graphs/check_bipartite_graph_dfs.py) -- [Strongly Connected Components Kosaraju](./graphs/scc_kosaraju.py) -- [Multi Hueristic Astar](./graphs/multi_hueristic_astar.py) -- [Page Rank](./graphs/page_rank.py) -- [Eulerian Path And Circuit For Undirected Graph](./graphs/Eulerian_path_and_circuit_for_undirected_graph.py) -- [Edmonds Karp Multiple Source And Sink](./graphs/edmonds_karp_multiple_source_and_sink.py) -- [Floyd Warshall](./graphs/floyd_warshall.py) -- [Minimum Spanning Tree Kruskal](./graphs/minimum_spanning_tree_kruskal.py) -- [Prim](./graphs/prim.py) -- [Kahns Algorithm Topo](./graphs/kahns_algorithm_topo.py) -- [BFS](./graphs/BFS.py) -- [Finding Bridges](./graphs/finding_bridges.py) -- [Graph List](./graphs/graph_list.py) -- [Dijkstra Algorithm](./graphs/dijkstra_algorithm.py) -- [A Star](./graphs/a_star.py) -- [Even Tree](./graphs/even_tree.py) -- [DFS](./graphs/DFS.py) - -## Networking Flow - -- [Minimum Cut](./networking_flow/minimum_cut.py) -- [Ford Fulkerson](./networking_flow/ford_fulkerson.py) - -## Matrix - -- [Matrix Operation](./matrix/matrix_operation.py) -- [Searching In Sorted Matrix](./matrix/searching_in_sorted_matrix.py) -- [Spiral Print](./matrix/spiral_print.py) - -## Searches - -- [Quick Select](./searches/quick_select.py) -- [Binary Search](./searches/binary_search.py) -- [Interpolation Search](./searches/interpolation_search.py) -- [Jump Search](./searches/jump_search.py) -- [Linear Search](./searches/linear_search.py) -- [Ternary Search](./searches/ternary_search.py) -- [Tabu Search](./searches/tabu_search.py) -- [Sentinel Linear Search](./searches/sentinel_linear_search.py) - -## Conversions - -- [Decimal To Binary](./conversions/decimal_to_binary.py) -- [Decimal To Octal](./conversions/decimal_to_octal.py) - -## Dynamic Programming - -- [Fractional Knapsack](./dynamic_programming/Fractional_Knapsack.py) -- [Sum Of Subset](./dynamic_programming/sum_of_subset.py) -- [Fast Fibonacci](./dynamic_programming/fast_fibonacci.py) -- [Bitmask](./dynamic_programming/bitmask.py) -- [Abbreviation](./dynamic_programming/abbreviation.py) -- [Rod Cutting](./dynamic_programming/rod_cutting.py) -- [Knapsack](./dynamic_programming/knapsack.py) -- [Max Sub Array](./dynamic_programming/max_sub_array.py) -- [Fibonacci](./dynamic_programming/fibonacci.py) -- [Minimum Partition](./dynamic_programming/minimum_partition.py) -- [K Means Clustering Tensorflow](./dynamic_programming/k_means_clustering_tensorflow.py) -- [Coin Change](./dynamic_programming/coin_change.py) -- [Subset Generation](./dynamic_programming/subset_generation.py) -- [Floyd Warshall](./dynamic_programming/floyd_warshall.py) -- [Longest Sub Array](./dynamic_programming/longest_sub_array.py) -- [Integer Partition](./dynamic_programming/integer_partition.py) -- [Matrix Chain Order](./dynamic_programming/matrix_chain_order.py) -- [Edit Distance](./dynamic_programming/edit_distance.py) -- [Longest Common Subsequence](./dynamic_programming/longest_common_subsequence.py) -- [Longest Increasing Subsequence O(nlogn)](<./dynamic_programming/longest_increasing_subsequence_O(nlogn).py>) -- [Longest Increasing Subsequence](./dynamic_programming/longest_increasing_subsequence.py) - -## Divide And Conquer - -- [Max Subarray Sum](./divide_and_conquer/max_subarray_sum.py) -- [Closest Pair Of Points](./divide_and_conquer/closest_pair_of_points.py) - -## Strings - -- [Knuth Morris Pratt](./strings/knuth_morris_pratt.py) -- [Rabin Karp](./strings/rabin_karp.py) -- [Naive String Search](./strings/naive_String_Search.py) -- [Levenshtein Distance](./strings/levenshtein_distance.py) -- [Min Cost String Conversion](./strings/min_cost_string_conversion.py) -- [Boyer Moore Search](./strings/Boyer_Moore_Search.py) -- [Manacher](./strings/manacher.py) - -## Sorts - -- [Quick Sort](./sorts/quick_sort.py) -- [Selection Sort](./sorts/selection_sort.py) -- [Bitonic Sort](./sorts/Bitonic_Sort.py) -- [Cycle Sort](./sorts/cycle_sort.py) -- [Comb Sort](./sorts/comb_sort.py) -- [Topological Sort](./sorts/topological_sort.py) -- [Merge Sort Fastest](./sorts/merge_sort_fastest.py) -- [Random Pivot Quick Sort](./sorts/random_pivot_quick_sort.py) -- [Heap Sort](./sorts/heap_sort.py) -- [Insertion Sort](./sorts/insertion_sort.py) -- [Counting Sort](./sorts/counting_sort.py) -- [Bucket Sort](./sorts/bucket_sort.py) -- [Quick Sort 3 Partition](./sorts/quick_sort_3_partition.py) -- [Bogo Sort](./sorts/bogo_sort.py) -- [Shell Sort](./sorts/shell_sort.py) -- [Pigeon Sort](./sorts/pigeon_sort.py) -- [Odd-Even Transposition Parallel](./sorts/Odd-Even_transposition_parallel.py) -- [Tree Sort](./sorts/tree_sort.py) -- [Cocktail Shaker Sort](./sorts/cocktail_shaker_sort.py) -- [Random Normal Distribution Quicksort](./sorts/random_normal_distribution_quicksort.py) -- [Wiggle Sort](./sorts/wiggle_sort.py) -- [Pancake Sort](./sorts/pancake_sort.py) -- [External Sort](./sorts/external_sort.py) -- [Tim Sort](./sorts/tim_sort.py) -- [Sorting Graphs.png](./sorts/sorting_graphs.png) -- [Radix Sort](./sorts/radix_sort.py) -- [Odd-Even Transposition Single-threaded](./sorts/Odd-Even_transposition_single-threaded.py) -- [Bubble Sort](./sorts/bubble_sort.py) -- [Gnome Sort](./sorts/gnome_sort.py) -- [Merge Sort](./sorts/merge_sort.py) - -## Machine Learning - -- [Perceptron](./machine_learning/perceptron.py) -- [Random Forest Classifier](./machine_learning/random_forest_classification/random_forest_classifier.ipynb) -- [NaiveBayes.ipynb](./machine_learning/NaiveBayes.ipynb) -- [Scoring Functions](./machine_learning/scoring_functions.py) -- [Logistic Regression](./machine_learning/logistic_regression.py) -- [Gradient Descent](./machine_learning/gradient_descent.py) -- [Linear Regression](./machine_learning/linear_regression.py) -- [Random Forest Regression](./machine_learning/random_forest_regression/random_forest_regression.py) -- [Random Forest Regression](./machine_learning/random_forest_regression/random_forest_regression.ipynb) -- [Reuters One Vs Rest Classifier.ipynb](./machine_learning/reuters_one_vs_rest_classifier.ipynb) -- [Decision Tree](./machine_learning/decision_tree.py) -- [Knn Sklearn](./machine_learning/knn_sklearn.py) -- [K Means Clust](./machine_learning/k_means_clust.py) - -## Neural Network - -- [Perceptron](./neural_network/perceptron.py) -- [Fully Connected Neural Network](./neural_network/fully_connected_neural_network.ipynb) -- [Convolution Neural Network](./neural_network/convolution_neural_network.py) -- [Back Propagation Neural Network](./neural_network/back_propagation_neural_network.py) - -## Data Structures - -- ## Binary Tree - - - [Basic Binary Tree](./data_structures/binary_tree/basic_binary_tree.py) - - [Red Black Tree](./data_structures/binary_tree/red_black_tree.py) - - [Fenwick Tree](./data_structures/binary_tree/fenwick_tree.py) - - [Treap](./data_structures/binary_tree/treap.py) - - [AVL Tree](./data_structures/binary_tree/AVL_tree.py) - - [Segment Tree](./data_structures/binary_tree/segment_tree.py) - - [Lazy Segment Tree](./data_structures/binary_tree/lazy_segment_tree.py) - - [Binary Search Tree](./data_structures/binary_tree/binary_search_tree.py) - -- ## Trie - - - [Trie](./data_structures/trie/trie.py) - -- ## Linked List - - - [Swap Nodes](./data_structures/linked_list/swap_nodes.py) - - [Doubly Linked List](./data_structures/linked_list/doubly_linked_list.py) - - [Singly Linked List](./data_structures/linked_list/singly_linked_list.py) - - [Is Palindrome](./data_structures/linked_list/is_Palindrome.py) - -- ## Stacks - - - [Postfix Evaluation](./data_structures/stacks/postfix_evaluation.py) - - [Balanced Parentheses](./data_structures/stacks/balanced_parentheses.py) - - [Infix To Prefix Conversion](./data_structures/stacks/infix_to_prefix_conversion.py) - - [Stack](./data_structures/stacks/stack.py) - - [Infix To Postfix Conversion](./data_structures/stacks/infix_to_postfix_conversion.py) - - [Next Greater Element](./data_structures/stacks/next_greater_element.py) - - [Stock Span Problem](./data_structures/stacks/stock_span_problem.py) - -- ## Queue - - - [Queue On Pseudo Stack](./data_structures/queue/queue_on_pseudo_stack.py) - - [Double Ended Queue](./data_structures/queue/double_ended_queue.py) - - [Queue On List](./data_structures/queue/queue_on_list.py) - -- ## Heap - - - [Heap](./data_structures/heap/heap.py) - -- ## Hashing - - - [Hash Table With Linked List](./data_structures/hashing/hash_table_with_linked_list.py) - - [Quadratic Probing](./data_structures/hashing/quadratic_probing.py) - - [Hash Table](./data_structures/hashing/hash_table.py) - - [Double Hash](./data_structures/hashing/double_hash.py) - - -## Other - -- [Detecting English Programmatically](./other/detecting_english_programmatically.py) -- [Fischer Yates Shuffle](./other/fischer_yates_shuffle.py) -- [Primelib](./other/primelib.py) -- [Binary Exponentiation 2](./other/binary_exponentiation_2.py) -- [Anagrams](./other/anagrams.py) -- [Palindrome](./other/palindrome.py) -- [Finding Primes](./other/finding_Primes.py) -- [Two Sum](./other/two_sum.py) -- [Password Generator](./other/password_generator.py) -- [Linear Congruential Generator](./other/linear_congruential_generator.py) -- [Frequency Finder](./other/frequency_finder.py) -- [Euclidean Gcd](./other/euclidean_gcd.py) -- [Word Patterns](./other/word_patterns.py) -- [Nested Brackets](./other/nested_brackets.py) -- [Binary Exponentiation](./other/binary_exponentiation.py) -- [Sierpinski Triangle](./other/sierpinski_triangle.py) -- [Game Of Life](./other/game_of_life.py) -- [Tower Of Hanoi](./other/tower_of_hanoi.py) +See our [directory](DIRECTORY.md). diff --git a/ciphers/Atbash.py b/ciphers/Atbash.py index 162614c727ee..5653f0213745 100644 --- a/ciphers/Atbash.py +++ b/ciphers/Atbash.py @@ -18,4 +18,6 @@ def Atbash(): output+=i print(output) -Atbash() + +if __name__ == '__main__': + Atbash() diff --git a/ciphers/caesar_cipher.py b/ciphers/caesar_cipher.py index e22f19b4851d..872b5d8195c1 100644 --- a/ciphers/caesar_cipher.py +++ b/ciphers/caesar_cipher.py @@ -59,4 +59,7 @@ def main(): elif choice == '4': print ("Goodbye.") break -main() + + +if __name__ == '__main__': + main() diff --git a/other/detecting_english_programmatically.py b/other/detecting_english_programmatically.py index 005fd3c10ca3..8b73ff6cf0c3 100644 --- a/other/detecting_english_programmatically.py +++ b/other/detecting_english_programmatically.py @@ -6,7 +6,7 @@ def loadDictionary(): path = os.path.split(os.path.realpath(__file__)) englishWords = {} - with open(path[0] + '/Dictionary.txt') as dictionaryFile: + with open(path[0] + '/dictionary.txt') as dictionaryFile: for word in dictionaryFile.read().split('\n'): englishWords[word] = None return englishWords diff --git a/other/sierpinski_triangle.py b/other/sierpinski_triangle.py index 329a8ce5c43f..fc22aad96059 100644 --- a/other/sierpinski_triangle.py +++ b/other/sierpinski_triangle.py @@ -27,13 +27,6 @@ import turtle import sys PROGNAME = 'Sierpinski Triangle' -if len(sys.argv) !=2: - raise Exception('right format for using this script: $python fractals.py ') - -myPen = turtle.Turtle() -myPen.ht() -myPen.speed(5) -myPen.pencolor('red') points = [[-175,-125],[0,175],[175,-125]] #size of triangle @@ -64,4 +57,12 @@ def triangle(points,depth): depth-1) -triangle(points,int(sys.argv[1])) +if __name__ == '__main__': + if len(sys.argv) !=2: + raise ValueError('right format for using this script: ' + '$python fractals.py ') + myPen = turtle.Turtle() + myPen.ht() + myPen.speed(5) + myPen.pencolor('red') + triangle(points,int(sys.argv[1])) diff --git a/other/tower_of_hanoi.py b/other/tower_of_hanoi.py index dc15b2ce8e58..9cc5b9e40543 100644 --- a/other/tower_of_hanoi.py +++ b/other/tower_of_hanoi.py @@ -16,10 +16,10 @@ def moveTower(height, fromPole, toPole, withPole): moveTower(height-1, withPole, toPole, fromPole) def moveDisk(fp,tp): - print(('moving disk from', fp, 'to', tp)) + print('moving disk from', fp, 'to', tp) def main(): - height = int(input('Height of hanoi: ')) + height = int(input('Height of hanoi: ').strip()) moveTower(height, 'A', 'B', 'C') if __name__ == '__main__': diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 000000000000..30179ac345b3 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +black +flake8 +matplotlib +mypy +numpy +pytest diff --git a/searches/tabu_search.py b/searches/tabu_search.py index e21ddd53cc78..ffd84f8ac031 100644 --- a/searches/tabu_search.py +++ b/searches/tabu_search.py @@ -38,7 +38,7 @@ def generate_neighbours(path): and the cost (distance) for each neighbor. Example of dict_of_neighbours: - >>> dict_of_neighbours[a] + >>) dict_of_neighbours[a] [[b,20],[c,18],[d,22],[e,26]] This indicates the neighbors of node (city) 'a', which has neighbor the node 'b' with distance 20, @@ -130,7 +130,7 @@ def find_neighborhood(solution, dict_of_neighbours): Example: - >>> find_neighborhood(['a','c','b','d','e','a']) + >>) find_neighborhood(['a','c','b','d','e','a']) [['a','e','b','d','c','a',90], [['a','c','d','b','e','a',90],['a','d','b','c','e','a',93], ['a','c','b','e','d','a',102], ['a','c','e','d','b','a',113], ['a','b','c','d','e','a',93]] diff --git a/sorts/Bitonic_Sort.py b/sorts/Bitonic_Sort.py index bae95b4346f6..ba40a1f698ee 100644 --- a/sorts/Bitonic_Sort.py +++ b/sorts/Bitonic_Sort.py @@ -42,15 +42,16 @@ def sort(a, N, up): bitonicSort(a, 0, N, up) -# Driver code to test above -a = [] - -n = int(input()) -for i in range(n): - a.append(int(input())) -up = 1 - -sort(a, n, up) -print("\n\nSorted array is") -for i in range(n): - print("%d" % a[i]) +if __name__ == "__main__": + # Driver code to test above + a = [] + + n = int(input().strip()) + for i in range(n): + a.append(int(input().strip())) + up = 1 + + sort(a, n, up) + print("\n\nSorted array is") + for i in range(n): + print("%d" % a[i]) diff --git a/sorts/bubble_sort.py b/sorts/bubble_sort.py index e17fc3358d53..4e2c19b65e02 100644 --- a/sorts/bubble_sort.py +++ b/sorts/bubble_sort.py @@ -19,7 +19,7 @@ def bubble_sort(collection): [-45, -5, -2] >>> bubble_sort([-23,0,6,-4,34]) - [-23,-4,0,6,34] + [-23, -4, 0, 6, 34] """ length = len(collection) for i in range(length-1): From 78cd3df3fc5935922f13db36c61c3b2680f138d7 Mon Sep 17 00:00:00 2001 From: cclauss Date: Mon, 8 Jul 2019 17:38:47 +0200 Subject: [PATCH 052/193] Update CONTRIBUTING.md to match #964 (#969) * Update CONTRIBUTING.md to match #964 Blocked by #964 * Do not modify README or DIRECTORY file. * Update CONTRIBUTING.md --- CONTRIBUTING.md | 27 ++++++++++++++++++++++----- 1 file changed, 22 insertions(+), 5 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 03de387a8acd..02235ee89973 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -28,6 +28,13 @@ We appreciate any contribution, from fixing a grammar mistake in a comment to im We want your work to be readable by others; therefore, we encourage you to note the following: - Please write in Python 3.x. +- Please consider running [__python/black__](https://github.com/python/black) on your Python file(s) before submitting your pull request. This is not a requirement but it does make your code more readable. There are other code formatters (autopep8, yapf) but the __black__ style is now the recommendation of the Python core team. To use it, + ```bash + pip3 install black # only required the first time + black my-submission.py + ``` + +- All submissions will need to pass the test __flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics__ before they will be accepted so if possible, try this test locally on your Python file(s) before submitting your pull request. - If you know [PEP 8](https://www.python.org/dev/peps/pep-0008/) already, you will have no problem in coding style, though we do not follow it strictly. Read the remaining section and have fun coding! @@ -74,11 +81,17 @@ We want your work to be readable by others; therefore, we encourage you to note The following "testing" approaches are **not** encouraged: - ```python* + ```python input('Enter your input:') # Or even worse... input = eval(raw_input("Enter your input: ")) ``` + + However, if your code uses __input()__ then we encourage you to gracefully deal with leading and trailing whitespace in user input by adding __.strip()__ to the end as in: + + ```python + starting_value = int(input("Please enter a starting value: ").strip()) + ``` Please write down your test case, like the following: @@ -92,8 +105,10 @@ We want your work to be readable by others; therefore, we encourage you to note print("1 + 2 = ", sumab(1,2)) # 1+2 = 3 print("6 + 4 = ", sumab(6,4)) # 6+4 = 10 ``` + + Better yet, if you know how to write [__doctests__](https://docs.python.org/3/library/doctest.html), please consider adding them. -- Avoid importing external libraries for basic algorithms. Use those libraries for complicated algorithms. +- Avoid importing external libraries for basic algorithms. Only use those libraries for complicated algorithms. #### Other Standard While Submitting Your Work @@ -105,15 +120,17 @@ We want your work to be readable by others; therefore, we encourage you to note - If you have modified/added code work, make sure the code compiles before submitting. -- If you have modified/added documentation work, make sure your language is concise and contains no grammar mistake. +- If you have modified/added documentation work, ensure your language is concise and contains no grammar errors. -- Update the README file if you have added any new algorithm. Only entry corresponding to the algorithm is to be made, not need to add sample data, test files or solutions to problems like Project Euler, in the README. +- Do not update the README.md or DIRECTORY.md file which will be periodically autogenerated by our Travis CI processes. - Add a corresponding explanation to [Algorithms-Explanation](https://github.com/TheAlgorithms/Algorithms-Explanation) (Optional but recommended). +- All submissions will be tested with [__mypy__](http://www.mypy-lang.org) so we encourage to add [__Python type hints__](https://docs.python.org/3/library/typing.html) where it makes sense to do so. + - Most importantly, - - **Be consistent with this guidelines while submitting.** + - **Be consistent in the use of these guidelines when submitting.** - **Join** [Gitter](https://gitter.im/TheAlgorithms) **now!** - Happy coding! From 32d5c1a9b270dec417bb93ecf0e5d9a25170224f Mon Sep 17 00:00:00 2001 From: Animesh Singh <46104817+Blues1998@users.noreply.github.com> Date: Mon, 8 Jul 2019 22:28:58 +0530 Subject: [PATCH 053/193] Project Euler Problem #13 Python Solution (#935) * Create text file for numbers * Create sol2.py * Pythonic version of Problem #16 solution * Update sol2.py * Valid Python code for Python version 2-3 * Update sol2.py --- project_euler/problem_13/num.txt | 100 +++++++++++++++++++++++++++++++ project_euler/problem_13/sol2.py | 5 ++ project_euler/problem_16/sol2.py | 6 ++ 3 files changed, 111 insertions(+) create mode 100644 project_euler/problem_13/num.txt create mode 100644 project_euler/problem_13/sol2.py create mode 100644 project_euler/problem_16/sol2.py diff --git a/project_euler/problem_13/num.txt b/project_euler/problem_13/num.txt new file mode 100644 index 000000000000..43b568e812a8 --- /dev/null +++ b/project_euler/problem_13/num.txt @@ -0,0 +1,100 @@ +37107287533902102798797998220837590246510135740250 +46376937677490009712648124896970078050417018260538 +74324986199524741059474233309513058123726617309629 +91942213363574161572522430563301811072406154908250 +23067588207539346171171980310421047513778063246676 +89261670696623633820136378418383684178734361726757 +28112879812849979408065481931592621691275889832738 +44274228917432520321923589422876796487670272189318 +47451445736001306439091167216856844588711603153276 +70386486105843025439939619828917593665686757934951 +62176457141856560629502157223196586755079324193331 +64906352462741904929101432445813822663347944758178 +92575867718337217661963751590579239728245598838407 +58203565325359399008402633568948830189458628227828 +80181199384826282014278194139940567587151170094390 +35398664372827112653829987240784473053190104293586 +86515506006295864861532075273371959191420517255829 +71693888707715466499115593487603532921714970056938 +54370070576826684624621495650076471787294438377604 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+34829543829199918180278916522431027392251122869539 +40957953066405232632538044100059654939159879593635 +29746152185502371307642255121183693803580388584903 +41698116222072977186158236678424689157993532961922 +62467957194401269043877107275048102390895523597457 +23189706772547915061505504953922979530901129967519 +86188088225875314529584099251203829009407770775672 +11306739708304724483816533873502340845647058077308 +82959174767140363198008187129011875491310547126581 +97623331044818386269515456334926366572897563400500 +42846280183517070527831839425882145521227251250327 +55121603546981200581762165212827652751691296897789 +32238195734329339946437501907836945765883352399886 +75506164965184775180738168837861091527357929701337 +62177842752192623401942399639168044983993173312731 +32924185707147349566916674687634660915035914677504 +99518671430235219628894890102423325116913619626622 +73267460800591547471830798392868535206946944540724 +76841822524674417161514036427982273348055556214818 +97142617910342598647204516893989422179826088076852 +87783646182799346313767754307809363333018982642090 +10848802521674670883215120185883543223812876952786 +71329612474782464538636993009049310363619763878039 +62184073572399794223406235393808339651327408011116 +66627891981488087797941876876144230030984490851411 +60661826293682836764744779239180335110989069790714 +85786944089552990653640447425576083659976645795096 +66024396409905389607120198219976047599490197230297 +64913982680032973156037120041377903785566085089252 +16730939319872750275468906903707539413042652315011 +94809377245048795150954100921645863754710598436791 +78639167021187492431995700641917969777599028300699 +15368713711936614952811305876380278410754449733078 +40789923115535562561142322423255033685442488917353 +44889911501440648020369068063960672322193204149535 +41503128880339536053299340368006977710650566631954 +81234880673210146739058568557934581403627822703280 +82616570773948327592232845941706525094512325230608 +22918802058777319719839450180888072429661980811197 +77158542502016545090413245809786882778948721859617 +72107838435069186155435662884062257473692284509516 +20849603980134001723930671666823555245252804609722 +53503534226472524250874054075591789781264330331690 diff --git a/project_euler/problem_13/sol2.py b/project_euler/problem_13/sol2.py new file mode 100644 index 000000000000..c1416bcd6e7d --- /dev/null +++ b/project_euler/problem_13/sol2.py @@ -0,0 +1,5 @@ +sum = 0 +with open("num.txt",'r') as f: + for line in f: + sum += int(line) +print(str(sum)[:10]) diff --git a/project_euler/problem_16/sol2.py b/project_euler/problem_16/sol2.py new file mode 100644 index 000000000000..cce3d2354bb1 --- /dev/null +++ b/project_euler/problem_16/sol2.py @@ -0,0 +1,6 @@ +from __future__ import print_function +n = 2**1000 +r = 0 +while n: + r, n = r + n % 10, n // 10 +print(r) From e2d9953952053130c63c51ce62c5ceb6cb084e91 Mon Sep 17 00:00:00 2001 From: Shoujue Xu Date: Tue, 9 Jul 2019 01:26:26 +0800 Subject: [PATCH 054/193] convolve and sobel (#971) * add gaussian filter algorithm and lena.jpg * add img_convolve algorithm and sobel_filter --- digital_image_processing/filters/convolve.py | 49 +++++++++++++++++++ .../filters/sobel_filter.py | 31 ++++++++++++ 2 files changed, 80 insertions(+) create mode 100644 digital_image_processing/filters/convolve.py create mode 100644 digital_image_processing/filters/sobel_filter.py diff --git a/digital_image_processing/filters/convolve.py b/digital_image_processing/filters/convolve.py new file mode 100644 index 000000000000..b7600d74c294 --- /dev/null +++ b/digital_image_processing/filters/convolve.py @@ -0,0 +1,49 @@ +# @Author : lightXu +# @File : convolve.py +# @Time : 2019/7/8 0008 下午 16:13 +from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey +from numpy import array, zeros, ravel, pad, dot, uint8 + + +def im2col(image, block_size): + rows, cols = image.shape + dst_height = cols - block_size[1] + 1 + dst_width = rows - block_size[0] + 1 + image_array = zeros((dst_height * dst_width, block_size[1] * block_size[0])) + row = 0 + for i in range(0, dst_height): + for j in range(0, dst_width): + window = ravel(image[i:i + block_size[0], j:j + block_size[1]]) + image_array[row, :] = window + row += 1 + + return image_array + + +def img_convolve(image, filter_kernel): + height, width = image.shape[0], image.shape[1] + k_size = filter_kernel.shape[0] + pad_size = k_size//2 + # Pads image with the edge values of array. + image_tmp = pad(image, pad_size, mode='edge') + + # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows + image_array = im2col(image_tmp, (k_size, k_size)) + + # turn the kernel into shape(k*k, 1) + kernel_array = ravel(filter_kernel) + # reshape and get the dst image + dst = dot(image_array, kernel_array).reshape(height, width) + return dst + + +if __name__ == '__main__': + # read original image + img = imread(r'../image_data/lena.jpg') + # turn image in gray scale value + gray = cvtColor(img, COLOR_BGR2GRAY) + # Laplace operator + Laplace_kernel = array([[0, 1, 0], [1, -4, 1], [0, 1, 0]]) + out = img_convolve(gray, Laplace_kernel).astype(uint8) + imshow('Laplacian', out) + waitKey(0) diff --git a/digital_image_processing/filters/sobel_filter.py b/digital_image_processing/filters/sobel_filter.py new file mode 100644 index 000000000000..0c797320a110 --- /dev/null +++ b/digital_image_processing/filters/sobel_filter.py @@ -0,0 +1,31 @@ +# @Author : lightXu +# @File : sobel_filter.py +# @Time : 2019/7/8 0008 下午 16:26 +import numpy as np +from cv2 import imread, cvtColor, COLOR_BGR2GRAY, imshow, waitKey +from digital_image_processing.filters.convolve import img_convolve + + +def sobel_filter(image): + kernel_x = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) + kernel_y = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]) + + dst_x = img_convolve(image, kernel_x) + dst_y = img_convolve(image, kernel_y) + dst = np.sqrt((np.square(dst_x)) + (np.square(dst_y))).astype(np.uint8) + degree = np.arctan2(dst_y, dst_x) + return dst, degree + + +if __name__ == '__main__': + # read original image + img = imread('../image_data/lena.jpg') + # turn image in gray scale value + gray = cvtColor(img, COLOR_BGR2GRAY) + + sobel, d = sobel_filter(gray) + + # show result images + imshow('sobel filter', sobel) + imshow('sobel degree', d) + waitKey(0) From 8b2d1b7f509a1124abdd037803db32880402da19 Mon Sep 17 00:00:00 2001 From: Jasper <46252815+jasper256@users.noreply.github.com> Date: Tue, 9 Jul 2019 03:03:18 -0400 Subject: [PATCH 055/193] added decimal to hexadecimal conversion (#977) * added decimal to hexadecimal conversion * fixed error occuring as more digits were needed --- conversions/decimal_to_hexadecimal.py | 43 +++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 conversions/decimal_to_hexadecimal.py diff --git a/conversions/decimal_to_hexadecimal.py b/conversions/decimal_to_hexadecimal.py new file mode 100644 index 000000000000..f91fac063adc --- /dev/null +++ b/conversions/decimal_to_hexadecimal.py @@ -0,0 +1,43 @@ +""" Convert Base 10 (Decimal) Values to Hexadecimal Representations """ + +# set decimal value for each hexadecimal digit +values = { + 0:'0', + 1:'1', + 2:'2', + 3:'3', + 4:'4', + 5:'5', + 6:'6', + 7:'7', + 8:'8', + 9:'9', + 10:'a', + 11:'b', + 12:'c', + 13:'d', + 14:'e', + 15:'f' +} + +def decimal_to_hexadecimal(decimal): + """ take decimal value, return hexadecimal representation as str """ + hexadecimal = '' + while decimal > 0: + remainder = decimal % 16 + decimal -= remainder + hexadecimal = values[remainder] + hexadecimal + decimal /= 16 + return hexadecimal + +def main(): + """ print test cases """ + print("5 in hexadecimal is", decimal_to_hexadecimal(5)) + print("15 in hexadecimal is", decimal_to_hexadecimal(15)) + print("37 in hexadecimal is", decimal_to_hexadecimal(37)) + print("255 in hexadecimal is", decimal_to_hexadecimal(255)) + print("4096 in hexadecimal is", decimal_to_hexadecimal(4096)) + print("999098 in hexadecimal is", decimal_to_hexadecimal(999098)) + +if __name__ == '__main__': + main() \ No newline at end of file From c85312da89dcc5bb1ad397feffc0e055dc576e85 Mon Sep 17 00:00:00 2001 From: Dharni0607 <30770547+Dharni0607@users.noreply.github.com> Date: Tue, 9 Jul 2019 20:50:43 +0530 Subject: [PATCH 056/193] updates in closest pair of points algorithm (#979) * updated closest pair of points (n*(logn)^2) to (n*logn) --- divide_and_conquer/closest_pair_of_points.py | 49 +++++++++++--------- 1 file changed, 28 insertions(+), 21 deletions(-) diff --git a/divide_and_conquer/closest_pair_of_points.py b/divide_and_conquer/closest_pair_of_points.py index cc5be428db79..ee06d27063df 100644 --- a/divide_and_conquer/closest_pair_of_points.py +++ b/divide_and_conquer/closest_pair_of_points.py @@ -1,27 +1,27 @@ """ -The algorithm finds distance btw closest pair of points in the given n points. +The algorithm finds distance between closest pair of points +in the given n points. Approach used -> Divide and conquer -The points are sorted based on Xco-ords -& by applying divide and conquer approach, +The points are sorted based on Xco-ords and +then based on Yco-ords separately. +And by applying divide and conquer approach, minimum distance is obtained recursively. ->> closest points lie on different sides of partition +>> Closest points can lie on different sides of partition. This case handled by forming a strip of points whose Xco-ords distance is less than closest_pair_dis -from mid-point's Xco-ords. +from mid-point's Xco-ords. Points sorted based on Yco-ords +are used in this step to reduce sorting time. Closest pair distance is found in the strip of points. (closest_in_strip) min(closest_pair_dis, closest_in_strip) would be the final answer. -Time complexity: O(n * (logn)^2) +Time complexity: O(n * log n) """ -import math - - def euclidean_distance_sqr(point1, point2): - return pow(point1[0] - point2[0], 2) + pow(point1[1] - point2[1], 2) + return (point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2 def column_based_sort(array, column = 0): @@ -66,7 +66,7 @@ def dis_between_closest_in_strip(points, points_counts, min_dis = float("inf")): return min_dis -def closest_pair_of_points_sqr(points, points_counts): +def closest_pair_of_points_sqr(points_sorted_on_x, points_sorted_on_y, points_counts): """ divide and conquer approach Parameters : @@ -79,12 +79,16 @@ def closest_pair_of_points_sqr(points, points_counts): # base case if points_counts <= 3: - return dis_between_closest_pair(points, points_counts) + return dis_between_closest_pair(points_sorted_on_x, points_counts) # recursion mid = points_counts//2 - closest_in_left = closest_pair_of_points(points[:mid], mid) - closest_in_right = closest_pair_of_points(points[mid:], points_counts - mid) + closest_in_left = closest_pair_of_points_sqr(points_sorted_on_x, + points_sorted_on_y[:mid], + mid) + closest_in_right = closest_pair_of_points_sqr(points_sorted_on_y, + points_sorted_on_y[mid:], + points_counts - mid) closest_pair_dis = min(closest_in_left, closest_in_right) """ cross_strip contains the points, whose Xcoords are at a @@ -92,22 +96,25 @@ def closest_pair_of_points_sqr(points, points_counts): """ cross_strip = [] - for point in points: - if abs(point[0] - points[mid][0]) < closest_pair_dis: + for point in points_sorted_on_x: + if abs(point[0] - points_sorted_on_x[mid][0]) < closest_pair_dis: cross_strip.append(point) - cross_strip = column_based_sort(cross_strip, 1) closest_in_strip = dis_between_closest_in_strip(cross_strip, len(cross_strip), closest_pair_dis) return min(closest_pair_dis, closest_in_strip) def closest_pair_of_points(points, points_counts): - return math.sqrt(closest_pair_of_points_sqr(points, points_counts)) + points_sorted_on_x = column_based_sort(points, column = 0) + points_sorted_on_y = column_based_sort(points, column = 1) + return (closest_pair_of_points_sqr(points_sorted_on_x, + points_sorted_on_y, + points_counts)) ** 0.5 -points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (0, 2), (5, 6), (1, 2)] -points = column_based_sort(points) -print("Distance:", closest_pair_of_points(points, len(points))) +if __name__ == "__main__": + points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)] + print("Distance:", closest_pair_of_points(points, len(points))) From e6eaa078e292dc856f1027331e8de30b43a918f9 Mon Sep 17 00:00:00 2001 From: cclauss Date: Wed, 10 Jul 2019 06:59:39 +0200 Subject: [PATCH 057/193] Pytest the entire repo (#980) * Pytest the entire repo * Do each directory for now... * YAML files hate tabs * Add more requirements * pip install opencv-python * Comment out FTP * Add pandas and sklearn to requirements * Comment out FTP, graphs, machine_learning, maths, neural_network, project_euler * Update .travis.yml * Comment out Data structures * if __name__ == "__main__": * pytest --ignore= * pytest . * Update .travis.yml * pytest . --doctest-modules --ignore=${IGNORE} * Ignore --ignore because it just hangs --- .travis.yml | 24 ++++++++++++++++++- ...longest_increasing_subsequence_O(nlogn).py | 5 ++-- requirements.txt | 5 ++++ 3 files changed, 31 insertions(+), 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index e67bd431a7c1..0e35fd084268 100644 --- a/.travis.yml +++ b/.travis.yml @@ -2,13 +2,35 @@ language: python dist: xenial # required for Python >= 3.7 python: 3.7 cache: pip +before_install: pip install --upgrade pip setuptools install: pip install -r requirements.txt before_script: - black --check . || true - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics script: - mypy --ignore-missing-imports . - - pytest --doctest-modules ./ciphers ./other ./searches ./sorts ./strings + #- IGNORE="data_structures,file_transfer_protocol,graphs,machine_learning,maths,neural_network,project_euler" + #- pytest . --doctest-modules --ignore=${IGNORE} + - pytest --doctest-modules + arithmetic_analysis + backtracking + boolean_algebra + ciphers + compression + conversions + digital_image_processing + divide_and_conquer + dynamic_programming + hashes + linear_algebra_python + matrix + networking_flow + other + searches + sorts + strings + traversals + after_success: - python ./~script.py - cat DIRECTORY.md diff --git a/dynamic_programming/longest_increasing_subsequence_O(nlogn).py b/dynamic_programming/longest_increasing_subsequence_O(nlogn).py index 21122a04d69f..86bec089adc7 100644 --- a/dynamic_programming/longest_increasing_subsequence_O(nlogn).py +++ b/dynamic_programming/longest_increasing_subsequence_O(nlogn).py @@ -37,5 +37,6 @@ def LongestIncreasingSubsequenceLength(v): return length -v = [2, 5, 3, 7, 11, 8, 10, 13, 6] -print(LongestIncreasingSubsequenceLength(v)) +if __name__ == "__main__": + v = [2, 5, 3, 7, 11, 8, 10, 13, 6] + print(LongestIncreasingSubsequenceLength(v)) diff --git a/requirements.txt b/requirements.txt index 30179ac345b3..91d3df33323d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,4 +3,9 @@ flake8 matplotlib mypy numpy +opencv-python +pandas pytest +sklearn +sympy +tensorflow From add1aef0645790f2688c8ad44ddfdf6638ab8cb1 Mon Sep 17 00:00:00 2001 From: PatOnTheBack <51241310+PatOnTheBack@users.noreply.github.com> Date: Wed, 10 Jul 2019 01:21:04 -0400 Subject: [PATCH 058/193] Rename average.py to average_mean.py (#939) 'average.py' is ambiguous. There are several kinds of averages, so 'average_mean.py' is a more precise name. --- maths/{average.py => average_mean.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename maths/{average.py => average_mean.py} (100%) diff --git a/maths/average.py b/maths/average_mean.py similarity index 100% rename from maths/average.py rename to maths/average_mean.py From 34dee749a725b0d47811161b0598b9f806ce88cd Mon Sep 17 00:00:00 2001 From: Shoujue Xu Date: Wed, 10 Jul 2019 22:41:05 +0800 Subject: [PATCH 059/193] add canny edge detection algorithm and modify sobel_filter (#991) * add gaussian filter algorithm and lena.jpg * add img_convolve algorithm and sobel_filter * add canny edge detection algorithm and modify sobel_filter * format to avoid the backslashes --- .../edge_detection/__init__.py | 0 .../edge_detection/canny.py | 107 ++++++++++++++++++ .../filters/sobel_filter.py | 23 ++-- 3 files changed, 122 insertions(+), 8 deletions(-) create mode 100644 digital_image_processing/edge_detection/__init__.py create mode 100644 digital_image_processing/edge_detection/canny.py diff --git a/digital_image_processing/edge_detection/__init__.py b/digital_image_processing/edge_detection/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/digital_image_processing/edge_detection/canny.py b/digital_image_processing/edge_detection/canny.py new file mode 100644 index 000000000000..7fde75a90a48 --- /dev/null +++ b/digital_image_processing/edge_detection/canny.py @@ -0,0 +1,107 @@ +import cv2 +import numpy as np +from digital_image_processing.filters.convolve import img_convolve +from digital_image_processing.filters.sobel_filter import sobel_filter + +PI = 180 + + +def gen_gaussian_kernel(k_size, sigma): + center = k_size // 2 + x, y = np.mgrid[0 - center:k_size - center, 0 - center:k_size - center] + g = 1 / (2 * np.pi * sigma) * np.exp(-(np.square(x) + np.square(y)) / (2 * np.square(sigma))) + return g + + +def canny(image, threshold_low=15, threshold_high=30, weak=128, strong=255): + image_row, image_col = image.shape[0], image.shape[1] + # gaussian_filter + gaussian_out = img_convolve(image, gen_gaussian_kernel(9, sigma=1.4)) + # get the gradient and degree by sobel_filter + sobel_grad, sobel_theta = sobel_filter(gaussian_out) + gradient_direction = np.rad2deg(sobel_theta) + gradient_direction += PI + + dst = np.zeros((image_row, image_col)) + + """ + Non-maximum suppression. If the edge strength of the current pixel is the largest compared to the other pixels + in the mask with the same direction, the value will be preserved. Otherwise, the value will be suppressed. + """ + for row in range(1, image_row - 1): + for col in range(1, image_col - 1): + direction = gradient_direction[row, col] + + if ( + 0 <= direction < 22.5 + or 15 * PI / 8 <= direction <= 2 * PI + or 7 * PI / 8 <= direction <= 9 * PI / 8 + ): + W = sobel_grad[row, col - 1] + E = sobel_grad[row, col + 1] + if sobel_grad[row, col] >= W and sobel_grad[row, col] >= E: + dst[row, col] = sobel_grad[row, col] + + elif (PI / 8 <= direction < 3 * PI / 8) or (9 * PI / 8 <= direction < 11 * PI / 8): + SW = sobel_grad[row + 1, col - 1] + NE = sobel_grad[row - 1, col + 1] + if sobel_grad[row, col] >= SW and sobel_grad[row, col] >= NE: + dst[row, col] = sobel_grad[row, col] + + elif (3 * PI / 8 <= direction < 5 * PI / 8) or (11 * PI / 8 <= direction < 13 * PI / 8): + N = sobel_grad[row - 1, col] + S = sobel_grad[row + 1, col] + if sobel_grad[row, col] >= N and sobel_grad[row, col] >= S: + dst[row, col] = sobel_grad[row, col] + + elif (5 * PI / 8 <= direction < 7 * PI / 8) or (13 * PI / 8 <= direction < 15 * PI / 8): + NW = sobel_grad[row - 1, col - 1] + SE = sobel_grad[row + 1, col + 1] + if sobel_grad[row, col] >= NW and sobel_grad[row, col] >= SE: + dst[row, col] = sobel_grad[row, col] + + """ + High-Low threshold detection. If an edge pixel’s gradient value is higher than the high threshold + value, it is marked as a strong edge pixel. If an edge pixel’s gradient value is smaller than the high + threshold value and larger than the low threshold value, it is marked as a weak edge pixel. If an edge + pixel's value is smaller than the low threshold value, it will be suppressed. + """ + if dst[row, col] >= threshold_high: + dst[row, col] = strong + elif dst[row, col] <= threshold_low: + dst[row, col] = 0 + else: + dst[row, col] = weak + + """ + Edge tracking. Usually a weak edge pixel caused from true edges will be connected to a strong edge pixel while + noise responses are unconnected. As long as there is one strong edge pixel that is involved in its 8-connected + neighborhood, that weak edge point can be identified as one that should be preserved. + """ + for row in range(1, image_row): + for col in range(1, image_col): + if dst[row, col] == weak: + if 255 in ( + dst[row, col + 1], + dst[row, col - 1], + dst[row - 1, col], + dst[row + 1, col], + dst[row - 1, col - 1], + dst[row + 1, col - 1], + dst[row - 1, col + 1], + dst[row + 1, col + 1], + ): + dst[row, col] = strong + else: + dst[row, col] = 0 + + return dst + + +if __name__ == '__main__': + # read original image in gray mode + lena = cv2.imread(r'../image_data/lena.jpg', 0) + # canny edge detection + canny_dst = canny(lena) + cv2.imshow('canny', canny_dst) + cv2.waitKey(0) diff --git a/digital_image_processing/filters/sobel_filter.py b/digital_image_processing/filters/sobel_filter.py index 0c797320a110..f3ef407d49e5 100644 --- a/digital_image_processing/filters/sobel_filter.py +++ b/digital_image_processing/filters/sobel_filter.py @@ -10,11 +10,18 @@ def sobel_filter(image): kernel_x = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) kernel_y = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]) - dst_x = img_convolve(image, kernel_x) - dst_y = img_convolve(image, kernel_y) - dst = np.sqrt((np.square(dst_x)) + (np.square(dst_y))).astype(np.uint8) - degree = np.arctan2(dst_y, dst_x) - return dst, degree + dst_x = np.abs(img_convolve(image, kernel_x)) + dst_y = np.abs(img_convolve(image, kernel_y)) + # modify the pix within [0, 255] + dst_x = dst_x * 255/np.max(dst_x) + dst_y = dst_y * 255/np.max(dst_y) + + dst_xy = np.sqrt((np.square(dst_x)) + (np.square(dst_y))) + dst_xy = dst_xy * 255/np.max(dst_xy) + dst = dst_xy.astype(np.uint8) + + theta = np.arctan2(dst_y, dst_x) + return dst, theta if __name__ == '__main__': @@ -23,9 +30,9 @@ def sobel_filter(image): # turn image in gray scale value gray = cvtColor(img, COLOR_BGR2GRAY) - sobel, d = sobel_filter(gray) + sobel_grad, sobel_theta = sobel_filter(gray) # show result images - imshow('sobel filter', sobel) - imshow('sobel degree', d) + imshow('sobel filter', sobel_grad) + imshow('sobel theta', sobel_theta) waitKey(0) From 2ad5be99192ade89c26752fb9d6fb7c32bc0337f Mon Sep 17 00:00:00 2001 From: PatOnTheBack <51241310+PatOnTheBack@users.noreply.github.com> Date: Wed, 10 Jul 2019 16:00:30 -0400 Subject: [PATCH 060/193] Modified Docstrings to Fix Errors (#975) I modified the Docstrings at the beginning of the file to fix D400, W0105, and E402. --- graphs/BFS.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/graphs/BFS.py b/graphs/BFS.py index bf9b572cec50..6bbdd9e25435 100644 --- a/graphs/BFS.py +++ b/graphs/BFS.py @@ -1,6 +1,8 @@ -"""pseudo-code""" - """ +BFS. + +pseudo-code: + BFS(graph G, start vertex s): // all nodes initially unexplored mark s as explored From 897f1d0fb4c4f78dd8a7e82820e843f7f9f38738 Mon Sep 17 00:00:00 2001 From: PatOnTheBack <51241310+PatOnTheBack@users.noreply.github.com> Date: Wed, 10 Jul 2019 16:09:24 -0400 Subject: [PATCH 061/193] Improved Formatting and Style of Math Algos (#960) * Improved Formatting and Style I improved formatting and style to make PyLama happier. Linters used: - mccabe - pep257 - pydocstyle - pep8 - pycodestyle - pyflakes - pylint - isort * Create volume.py This script calculates the volumes of various shapes. * Delete lucasSeries.py * Revert "Delete lucasSeries.py" This reverts commit 64c19f7a6c8b74e15bed07f0f0337598a001ceb4. * Update lucasSeries.py --- maths/Binary_Exponentiation.py | 26 +++---- maths/Find_Min.py | 17 +++-- maths/Prime_Check.py | 49 +++++++------ maths/abs.py | 13 ++-- maths/extended_euclidean_algorithm.py | 53 +++++++++----- maths/fermat_little_theorem.py | 6 +- maths/greater_common_divisor.py | 18 +++-- maths/modular_exponential.py | 31 ++++---- maths/segmented_sieve.py | 43 ++++++----- maths/sieve_of_eratosthenes.py | 31 ++++---- maths/simpson_rule.py | 60 ++++++++-------- maths/trapezoidal_rule.py | 24 +++---- maths/volume.py | 100 ++++++++++++++++++++++++++ 13 files changed, 314 insertions(+), 157 deletions(-) create mode 100644 maths/volume.py diff --git a/maths/Binary_Exponentiation.py b/maths/Binary_Exponentiation.py index 2411cd58a76b..cf789afc6f22 100644 --- a/maths/Binary_Exponentiation.py +++ b/maths/Binary_Exponentiation.py @@ -1,25 +1,27 @@ -#Author : Junth Basnet -#Time Complexity : O(logn) +"""Binary Exponentiation.""" + +# Author : Junth Basnet +# Time Complexity : O(logn) + def binary_exponentiation(a, n): - + if (n == 0): return 1 - + elif (n % 2 == 1): return binary_exponentiation(a, n - 1) * a - + else: b = binary_exponentiation(a, n / 2) return b * b - + try: - base = int(input('Enter Base : ')) - power = int(input("Enter Power : ")) + BASE = int(input('Enter Base : ')) + POWER = int(input("Enter Power : ")) except ValueError: - print ("Invalid literal for integer") + print("Invalid literal for integer") -result = binary_exponentiation(base, power) -print("{}^({}) : {}".format(base, power, result)) - +RESULT = binary_exponentiation(BASE, POWER) +print("{}^({}) : {}".format(BASE, POWER, RESULT)) diff --git a/maths/Find_Min.py b/maths/Find_Min.py index 86207984e3da..c720da268a25 100644 --- a/maths/Find_Min.py +++ b/maths/Find_Min.py @@ -1,12 +1,17 @@ +"""Find Minimum Number in a List.""" + + def main(): - def findMin(x): - minNum = x[0] + """Find Minimum Number in a List.""" + def find_min(x): + min_num = x[0] for i in x: - if minNum > i: - minNum = i - return minNum + if min_num > i: + min_num = i + return min_num + + print(find_min([0, 1, 2, 3, 4, 5, -3, 24, -56])) # = -56 - print(findMin([0,1,2,3,4,5,-3,24,-56])) # = -56 if __name__ == '__main__': main() diff --git a/maths/Prime_Check.py b/maths/Prime_Check.py index 8c5c181689dd..9249834dc069 100644 --- a/maths/Prime_Check.py +++ b/maths/Prime_Check.py @@ -1,9 +1,13 @@ +"""Prime Check.""" + import math import unittest -def primeCheck(number): +def prime_check(number): """ + Check to See if a Number is Prime. + A number is prime if it has exactly two dividers: 1 and itself. """ if number < 2: @@ -24,31 +28,30 @@ def primeCheck(number): class Test(unittest.TestCase): def test_primes(self): - self.assertTrue(primeCheck(2)) - self.assertTrue(primeCheck(3)) - self.assertTrue(primeCheck(5)) - self.assertTrue(primeCheck(7)) - self.assertTrue(primeCheck(11)) - self.assertTrue(primeCheck(13)) - self.assertTrue(primeCheck(17)) - self.assertTrue(primeCheck(19)) - self.assertTrue(primeCheck(23)) - self.assertTrue(primeCheck(29)) + self.assertTrue(prime_check(2)) + self.assertTrue(prime_check(3)) + self.assertTrue(prime_check(5)) + self.assertTrue(prime_check(7)) + self.assertTrue(prime_check(11)) + self.assertTrue(prime_check(13)) + self.assertTrue(prime_check(17)) + self.assertTrue(prime_check(19)) + self.assertTrue(prime_check(23)) + self.assertTrue(prime_check(29)) def test_not_primes(self): - self.assertFalse(primeCheck(-19), - "Negative numbers are not prime.") - self.assertFalse(primeCheck(0), - "Zero doesn't have any divider, primes must have two") - self.assertFalse(primeCheck(1), - "One just have 1 divider, primes must have two.") - self.assertFalse(primeCheck(2 * 2)) - self.assertFalse(primeCheck(2 * 3)) - self.assertFalse(primeCheck(3 * 3)) - self.assertFalse(primeCheck(3 * 5)) - self.assertFalse(primeCheck(3 * 5 * 7)) + self.assertFalse(prime_check(-19), + "Negative numbers are not prime.") + self.assertFalse(prime_check(0), + "Zero doesn't have any divider, primes must have two") + self.assertFalse(prime_check(1), + "One just have 1 divider, primes must have two.") + self.assertFalse(prime_check(2 * 2)) + self.assertFalse(prime_check(2 * 3)) + self.assertFalse(prime_check(3 * 3)) + self.assertFalse(prime_check(3 * 5)) + self.assertFalse(prime_check(3 * 5 * 7)) if __name__ == '__main__': unittest.main() - diff --git a/maths/abs.py b/maths/abs.py index 624823fc183e..2734e58ceee6 100644 --- a/maths/abs.py +++ b/maths/abs.py @@ -1,24 +1,25 @@ """Absolute Value.""" -def absVal(num): +def abs_val(num): """ Find the absolute value of a number. - >>absVal(-5) + >>abs_val(-5) 5 - >>absVal(0) + >>abs_val(0) 0 """ if num < 0: return -num - else: - return num + + # Returns if number is not < 0 + return num def main(): """Print absolute value of -34.""" - print(absVal(-34)) # = 34 + print(abs_val(-34)) # = 34 if __name__ == '__main__': diff --git a/maths/extended_euclidean_algorithm.py b/maths/extended_euclidean_algorithm.py index f5a3cc88e474..fc3798e7e432 100644 --- a/maths/extended_euclidean_algorithm.py +++ b/maths/extended_euclidean_algorithm.py @@ -1,20 +1,38 @@ +""" +Extended Euclidean Algorithm. + +Finds 2 numbers a and b such that it satisfies +the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity) +""" + # @Author: S. Sharma # @Date: 2019-02-25T12:08:53-06:00 # @Email: silentcat@protonmail.com -# @Last modified by: silentcat -# @Last modified time: 2019-02-26T07:07:38-06:00 +# @Last modified by: PatOnTheBack +# @Last modified time: 2019-07-05 import sys -# Finds 2 numbers a and b such that it satisfies -# the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity) + def extended_euclidean_algorithm(m, n): - a = 0; aprime = 1; b = 1; bprime = 0 - q = 0; r = 0 + """ + Extended Euclidean Algorithm. + + Finds 2 numbers a and b such that it satisfies + the equation am + bn = gcd(m, n) (a.k.a Bezout's Identity) + """ + a = 0 + a_prime = 1 + b = 1 + b_prime = 0 + q = 0 + r = 0 if m > n: - c = m; d = n + c = m + d = n else: - c = n; d = m + c = n + d = m while True: q = int(c / d) @@ -24,22 +42,24 @@ def extended_euclidean_algorithm(m, n): c = d d = r - t = aprime - aprime = a - a = t - q*a + t = a_prime + a_prime = a + a = t - q * a - t = bprime - bprime = b - b = t - q*b + t = b_prime + b_prime = b + b = t - q * b pair = None if m > n: - pair = (a,b) + pair = (a, b) else: - pair = (b,a) + pair = (b, a) return pair + def main(): + """Call Extended Euclidean Algorithm.""" if len(sys.argv) < 3: print('2 integer arguments required') exit(1) @@ -47,5 +67,6 @@ def main(): n = int(sys.argv[2]) print(extended_euclidean_algorithm(m, n)) + if __name__ == '__main__': main() diff --git a/maths/fermat_little_theorem.py b/maths/fermat_little_theorem.py index 93af98684894..8cf60dafe3ca 100644 --- a/maths/fermat_little_theorem.py +++ b/maths/fermat_little_theorem.py @@ -5,13 +5,13 @@ def binary_exponentiation(a, n, mod): - + if (n == 0): return 1 - + elif (n % 2 == 1): return (binary_exponentiation(a, n - 1, mod) * a) % mod - + else: b = binary_exponentiation(a, n / 2, mod) return (b * b) % mod diff --git a/maths/greater_common_divisor.py b/maths/greater_common_divisor.py index 15adaca1fb8d..adc7811e8317 100644 --- a/maths/greater_common_divisor.py +++ b/maths/greater_common_divisor.py @@ -1,15 +1,25 @@ -# Greater Common Divisor - https://en.wikipedia.org/wiki/Greatest_common_divisor +""" +Greater Common Divisor. + +Wikipedia reference: https://en.wikipedia.org/wiki/Greatest_common_divisor +""" + + def gcd(a, b): + """Calculate Greater Common Divisor (GCD).""" return b if a == 0 else gcd(b % a, a) + def main(): + """Call GCD Function.""" try: nums = input("Enter two Integers separated by comma (,): ").split(',') - num1 = int(nums[0]); num2 = int(nums[1]) + num_1 = int(nums[0]) + num_2 = int(nums[1]) except (IndexError, UnboundLocalError, ValueError): print("Wrong Input") - print(f"gcd({num1}, {num2}) = {gcd(num1, num2)}") + print(f"gcd({num_1}, {num_2}) = {gcd(num_1, num_2)}") + if __name__ == '__main__': main() - diff --git a/maths/modular_exponential.py b/maths/modular_exponential.py index b3f4c00bd5d8..750de7cba99e 100644 --- a/maths/modular_exponential.py +++ b/maths/modular_exponential.py @@ -1,20 +1,25 @@ -def modularExponential(base, power, mod): - if power < 0: - return -1 - base %= mod - result = 1 +"""Modular Exponential.""" - while power > 0: - if power & 1: - result = (result * base) % mod - power = power >> 1 - base = (base * base) % mod - return result + +def modular_exponential(base, power, mod): + """Calculate Modular Exponential.""" + if power < 0: + return -1 + base %= mod + result = 1 + + while power > 0: + if power & 1: + result = (result * base) % mod + power = power >> 1 + base = (base * base) % mod + return result def main(): - print(modularExponential(3, 200, 13)) + """Call Modular Exponential Function.""" + print(modular_exponential(3, 200, 13)) if __name__ == '__main__': - main() + main() diff --git a/maths/segmented_sieve.py b/maths/segmented_sieve.py index 52ca6fbe601d..b15ec2480678 100644 --- a/maths/segmented_sieve.py +++ b/maths/segmented_sieve.py @@ -1,46 +1,51 @@ +"""Segmented Sieve.""" + import math + def sieve(n): + """Segmented Sieve.""" in_prime = [] start = 2 - end = int(math.sqrt(n)) # Size of every segment + end = int(math.sqrt(n)) # Size of every segment temp = [True] * (end + 1) prime = [] - - while(start <= end): - if temp[start] == True: + + while start <= end: + if temp[start] is True: in_prime.append(start) - for i in range(start*start, end+1, start): - if temp[i] == True: + for i in range(start * start, end + 1, start): + if temp[i] is True: temp[i] = False start += 1 prime += in_prime - + low = end + 1 high = low + end - 1 if high > n: high = n - - while(low <= n): - temp = [True] * (high-low+1) + + while low <= n: + temp = [True] * (high - low + 1) for each in in_prime: - + t = math.floor(low / each) * each if t < low: t += each - - for j in range(t, high+1, each): + + for j in range(t, high + 1, each): temp[j - low] = False - + for j in range(len(temp)): - if temp[j] == True: - prime.append(j+low) - + if temp[j] is True: + prime.append(j + low) + low = high + 1 high = low + end - 1 if high > n: high = n - + return prime -print(sieve(10**6)) \ No newline at end of file + +print(sieve(10**6)) diff --git a/maths/sieve_of_eratosthenes.py b/maths/sieve_of_eratosthenes.py index 26c17fa6ffec..11c123693694 100644 --- a/maths/sieve_of_eratosthenes.py +++ b/maths/sieve_of_eratosthenes.py @@ -1,24 +1,29 @@ +"""Sieve of Eratosthones.""" + import math -n = int(input("Enter n: ")) + +N = int(input("Enter n: ")) + def sieve(n): - l = [True] * (n+1) + """Sieve of Eratosthones.""" + l = [True] * (n + 1) prime = [] start = 2 - end = int(math.sqrt(n)) - while(start <= end): - if l[start] == True: + end = int(math.sqrt(n)) + while start <= end: + if l[start] is True: prime.append(start) - for i in range(start*start, n+1, start): - if l[i] == True: + for i in range(start * start, n + 1, start): + if l[i] is True: l[i] = False start += 1 - - for j in range(end+1,n+1): - if l[j] == True: + + for j in range(end + 1, n + 1): + if l[j] is True: prime.append(j) - + return prime -print(sieve(n)) - + +print(sieve(N)) diff --git a/maths/simpson_rule.py b/maths/simpson_rule.py index 091c86c17f1b..2b237d2e1a4e 100644 --- a/maths/simpson_rule.py +++ b/maths/simpson_rule.py @@ -1,49 +1,49 @@ -''' +""" Numerical integration or quadrature for a smooth function f with known values at x_i -This method is the classical approch of suming 'Equally Spaced Abscissas' +This method is the classical approch of suming 'Equally Spaced Abscissas' -method 2: +method 2: "Simpson Rule" -''' +""" from __future__ import print_function def method_2(boundary, steps): # "Simpson Rule" # int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn) - h = (boundary[1] - boundary[0]) / steps - a = boundary[0] - b = boundary[1] - x_i = makePoints(a,b,h) - y = 0.0 - y += (h/3.0)*f(a) - cnt = 2 - for i in x_i: - y += (h/3)*(4-2*(cnt%2))*f(i) - cnt += 1 - y += (h/3.0)*f(b) - return y - -def makePoints(a,b,h): - x = a + h - while x < (b-h): - yield x - x = x + h + h = (boundary[1] - boundary[0]) / steps + a = boundary[0] + b = boundary[1] + x_i = make_points(a,b,h) + y = 0.0 + y += (h/3.0)*f(a) + cnt = 2 + for i in x_i: + y += (h/3)*(4-2*(cnt%2))*f(i) + cnt += 1 + y += (h/3.0)*f(b) + return y + +def make_points(a,b,h): + x = a + h + while x < (b-h): + yield x + x = x + h def f(x): #enter your function here - y = (x-0)*(x-0) - return y + y = (x-0)*(x-0) + return y def main(): - a = 0.0 #Lower bound of integration - b = 1.0 #Upper bound of integration - steps = 10.0 #define number of steps or resolution - boundary = [a, b] #define boundary of integration - y = method_2(boundary, steps) - print('y = {0}'.format(y)) + a = 0.0 #Lower bound of integration + b = 1.0 #Upper bound of integration + steps = 10.0 #define number of steps or resolution + boundary = [a, b] #define boundary of integration + y = method_2(boundary, steps) + print('y = {0}'.format(y)) if __name__ == '__main__': main() diff --git a/maths/trapezoidal_rule.py b/maths/trapezoidal_rule.py index 52310c1ed3b0..789f263c6991 100644 --- a/maths/trapezoidal_rule.py +++ b/maths/trapezoidal_rule.py @@ -1,12 +1,12 @@ -''' +""" Numerical integration or quadrature for a smooth function f with known values at x_i -This method is the classical approch of suming 'Equally Spaced Abscissas' +This method is the classical approch of suming 'Equally Spaced Abscissas' -method 1: +method 1: "extended trapezoidal rule" -''' +""" from __future__ import print_function def method_1(boundary, steps): @@ -15,21 +15,21 @@ def method_1(boundary, steps): h = (boundary[1] - boundary[0]) / steps a = boundary[0] b = boundary[1] - x_i = makePoints(a,b,h) - y = 0.0 + x_i = make_points(a,b,h) + y = 0.0 y += (h/2.0)*f(a) for i in x_i: #print(i) y += h*f(i) - y += (h/2.0)*f(b) - return y + y += (h/2.0)*f(b) + return y -def makePoints(a,b,h): - x = a + h +def make_points(a,b,h): + x = a + h while x < (b-h): yield x x = x + h - + def f(x): #enter your function here y = (x-0)*(x-0) return y @@ -37,7 +37,7 @@ def f(x): #enter your function here def main(): a = 0.0 #Lower bound of integration b = 1.0 #Upper bound of integration - steps = 10.0 #define number of steps or resolution + steps = 10.0 #define number of steps or resolution boundary = [a, b] #define boundary of integration y = method_1(boundary, steps) print('y = {0}'.format(y)) diff --git a/maths/volume.py b/maths/volume.py new file mode 100644 index 000000000000..171bc538f5a4 --- /dev/null +++ b/maths/volume.py @@ -0,0 +1,100 @@ +""" +Find Volumes of Various Shapes. + +Wikipedia reference: https://en.wikipedia.org/wiki/Volume +""" + +from math import pi + +PI = pi + + +def vol_cube(side_length): + """Calculate the Volume of a Cube.""" + # Cube side_length. + return float(side_length ** 3) + + +def vol_cuboid(width, height, length): + """Calculate the Volume of a Cuboid.""" + # Multiply lengths together. + return float(width * height * length) + + +def vol_cone(area_of_base, height): + """ + Calculate the Volume of a Cone. + + Wikipedia reference: https://en.wikipedia.org/wiki/Cone + volume = (1/3) * area_of_base * height + """ + return (float(1) / 3) * area_of_base * height + + +def vol_right_circ_cone(radius, height): + """ + Calculate the Volume of a Right Circular Cone. + + Wikipedia reference: https://en.wikipedia.org/wiki/Cone + volume = (1/3) * pi * radius^2 * height + """ + + import math + + return (float(1) / 3) * PI * (radius ** 2) * height + + +def vol_prism(area_of_base, height): + """ + Calculate the Volume of a Prism. + + V = Bh + Wikipedia reference: https://en.wikipedia.org/wiki/Prism_(geometry) + """ + return float(area_of_base * height) + + +def vol_pyramid(area_of_base, height): + """ + Calculate the Volume of a Prism. + + V = (1/3) * Bh + Wikipedia reference: https://en.wikipedia.org/wiki/Pyramid_(geometry) + """ + return (float(1) / 3) * area_of_base * height + + +def vol_sphere(radius): + """ + Calculate the Volume of a Sphere. + + V = (4/3) * pi * r^3 + Wikipedia reference: https://en.wikipedia.org/wiki/Sphere + """ + return (float(4) / 3) * PI * radius ** 3 + + +def vol_circular_cylinder(radius, height): + """Calculate the Volume of a Circular Cylinder. + + Wikipedia reference: https://en.wikipedia.org/wiki/Cylinder + volume = pi * radius^2 * height + """ + return PI * radius ** 2 * height + + +def main(): + """Print the Results of Various Volume Calculations.""" + print("Volumes:") + print("Cube: " + str(vol_cube(2))) # = 8 + print("Cuboid: " + str(vol_cuboid(2, 2, 2))) # = 8 + print("Cone: " + str(vol_cone(2, 2))) # ~= 1.33 + print("Right Circular Cone: " + str(vol_right_circ_cone(2, 2))) # ~= 8.38 + print("Prism: " + str(vol_prism(2, 2))) # = 4 + print("Pyramid: " + str(vol_pyramid(2, 2))) # ~= 1.33 + print("Sphere: " + str(vol_sphere(2))) # ~= 33.5 + print("Circular Cylinder: " + str(vol_circular_cylinder(2, 2))) # ~= 25.1 + + +if __name__ == "__main__": + main() From 37fbd8ca2ed404767ad8514edd6e0330c0306a58 Mon Sep 17 00:00:00 2001 From: Sanders Lin <45224617+SandersLin@users.noreply.github.com> Date: Thu, 11 Jul 2019 04:38:10 +0800 Subject: [PATCH 062/193] Update average_median.py (#998) added doctest, fixed TypeError: list indices must be integers or slices, not float error due to number/2 producing float as index. --- maths/average_median.py | 43 +++++++++++++++++------------------------ 1 file changed, 18 insertions(+), 25 deletions(-) diff --git a/maths/average_median.py b/maths/average_median.py index 565bb4afd112..eab0107d8da8 100644 --- a/maths/average_median.py +++ b/maths/average_median.py @@ -1,41 +1,34 @@ -""" -Find median of a list of numbers. +def median(nums): + """ + Find median of a list of numbers. -Read more about medians: - https://en.wikipedia.org/wiki/Median -""" + >>> median([0]) + 0 + >>> median([4,1,3,2]) + 2.5 + Args: + nums: List of nums -def median(nums): - """Find median of a list of numbers.""" - # Sort list + Returns: + Median. + """ sorted_list = sorted(nums) - print("List of numbers:") - print(sorted_list) - - # Is number of items in list even? + med = None if len(sorted_list) % 2 == 0: - # Find index for first middle value. - mid_index_1 = len(sorted_list) / 2 - # Find index for second middle value. - mid_index_2 = -(len(sorted_list) / 2) - 1 - # Divide middle values by 2 to get average (mean). + mid_index_1 = len(sorted_list) // 2 + mid_index_2 = (len(sorted_list) // 2) - 1 med = (sorted_list[mid_index_1] + sorted_list[mid_index_2]) / float(2) - return med # Return makes `else:` unnecessary. - # Number of items is odd. - mid_index = (len(sorted_list) - 1) / 2 - # Middle index is median. - med = sorted_list[mid_index] + else: + mid_index = (len(sorted_list) - 1) // 2 + med = sorted_list[mid_index] return med - def main(): - """Call average module to find median of a specific list of numbers.""" print("Odd number of numbers:") print(median([2, 4, 6, 8, 20, 50, 70])) print("Even number of numbers:") print(median([2, 4, 6, 8, 20, 50])) - if __name__ == '__main__': main() From b79a197e8c05251cf9443cbc5c15bb66ae23f3d8 Mon Sep 17 00:00:00 2001 From: Sanders Lin <45224617+SandersLin@users.noreply.github.com> Date: Thu, 11 Jul 2019 12:43:03 +0800 Subject: [PATCH 063/193] Update abs_Max.py (#997) * Update abs_Max.py fix docstring for doctest to work properly (add space after >>>) * Update abs_Max.py --- maths/abs_Max.py | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/maths/abs_Max.py b/maths/abs_Max.py index 7ff9e4d3ca09..28f631f0100e 100644 --- a/maths/abs_Max.py +++ b/maths/abs_Max.py @@ -1,8 +1,10 @@ -def absMax(x): +from typing import List + +def abs_max(x: List[int]) -> int: """ - #>>>absMax([0,5,1,11]) + >>> abs_max([0,5,1,11]) 11 - >>absMax([3,-10,-2]) + >>> abs_max([3,-10,-2]) -10 """ j =x[0] @@ -11,15 +13,20 @@ def absMax(x): j = i return j +def abs_max_sort(x): + """ + >>> abs_max_sort([0,5,1,11]) + 11 + >>> abs_max_sort([3,-10,-2]) + -10 + """ + return sorted(x,key=abs)[-1] def main(): a = [1,2,-11] - print(absMax(a)) # = -11 - + assert abs_max(a) == -11 + assert abs_max_sort(a) == -11 if __name__ == '__main__': main() -""" -print abs Max -""" From 5f991f7740f8bbb801d5442e07f6e2881e3a7e1d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alfonso=20Rodr=C3=ADguez=20Pereira?= Date: Thu, 11 Jul 2019 11:16:42 +0200 Subject: [PATCH 064/193] #315 Renamed all files to snake_case (#993) --- ciphers/{Atbash.py => atbash.py} | 0 ...{morse_Code_implementation.py => morse_code_implementation.py} | 0 data_structures/binary_tree/{AVL_tree.py => avl_tree.py} | 0 data_structures/binary_tree/{LCA.py => lca.py} | 0 .../linked_list/{is_Palindrome.py => is_palindrome.py} | 0 .../{Fractional_Knapsack.py => fractional_knapsack.py} | 0 ...nce_O(nlogn).py => longest_increasing_subsequence_o(nlogn).py} | 0 graphs/{BFS.py => bfs.py} | 0 graphs/{DFS.py => dfs.py} | 0 ...hted)_Graph.py => directed_and_undirected_(weighted)_graph.py} | 0 ...graph.py => eulerian_path_and_circuit_for_undirected_graph.py} | 0 maths/{abs_Max.py => abs_max.py} | 0 maths/{abs_Min.py => abs_min.py} | 0 maths/{Binary_Exponentiation.py => binary_exponentiation.py} | 0 maths/{Find_Max.py => find_max.py} | 0 maths/{Find_Min.py => find_min.py} | 0 maths/{Prime_Check.py => prime_check.py} | 0 other/{finding_Primes.py => finding_primes.py} | 0 sorts/{Bitonic_Sort.py => bitonic_sort.py} | 0 ...ansposition_parallel.py => odd_even_transposition_parallel.py} | 0 ...ngle-threaded.py => odd_even_transposition_single_threaded.py} | 0 strings/{Boyer_Moore_Search.py => boyer_moore_search.py} | 0 strings/{naive_String_Search.py => naive_string_search.py} | 0 23 files changed, 0 insertions(+), 0 deletions(-) rename ciphers/{Atbash.py => atbash.py} (100%) rename ciphers/{morse_Code_implementation.py => morse_code_implementation.py} (100%) rename data_structures/binary_tree/{AVL_tree.py => avl_tree.py} (100%) rename data_structures/binary_tree/{LCA.py => lca.py} (100%) rename data_structures/linked_list/{is_Palindrome.py => is_palindrome.py} (100%) rename dynamic_programming/{Fractional_Knapsack.py => fractional_knapsack.py} (100%) rename dynamic_programming/{longest_increasing_subsequence_O(nlogn).py => longest_increasing_subsequence_o(nlogn).py} (100%) rename graphs/{BFS.py => bfs.py} (100%) rename graphs/{DFS.py => dfs.py} (100%) rename graphs/{Directed_and_Undirected_(Weighted)_Graph.py => directed_and_undirected_(weighted)_graph.py} (100%) rename graphs/{Eulerian_path_and_circuit_for_undirected_graph.py => eulerian_path_and_circuit_for_undirected_graph.py} (100%) rename maths/{abs_Max.py => abs_max.py} (100%) rename maths/{abs_Min.py => abs_min.py} (100%) rename maths/{Binary_Exponentiation.py => binary_exponentiation.py} (100%) rename maths/{Find_Max.py => find_max.py} (100%) rename maths/{Find_Min.py => find_min.py} (100%) rename maths/{Prime_Check.py => prime_check.py} (100%) rename other/{finding_Primes.py => finding_primes.py} (100%) rename sorts/{Bitonic_Sort.py => bitonic_sort.py} (100%) rename sorts/{Odd-Even_transposition_parallel.py => odd_even_transposition_parallel.py} (100%) rename sorts/{Odd-Even_transposition_single-threaded.py => odd_even_transposition_single_threaded.py} (100%) rename strings/{Boyer_Moore_Search.py => boyer_moore_search.py} (100%) rename strings/{naive_String_Search.py => naive_string_search.py} (100%) diff --git a/ciphers/Atbash.py b/ciphers/atbash.py similarity index 100% rename from ciphers/Atbash.py rename to ciphers/atbash.py diff --git a/ciphers/morse_Code_implementation.py b/ciphers/morse_code_implementation.py similarity index 100% rename from ciphers/morse_Code_implementation.py rename to ciphers/morse_code_implementation.py diff --git a/data_structures/binary_tree/AVL_tree.py b/data_structures/binary_tree/avl_tree.py similarity index 100% rename from data_structures/binary_tree/AVL_tree.py rename to data_structures/binary_tree/avl_tree.py diff --git a/data_structures/binary_tree/LCA.py b/data_structures/binary_tree/lca.py similarity index 100% rename from data_structures/binary_tree/LCA.py rename to data_structures/binary_tree/lca.py diff --git a/data_structures/linked_list/is_Palindrome.py b/data_structures/linked_list/is_palindrome.py similarity index 100% rename from data_structures/linked_list/is_Palindrome.py rename to data_structures/linked_list/is_palindrome.py diff --git a/dynamic_programming/Fractional_Knapsack.py b/dynamic_programming/fractional_knapsack.py similarity index 100% rename from dynamic_programming/Fractional_Knapsack.py rename to dynamic_programming/fractional_knapsack.py diff --git a/dynamic_programming/longest_increasing_subsequence_O(nlogn).py b/dynamic_programming/longest_increasing_subsequence_o(nlogn).py similarity index 100% rename from dynamic_programming/longest_increasing_subsequence_O(nlogn).py rename to dynamic_programming/longest_increasing_subsequence_o(nlogn).py diff --git a/graphs/BFS.py b/graphs/bfs.py similarity index 100% rename from graphs/BFS.py rename to graphs/bfs.py diff --git a/graphs/DFS.py b/graphs/dfs.py similarity index 100% rename from graphs/DFS.py rename to graphs/dfs.py diff --git a/graphs/Directed_and_Undirected_(Weighted)_Graph.py b/graphs/directed_and_undirected_(weighted)_graph.py similarity index 100% rename from graphs/Directed_and_Undirected_(Weighted)_Graph.py rename to graphs/directed_and_undirected_(weighted)_graph.py diff --git a/graphs/Eulerian_path_and_circuit_for_undirected_graph.py b/graphs/eulerian_path_and_circuit_for_undirected_graph.py similarity index 100% rename from graphs/Eulerian_path_and_circuit_for_undirected_graph.py rename to graphs/eulerian_path_and_circuit_for_undirected_graph.py diff --git a/maths/abs_Max.py b/maths/abs_max.py similarity index 100% rename from maths/abs_Max.py rename to maths/abs_max.py diff --git a/maths/abs_Min.py b/maths/abs_min.py similarity index 100% rename from maths/abs_Min.py rename to maths/abs_min.py diff --git a/maths/Binary_Exponentiation.py b/maths/binary_exponentiation.py similarity index 100% rename from maths/Binary_Exponentiation.py rename to maths/binary_exponentiation.py diff --git a/maths/Find_Max.py b/maths/find_max.py similarity index 100% rename from maths/Find_Max.py rename to maths/find_max.py diff --git a/maths/Find_Min.py b/maths/find_min.py similarity index 100% rename from maths/Find_Min.py rename to maths/find_min.py diff --git a/maths/Prime_Check.py b/maths/prime_check.py similarity index 100% rename from maths/Prime_Check.py rename to maths/prime_check.py diff --git a/other/finding_Primes.py b/other/finding_primes.py similarity index 100% rename from other/finding_Primes.py rename to other/finding_primes.py diff --git a/sorts/Bitonic_Sort.py b/sorts/bitonic_sort.py similarity index 100% rename from sorts/Bitonic_Sort.py rename to sorts/bitonic_sort.py diff --git a/sorts/Odd-Even_transposition_parallel.py b/sorts/odd_even_transposition_parallel.py similarity index 100% rename from sorts/Odd-Even_transposition_parallel.py rename to sorts/odd_even_transposition_parallel.py diff --git a/sorts/Odd-Even_transposition_single-threaded.py b/sorts/odd_even_transposition_single_threaded.py similarity index 100% rename from sorts/Odd-Even_transposition_single-threaded.py rename to sorts/odd_even_transposition_single_threaded.py diff --git a/strings/Boyer_Moore_Search.py b/strings/boyer_moore_search.py similarity index 100% rename from strings/Boyer_Moore_Search.py rename to strings/boyer_moore_search.py diff --git a/strings/naive_String_Search.py b/strings/naive_string_search.py similarity index 100% rename from strings/naive_String_Search.py rename to strings/naive_string_search.py From c2f2fa8b231999905564006cc3dd2db55de5ecc7 Mon Sep 17 00:00:00 2001 From: Sanders Lin <45224617+SandersLin@users.noreply.github.com> Date: Fri, 12 Jul 2019 00:20:41 +0800 Subject: [PATCH 065/193] Update abs_Min.py (#1004) * Update abs_Min.py * Create __init__.py * Rename abs_Min.py to abs_min.py * Update abs_min.py --- maths/__init__.py | 1 + maths/abs_min.py | 5 +++-- 2 files changed, 4 insertions(+), 2 deletions(-) create mode 100644 maths/__init__.py diff --git a/maths/__init__.py b/maths/__init__.py new file mode 100644 index 000000000000..8b137891791f --- /dev/null +++ b/maths/__init__.py @@ -0,0 +1 @@ + diff --git a/maths/abs_min.py b/maths/abs_min.py index 67d510551907..d546196aa1b5 100644 --- a/maths/abs_min.py +++ b/maths/abs_min.py @@ -1,4 +1,5 @@ -from Maths.abs import absVal +from abs import abs_val + def absMin(x): """ # >>>absMin([0,5,1,11]) @@ -8,7 +9,7 @@ def absMin(x): """ j = x[0] for i in x: - if absVal(i) < absVal(j): + if abs_val(i) < abs_val(j): j = i return j From f2eb965604ef704d1ffc1a79acaba8b8c58e204b Mon Sep 17 00:00:00 2001 From: FrogBattle <44649323+FrogBattle@users.noreply.github.com> Date: Thu, 11 Jul 2019 17:21:48 +0100 Subject: [PATCH 066/193] Update ~script.py (#990) Changing the boolean expression avoids the use of a continue statement. This way the code becomes easier/faster to compute on lower level and it has a better coding style. --- ~script.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/~script.py b/~script.py index c44f3436fcec..1fbb1e838c62 100644 --- a/~script.py +++ b/~script.py @@ -20,16 +20,15 @@ def _markdown(parent, ignores, ignores_ext, depth): for i in os.listdir(parent): full = os.path.join(parent, i) name, ext = os.path.splitext(i) - if i in ignores or ext in ignores_ext: - continue - if os.path.isfile(full): - # generate list - pre = parent.replace("./", "").replace(" ", "%20") - # replace all spaces to safe URL - child = i.replace(" ", "%20") - files.append((pre, child, name)) - else: - dirs.append(i) + if i not in ignores and ext not in ignores_ext: + if os.path.isfile(full): + # generate list + pre = parent.replace("./", "").replace(" ", "%20") + # replace all spaces to safe URL + child = i.replace(" ", "%20") + files.append((pre, child, name)) + else: + dirs.append(i) # Sort files files.sort(key=lambda e: e[2].lower()) for f in files: From 1dc9ec8fb2c51372800762b8e3f734eb93ec1814 Mon Sep 17 00:00:00 2001 From: obelisk0114 Date: Fri, 12 Jul 2019 08:16:14 -0700 Subject: [PATCH 067/193] Update Bucket Sort time complexity analysis (#918) --- sorts/bucket_sort.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/sorts/bucket_sort.py b/sorts/bucket_sort.py index 5c4a71513ed3..0678b1194657 100644 --- a/sorts/bucket_sort.py +++ b/sorts/bucket_sort.py @@ -17,7 +17,12 @@ # number of buckets. # Time Complexity of Solution: -# Best Case O(n); Average Case O(n); Worst Case O(n) +# Worst case scenario occurs when all the elements are placed in a single bucket. The overall performance +# would then be dominated by the algorithm used to sort each bucket. In this case, O(n log n), because of TimSort +# +# Average Case O(n + (n^2)/k + k), where k is the number of buckets +# +# If k = O(n), time complexity is O(n) DEFAULT_BUCKET_SIZE = 5 From 1e0b33d3dd7eda32a97fe73df09df20e2004eb98 Mon Sep 17 00:00:00 2001 From: Sanders Lin <45224617+SandersLin@users.noreply.github.com> Date: Sat, 13 Jul 2019 15:04:43 +0800 Subject: [PATCH 068/193] Update 3n+1.py (#996) * Update 3n+1.py Made variable names more meaningful and removed nested functions. * Update 3n+1.py * Update 3n+1.py * Update 3n+1.py --- maths/3n+1.py | 41 ++++++++++++++++++++++++++--------------- 1 file changed, 26 insertions(+), 15 deletions(-) diff --git a/maths/3n+1.py b/maths/3n+1.py index 6424fe0d8f15..d6c14ff0f47d 100644 --- a/maths/3n+1.py +++ b/maths/3n+1.py @@ -1,19 +1,30 @@ -def main(): - def n31(a):# a = initial number - c = 0 - l = [a] - while a != 1: - if a % 2 == 0:#if even divide it by 2 - a = a // 2 - elif a % 2 == 1:#if odd 3n+1 - a = 3*a +1 - c += 1#counter - l += [a] +from typing import Tuple, List + +def n31(a: int) -> Tuple[List[int], int]: + """ + Returns the Collatz sequence and its length of any postiver integer. + >>> n31(4) + ([4, 2, 1], 3) + """ - return l , c - print(n31(43)) - print(n31(98)[0][-1])# = a - print("It took {0} steps.".format(n31(13)[1]))#optional finish + if not isinstance(a, int): + raise TypeError('Must be int, not {0}'.format(type(a).__name__)) + if a < 1: + raise ValueError('Given integer must be greater than 1, not {0}'.format(a)) + + path = [a] + while a != 1: + if a % 2 == 0: + a = a // 2 + else: + a = 3*a +1 + path += [a] + return path, len(path) + +def main(): + num = 4 + path , length = n31(num) + print("The Collatz sequence of {0} took {1} steps. \nPath: {2}".format(num,length, path)) if __name__ == '__main__': main() From 7a6ebb85a2e8eda43131803f2104cdbba07cb164 Mon Sep 17 00:00:00 2001 From: Sanders Lin <45224617+SandersLin@users.noreply.github.com> Date: Sat, 13 Jul 2019 15:10:02 +0800 Subject: [PATCH 069/193] Update edit_distance.py (#1001) added bottom up method. --- dynamic_programming/edit_distance.py | 34 ++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/dynamic_programming/edit_distance.py b/dynamic_programming/edit_distance.py index 335e5196ed53..b71f80d68935 100644 --- a/dynamic_programming/edit_distance.py +++ b/dynamic_programming/edit_distance.py @@ -52,6 +52,35 @@ def solve(self, A, B): return self.__solveDP(len(A)-1, len(B)-1) + +def min_distance_bottom_up(word1: str, word2: str) -> int: + """ + >>> min_distance_bottom_up("intention", "execution") + 5 + >>> min_distance_bottom_up("intention", "") + 9 + >>> min_distance_bottom_up("", "") + 0 + """ + m = len(word1) + n = len(word2) + dp = [[0 for _ in range(n+1) ] for _ in range(m+1)] + for i in range(m+1): + for j in range(n+1): + + if i == 0: #first string is empty + dp[i][j] = j + elif j == 0: #second string is empty + dp[i][j] = i + elif word1[i-1] == word2[j-1]: #last character of both substing is equal + dp[i][j] = dp[i-1][j-1] + else: + insert = dp[i][j-1] + delete = dp[i-1][j] + replace = dp[i-1][j-1] + dp[i][j] = 1 + min(insert, delete, replace) + return dp[m][n] + if __name__ == '__main__': try: raw_input # Python 2 @@ -71,5 +100,10 @@ def solve(self, A, B): print() print("The minimum Edit Distance is: %d" % (solver.solve(S1, S2))) + print("The minimum Edit Distance is: %d" % (min_distance_bottom_up(S1, S2))) print() print("*************** End of Testing Edit Distance DP Algorithm ***************") + + + + From 7271c0d64acf4881636f08c4c2dcb00cbe757798 Mon Sep 17 00:00:00 2001 From: Sanders Lin <45224617+SandersLin@users.noreply.github.com> Date: Sat, 13 Jul 2019 15:12:54 +0800 Subject: [PATCH 070/193] Update rod_cutting.py (#995) * Update rod_cutting.py A hopefully clearer implementation without dependence on global variables. * Update rod_cutting.py added doctests * Update rod_cutting.py * Update rod_cutting.py --- dynamic_programming/rod_cutting.py | 115 ++++++++++++++--------------- 1 file changed, 57 insertions(+), 58 deletions(-) diff --git a/dynamic_programming/rod_cutting.py b/dynamic_programming/rod_cutting.py index 34350cb8202b..c3111dcfc8a1 100644 --- a/dynamic_programming/rod_cutting.py +++ b/dynamic_programming/rod_cutting.py @@ -1,58 +1,57 @@ -### PROBLEM ### -""" -We are given a rod of length n and we are given the array of prices, also of -length n. This array contains the price for selling a rod at a certain length. -For example, prices[5] shows the price we can sell a rod of length 5. -Generalising, prices[x] shows the price a rod of length x can be sold. -We are tasked to find the optimal solution to sell the given rod. -""" - -### SOLUTION ### -""" -Profit(n) = max(1 m): - m = yesCut[i] - - solutions[n] = m - return m - - - -### EXAMPLE ### -length = 5 -#The first price, 0, is for when we have no rod. -prices = [0, 1, 3, 7, 9, 11, 13, 17, 21, 21, 30] -solutions = [-1 for x in range(length+1)] - -print(CutRod(length)) +from typing import List + +def rod_cutting(prices: List[int],length: int) -> int: + """ + Given a rod of length n and array of prices that indicate price at each length. + Determine the maximum value obtainable by cutting up the rod and selling the pieces + + >>> rod_cutting([1,5,8,9],4) + 10 + >>> rod_cutting([1,1,1],3) + 3 + >>> rod_cutting([1,2,3], -1) + Traceback (most recent call last): + ValueError: Given integer must be greater than 1, not -1 + >>> rod_cutting([1,2,3], 3.2) + Traceback (most recent call last): + TypeError: Must be int, not float + >>> rod_cutting([], 3) + Traceback (most recent call last): + AssertionError: prices list is shorted than length: 3 + + + + Args: + prices: list indicating price at each length, where prices[0] = 0 indicating rod of zero length has no value + length: length of rod + + Returns: + Maximum revenue attainable by cutting up the rod in any way. + """ + + prices.insert(0, 0) + if not isinstance(length, int): + raise TypeError('Must be int, not {0}'.format(type(length).__name__)) + if length < 0: + raise ValueError('Given integer must be greater than 1, not {0}'.format(length)) + assert len(prices) - 1 >= length, "prices list is shorted than length: {0}".format(length) + + return rod_cutting_recursive(prices, length) + +def rod_cutting_recursive(prices: List[int],length: int) -> int: + #base case + if length == 0: + return 0 + value = float('-inf') + for firstCutLocation in range(1,length+1): + value = max(value, prices[firstCutLocation]+rod_cutting_recursive(prices,length - firstCutLocation)) + return value + + +def main(): + assert rod_cutting([1,5,8,9,10,17,17,20,24,30],10) == 30 + # print(rod_cutting([],0)) + +if __name__ == '__main__': + main() + From d72586c5f4164ed2b518d6badea9d9a89e756689 Mon Sep 17 00:00:00 2001 From: Hector S Date: Sat, 13 Jul 2019 15:50:37 -0400 Subject: [PATCH 071/193] Updated ~script.py per #978 (#1013) * Added print function into matrix_multiplication_addition.py and removed blank space in data_structures/binary tree directory * Removed .vs/ folder per #893 * Rename matrix_multiplication_addition.py to matrix_operation.py * Moved and renamed ~script.py to scripts/build_directory_md.py Updated DIRECTORY.MD file * Modified .travis.yml per suggestion in #1013 * Fixed typo per suggestions in #1013 --- .travis.yml | 2 +- DIRECTORY.md | 123 +++++++++++--------- ~script.py => scripts/build_directory_md.py | 4 +- 3 files changed, 73 insertions(+), 56 deletions(-) rename ~script.py => scripts/build_directory_md.py (97%) diff --git a/.travis.yml b/.travis.yml index 0e35fd084268..9afc0c93a037 100644 --- a/.travis.yml +++ b/.travis.yml @@ -32,5 +32,5 @@ script: traversals after_success: - - python ./~script.py + - python scripts/build_directory_md.py - cat DIRECTORY.md diff --git a/DIRECTORY.md b/DIRECTORY.md index befd634c1eb0..66128228abc3 100644 --- a/DIRECTORY.md +++ b/DIRECTORY.md @@ -4,13 +4,17 @@ * [lu decomposition](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/lu_decomposition.py) * [newton method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_method.py) * [newton raphson method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_raphson_method.py) -## Binary Tree - * [basic binary tree](https://github.com/TheAlgorithms/Python/blob/master/binary_tree/basic_binary_tree.py) +## Backtracking + * [all permutations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_permutations.py) + * [all subsequences](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_subsequences.py) + * [minimax](https://github.com/TheAlgorithms/Python/blob/master/backtracking/minimax.py) + * [n queens](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens.py) + * [sum of subsets](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sum_of_subsets.py) ## Boolean Algebra * [quine mc cluskey](https://github.com/TheAlgorithms/Python/blob/master/boolean_algebra/quine_mc_cluskey.py) ## Ciphers * [affine cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/affine_cipher.py) - * [Atbash](https://github.com/TheAlgorithms/Python/blob/master/ciphers/Atbash.py) + * [atbash](https://github.com/TheAlgorithms/Python/blob/master/ciphers/atbash.py) * [base16](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base16.py) * [base32](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base32.py) * [base64 cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base64_cipher.py) @@ -20,7 +24,7 @@ * [cryptomath module](https://github.com/TheAlgorithms/Python/blob/master/ciphers/cryptomath_module.py) * [elgamal key generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/elgamal_key_generator.py) * [hill cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/hill_cipher.py) - * [morse Code implementation](https://github.com/TheAlgorithms/Python/blob/master/ciphers/morse_Code_implementation.py) + * [morse code implementation](https://github.com/TheAlgorithms/Python/blob/master/ciphers/morse_code_implementation.py) * [onepad cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/onepad_cipher.py) * [playfair cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/playfair_cipher.py) * [prehistoric men](https://github.com/TheAlgorithms/Python/blob/master/ciphers/prehistoric_men.txt) @@ -36,17 +40,21 @@ * [xor cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/xor_cipher.py) ## Compression * [huffman](https://github.com/TheAlgorithms/Python/blob/master/compression/huffman.py) -## Compression Analysis - * [psnr](https://github.com/TheAlgorithms/Python/blob/master/compression_analysis/psnr.py) + * [peak signal to noise ratio](https://github.com/TheAlgorithms/Python/blob/master/compression/peak_signal_to_noise_ratio.py) + * Image Data +## Conversions + * [decimal to binary](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary.py) + * [decimal to hexadecimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_hexadecimal.py) + * [decimal to octal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_octal.py) ## Data Structures - * [arrays](https://github.com/TheAlgorithms/Python/blob/master/data_structures/arrays.py) - * [avl](https://github.com/TheAlgorithms/Python/blob/master/data_structures/avl.py) - * [LCA](https://github.com/TheAlgorithms/Python/blob/master/data_structures/LCA.py) * Binary Tree - * [AVL tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/AVL_tree.py) + * [avl tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/avl_tree.py) + * [basic binary tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/basic_binary_tree.py) * [binary search tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_search_tree.py) * [fenwick tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/fenwick_tree.py) * [lazy segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lazy_segment_tree.py) + * [lca](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lca.py) + * [red black tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/red_black_tree.py) * [segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/segment_tree.py) * [treap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/treap.py) * Hashing @@ -60,9 +68,9 @@ * [heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap.py) * Linked List * [doubly linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/doubly_linked_list.py) - * [is Palindrome](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/is_Palindrome.py) + * [is palindrome](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/is_palindrome.py) * [singly linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/singly_linked_list.py) - * [swapNodes](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/swapNodes.py) + * [swap nodes](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/swap_nodes.py) * Queue * [double ended queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/double_ended_queue.py) * [queue on list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_list.py) @@ -71,18 +79,24 @@ * [balanced parentheses](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/balanced_parentheses.py) * [infix to postfix conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_postfix_conversion.py) * [infix to prefix conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_prefix_conversion.py) - * [next](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/next.py) + * [next greater element](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/next_greater_element.py) * [postfix evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/postfix_evaluation.py) * [stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack.py) * [stock span problem](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stock_span_problem.py) * Trie * [trie](https://github.com/TheAlgorithms/Python/blob/master/data_structures/trie/trie.py) - * Union Find - * [tests union find](https://github.com/TheAlgorithms/Python/blob/master/data_structures/union_find/tests_union_find.py) - * [union find](https://github.com/TheAlgorithms/Python/blob/master/data_structures/union_find/union_find.py) ## Digital Image Processing + * Edge Detection + * [canny](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/edge_detection/canny.py) * Filters + * [convolve](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/convolve.py) + * [gaussian filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/gaussian_filter.py) * [median filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/median_filter.py) + * [sobel filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/sobel_filter.py) + * Image Data +## Divide And Conquer + * [closest pair of points](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/closest_pair_of_points.py) + * [max subarray sum](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/max_subarray_sum.py) ## Dynamic Programming * [abbreviation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/abbreviation.py) * [bitmask](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/bitmask.py) @@ -91,19 +105,20 @@ * [fast fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fast_fibonacci.py) * [fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fibonacci.py) * [floyd warshall](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/floyd_warshall.py) - * [Fractional Knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/Fractional_Knapsack.py) + * [fractional knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fractional_knapsack.py) * [integer partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/integer_partition.py) * [k means clustering tensorflow](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/k_means_clustering_tensorflow.py) * [knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/knapsack.py) * [longest common subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_common_subsequence.py) * [longest increasing subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence.py) - * [longest increasing subsequence O(nlogn)](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence_O(nlogn).py) + * [longest increasing subsequence o(nlogn)](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence_o(nlogn).py) * [longest sub array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_sub_array.py) * [matrix chain order](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/matrix_chain_order.py) * [max sub array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sub_array.py) * [minimum partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_partition.py) * [rod cutting](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/rod_cutting.py) * [subset generation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/subset_generation.py) + * [sum of subset](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/sum_of_subset.py) ## File Transfer Protocol * [ftp client server](https://github.com/TheAlgorithms/Python/blob/master/file_transfer_protocol/ftp_client_server.py) * [ftp send receive](https://github.com/TheAlgorithms/Python/blob/master/file_transfer_protocol/ftp_send_receive.py) @@ -112,19 +127,19 @@ * [articulation points](https://github.com/TheAlgorithms/Python/blob/master/graphs/articulation_points.py) * [basic graphs](https://github.com/TheAlgorithms/Python/blob/master/graphs/basic_graphs.py) * [bellman ford](https://github.com/TheAlgorithms/Python/blob/master/graphs/bellman_ford.py) - * [BFS](https://github.com/TheAlgorithms/Python/blob/master/graphs/BFS.py) + * [bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs.py) * [bfs shortest path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_shortest_path.py) * [breadth first search](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search.py) * [check bipartite graph bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_bfs.py) * [check bipartite graph dfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_dfs.py) * [depth first search](https://github.com/TheAlgorithms/Python/blob/master/graphs/depth_first_search.py) - * [DFS](https://github.com/TheAlgorithms/Python/blob/master/graphs/DFS.py) + * [dfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/dfs.py) * [dijkstra](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra.py) * [dijkstra 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_2.py) * [dijkstra algorithm](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_algorithm.py) - * [Directed and Undirected (Weighted) Graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/Directed_and_Undirected_(Weighted)_Graph.py) + * [directed and undirected (weighted) graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/directed_and_undirected_(weighted)_graph.py) * [edmonds karp multiple source and sink](https://github.com/TheAlgorithms/Python/blob/master/graphs/edmonds_karp_multiple_source_and_sink.py) - * [Eulerian path and circuit for undirected graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/Eulerian_path_and_circuit_for_undirected_graph.py) + * [eulerian path and circuit for undirected graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/eulerian_path_and_circuit_for_undirected_graph.py) * [even tree](https://github.com/TheAlgorithms/Python/blob/master/graphs/even_tree.py) * [finding bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py) * [floyd warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/floyd_warshall.py) @@ -141,6 +156,7 @@ * [tarjans scc](https://github.com/TheAlgorithms/Python/blob/master/graphs/tarjans_scc.py) ## Hashes * [chaos machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/chaos_machine.py) + * [enigma machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/enigma_machine.py) * [md5](https://github.com/TheAlgorithms/Python/blob/master/hashes/md5.py) * [sha1](https://github.com/TheAlgorithms/Python/blob/master/hashes/sha1.py) ## Linear Algebra Python @@ -151,24 +167,26 @@ * [decision tree](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/decision_tree.py) * [gradient descent](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_descent.py) * [k means clust](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_means_clust.py) + * [knn sklearn](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/knn_sklearn.py) * [linear regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_regression.py) * [logistic regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/logistic_regression.py) * [perceptron](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/perceptron.py) * [scoring functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/scoring_functions.py) * Random Forest Classification - * [random forest classification](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/Random%20Forest%20Classification/random_forest_classification.py) - * [Social Network Ads](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/Random%20Forest%20Classification/Social_Network_Ads.csv) + * [random forest classification](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classification/random_forest_classification.py) + * [Social Network Ads](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classification/Social_Network_Ads.csv) * Random Forest Regression - * [Position Salaries](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/Random%20Forest%20Regression/Position_Salaries.csv) - * [random forest regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/Random%20Forest%20Regression/random_forest_regression.py) + * [Position Salaries](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression/Position_Salaries.csv) + * [random forest regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression/random_forest_regression.py) ## Maths * [3n+1](https://github.com/TheAlgorithms/Python/blob/master/maths/3n+1.py) * [abs](https://github.com/TheAlgorithms/Python/blob/master/maths/abs.py) - * [abs Max](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_Max.py) - * [abs Min](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_Min.py) - * [average](https://github.com/TheAlgorithms/Python/blob/master/maths/average.py) + * [abs max](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_max.py) + * [abs min](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_min.py) + * [average mean](https://github.com/TheAlgorithms/Python/blob/master/maths/average_mean.py) + * [average median](https://github.com/TheAlgorithms/Python/blob/master/maths/average_median.py) * [basic maths](https://github.com/TheAlgorithms/Python/blob/master/maths/basic_maths.py) - * [Binary Exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/Binary_Exponentiation.py) + * [binary exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation.py) * [extended euclidean algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/extended_euclidean_algorithm.py) * [factorial python](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_python.py) * [factorial recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_recursive.py) @@ -176,29 +194,27 @@ * [fibonacci](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci.py) * [fibonacci sequence recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci_sequence_recursion.py) * [find lcm](https://github.com/TheAlgorithms/Python/blob/master/maths/find_lcm.py) - * [Find Max](https://github.com/TheAlgorithms/Python/blob/master/maths/Find_Max.py) - * [Find Min](https://github.com/TheAlgorithms/Python/blob/master/maths/Find_Min.py) + * [find max](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max.py) + * [find min](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min.py) * [greater common divisor](https://github.com/TheAlgorithms/Python/blob/master/maths/greater_common_divisor.py) - * [Hanoi](https://github.com/TheAlgorithms/Python/blob/master/maths/Hanoi.py) - * [lucasSeries](https://github.com/TheAlgorithms/Python/blob/master/maths/lucasSeries.py) + * [lucas series](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas%20series.py) * [modular exponential](https://github.com/TheAlgorithms/Python/blob/master/maths/modular_exponential.py) * [newton raphson](https://github.com/TheAlgorithms/Python/blob/master/maths/newton_raphson.py) - * [Prime Check](https://github.com/TheAlgorithms/Python/blob/master/maths/Prime_Check.py) + * [prime check](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_check.py) * [segmented sieve](https://github.com/TheAlgorithms/Python/blob/master/maths/segmented_sieve.py) * [sieve of eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/sieve_of_eratosthenes.py) * [simpson rule](https://github.com/TheAlgorithms/Python/blob/master/maths/simpson_rule.py) * [trapezoidal rule](https://github.com/TheAlgorithms/Python/blob/master/maths/trapezoidal_rule.py) - * Tests - * [test fibonacci](https://github.com/TheAlgorithms/Python/blob/master/maths/tests/test_fibonacci.py) + * [volume](https://github.com/TheAlgorithms/Python/blob/master/maths/volume.py) ## Matrix * [matrix operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_operation.py) * [searching in sorted matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/searching_in_sorted_matrix.py) - * [spiralPrint](https://github.com/TheAlgorithms/Python/blob/master/matrix/spiralPrint.py) + * [spiral print](https://github.com/TheAlgorithms/Python/blob/master/matrix/spiral_print.py) ## Networking Flow * [ford fulkerson](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/ford_fulkerson.py) * [minimum cut](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/minimum_cut.py) ## Neural Network - * [bpnn](https://github.com/TheAlgorithms/Python/blob/master/neural_network/bpnn.py) + * [back propagation neural network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/back_propagation_neural_network.py) * [convolution neural network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/convolution_neural_network.py) * [perceptron](https://github.com/TheAlgorithms/Python/blob/master/neural_network/perceptron.py) ## Other @@ -208,11 +224,11 @@ * [detecting english programmatically](https://github.com/TheAlgorithms/Python/blob/master/other/detecting_english_programmatically.py) * [dictionary](https://github.com/TheAlgorithms/Python/blob/master/other/dictionary.txt) * [euclidean gcd](https://github.com/TheAlgorithms/Python/blob/master/other/euclidean_gcd.py) - * [finding Primes](https://github.com/TheAlgorithms/Python/blob/master/other/finding_Primes.py) + * [finding primes](https://github.com/TheAlgorithms/Python/blob/master/other/finding_primes.py) * [fischer yates shuffle](https://github.com/TheAlgorithms/Python/blob/master/other/fischer_yates_shuffle.py) * [frequency finder](https://github.com/TheAlgorithms/Python/blob/master/other/frequency_finder.py) + * [game of life](https://github.com/TheAlgorithms/Python/blob/master/other/game_of_life.py) * [linear congruential generator](https://github.com/TheAlgorithms/Python/blob/master/other/linear_congruential_generator.py) - * [n queens](https://github.com/TheAlgorithms/Python/blob/master/other/n_queens.py) * [nested brackets](https://github.com/TheAlgorithms/Python/blob/master/other/nested_brackets.py) * [palindrome](https://github.com/TheAlgorithms/Python/blob/master/other/palindrome.py) * [password generator](https://github.com/TheAlgorithms/Python/blob/master/other/password_generator.py) @@ -222,9 +238,8 @@ * [two sum](https://github.com/TheAlgorithms/Python/blob/master/other/two_sum.py) * [word patterns](https://github.com/TheAlgorithms/Python/blob/master/other/word_patterns.py) * [words](https://github.com/TheAlgorithms/Python/blob/master/other/words) - * Game Of Life - * [game o life](https://github.com/TheAlgorithms/Python/blob/master/other/game_of_life/game_o_life.py) - * [sample](https://github.com/TheAlgorithms/Python/blob/master/other/game_of_life/sample.gif) + * Pycache + * [password generator.cpython-37](https://github.com/TheAlgorithms/Python/blob/master/other/__pycache__/password_generator.cpython-37.pyc) ## Project Euler * Problem 01 * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol1.py) @@ -273,7 +288,9 @@ * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_12/sol1.py) * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_12/sol2.py) * Problem 13 + * [num](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_13/num.txt) * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_13/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_13/sol2.py) * Problem 14 * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_14/sol1.py) * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_14/sol2.py) @@ -281,6 +298,7 @@ * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_15/sol1.py) * Problem 16 * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_16/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_16/sol2.py) * Problem 17 * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_17/sol1.py) * Problem 19 @@ -319,6 +337,8 @@ * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_53/sol1.py) * Problem 76 * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_76/sol1.py) +## Scripts + * [build directory md](https://github.com/TheAlgorithms/Python/blob/master/scripts/build_directory_md.py) ## Searches * [binary search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py) * [interpolation search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py) @@ -329,13 +349,8 @@ * [tabu search](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_search.py) * [tabu test data](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_test_data.txt) * [ternary search](https://github.com/TheAlgorithms/Python/blob/master/searches/ternary_search.py) - * [test interpolation search](https://github.com/TheAlgorithms/Python/blob/master/searches/test_interpolation_search.py) - * [test tabu search](https://github.com/TheAlgorithms/Python/blob/master/searches/test_tabu_search.py) -## Simple Client - * [client](https://github.com/TheAlgorithms/Python/blob/master/simple_client/client.py) - * [server](https://github.com/TheAlgorithms/Python/blob/master/simple_client/server.py) ## Sorts - * [Bitonic Sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/Bitonic_Sort.py) + * [bitonic sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bitonic_sort.py) * [bogo sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bogo_sort.py) * [bubble sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bubble_sort.py) * [bucket sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bucket_sort.py) @@ -349,8 +364,8 @@ * [insertion sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/insertion_sort.py) * [merge sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort.py) * [merge sort fastest](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort_fastest.py) - * [Odd-Even transposition parallel](https://github.com/TheAlgorithms/Python/blob/master/sorts/Odd-Even_transposition_parallel.py) - * [Odd-Even transposition single-threaded](https://github.com/TheAlgorithms/Python/blob/master/sorts/Odd-Even_transposition_single-threaded.py) + * [odd even transposition parallel](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_parallel.py) + * [odd even transposition single threaded](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_single_threaded.py) * [pancake sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pancake_sort.py) * [pigeon sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pigeon_sort.py) * [quick sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort.py) @@ -360,17 +375,17 @@ * [random pivot quick sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_pivot_quick_sort.py) * [selection sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/selection_sort.py) * [shell sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/shell_sort.py) - * [tests](https://github.com/TheAlgorithms/Python/blob/master/sorts/tests.py) * [tim sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tim_sort.py) * [topological sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/topological_sort.py) * [tree sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tree_sort.py) * [wiggle sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/wiggle_sort.py) ## Strings + * [boyer moore search](https://github.com/TheAlgorithms/Python/blob/master/strings/boyer_moore_search.py) * [knuth morris pratt](https://github.com/TheAlgorithms/Python/blob/master/strings/knuth_morris_pratt.py) * [levenshtein distance](https://github.com/TheAlgorithms/Python/blob/master/strings/levenshtein_distance.py) * [manacher](https://github.com/TheAlgorithms/Python/blob/master/strings/manacher.py) * [min cost string conversion](https://github.com/TheAlgorithms/Python/blob/master/strings/min_cost_string_conversion.py) - * [naive String Search](https://github.com/TheAlgorithms/Python/blob/master/strings/naive_String_Search.py) + * [naive string search](https://github.com/TheAlgorithms/Python/blob/master/strings/naive_string_search.py) * [rabin karp](https://github.com/TheAlgorithms/Python/blob/master/strings/rabin_karp.py) ## Traversals * [binary tree traversals](https://github.com/TheAlgorithms/Python/blob/master/traversals/binary_tree_traversals.py) diff --git a/~script.py b/scripts/build_directory_md.py similarity index 97% rename from ~script.py rename to scripts/build_directory_md.py index 1fbb1e838c62..47192701880d 100644 --- a/~script.py +++ b/scripts/build_directory_md.py @@ -51,8 +51,10 @@ def _markdown(parent, ignores, ignores_ext, depth): ignores = [".vs", ".gitignore", ".git", - "~script.py", + "scripts", "__init__.py", + "requirements.txt", + ".github" ] # Files with given entensions will be ignored ignores_ext = [ From 0d615398830e8a6fd1c3aa4bd6896de1b8200561 Mon Sep 17 00:00:00 2001 From: Rakshit Parashar <34675136+rishu2403@users.noreply.github.com> Date: Sat, 13 Jul 2019 12:54:38 -0700 Subject: [PATCH 072/193] Log_likelihood update (#1008) * Add files via upload This is a simple exploratory notebook that heavily expolits pandas and seaborn * Update logistic_regression.py * Update logistic_regression.py * Rename Food wastage analysis from 1961-2013 (FAO).ipynb to other/Food wastage analysis from 1961-2013 (FAO).ipynb * Update logistic_regression.py * Update logistic_regression.py * Update logistic_regression.py * Update logistic_regression.py * Update logistic_regression.py * Update logistic_regression.py * Update logistic_regression.py --- machine_learning/logistic_regression.py | 28 +- ...astage analysis from 1961-2013 (FAO).ipynb | 5916 +++++++++++++++++ 2 files changed, 5933 insertions(+), 11 deletions(-) create mode 100644 other/Food wastage analysis from 1961-2013 (FAO).ipynb diff --git a/machine_learning/logistic_regression.py b/machine_learning/logistic_regression.py index 71952e792e81..9a60831862da 100644 --- a/machine_learning/logistic_regression.py +++ b/machine_learning/logistic_regression.py @@ -31,13 +31,16 @@ def sigmoid_function(z): def cost_function(h, y): return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean() +def log_likelihood(X, Y, weights): + scores = np.dot(X, weights) + return np.sum(Y*scores - np.log(1 + np.exp(scores)) ) # here alpha is the learning rate, X is the feature matrix,y is the target matrix - def logistic_reg( alpha, X, y, + num_steps, max_iterations=70000, ): converged = False @@ -49,21 +52,24 @@ def logistic_reg( h = sigmoid_function(z) gradient = np.dot(X.T, h - y) / y.size theta = theta - alpha * gradient - z = np.dot(X, theta) h = sigmoid_function(z) J = cost_function(h, y) - iterations += 1 # update iterations - - if iterations == max_iterations: - print ('Maximum iterations exceeded!') - print ('Minimal cost function J=', J) - converged = True - + weights = np.zeros(X.shape[1]) + for step in range(num_steps): + scores = np.dot(X, weights) + predictions = sigmoid_function(scores) + if step % 10000 == 0: + print(log_likelihood(X,y,weights)) # Print log-likelihood every so often + return weights + + if iterations == max_iterations: + print ('Maximum iterations exceeded!') + print ('Minimal cost function J=', J) + converged = True return theta - # In[68]: if __name__ == '__main__': @@ -72,7 +78,7 @@ def logistic_reg( y = (iris.target != 0) * 1 alpha = 0.1 - theta = logistic_reg(alpha, X, y, max_iterations=70000) + theta = logistic_reg(alpha,X,y,max_iterations=70000,num_steps=30000) print (theta) diff --git a/other/Food wastage analysis from 1961-2013 (FAO).ipynb b/other/Food wastage analysis from 1961-2013 (FAO).ipynb new file mode 100644 index 000000000000..384314c7e8f1 --- /dev/null +++ b/other/Food wastage analysis from 1961-2013 (FAO).ipynb @@ -0,0 +1,5916 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "1eecdb4a-89ca-4a1e-9c4c-7c44b2e628a1", + "_uuid": "110a8132a8179a9bed2fc8f1096592dc791f1661" + }, + "source": [ + "# About the dataset\n", + "\n", + "Context\n", + "Our world population is expected to grow from 7.3 billion today to 9.7 billion in the year 2050. Finding solutions for feeding the growing world population has become a hot topic for food and agriculture organizations, entrepreneurs and philanthropists. These solutions range from changing the way we grow our food to changing the way we eat. To make things harder, the world's climate is changing and it is both affecting and affected by the way we grow our food – agriculture. This dataset provides an insight on our worldwide food production - focusing on a comparison between food produced for human consumption and feed produced for animals.\n", + "\n", + "Content\n", + "The Food and Agriculture Organization of the United Nations provides free access to food and agriculture data for over 245 countries and territories, from the year 1961 to the most recent update (depends on the dataset). One dataset from the FAO's database is the Food Balance Sheets. It presents a comprehensive picture of the pattern of a country's food supply during a specified reference period, the last time an update was loaded to the FAO database was in 2013. The food balance sheet shows for each food item the sources of supply and its utilization. This chunk of the dataset is focused on two utilizations of each food item available:\n", + "\n", + "Food - refers to the total amount of the food item available as human food during the reference period.\n", + "Feed - refers to the quantity of the food item available for feeding to the livestock and poultry during the reference period.\n", + "Dataset's attributes:\n", + "\n", + "Area code - Country name abbreviation\n", + "Area - County name\n", + "Item - Food item\n", + "Element - Food or Feed\n", + "Latitude - geographic coordinate that specifies the north–south position of a point on the Earth's surface\n", + "Longitude - geographic coordinate that specifies the east-west position of a point on the Earth's surface\n", + "Production per year - Amount of food item produced in 1000 tonnes\n", + "\n", + "This is a simple exploratory notebook that heavily expolits pandas and seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" + }, + "outputs": [], + "source": [ + "# Importing libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "%matplotlib inline\n", + "# importing data\n", + "df = pd.read_csv(\"FAO.csv\", encoding = \"ISO-8859-1\")\n", + "pd.options.mode.chained_assignment = None\n", + "from sklearn.linear_model import LinearRegression" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Area AbbreviationArea CodeAreaItem CodeItemElement CodeElementUnitlatitudelongitude...Y2004Y2005Y2006Y2007Y2008Y2009Y2010Y2011Y2012Y2013
0AFG2Afghanistan2511Wheat and products5142Food1000 tonnes33.9467.71...3249.03486.03704.04164.04252.04538.04605.04711.048104895
1AFG2Afghanistan2805Rice (Milled Equivalent)5142Food1000 tonnes33.9467.71...419.0445.0546.0455.0490.0415.0442.0476.0425422
2AFG2Afghanistan2513Barley and products5521Feed1000 tonnes33.9467.71...58.0236.0262.0263.0230.0379.0315.0203.0367360
3AFG2Afghanistan2513Barley and products5142Food1000 tonnes33.9467.71...185.043.044.048.062.055.060.072.07889
4AFG2Afghanistan2514Maize and products5521Feed1000 tonnes33.9467.71...120.0208.0233.0249.0247.0195.0178.0191.0200200
5AFG2Afghanistan2514Maize and products5142Food1000 tonnes33.9467.71...231.067.082.067.069.071.082.073.07776
6AFG2Afghanistan2517Millet and products5142Food1000 tonnes33.9467.71...15.021.011.019.021.018.014.014.01412
7AFG2Afghanistan2520Cereals, Other5142Food1000 tonnes33.9467.71...2.01.01.00.00.00.00.00.000
8AFG2Afghanistan2531Potatoes and products5142Food1000 tonnes33.9467.71...276.0294.0294.0260.0242.0250.0192.0169.0196230
9AFG2Afghanistan2536Sugar cane5521Feed1000 tonnes33.9467.71...50.029.061.065.054.0114.083.083.06981
10AFG2Afghanistan2537Sugar beet5521Feed1000 tonnes33.9467.71...0.00.00.00.00.00.00.00.000
11AFG2Afghanistan2542Sugar (Raw Equivalent)5142Food1000 tonnes33.9467.71...124.0152.0169.0192.0217.0231.0240.0240.0250255
12AFG2Afghanistan2543Sweeteners, Other5142Food1000 tonnes33.9467.71...9.015.012.06.011.02.09.021.02416
13AFG2Afghanistan2745Honey5142Food1000 tonnes33.9467.71...3.03.03.03.03.03.03.02.022
14AFG2Afghanistan2549Pulses, Other and products5521Feed1000 tonnes33.9467.71...3.02.03.03.03.05.04.05.044
15AFG2Afghanistan2549Pulses, Other and products5142Food1000 tonnes33.9467.71...17.035.037.040.054.080.066.081.06374
16AFG2Afghanistan2551Nuts and products5142Food1000 tonnes33.9467.71...11.013.024.034.042.028.066.071.07044
17AFG2Afghanistan2560Coconuts - Incl Copra5142Food1000 tonnes33.9467.71...0.00.00.00.00.00.00.00.000
18AFG2Afghanistan2561Sesame seed5142Food1000 tonnes33.9467.71...16.016.013.016.016.016.019.017.01616
19AFG2Afghanistan2563Olives (including preserved)5142Food1000 tonnes33.9467.71...1.01.00.00.02.03.02.02.022
20AFG2Afghanistan2571Soyabean Oil5142Food1000 tonnes33.9467.71...6.035.018.021.011.06.015.016.01616
21AFG2Afghanistan2572Groundnut Oil5142Food1000 tonnes33.9467.71...0.00.00.00.00.00.00.00.000
22AFG2Afghanistan2573Sunflowerseed Oil5142Food1000 tonnes33.9467.71...4.06.05.09.03.08.015.016.01723
23AFG2Afghanistan2574Rape and Mustard Oil5142Food1000 tonnes33.9467.71...0.01.03.05.06.06.01.02.022
24AFG2Afghanistan2575Cottonseed Oil5142Food1000 tonnes33.9467.71...2.03.03.03.03.04.03.03.034
25AFG2Afghanistan2577Palm Oil5142Food1000 tonnes33.9467.71...71.069.056.051.036.053.059.051.06164
26AFG2Afghanistan2579Sesameseed Oil5142Food1000 tonnes33.9467.71...1.01.01.02.02.01.01.02.011
27AFG2Afghanistan2580Olive Oil5142Food1000 tonnes33.9467.71...0.00.00.00.00.01.01.01.011
28AFG2Afghanistan2586Oilcrops Oil, Other5142Food1000 tonnes33.9467.71...0.01.00.00.03.01.02.02.022
29AFG2Afghanistan2601Tomatoes and products5142Food1000 tonnes33.9467.71...2.02.08.01.00.00.00.00.000
..................................................................
21447ZWE181Zimbabwe2765Crustaceans5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21448ZWE181Zimbabwe2766Cephalopods5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21449ZWE181Zimbabwe2767Molluscs, Other5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.01.00.000
21450ZWE181Zimbabwe2775Aquatic Plants5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21451ZWE181Zimbabwe2680Infant food5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21452ZWE181Zimbabwe2905Cereals - Excluding Beer5521Feed1000 tonnes-19.0229.15...75.054.075.055.063.062.055.055.05555
21453ZWE181Zimbabwe2905Cereals - Excluding Beer5142Food1000 tonnes-19.0229.15...1844.01842.01944.01962.01918.01980.02011.02094.020712016
21454ZWE181Zimbabwe2907Starchy Roots5142Food1000 tonnes-19.0229.15...223.0236.0238.0228.0245.0258.0258.0269.0272276
21455ZWE181Zimbabwe2908Sugar Crops5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21456ZWE181Zimbabwe2909Sugar & Sweeteners5142Food1000 tonnes-19.0229.15...335.0313.0339.0302.0285.0287.0314.0336.0396416
21457ZWE181Zimbabwe2911Pulses5142Food1000 tonnes-19.0229.15...63.059.061.057.069.078.068.056.05255
21458ZWE181Zimbabwe2912Treenuts5142Food1000 tonnes-19.0229.15...1.02.01.02.02.03.04.02.043
21459ZWE181Zimbabwe2913Oilcrops5521Feed1000 tonnes-19.0229.15...36.046.041.033.031.019.024.017.02730
21460ZWE181Zimbabwe2913Oilcrops5142Food1000 tonnes-19.0229.15...60.059.061.062.048.044.041.040.03838
21461ZWE181Zimbabwe2914Vegetable Oils5142Food1000 tonnes-19.0229.15...111.0114.0112.0114.0134.0135.0137.0147.0159160
21462ZWE181Zimbabwe2918Vegetables5142Food1000 tonnes-19.0229.15...161.0166.0208.0185.0137.0179.0215.0217.0227227
21463ZWE181Zimbabwe2919Fruits - Excluding Wine5142Food1000 tonnes-19.0229.15...191.0134.0167.0177.0185.0184.0211.0230.0246217
21464ZWE181Zimbabwe2922Stimulants5142Food1000 tonnes-19.0229.15...7.021.014.024.016.011.023.011.01010
21465ZWE181Zimbabwe2923Spices5142Food1000 tonnes-19.0229.15...7.011.07.012.016.016.014.011.01212
21466ZWE181Zimbabwe2924Alcoholic Beverages5142Food1000 tonnes-19.0229.15...294.0290.0316.0355.0398.0437.0448.0476.0525516
21467ZWE181Zimbabwe2943Meat5142Food1000 tonnes-19.0229.15...222.0228.0233.0238.0242.0265.0262.0277.0280258
21468ZWE181Zimbabwe2945Offals5142Food1000 tonnes-19.0229.15...20.020.021.021.021.021.021.021.02222
21469ZWE181Zimbabwe2946Animal fats5142Food1000 tonnes-19.0229.15...26.026.029.029.027.031.030.025.02620
21470ZWE181Zimbabwe2949Eggs5142Food1000 tonnes-19.0229.15...15.018.018.021.022.027.027.024.02425
21471ZWE181Zimbabwe2948Milk - Excluding Butter5521Feed1000 tonnes-19.0229.15...21.021.021.021.021.023.025.025.03031
21472ZWE181Zimbabwe2948Milk - Excluding Butter5142Food1000 tonnes-19.0229.15...373.0357.0359.0356.0341.0385.0418.0457.0426451
21473ZWE181Zimbabwe2960Fish, Seafood5521Feed1000 tonnes-19.0229.15...5.04.09.06.09.05.015.015.01515
21474ZWE181Zimbabwe2960Fish, Seafood5142Food1000 tonnes-19.0229.15...18.014.017.014.015.018.029.040.04040
21475ZWE181Zimbabwe2961Aquatic Products, Other5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21476ZWE181Zimbabwe2928Miscellaneous5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
\n", + "

21477 rows × 63 columns

\n", + "
" + ], + "text/plain": [ + " Area Abbreviation Area Code Area Item Code \\\n", + "0 AFG 2 Afghanistan 2511 \n", + "1 AFG 2 Afghanistan 2805 \n", + "2 AFG 2 Afghanistan 2513 \n", + "3 AFG 2 Afghanistan 2513 \n", + "4 AFG 2 Afghanistan 2514 \n", + "5 AFG 2 Afghanistan 2514 \n", + "6 AFG 2 Afghanistan 2517 \n", + "7 AFG 2 Afghanistan 2520 \n", + "8 AFG 2 Afghanistan 2531 \n", + "9 AFG 2 Afghanistan 2536 \n", + "10 AFG 2 Afghanistan 2537 \n", + "11 AFG 2 Afghanistan 2542 \n", + "12 AFG 2 Afghanistan 2543 \n", + "13 AFG 2 Afghanistan 2745 \n", + "14 AFG 2 Afghanistan 2549 \n", + "15 AFG 2 Afghanistan 2549 \n", + "16 AFG 2 Afghanistan 2551 \n", + "17 AFG 2 Afghanistan 2560 \n", + "18 AFG 2 Afghanistan 2561 \n", + "19 AFG 2 Afghanistan 2563 \n", + "20 AFG 2 Afghanistan 2571 \n", + "21 AFG 2 Afghanistan 2572 \n", + "22 AFG 2 Afghanistan 2573 \n", + "23 AFG 2 Afghanistan 2574 \n", + "24 AFG 2 Afghanistan 2575 \n", + "25 AFG 2 Afghanistan 2577 \n", + "26 AFG 2 Afghanistan 2579 \n", + "27 AFG 2 Afghanistan 2580 \n", + "28 AFG 2 Afghanistan 2586 \n", + "29 AFG 2 Afghanistan 2601 \n", + "... ... ... ... ... \n", + "21447 ZWE 181 Zimbabwe 2765 \n", + "21448 ZWE 181 Zimbabwe 2766 \n", + "21449 ZWE 181 Zimbabwe 2767 \n", + "21450 ZWE 181 Zimbabwe 2775 \n", + "21451 ZWE 181 Zimbabwe 2680 \n", + "21452 ZWE 181 Zimbabwe 2905 \n", + "21453 ZWE 181 Zimbabwe 2905 \n", + "21454 ZWE 181 Zimbabwe 2907 \n", + "21455 ZWE 181 Zimbabwe 2908 \n", + "21456 ZWE 181 Zimbabwe 2909 \n", + "21457 ZWE 181 Zimbabwe 2911 \n", + "21458 ZWE 181 Zimbabwe 2912 \n", + "21459 ZWE 181 Zimbabwe 2913 \n", + "21460 ZWE 181 Zimbabwe 2913 \n", + "21461 ZWE 181 Zimbabwe 2914 \n", + "21462 ZWE 181 Zimbabwe 2918 \n", + "21463 ZWE 181 Zimbabwe 2919 \n", + "21464 ZWE 181 Zimbabwe 2922 \n", + "21465 ZWE 181 Zimbabwe 2923 \n", + "21466 ZWE 181 Zimbabwe 2924 \n", + "21467 ZWE 181 Zimbabwe 2943 \n", + "21468 ZWE 181 Zimbabwe 2945 \n", + "21469 ZWE 181 Zimbabwe 2946 \n", + "21470 ZWE 181 Zimbabwe 2949 \n", + "21471 ZWE 181 Zimbabwe 2948 \n", + "21472 ZWE 181 Zimbabwe 2948 \n", + "21473 ZWE 181 Zimbabwe 2960 \n", + "21474 ZWE 181 Zimbabwe 2960 \n", + "21475 ZWE 181 Zimbabwe 2961 \n", + "21476 ZWE 181 Zimbabwe 2928 \n", + "\n", + " Item Element Code Element Unit \\\n", + "0 Wheat and products 5142 Food 1000 tonnes \n", + "1 Rice (Milled Equivalent) 5142 Food 1000 tonnes \n", + "2 Barley and products 5521 Feed 1000 tonnes \n", + "3 Barley and products 5142 Food 1000 tonnes \n", + "4 Maize and products 5521 Feed 1000 tonnes \n", + "5 Maize and products 5142 Food 1000 tonnes \n", + "6 Millet and products 5142 Food 1000 tonnes \n", + "7 Cereals, Other 5142 Food 1000 tonnes \n", + "8 Potatoes and products 5142 Food 1000 tonnes \n", + "9 Sugar cane 5521 Feed 1000 tonnes \n", + "10 Sugar beet 5521 Feed 1000 tonnes \n", + "11 Sugar (Raw Equivalent) 5142 Food 1000 tonnes \n", + "12 Sweeteners, Other 5142 Food 1000 tonnes \n", + "13 Honey 5142 Food 1000 tonnes \n", + "14 Pulses, Other and products 5521 Feed 1000 tonnes \n", + "15 Pulses, Other and products 5142 Food 1000 tonnes \n", + "16 Nuts and products 5142 Food 1000 tonnes \n", + "17 Coconuts - Incl Copra 5142 Food 1000 tonnes \n", + "18 Sesame seed 5142 Food 1000 tonnes \n", + "19 Olives (including preserved) 5142 Food 1000 tonnes \n", + "20 Soyabean Oil 5142 Food 1000 tonnes \n", + "21 Groundnut Oil 5142 Food 1000 tonnes \n", + "22 Sunflowerseed Oil 5142 Food 1000 tonnes \n", + "23 Rape and Mustard Oil 5142 Food 1000 tonnes \n", + "24 Cottonseed Oil 5142 Food 1000 tonnes \n", + "25 Palm Oil 5142 Food 1000 tonnes \n", + "26 Sesameseed Oil 5142 Food 1000 tonnes \n", + "27 Olive Oil 5142 Food 1000 tonnes \n", + "28 Oilcrops Oil, Other 5142 Food 1000 tonnes \n", + "29 Tomatoes and products 5142 Food 1000 tonnes \n", + "... ... ... ... ... \n", + "21447 Crustaceans 5142 Food 1000 tonnes \n", + "21448 Cephalopods 5142 Food 1000 tonnes \n", + "21449 Molluscs, Other 5142 Food 1000 tonnes \n", + "21450 Aquatic Plants 5142 Food 1000 tonnes \n", + "21451 Infant food 5142 Food 1000 tonnes \n", + "21452 Cereals - Excluding Beer 5521 Feed 1000 tonnes \n", + "21453 Cereals - Excluding Beer 5142 Food 1000 tonnes \n", + "21454 Starchy Roots 5142 Food 1000 tonnes \n", + "21455 Sugar Crops 5142 Food 1000 tonnes \n", + "21456 Sugar & Sweeteners 5142 Food 1000 tonnes \n", + "21457 Pulses 5142 Food 1000 tonnes \n", + "21458 Treenuts 5142 Food 1000 tonnes \n", + "21459 Oilcrops 5521 Feed 1000 tonnes \n", + "21460 Oilcrops 5142 Food 1000 tonnes \n", + "21461 Vegetable Oils 5142 Food 1000 tonnes \n", + "21462 Vegetables 5142 Food 1000 tonnes \n", + "21463 Fruits - Excluding Wine 5142 Food 1000 tonnes \n", + "21464 Stimulants 5142 Food 1000 tonnes \n", + "21465 Spices 5142 Food 1000 tonnes \n", + "21466 Alcoholic Beverages 5142 Food 1000 tonnes \n", + "21467 Meat 5142 Food 1000 tonnes \n", + "21468 Offals 5142 Food 1000 tonnes \n", + "21469 Animal fats 5142 Food 1000 tonnes \n", + "21470 Eggs 5142 Food 1000 tonnes \n", + "21471 Milk - Excluding Butter 5521 Feed 1000 tonnes \n", + "21472 Milk - Excluding Butter 5142 Food 1000 tonnes \n", + "21473 Fish, Seafood 5521 Feed 1000 tonnes \n", + "21474 Fish, Seafood 5142 Food 1000 tonnes \n", + "21475 Aquatic Products, Other 5142 Food 1000 tonnes \n", + "21476 Miscellaneous 5142 Food 1000 tonnes \n", + "\n", + " latitude longitude ... Y2004 Y2005 Y2006 Y2007 Y2008 \\\n", + "0 33.94 67.71 ... 3249.0 3486.0 3704.0 4164.0 4252.0 \n", + "1 33.94 67.71 ... 419.0 445.0 546.0 455.0 490.0 \n", + "2 33.94 67.71 ... 58.0 236.0 262.0 263.0 230.0 \n", + "3 33.94 67.71 ... 185.0 43.0 44.0 48.0 62.0 \n", + "4 33.94 67.71 ... 120.0 208.0 233.0 249.0 247.0 \n", + "5 33.94 67.71 ... 231.0 67.0 82.0 67.0 69.0 \n", + "6 33.94 67.71 ... 15.0 21.0 11.0 19.0 21.0 \n", + "7 33.94 67.71 ... 2.0 1.0 1.0 0.0 0.0 \n", + "8 33.94 67.71 ... 276.0 294.0 294.0 260.0 242.0 \n", + "9 33.94 67.71 ... 50.0 29.0 61.0 65.0 54.0 \n", + "10 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "11 33.94 67.71 ... 124.0 152.0 169.0 192.0 217.0 \n", + "12 33.94 67.71 ... 9.0 15.0 12.0 6.0 11.0 \n", + "13 33.94 67.71 ... 3.0 3.0 3.0 3.0 3.0 \n", + "14 33.94 67.71 ... 3.0 2.0 3.0 3.0 3.0 \n", + "15 33.94 67.71 ... 17.0 35.0 37.0 40.0 54.0 \n", + "16 33.94 67.71 ... 11.0 13.0 24.0 34.0 42.0 \n", + "17 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "18 33.94 67.71 ... 16.0 16.0 13.0 16.0 16.0 \n", + "19 33.94 67.71 ... 1.0 1.0 0.0 0.0 2.0 \n", + "20 33.94 67.71 ... 6.0 35.0 18.0 21.0 11.0 \n", + "21 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "22 33.94 67.71 ... 4.0 6.0 5.0 9.0 3.0 \n", + "23 33.94 67.71 ... 0.0 1.0 3.0 5.0 6.0 \n", + "24 33.94 67.71 ... 2.0 3.0 3.0 3.0 3.0 \n", + "25 33.94 67.71 ... 71.0 69.0 56.0 51.0 36.0 \n", + "26 33.94 67.71 ... 1.0 1.0 1.0 2.0 2.0 \n", + "27 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "28 33.94 67.71 ... 0.0 1.0 0.0 0.0 3.0 \n", + "29 33.94 67.71 ... 2.0 2.0 8.0 1.0 0.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "21447 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21448 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21449 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21450 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21451 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21452 -19.02 29.15 ... 75.0 54.0 75.0 55.0 63.0 \n", + "21453 -19.02 29.15 ... 1844.0 1842.0 1944.0 1962.0 1918.0 \n", + "21454 -19.02 29.15 ... 223.0 236.0 238.0 228.0 245.0 \n", + "21455 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21456 -19.02 29.15 ... 335.0 313.0 339.0 302.0 285.0 \n", + "21457 -19.02 29.15 ... 63.0 59.0 61.0 57.0 69.0 \n", + "21458 -19.02 29.15 ... 1.0 2.0 1.0 2.0 2.0 \n", + "21459 -19.02 29.15 ... 36.0 46.0 41.0 33.0 31.0 \n", + "21460 -19.02 29.15 ... 60.0 59.0 61.0 62.0 48.0 \n", + "21461 -19.02 29.15 ... 111.0 114.0 112.0 114.0 134.0 \n", + "21462 -19.02 29.15 ... 161.0 166.0 208.0 185.0 137.0 \n", + "21463 -19.02 29.15 ... 191.0 134.0 167.0 177.0 185.0 \n", + "21464 -19.02 29.15 ... 7.0 21.0 14.0 24.0 16.0 \n", + "21465 -19.02 29.15 ... 7.0 11.0 7.0 12.0 16.0 \n", + "21466 -19.02 29.15 ... 294.0 290.0 316.0 355.0 398.0 \n", + "21467 -19.02 29.15 ... 222.0 228.0 233.0 238.0 242.0 \n", + "21468 -19.02 29.15 ... 20.0 20.0 21.0 21.0 21.0 \n", + "21469 -19.02 29.15 ... 26.0 26.0 29.0 29.0 27.0 \n", + "21470 -19.02 29.15 ... 15.0 18.0 18.0 21.0 22.0 \n", + "21471 -19.02 29.15 ... 21.0 21.0 21.0 21.0 21.0 \n", + "21472 -19.02 29.15 ... 373.0 357.0 359.0 356.0 341.0 \n", + "21473 -19.02 29.15 ... 5.0 4.0 9.0 6.0 9.0 \n", + "21474 -19.02 29.15 ... 18.0 14.0 17.0 14.0 15.0 \n", + "21475 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21476 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " Y2009 Y2010 Y2011 Y2012 Y2013 \n", + "0 4538.0 4605.0 4711.0 4810 4895 \n", + "1 415.0 442.0 476.0 425 422 \n", + "2 379.0 315.0 203.0 367 360 \n", + "3 55.0 60.0 72.0 78 89 \n", + "4 195.0 178.0 191.0 200 200 \n", + "5 71.0 82.0 73.0 77 76 \n", + "6 18.0 14.0 14.0 14 12 \n", + "7 0.0 0.0 0.0 0 0 \n", + "8 250.0 192.0 169.0 196 230 \n", + "9 114.0 83.0 83.0 69 81 \n", + "10 0.0 0.0 0.0 0 0 \n", + "11 231.0 240.0 240.0 250 255 \n", + "12 2.0 9.0 21.0 24 16 \n", + "13 3.0 3.0 2.0 2 2 \n", + "14 5.0 4.0 5.0 4 4 \n", + "15 80.0 66.0 81.0 63 74 \n", + "16 28.0 66.0 71.0 70 44 \n", + "17 0.0 0.0 0.0 0 0 \n", + "18 16.0 19.0 17.0 16 16 \n", + "19 3.0 2.0 2.0 2 2 \n", + "20 6.0 15.0 16.0 16 16 \n", + "21 0.0 0.0 0.0 0 0 \n", + "22 8.0 15.0 16.0 17 23 \n", + "23 6.0 1.0 2.0 2 2 \n", + "24 4.0 3.0 3.0 3 4 \n", + "25 53.0 59.0 51.0 61 64 \n", + "26 1.0 1.0 2.0 1 1 \n", + "27 1.0 1.0 1.0 1 1 \n", + "28 1.0 2.0 2.0 2 2 \n", + "29 0.0 0.0 0.0 0 0 \n", + "... ... ... ... ... ... \n", + "21447 0.0 0.0 0.0 0 0 \n", + "21448 0.0 0.0 0.0 0 0 \n", + "21449 0.0 1.0 0.0 0 0 \n", + "21450 0.0 0.0 0.0 0 0 \n", + "21451 0.0 0.0 0.0 0 0 \n", + "21452 62.0 55.0 55.0 55 55 \n", + "21453 1980.0 2011.0 2094.0 2071 2016 \n", + "21454 258.0 258.0 269.0 272 276 \n", + "21455 0.0 0.0 0.0 0 0 \n", + "21456 287.0 314.0 336.0 396 416 \n", + "21457 78.0 68.0 56.0 52 55 \n", + "21458 3.0 4.0 2.0 4 3 \n", + "21459 19.0 24.0 17.0 27 30 \n", + "21460 44.0 41.0 40.0 38 38 \n", + "21461 135.0 137.0 147.0 159 160 \n", + "21462 179.0 215.0 217.0 227 227 \n", + "21463 184.0 211.0 230.0 246 217 \n", + "21464 11.0 23.0 11.0 10 10 \n", + "21465 16.0 14.0 11.0 12 12 \n", + "21466 437.0 448.0 476.0 525 516 \n", + "21467 265.0 262.0 277.0 280 258 \n", + "21468 21.0 21.0 21.0 22 22 \n", + "21469 31.0 30.0 25.0 26 20 \n", + "21470 27.0 27.0 24.0 24 25 \n", + "21471 23.0 25.0 25.0 30 31 \n", + "21472 385.0 418.0 457.0 426 451 \n", + "21473 5.0 15.0 15.0 15 15 \n", + "21474 18.0 29.0 40.0 40 40 \n", + "21475 0.0 0.0 0.0 0 0 \n", + "21476 0.0 0.0 0.0 0 0 \n", + "\n", + "[21477 rows x 63 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "731a952c-b292-46e3-be7a-4afffe2b4ff1", + "_uuid": "5d165c279ce22afc0a874e32931d7b0ebb0717f9" + }, + "source": [ + "Let's see what the data looks like..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", + "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a", + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "25c3f986-fd14-4a3f-baff-02571ad665eb", + "_uuid": "5a7da58320ab35ab1bcf83a62209afbe40b672fe" + }, + "source": [ + "# Plot for annual produce of different countries with quantity in y-axis and years in x-axis" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Area AbbreviationArea CodeAreaItem CodeItemElement CodeElementUnitlatitudelongitude...Y2004Y2005Y2006Y2007Y2008Y2009Y2010Y2011Y2012Y2013
0AFG2Afghanistan2511Wheat and products5142Food1000 tonnes33.9467.71...3249.03486.03704.04164.04252.04538.04605.04711.048104895
1AFG2Afghanistan2805Rice (Milled Equivalent)5142Food1000 tonnes33.9467.71...419.0445.0546.0455.0490.0415.0442.0476.0425422
2AFG2Afghanistan2513Barley and products5521Feed1000 tonnes33.9467.71...58.0236.0262.0263.0230.0379.0315.0203.0367360
3AFG2Afghanistan2513Barley and products5142Food1000 tonnes33.9467.71...185.043.044.048.062.055.060.072.07889
4AFG2Afghanistan2514Maize and products5521Feed1000 tonnes33.9467.71...120.0208.0233.0249.0247.0195.0178.0191.0200200
5AFG2Afghanistan2514Maize and products5142Food1000 tonnes33.9467.71...231.067.082.067.069.071.082.073.07776
6AFG2Afghanistan2517Millet and products5142Food1000 tonnes33.9467.71...15.021.011.019.021.018.014.014.01412
7AFG2Afghanistan2520Cereals, Other5142Food1000 tonnes33.9467.71...2.01.01.00.00.00.00.00.000
8AFG2Afghanistan2531Potatoes and products5142Food1000 tonnes33.9467.71...276.0294.0294.0260.0242.0250.0192.0169.0196230
9AFG2Afghanistan2536Sugar cane5521Feed1000 tonnes33.9467.71...50.029.061.065.054.0114.083.083.06981
10AFG2Afghanistan2537Sugar beet5521Feed1000 tonnes33.9467.71...0.00.00.00.00.00.00.00.000
11AFG2Afghanistan2542Sugar (Raw Equivalent)5142Food1000 tonnes33.9467.71...124.0152.0169.0192.0217.0231.0240.0240.0250255
12AFG2Afghanistan2543Sweeteners, Other5142Food1000 tonnes33.9467.71...9.015.012.06.011.02.09.021.02416
13AFG2Afghanistan2745Honey5142Food1000 tonnes33.9467.71...3.03.03.03.03.03.03.02.022
14AFG2Afghanistan2549Pulses, Other and products5521Feed1000 tonnes33.9467.71...3.02.03.03.03.05.04.05.044
15AFG2Afghanistan2549Pulses, Other and products5142Food1000 tonnes33.9467.71...17.035.037.040.054.080.066.081.06374
16AFG2Afghanistan2551Nuts and products5142Food1000 tonnes33.9467.71...11.013.024.034.042.028.066.071.07044
17AFG2Afghanistan2560Coconuts - Incl Copra5142Food1000 tonnes33.9467.71...0.00.00.00.00.00.00.00.000
18AFG2Afghanistan2561Sesame seed5142Food1000 tonnes33.9467.71...16.016.013.016.016.016.019.017.01616
19AFG2Afghanistan2563Olives (including preserved)5142Food1000 tonnes33.9467.71...1.01.00.00.02.03.02.02.022
20AFG2Afghanistan2571Soyabean Oil5142Food1000 tonnes33.9467.71...6.035.018.021.011.06.015.016.01616
21AFG2Afghanistan2572Groundnut Oil5142Food1000 tonnes33.9467.71...0.00.00.00.00.00.00.00.000
22AFG2Afghanistan2573Sunflowerseed Oil5142Food1000 tonnes33.9467.71...4.06.05.09.03.08.015.016.01723
23AFG2Afghanistan2574Rape and Mustard Oil5142Food1000 tonnes33.9467.71...0.01.03.05.06.06.01.02.022
24AFG2Afghanistan2575Cottonseed Oil5142Food1000 tonnes33.9467.71...2.03.03.03.03.04.03.03.034
25AFG2Afghanistan2577Palm Oil5142Food1000 tonnes33.9467.71...71.069.056.051.036.053.059.051.06164
26AFG2Afghanistan2579Sesameseed Oil5142Food1000 tonnes33.9467.71...1.01.01.02.02.01.01.02.011
27AFG2Afghanistan2580Olive Oil5142Food1000 tonnes33.9467.71...0.00.00.00.00.01.01.01.011
28AFG2Afghanistan2586Oilcrops Oil, Other5142Food1000 tonnes33.9467.71...0.01.00.00.03.01.02.02.022
29AFG2Afghanistan2601Tomatoes and products5142Food1000 tonnes33.9467.71...2.02.08.01.00.00.00.00.000
..................................................................
21447ZWE181Zimbabwe2765Crustaceans5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21448ZWE181Zimbabwe2766Cephalopods5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21449ZWE181Zimbabwe2767Molluscs, Other5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.01.00.000
21450ZWE181Zimbabwe2775Aquatic Plants5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21451ZWE181Zimbabwe2680Infant food5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21452ZWE181Zimbabwe2905Cereals - Excluding Beer5521Feed1000 tonnes-19.0229.15...75.054.075.055.063.062.055.055.05555
21453ZWE181Zimbabwe2905Cereals - Excluding Beer5142Food1000 tonnes-19.0229.15...1844.01842.01944.01962.01918.01980.02011.02094.020712016
21454ZWE181Zimbabwe2907Starchy Roots5142Food1000 tonnes-19.0229.15...223.0236.0238.0228.0245.0258.0258.0269.0272276
21455ZWE181Zimbabwe2908Sugar Crops5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21456ZWE181Zimbabwe2909Sugar & Sweeteners5142Food1000 tonnes-19.0229.15...335.0313.0339.0302.0285.0287.0314.0336.0396416
21457ZWE181Zimbabwe2911Pulses5142Food1000 tonnes-19.0229.15...63.059.061.057.069.078.068.056.05255
21458ZWE181Zimbabwe2912Treenuts5142Food1000 tonnes-19.0229.15...1.02.01.02.02.03.04.02.043
21459ZWE181Zimbabwe2913Oilcrops5521Feed1000 tonnes-19.0229.15...36.046.041.033.031.019.024.017.02730
21460ZWE181Zimbabwe2913Oilcrops5142Food1000 tonnes-19.0229.15...60.059.061.062.048.044.041.040.03838
21461ZWE181Zimbabwe2914Vegetable Oils5142Food1000 tonnes-19.0229.15...111.0114.0112.0114.0134.0135.0137.0147.0159160
21462ZWE181Zimbabwe2918Vegetables5142Food1000 tonnes-19.0229.15...161.0166.0208.0185.0137.0179.0215.0217.0227227
21463ZWE181Zimbabwe2919Fruits - Excluding Wine5142Food1000 tonnes-19.0229.15...191.0134.0167.0177.0185.0184.0211.0230.0246217
21464ZWE181Zimbabwe2922Stimulants5142Food1000 tonnes-19.0229.15...7.021.014.024.016.011.023.011.01010
21465ZWE181Zimbabwe2923Spices5142Food1000 tonnes-19.0229.15...7.011.07.012.016.016.014.011.01212
21466ZWE181Zimbabwe2924Alcoholic Beverages5142Food1000 tonnes-19.0229.15...294.0290.0316.0355.0398.0437.0448.0476.0525516
21467ZWE181Zimbabwe2943Meat5142Food1000 tonnes-19.0229.15...222.0228.0233.0238.0242.0265.0262.0277.0280258
21468ZWE181Zimbabwe2945Offals5142Food1000 tonnes-19.0229.15...20.020.021.021.021.021.021.021.02222
21469ZWE181Zimbabwe2946Animal fats5142Food1000 tonnes-19.0229.15...26.026.029.029.027.031.030.025.02620
21470ZWE181Zimbabwe2949Eggs5142Food1000 tonnes-19.0229.15...15.018.018.021.022.027.027.024.02425
21471ZWE181Zimbabwe2948Milk - Excluding Butter5521Feed1000 tonnes-19.0229.15...21.021.021.021.021.023.025.025.03031
21472ZWE181Zimbabwe2948Milk - Excluding Butter5142Food1000 tonnes-19.0229.15...373.0357.0359.0356.0341.0385.0418.0457.0426451
21473ZWE181Zimbabwe2960Fish, Seafood5521Feed1000 tonnes-19.0229.15...5.04.09.06.09.05.015.015.01515
21474ZWE181Zimbabwe2960Fish, Seafood5142Food1000 tonnes-19.0229.15...18.014.017.014.015.018.029.040.04040
21475ZWE181Zimbabwe2961Aquatic Products, Other5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
21476ZWE181Zimbabwe2928Miscellaneous5142Food1000 tonnes-19.0229.15...0.00.00.00.00.00.00.00.000
\n", + "

21477 rows × 63 columns

\n", + "
" + ], + "text/plain": [ + " Area Abbreviation Area Code Area Item Code \\\n", + "0 AFG 2 Afghanistan 2511 \n", + "1 AFG 2 Afghanistan 2805 \n", + "2 AFG 2 Afghanistan 2513 \n", + "3 AFG 2 Afghanistan 2513 \n", + "4 AFG 2 Afghanistan 2514 \n", + "5 AFG 2 Afghanistan 2514 \n", + "6 AFG 2 Afghanistan 2517 \n", + "7 AFG 2 Afghanistan 2520 \n", + "8 AFG 2 Afghanistan 2531 \n", + "9 AFG 2 Afghanistan 2536 \n", + "10 AFG 2 Afghanistan 2537 \n", + "11 AFG 2 Afghanistan 2542 \n", + "12 AFG 2 Afghanistan 2543 \n", + "13 AFG 2 Afghanistan 2745 \n", + "14 AFG 2 Afghanistan 2549 \n", + "15 AFG 2 Afghanistan 2549 \n", + "16 AFG 2 Afghanistan 2551 \n", + "17 AFG 2 Afghanistan 2560 \n", + "18 AFG 2 Afghanistan 2561 \n", + "19 AFG 2 Afghanistan 2563 \n", + "20 AFG 2 Afghanistan 2571 \n", + "21 AFG 2 Afghanistan 2572 \n", + "22 AFG 2 Afghanistan 2573 \n", + "23 AFG 2 Afghanistan 2574 \n", + "24 AFG 2 Afghanistan 2575 \n", + "25 AFG 2 Afghanistan 2577 \n", + "26 AFG 2 Afghanistan 2579 \n", + "27 AFG 2 Afghanistan 2580 \n", + "28 AFG 2 Afghanistan 2586 \n", + "29 AFG 2 Afghanistan 2601 \n", + "... ... ... ... ... \n", + "21447 ZWE 181 Zimbabwe 2765 \n", + "21448 ZWE 181 Zimbabwe 2766 \n", + "21449 ZWE 181 Zimbabwe 2767 \n", + "21450 ZWE 181 Zimbabwe 2775 \n", + "21451 ZWE 181 Zimbabwe 2680 \n", + "21452 ZWE 181 Zimbabwe 2905 \n", + "21453 ZWE 181 Zimbabwe 2905 \n", + "21454 ZWE 181 Zimbabwe 2907 \n", + "21455 ZWE 181 Zimbabwe 2908 \n", + "21456 ZWE 181 Zimbabwe 2909 \n", + "21457 ZWE 181 Zimbabwe 2911 \n", + "21458 ZWE 181 Zimbabwe 2912 \n", + "21459 ZWE 181 Zimbabwe 2913 \n", + "21460 ZWE 181 Zimbabwe 2913 \n", + "21461 ZWE 181 Zimbabwe 2914 \n", + "21462 ZWE 181 Zimbabwe 2918 \n", + "21463 ZWE 181 Zimbabwe 2919 \n", + "21464 ZWE 181 Zimbabwe 2922 \n", + "21465 ZWE 181 Zimbabwe 2923 \n", + "21466 ZWE 181 Zimbabwe 2924 \n", + "21467 ZWE 181 Zimbabwe 2943 \n", + "21468 ZWE 181 Zimbabwe 2945 \n", + "21469 ZWE 181 Zimbabwe 2946 \n", + "21470 ZWE 181 Zimbabwe 2949 \n", + "21471 ZWE 181 Zimbabwe 2948 \n", + "21472 ZWE 181 Zimbabwe 2948 \n", + "21473 ZWE 181 Zimbabwe 2960 \n", + "21474 ZWE 181 Zimbabwe 2960 \n", + "21475 ZWE 181 Zimbabwe 2961 \n", + "21476 ZWE 181 Zimbabwe 2928 \n", + "\n", + " Item Element Code Element Unit \\\n", + "0 Wheat and products 5142 Food 1000 tonnes \n", + "1 Rice (Milled Equivalent) 5142 Food 1000 tonnes \n", + "2 Barley and products 5521 Feed 1000 tonnes \n", + "3 Barley and products 5142 Food 1000 tonnes \n", + "4 Maize and products 5521 Feed 1000 tonnes \n", + "5 Maize and products 5142 Food 1000 tonnes \n", + "6 Millet and products 5142 Food 1000 tonnes \n", + "7 Cereals, Other 5142 Food 1000 tonnes \n", + "8 Potatoes and products 5142 Food 1000 tonnes \n", + "9 Sugar cane 5521 Feed 1000 tonnes \n", + "10 Sugar beet 5521 Feed 1000 tonnes \n", + "11 Sugar (Raw Equivalent) 5142 Food 1000 tonnes \n", + "12 Sweeteners, Other 5142 Food 1000 tonnes \n", + "13 Honey 5142 Food 1000 tonnes \n", + "14 Pulses, Other and products 5521 Feed 1000 tonnes \n", + "15 Pulses, Other and products 5142 Food 1000 tonnes \n", + "16 Nuts and products 5142 Food 1000 tonnes \n", + "17 Coconuts - Incl Copra 5142 Food 1000 tonnes \n", + "18 Sesame seed 5142 Food 1000 tonnes \n", + "19 Olives (including preserved) 5142 Food 1000 tonnes \n", + "20 Soyabean Oil 5142 Food 1000 tonnes \n", + "21 Groundnut Oil 5142 Food 1000 tonnes \n", + "22 Sunflowerseed Oil 5142 Food 1000 tonnes \n", + "23 Rape and Mustard Oil 5142 Food 1000 tonnes \n", + "24 Cottonseed Oil 5142 Food 1000 tonnes \n", + "25 Palm Oil 5142 Food 1000 tonnes \n", + "26 Sesameseed Oil 5142 Food 1000 tonnes \n", + "27 Olive Oil 5142 Food 1000 tonnes \n", + "28 Oilcrops Oil, Other 5142 Food 1000 tonnes \n", + "29 Tomatoes and products 5142 Food 1000 tonnes \n", + "... ... ... ... ... \n", + "21447 Crustaceans 5142 Food 1000 tonnes \n", + "21448 Cephalopods 5142 Food 1000 tonnes \n", + "21449 Molluscs, Other 5142 Food 1000 tonnes \n", + "21450 Aquatic Plants 5142 Food 1000 tonnes \n", + "21451 Infant food 5142 Food 1000 tonnes \n", + "21452 Cereals - Excluding Beer 5521 Feed 1000 tonnes \n", + "21453 Cereals - Excluding Beer 5142 Food 1000 tonnes \n", + "21454 Starchy Roots 5142 Food 1000 tonnes \n", + "21455 Sugar Crops 5142 Food 1000 tonnes \n", + "21456 Sugar & Sweeteners 5142 Food 1000 tonnes \n", + "21457 Pulses 5142 Food 1000 tonnes \n", + "21458 Treenuts 5142 Food 1000 tonnes \n", + "21459 Oilcrops 5521 Feed 1000 tonnes \n", + "21460 Oilcrops 5142 Food 1000 tonnes \n", + "21461 Vegetable Oils 5142 Food 1000 tonnes \n", + "21462 Vegetables 5142 Food 1000 tonnes \n", + "21463 Fruits - Excluding Wine 5142 Food 1000 tonnes \n", + "21464 Stimulants 5142 Food 1000 tonnes \n", + "21465 Spices 5142 Food 1000 tonnes \n", + "21466 Alcoholic Beverages 5142 Food 1000 tonnes \n", + "21467 Meat 5142 Food 1000 tonnes \n", + "21468 Offals 5142 Food 1000 tonnes \n", + "21469 Animal fats 5142 Food 1000 tonnes \n", + "21470 Eggs 5142 Food 1000 tonnes \n", + "21471 Milk - Excluding Butter 5521 Feed 1000 tonnes \n", + "21472 Milk - Excluding Butter 5142 Food 1000 tonnes \n", + "21473 Fish, Seafood 5521 Feed 1000 tonnes \n", + "21474 Fish, Seafood 5142 Food 1000 tonnes \n", + "21475 Aquatic Products, Other 5142 Food 1000 tonnes \n", + "21476 Miscellaneous 5142 Food 1000 tonnes \n", + "\n", + " latitude longitude ... Y2004 Y2005 Y2006 Y2007 Y2008 \\\n", + "0 33.94 67.71 ... 3249.0 3486.0 3704.0 4164.0 4252.0 \n", + "1 33.94 67.71 ... 419.0 445.0 546.0 455.0 490.0 \n", + "2 33.94 67.71 ... 58.0 236.0 262.0 263.0 230.0 \n", + "3 33.94 67.71 ... 185.0 43.0 44.0 48.0 62.0 \n", + "4 33.94 67.71 ... 120.0 208.0 233.0 249.0 247.0 \n", + "5 33.94 67.71 ... 231.0 67.0 82.0 67.0 69.0 \n", + "6 33.94 67.71 ... 15.0 21.0 11.0 19.0 21.0 \n", + "7 33.94 67.71 ... 2.0 1.0 1.0 0.0 0.0 \n", + "8 33.94 67.71 ... 276.0 294.0 294.0 260.0 242.0 \n", + "9 33.94 67.71 ... 50.0 29.0 61.0 65.0 54.0 \n", + "10 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "11 33.94 67.71 ... 124.0 152.0 169.0 192.0 217.0 \n", + "12 33.94 67.71 ... 9.0 15.0 12.0 6.0 11.0 \n", + "13 33.94 67.71 ... 3.0 3.0 3.0 3.0 3.0 \n", + "14 33.94 67.71 ... 3.0 2.0 3.0 3.0 3.0 \n", + "15 33.94 67.71 ... 17.0 35.0 37.0 40.0 54.0 \n", + "16 33.94 67.71 ... 11.0 13.0 24.0 34.0 42.0 \n", + "17 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "18 33.94 67.71 ... 16.0 16.0 13.0 16.0 16.0 \n", + "19 33.94 67.71 ... 1.0 1.0 0.0 0.0 2.0 \n", + "20 33.94 67.71 ... 6.0 35.0 18.0 21.0 11.0 \n", + "21 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "22 33.94 67.71 ... 4.0 6.0 5.0 9.0 3.0 \n", + "23 33.94 67.71 ... 0.0 1.0 3.0 5.0 6.0 \n", + "24 33.94 67.71 ... 2.0 3.0 3.0 3.0 3.0 \n", + "25 33.94 67.71 ... 71.0 69.0 56.0 51.0 36.0 \n", + "26 33.94 67.71 ... 1.0 1.0 1.0 2.0 2.0 \n", + "27 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n", + "28 33.94 67.71 ... 0.0 1.0 0.0 0.0 3.0 \n", + "29 33.94 67.71 ... 2.0 2.0 8.0 1.0 0.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "21447 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21448 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21449 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21450 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21451 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21452 -19.02 29.15 ... 75.0 54.0 75.0 55.0 63.0 \n", + "21453 -19.02 29.15 ... 1844.0 1842.0 1944.0 1962.0 1918.0 \n", + "21454 -19.02 29.15 ... 223.0 236.0 238.0 228.0 245.0 \n", + "21455 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21456 -19.02 29.15 ... 335.0 313.0 339.0 302.0 285.0 \n", + "21457 -19.02 29.15 ... 63.0 59.0 61.0 57.0 69.0 \n", + "21458 -19.02 29.15 ... 1.0 2.0 1.0 2.0 2.0 \n", + "21459 -19.02 29.15 ... 36.0 46.0 41.0 33.0 31.0 \n", + "21460 -19.02 29.15 ... 60.0 59.0 61.0 62.0 48.0 \n", + "21461 -19.02 29.15 ... 111.0 114.0 112.0 114.0 134.0 \n", + "21462 -19.02 29.15 ... 161.0 166.0 208.0 185.0 137.0 \n", + "21463 -19.02 29.15 ... 191.0 134.0 167.0 177.0 185.0 \n", + "21464 -19.02 29.15 ... 7.0 21.0 14.0 24.0 16.0 \n", + "21465 -19.02 29.15 ... 7.0 11.0 7.0 12.0 16.0 \n", + "21466 -19.02 29.15 ... 294.0 290.0 316.0 355.0 398.0 \n", + "21467 -19.02 29.15 ... 222.0 228.0 233.0 238.0 242.0 \n", + "21468 -19.02 29.15 ... 20.0 20.0 21.0 21.0 21.0 \n", + "21469 -19.02 29.15 ... 26.0 26.0 29.0 29.0 27.0 \n", + "21470 -19.02 29.15 ... 15.0 18.0 18.0 21.0 22.0 \n", + "21471 -19.02 29.15 ... 21.0 21.0 21.0 21.0 21.0 \n", + "21472 -19.02 29.15 ... 373.0 357.0 359.0 356.0 341.0 \n", + "21473 -19.02 29.15 ... 5.0 4.0 9.0 6.0 9.0 \n", + "21474 -19.02 29.15 ... 18.0 14.0 17.0 14.0 15.0 \n", + "21475 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "21476 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " Y2009 Y2010 Y2011 Y2012 Y2013 \n", + "0 4538.0 4605.0 4711.0 4810 4895 \n", + "1 415.0 442.0 476.0 425 422 \n", + "2 379.0 315.0 203.0 367 360 \n", + "3 55.0 60.0 72.0 78 89 \n", + "4 195.0 178.0 191.0 200 200 \n", + "5 71.0 82.0 73.0 77 76 \n", + "6 18.0 14.0 14.0 14 12 \n", + "7 0.0 0.0 0.0 0 0 \n", + "8 250.0 192.0 169.0 196 230 \n", + "9 114.0 83.0 83.0 69 81 \n", + "10 0.0 0.0 0.0 0 0 \n", + "11 231.0 240.0 240.0 250 255 \n", + "12 2.0 9.0 21.0 24 16 \n", + "13 3.0 3.0 2.0 2 2 \n", + "14 5.0 4.0 5.0 4 4 \n", + "15 80.0 66.0 81.0 63 74 \n", + "16 28.0 66.0 71.0 70 44 \n", + "17 0.0 0.0 0.0 0 0 \n", + "18 16.0 19.0 17.0 16 16 \n", + "19 3.0 2.0 2.0 2 2 \n", + "20 6.0 15.0 16.0 16 16 \n", + "21 0.0 0.0 0.0 0 0 \n", + "22 8.0 15.0 16.0 17 23 \n", + "23 6.0 1.0 2.0 2 2 \n", + "24 4.0 3.0 3.0 3 4 \n", + "25 53.0 59.0 51.0 61 64 \n", + "26 1.0 1.0 2.0 1 1 \n", + "27 1.0 1.0 1.0 1 1 \n", + "28 1.0 2.0 2.0 2 2 \n", + "29 0.0 0.0 0.0 0 0 \n", + "... ... ... ... ... ... \n", + "21447 0.0 0.0 0.0 0 0 \n", + "21448 0.0 0.0 0.0 0 0 \n", + "21449 0.0 1.0 0.0 0 0 \n", + "21450 0.0 0.0 0.0 0 0 \n", + "21451 0.0 0.0 0.0 0 0 \n", + "21452 62.0 55.0 55.0 55 55 \n", + "21453 1980.0 2011.0 2094.0 2071 2016 \n", + "21454 258.0 258.0 269.0 272 276 \n", + "21455 0.0 0.0 0.0 0 0 \n", + "21456 287.0 314.0 336.0 396 416 \n", + "21457 78.0 68.0 56.0 52 55 \n", + "21458 3.0 4.0 2.0 4 3 \n", + "21459 19.0 24.0 17.0 27 30 \n", + "21460 44.0 41.0 40.0 38 38 \n", + "21461 135.0 137.0 147.0 159 160 \n", + "21462 179.0 215.0 217.0 227 227 \n", + "21463 184.0 211.0 230.0 246 217 \n", + "21464 11.0 23.0 11.0 10 10 \n", + "21465 16.0 14.0 11.0 12 12 \n", + "21466 437.0 448.0 476.0 525 516 \n", + "21467 265.0 262.0 277.0 280 258 \n", + "21468 21.0 21.0 21.0 22 22 \n", + "21469 31.0 30.0 25.0 26 20 \n", + "21470 27.0 27.0 24.0 24 25 \n", + "21471 23.0 25.0 25.0 30 31 \n", + "21472 385.0 418.0 457.0 426 451 \n", + "21473 5.0 15.0 15.0 15 15 \n", + "21474 18.0 29.0 40.0 40 40 \n", + "21475 0.0 0.0 0.0 0 0 \n", + "21476 0.0 0.0 0.0 0 0 \n", + "\n", + "[21477 rows x 63 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "_cell_guid": "347e620f-b0e4-448e-81c7-e164f560c5a3", + "_uuid": "0acdd759950f5df3298224b0804562973663a11d", + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "area_list = list(df['Area'].unique())\n", + "year_list = list(df.iloc[:,10:].columns)\n", + "\n", + "plt.figure(figsize=(24,12))\n", + "for ar in area_list:\n", + " yearly_produce = []\n", + " for yr in year_list:\n", + " yearly_produce.append(df[yr][df['Area'] == ar].sum())\n", + " plt.plot(yearly_produce, label=ar)\n", + "plt.xticks(np.arange(53), tuple(year_list), rotation=60)\n", + "plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=8, mode=\"expand\", borderaxespad=0.)\n", + "plt.savefig('p.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(24,12))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "2ebe07e3-739b-4f39-8736-a512426c05bf", + "_uuid": "70900ec0ff5e248cd382ee53b5927cb671efa80e", + "collapsed": true + }, + "source": [ + "Clearly, China, India and US stand out here. So, these are the countries with most food and feed production.\n", + "\n", + "Now, let's have a close look at their food and feed data\n", + "\n", + "# Food and feed plot for the whole dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "_cell_guid": "ec0c911d-e154-4f8a-a79f-ced4896d5115", + "_uuid": "683dc56125b3a4c66b1e140098ec91490cbbe96f", + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n", + " warnings.warn(msg)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.factorplot(\"Element\", data=df, kind=\"count\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "189c74af-e6e4-4ddd-a73c-3725f3aa8124", + "_uuid": "bfd404fb5dbb48c3e3bd1dcd45fb27a5fb475a00" + }, + "source": [ + "So, there is a huge difference in food and feed production. Now, we have obvious assumptions about the following plots after looking at this huge difference.\n", + "\n", + "# Food and feed plot for the largest producers(India, USA, China)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "_cell_guid": "0bf44e4e-d4c4-4f74-ae9f-82f52139d182", + "_uuid": "be1bc3d49c8cee62f48a09ada0db3170adcedc17" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n", + " warnings.warn(msg)\n", + "/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n", + " warnings.warn(msg, UserWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.factorplot(\"Area\", data=df[(df['Area'] == \"India\") | (df['Area'] == \"China, mainland\") | (df['Area'] == \"United States of America\")], kind=\"count\", hue=\"Element\", size=8, aspect=.8)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "94c19dc8-b1e7-4b61-b81f-422c27184c4e", + "_uuid": "0d1cfc7acc74847dbc5813b9b3bd0eb9db450985" + }, + "source": [ + "Though, there is a huge difference between feed and food production, these countries' total production and their ranks depend on feed production." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "9dba87b4-fa51-43ef-95ae-f31396c20146", + "_uuid": "43e0f00abf706ab1782ebb78cefc38aca17316e6" + }, + "source": [ + "Now, we create a dataframe with countries as index and their annual produce as columns from 1961 to 2013." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "_cell_guid": "c4a5f859-0384-4c8e-b894-3f747aec8cf9", + "_uuid": "84dd7a2b601479728dd172d3100951553c2daff5", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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5 rows × 53 columns

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" + ], + "text/plain": [ + " Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 \\\n", + "Afghanistan 9481.0 9414.0 9194.0 10170.0 10473.0 10169.0 \n", + "Albania 1706.0 1749.0 1767.0 1889.0 1884.0 1995.0 \n", + "Algeria 7488.0 7235.0 6861.0 7255.0 7509.0 7536.0 \n", + "Angola 4834.0 4775.0 5240.0 5286.0 5527.0 5677.0 \n", + "Antigua and Barbuda 92.0 94.0 105.0 95.0 84.0 73.0 \n", + "\n", + " Y1967 Y1968 Y1969 Y1970 ... Y2004 \\\n", + "Afghanistan 11289.0 11508.0 11815.0 10454.0 ... 16542.0 \n", + "Albania 2046.0 2169.0 2230.0 2395.0 ... 6637.0 \n", + "Algeria 7986.0 8839.0 9003.0 9355.0 ... 48619.0 \n", + "Angola 5833.0 5685.0 6219.0 6460.0 ... 25541.0 \n", + "Antigua and Barbuda 64.0 59.0 68.0 77.0 ... 92.0 \n", + "\n", + " Y2005 Y2006 Y2007 Y2008 Y2009 Y2010 \\\n", + "Afghanistan 17658.0 18317.0 19248.0 19381.0 20661.0 21030.0 \n", + "Albania 6719.0 6911.0 6744.0 7168.0 7316.0 7907.0 \n", + "Algeria 49562.0 51067.0 49933.0 50916.0 57505.0 60071.0 \n", + "Angola 26696.0 28247.0 29877.0 32053.0 36985.0 38400.0 \n", + "Antigua and Barbuda 115.0 110.0 122.0 115.0 114.0 115.0 \n", + "\n", + " Y2011 Y2012 Y2013 \n", + "Afghanistan 21100.0 22706.0 23007.0 \n", + "Albania 8114.0 8221.0 8271.0 \n", + "Algeria 65852.0 69365.0 72161.0 \n", + "Angola 40573.0 38064.0 48639.0 \n", + "Antigua and Barbuda 118.0 113.0 119.0 \n", + "\n", + "[5 rows x 53 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_df = pd.DataFrame.transpose(new_df)\n", + "new_df.columns = year_list\n", + "\n", + "new_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "57929d23-e3d7-4955-92d1-6fa388eb774d", + "_uuid": "605f908af9ff88120fce2a2b59160816fcdcfa67" + }, + "source": [ + "Perfect! Now, we will do some feature engineering.\n", + "\n", + "# First, a new column which indicates mean produce of each state over the given years. Second, a ranking column which ranks countries on the basis of mean produce." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "_cell_guid": "ab91a322-0cb9-4edf-b5a2-cde82a237824", + "_uuid": "979f875019abef3ed85af75e000fe59d1de5a381" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Y1961Y1962Y1963Y1964Y1965Y1966Y1967Y1968Y1969Y1970...Y2006Y2007Y2008Y2009Y2010Y2011Y2012Y2013Mean_ProduceRank
Afghanistan9481.09414.09194.010170.010473.010169.011289.011508.011815.010454.0...18317.019248.019381.020661.021030.021100.022706.023007.013003.05660469.0
Albania1706.01749.01767.01889.01884.01995.02046.02169.02230.02395.0...6911.06744.07168.07316.07907.08114.08221.08271.04475.509434104.0
Algeria7488.07235.06861.07255.07509.07536.07986.08839.09003.09355.0...51067.049933.050916.057505.060071.065852.069365.072161.028879.49056638.0
Angola4834.04775.05240.05286.05527.05677.05833.05685.06219.06460.0...28247.029877.032053.036985.038400.040573.038064.048639.013321.05660468.0
Antigua and Barbuda92.094.0105.095.084.073.064.059.068.077.0...110.0122.0115.0114.0115.0118.0113.0119.083.886792172.0
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5 rows × 55 columns

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" + ], + "text/plain": [ + " Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 \\\n", + "Afghanistan 9481.0 9414.0 9194.0 10170.0 10473.0 10169.0 \n", + "Albania 1706.0 1749.0 1767.0 1889.0 1884.0 1995.0 \n", + "Algeria 7488.0 7235.0 6861.0 7255.0 7509.0 7536.0 \n", + "Angola 4834.0 4775.0 5240.0 5286.0 5527.0 5677.0 \n", + "Antigua and Barbuda 92.0 94.0 105.0 95.0 84.0 73.0 \n", + "\n", + " Y1967 Y1968 Y1969 Y1970 ... Y2006 \\\n", + "Afghanistan 11289.0 11508.0 11815.0 10454.0 ... 18317.0 \n", + "Albania 2046.0 2169.0 2230.0 2395.0 ... 6911.0 \n", + "Algeria 7986.0 8839.0 9003.0 9355.0 ... 51067.0 \n", + "Angola 5833.0 5685.0 6219.0 6460.0 ... 28247.0 \n", + "Antigua and Barbuda 64.0 59.0 68.0 77.0 ... 110.0 \n", + "\n", + " Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 \\\n", + "Afghanistan 19248.0 19381.0 20661.0 21030.0 21100.0 22706.0 \n", + "Albania 6744.0 7168.0 7316.0 7907.0 8114.0 8221.0 \n", + "Algeria 49933.0 50916.0 57505.0 60071.0 65852.0 69365.0 \n", + "Angola 29877.0 32053.0 36985.0 38400.0 40573.0 38064.0 \n", + "Antigua and Barbuda 122.0 115.0 114.0 115.0 118.0 113.0 \n", + "\n", + " Y2013 Mean_Produce Rank \n", + "Afghanistan 23007.0 13003.056604 69.0 \n", + "Albania 8271.0 4475.509434 104.0 \n", + "Algeria 72161.0 28879.490566 38.0 \n", + "Angola 48639.0 13321.056604 68.0 \n", + "Antigua and Barbuda 119.0 83.886792 172.0 \n", + "\n", + "[5 rows x 55 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_produce = []\n", + "for i in range(174):\n", + " mean_produce.append(new_df.iloc[i,:].values.mean())\n", + "new_df['Mean_Produce'] = mean_produce\n", + "\n", + "new_df['Rank'] = new_df['Mean_Produce'].rank(ascending=False)\n", + "\n", + "new_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "6f7c4fb7-1475-439f-9929-4cf4b29d8de7", + "_uuid": "da6c9c98eaff45edba1179103ae539bbfbe9753b" + }, + "source": [ + "Now, we create another dataframe with items and their total production each year from 1961 to 2013" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "_cell_guid": "bfd692bc-dce4-4870-9ab9-9775cf69a87f", + "_uuid": "9e11017d381f175eee714643bc5fa763600aaa0b" + }, + "outputs": [], + "source": [ + "item_list = list(df['Item'].unique())\n", + "\n", + "item_df = pd.DataFrame()\n", + "item_df['Item_Name'] = item_list\n", + "\n", + "for yr in year_list:\n", + " item_produce = []\n", + " for it in item_list:\n", + " item_produce.append(df[yr][df['Item']==it].sum())\n", + " item_df[yr] = item_produce\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "_cell_guid": "3b7ed0c2-6140-4285-861c-d0cd2324a1f5", + "_uuid": "cb4641df5ce90f516f88c536e8a6c6870c5b4f65" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Item_NameY1961Y1962Y1963Y1964Y1965Y1966Y1967Y1968Y1969...Y2004Y2005Y2006Y2007Y2008Y2009Y2010Y2011Y2012Y2013
0Wheat and products138829.0144643.0147325.0156273.0168822.0169832.0171469.0179530.0189658.0...527394.0532263.0537279.0529271.0562239.0557245.0549926.0578179.0576597587492
1Rice (Milled Equivalent)122700.0131842.0139507.0148304.0150056.0155583.0158587.0164614.0167922.0...361107.0366025.0372629.0378698.0389708.0394221.0398559.0404152.0406787410880
2Barley and products46180.048915.051642.054184.054945.055463.056424.060455.065501.0...102055.097185.0100981.093310.098209.099135.092563.092570.08876699452
3Maize and products168039.0168305.0172905.0175468.0190304.0200860.0213050.0215613.0221953.0...545024.0549036.0543280.0573892.0592231.0557940.0584337.0603297.0608730671300
4Millet and products19075.019019.019740.020353.018377.020860.022997.021785.023966.0...25789.025496.025997.026750.026373.024575.027039.025740.02610526346
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5 rows × 54 columns

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" + ], + "text/plain": [ + " Item_Name Y1961 Y1962 Y1963 Y1964 Y1965 \\\n", + "0 Wheat and products 138829.0 144643.0 147325.0 156273.0 168822.0 \n", + "1 Rice (Milled Equivalent) 122700.0 131842.0 139507.0 148304.0 150056.0 \n", + "2 Barley and products 46180.0 48915.0 51642.0 54184.0 54945.0 \n", + "3 Maize and products 168039.0 168305.0 172905.0 175468.0 190304.0 \n", + "4 Millet and products 19075.0 19019.0 19740.0 20353.0 18377.0 \n", + "\n", + " Y1966 Y1967 Y1968 Y1969 ... Y2004 Y2005 \\\n", + "0 169832.0 171469.0 179530.0 189658.0 ... 527394.0 532263.0 \n", + "1 155583.0 158587.0 164614.0 167922.0 ... 361107.0 366025.0 \n", + "2 55463.0 56424.0 60455.0 65501.0 ... 102055.0 97185.0 \n", + "3 200860.0 213050.0 215613.0 221953.0 ... 545024.0 549036.0 \n", + "4 20860.0 22997.0 21785.0 23966.0 ... 25789.0 25496.0 \n", + "\n", + " Y2006 Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013 \n", + "0 537279.0 529271.0 562239.0 557245.0 549926.0 578179.0 576597 587492 \n", + "1 372629.0 378698.0 389708.0 394221.0 398559.0 404152.0 406787 410880 \n", + "2 100981.0 93310.0 98209.0 99135.0 92563.0 92570.0 88766 99452 \n", + "3 543280.0 573892.0 592231.0 557940.0 584337.0 603297.0 608730 671300 \n", + "4 25997.0 26750.0 26373.0 24575.0 27039.0 25740.0 26105 26346 \n", + "\n", + "[5 rows x 54 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "item_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "3fa01e1f-bedd-431b-90c3-8d7d70545f34", + "_uuid": "56a647293f1c1aba7c184f249021e008a4d5a8f2" + }, + "source": [ + "# Some more feature engineering\n", + "\n", + "This time, we will use the new features to get some good conclusions.\n", + "\n", + "# 1. Total amount of item produced from 1961 to 2013\n", + "# 2. Providing a rank to the items to know the most produced item" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "_cell_guid": "3a6bb102-6749-4818-860d-59aaad6de07f", + "_uuid": "9e816786e7a161227ae72d164b25c0029e01e5b4", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Item_NameY1961Y1962Y1963Y1964Y1965Y1966Y1967Y1968Y1969...Y2006Y2007Y2008Y2009Y2010Y2011Y2012Y2013SumProduction_Rank
0Wheat and products138829.0144643.0147325.0156273.0168822.0169832.0171469.0179530.0189658.0...537279.0529271.0562239.0557245.0549926.0578179.057659758749219194671.06.0
1Rice (Milled Equivalent)122700.0131842.0139507.0148304.0150056.0155583.0158587.0164614.0167922.0...372629.0378698.0389708.0394221.0398559.0404152.040678741088014475448.08.0
2Barley and products46180.048915.051642.054184.054945.055463.056424.060455.065501.0...100981.093310.098209.099135.092563.092570.088766994524442742.020.0
3Maize and products168039.0168305.0172905.0175468.0190304.0200860.0213050.0215613.0221953.0...543280.0573892.0592231.0557940.0584337.0603297.060873067130019960640.05.0
4Millet and products19075.019019.019740.020353.018377.020860.022997.021785.023966.0...25997.026750.026373.024575.027039.025740.026105263461225400.038.0
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5 rows × 56 columns

\n", + "
" + ], + "text/plain": [ + " Item_Name Y1961 Y1962 Y1963 Y1964 Y1965 \\\n", + "0 Wheat and products 138829.0 144643.0 147325.0 156273.0 168822.0 \n", + "1 Rice (Milled Equivalent) 122700.0 131842.0 139507.0 148304.0 150056.0 \n", + "2 Barley and products 46180.0 48915.0 51642.0 54184.0 54945.0 \n", + "3 Maize and products 168039.0 168305.0 172905.0 175468.0 190304.0 \n", + "4 Millet and products 19075.0 19019.0 19740.0 20353.0 18377.0 \n", + "\n", + " Y1966 Y1967 Y1968 Y1969 ... Y2006 \\\n", + "0 169832.0 171469.0 179530.0 189658.0 ... 537279.0 \n", + "1 155583.0 158587.0 164614.0 167922.0 ... 372629.0 \n", + "2 55463.0 56424.0 60455.0 65501.0 ... 100981.0 \n", + "3 200860.0 213050.0 215613.0 221953.0 ... 543280.0 \n", + "4 20860.0 22997.0 21785.0 23966.0 ... 25997.0 \n", + "\n", + " Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013 \\\n", + "0 529271.0 562239.0 557245.0 549926.0 578179.0 576597 587492 \n", + "1 378698.0 389708.0 394221.0 398559.0 404152.0 406787 410880 \n", + "2 93310.0 98209.0 99135.0 92563.0 92570.0 88766 99452 \n", + "3 573892.0 592231.0 557940.0 584337.0 603297.0 608730 671300 \n", + "4 26750.0 26373.0 24575.0 27039.0 25740.0 26105 26346 \n", + "\n", + " Sum Production_Rank \n", + "0 19194671.0 6.0 \n", + "1 14475448.0 8.0 \n", + "2 4442742.0 20.0 \n", + "3 19960640.0 5.0 \n", + "4 1225400.0 38.0 \n", + "\n", + "[5 rows x 56 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sum_col = []\n", + "for i in range(115):\n", + " sum_col.append(item_df.iloc[i,1:].values.sum())\n", + "item_df['Sum'] = sum_col\n", + "item_df['Production_Rank'] = item_df['Sum'].rank(ascending=False)\n", + "\n", + "item_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "7e20740c-565b-4969-a52e-d986e462b750", + "_uuid": "f483c9add5f6af9af9162b5425f6d65eb1c5f4aa" + }, + "source": [ + "# Now, we find the most produced food items in the last half-century" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "_cell_guid": "3130fe83-404c-4b3c-addc-560b2e2f32bf", + "_uuid": "0403e9ab2e13587588e3a30d64b8b6638571d3d5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "56 Cereals - Excluding Beer\n", + "65 Fruits - Excluding Wine\n", + "3 Maize and products\n", + "53 Milk - Excluding Butter\n", + "6 Potatoes and products\n", + "1 Rice (Milled Equivalent)\n", + "57 Starchy Roots\n", + "64 Vegetables\n", + "27 Vegetables, Other\n", + "0 Wheat and products\n", + "Name: Item_Name, dtype: object" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "item_df['Item_Name'][item_df['Production_Rank'] < 11.0].sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "b6212fed-588b-426e-9271-6d857cd6aacb", + "_uuid": "e2c83f4c851b755ea6cf19f1bca168e705bd4edd" + }, + "source": [ + "So, cereals, fruits and maize are the most produced items in the last 50 years\n", + "\n", + "# Food and feed plot for most produced items " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "_cell_guid": "493f9940-1762-4718-acb4-fba5c4c73f4b", + "_uuid": "f8454c5200bdeb3995b9a0ada3deb5ca1c31f181" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n", + " warnings.warn(msg)\n", + "/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n", + " warnings.warn(msg, UserWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.factorplot(\"Item\", data=df[(df['Item']=='Wheat and products') | (df['Item']=='Rice (Milled Equivalent)') | (df['Item']=='Maize and products') | (df['Item']=='Potatoes and products') | (df['Item']=='Vegetables, Other') | (df['Item']=='Milk - Excluding Butter') | (df['Item']=='Cereals - Excluding Beer') | (df['Item']=='Starchy Roots') | (df['Item']=='Vegetables') | (df['Item']=='Fruits - Excluding Wine')], kind=\"count\", hue=\"Element\", size=20, aspect=.8)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "45dda825-49a0-41ab-9ebd-eaa609aac986", + "_uuid": "ce5b2d38ff24ea08da632c4e2773dbd0bd026b9d", + "collapsed": true + }, + "source": [ + "# Now, we plot a heatmap of correlation of produce in difference years" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "_cell_guid": "b1bab0ec-6615-452c-8d06-a81d4f2ae252", + "_uuid": "a2ed2aae2364810ce640648cf50880adcf2cdcc4" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "year_df = df.iloc[:,10:]\n", + "fig, ax = plt.subplots(figsize=(16,10))\n", + "sns.heatmap(year_df.corr(), ax=ax)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "43e1af94-ba07-4b95-8da3-1d774db940cd", + "_uuid": "70d2b0a7db9b8a5535b3c5b3c2eb927b904bf6d3" + }, + "source": [ + "So, we gather that a given year's production is more similar to its immediate previous and immediate following years." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "_cell_guid": "58cde27d-5ddc-4ebe-a8e1-80a8257f44c1", + "_uuid": "6f48b52c09ea6a207644044cace5a88c983bf316" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", + " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10,10))\n", + "ax1.set(xlabel='Y1968', ylabel='Y1961')\n", + "ax2.set(xlabel='Y1968', ylabel='Y1963')\n", + "ax3.set(xlabel='Y1968', ylabel='Y1986')\n", + "ax4.set(xlabel='Y1968', ylabel='Y2013')\n", + "sns.jointplot(x=\"Y1968\", y=\"Y1961\", data=df, kind=\"reg\", ax=ax1)\n", + "sns.jointplot(x=\"Y1968\", y=\"Y1963\", data=df, kind=\"reg\", ax=ax2)\n", + "sns.jointplot(x=\"Y1968\", y=\"Y1986\", data=df, kind=\"reg\", ax=ax3)\n", + "sns.jointplot(x=\"Y1968\", y=\"Y2013\", data=df, kind=\"reg\", ax=ax4)\n", + "plt.close(2)\n", + "plt.close(3)\n", + "plt.close(4)\n", + "plt.close(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "8a297a06-977f-4ff7-a9ad-c7e8804930a8", + "_uuid": "6b738ce8b15a764fab90fac96f9534f94c14342e" + }, + "source": [ + "# Heatmap of production of food items over years\n", + "\n", + "This will detect the items whose production has drastically increased over the years" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "_cell_guid": "588cebd9-e97c-460d-8ed5-e663ac293711", + "_uuid": "16ce47d43a3038874a74d8bbb9a2e26f6ee54437" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "new_item_df = item_df.drop([\"Item_Name\",\"Sum\",\"Production_Rank\"], axis = 1)\n", + "fig, ax = plt.subplots(figsize=(12,24))\n", + "sns.heatmap(new_item_df,ax=ax)\n", + "ax.set_yticklabels(item_df.Item_Name.values[::-1])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "825620f9-7ab5-4fe2-9529-c4f1a300138e", + "_uuid": "5c42595537332ea71089d8c3dc041d3bf7d41b55" + }, + "source": [ + "There is considerable growth in production of Palmkernel oil, Meat/Aquatic animals, ricebran oil, cottonseed, seafood, offals, roots, poultry meat, mutton, bear, cocoa, coffee and soyabean oil.\n", + "There has been exceptional growth in production of onions, cream, sugar crops, treenuts, butter/ghee and to some extent starchy roots." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "80428f51-2fd4-468d-9530-9279215b4218", + "_uuid": "4c9bb27cd76099c5348243a99448c509ef0c5ded" + }, + "source": [ + "Now, we look at clustering." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "a3f1db3a-1b82-4e42-8e7d-f1a26915693b", + "_uuid": "da167de5a5b92e164fc6993b32ebbfab4ef9a6e3", + "collapsed": true + }, + "source": [ + "# What is clustering?\n", + "Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "136315a0-b37d-4d89-bd0d-037727062c34", + "_uuid": "04ab802ec92eaf6a27706f2008933dcf3865855a" + }, + "source": [ + "# Today, we will form clusters to classify countries based on productivity scale" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "27ba0b5d-c57e-485d-9588-017e16fe1904", + "_uuid": "659afdada04e8854765b5e7208394915b30f859a" + }, + "source": [ + "For this, we will use k-means clustering algorithm.\n", + "# K-means clustering\n", + "(Source [Wikipedia](https://en.wikipedia.org/wiki/K-means_clustering#Standard_algorithm) )\n", + "![http://gdurl.com/5BbP](http://gdurl.com/5BbP)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "7aeb3175-33bd-4f49-903a-57d43380e90e", + "_uuid": "6b0b4881e623ed3c133b68b98e6fb6755e18fd78" + }, + "source": [ + "This is the data we will use." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "_cell_guid": "a5b99ea8-975f-4467-9895-bffe1db876eb", + "_uuid": "57aba4000bfc422e848b14ad24b02a570d6c0554" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Y1961Y1962Y1963Y1964Y1965Y1966Y1967Y1968Y1969Y1970...Y2006Y2007Y2008Y2009Y2010Y2011Y2012Y2013Mean_ProduceRank
Afghanistan9481.09414.09194.010170.010473.010169.011289.011508.011815.010454.0...18317.019248.019381.020661.021030.021100.022706.023007.013003.05660469.0
Albania1706.01749.01767.01889.01884.01995.02046.02169.02230.02395.0...6911.06744.07168.07316.07907.08114.08221.08271.04475.509434104.0
Algeria7488.07235.06861.07255.07509.07536.07986.08839.09003.09355.0...51067.049933.050916.057505.060071.065852.069365.072161.028879.49056638.0
Angola4834.04775.05240.05286.05527.05677.05833.05685.06219.06460.0...28247.029877.032053.036985.038400.040573.038064.048639.013321.05660468.0
Antigua and Barbuda92.094.0105.095.084.073.064.059.068.077.0...110.0122.0115.0114.0115.0118.0113.0119.083.886792172.0
\n", + "

5 rows × 55 columns

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" + ], + "text/plain": [ + " Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 \\\n", + "Afghanistan 9481.0 9414.0 9194.0 10170.0 10473.0 10169.0 \n", + "Albania 1706.0 1749.0 1767.0 1889.0 1884.0 1995.0 \n", + "Algeria 7488.0 7235.0 6861.0 7255.0 7509.0 7536.0 \n", + "Angola 4834.0 4775.0 5240.0 5286.0 5527.0 5677.0 \n", + "Antigua and Barbuda 92.0 94.0 105.0 95.0 84.0 73.0 \n", + "\n", + " Y1967 Y1968 Y1969 Y1970 ... Y2006 \\\n", + "Afghanistan 11289.0 11508.0 11815.0 10454.0 ... 18317.0 \n", + "Albania 2046.0 2169.0 2230.0 2395.0 ... 6911.0 \n", + "Algeria 7986.0 8839.0 9003.0 9355.0 ... 51067.0 \n", + "Angola 5833.0 5685.0 6219.0 6460.0 ... 28247.0 \n", + "Antigua and Barbuda 64.0 59.0 68.0 77.0 ... 110.0 \n", + "\n", + " Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 \\\n", + "Afghanistan 19248.0 19381.0 20661.0 21030.0 21100.0 22706.0 \n", + "Albania 6744.0 7168.0 7316.0 7907.0 8114.0 8221.0 \n", + "Algeria 49933.0 50916.0 57505.0 60071.0 65852.0 69365.0 \n", + "Angola 29877.0 32053.0 36985.0 38400.0 40573.0 38064.0 \n", + "Antigua and Barbuda 122.0 115.0 114.0 115.0 118.0 113.0 \n", + "\n", + " Y2013 Mean_Produce Rank \n", + "Afghanistan 23007.0 13003.056604 69.0 \n", + "Albania 8271.0 4475.509434 104.0 \n", + "Algeria 72161.0 28879.490566 38.0 \n", + "Angola 48639.0 13321.056604 68.0 \n", + "Antigua and Barbuda 119.0 83.886792 172.0 \n", + "\n", + "[5 rows x 55 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "_cell_guid": "66964df2-892d-4e55-a4b1-f94d10e4c7dd", + "_uuid": "19bdd89a3ad9df962959ad6b996946f6f3916d58" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n", + "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n", + " after removing the cwd from sys.path.\n" + ] + } + ], + "source": [ + "X = new_df.iloc[:,:-2].values\n", + "\n", + "X = pd.DataFrame(X)\n", + "X = X.convert_objects(convert_numeric=True)\n", + "X.columns = year_list" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "461e5bcc-0101-4ea1-ae13-20600f883929", + "_uuid": "0d3e50235c9505ebc255053d4a5aae547fc17d8d" + }, + "source": [ + "# Elbow method to select number of clusters\n", + "This method looks at the percentage of variance explained as a function of the number of clusters: One should choose a number of clusters so that adding another cluster doesn't give much better modeling of the data. More precisely, if one plots the percentage of variance explained by the clusters against the number of clusters, the first clusters will add much information (explain a lot of variance), but at some point the marginal gain will drop, giving an angle in the graph. The number of clusters is chosen at this point, hence the \"elbow criterion\". This \"elbow\" cannot always be unambiguously identified. Percentage of variance explained is the ratio of the between-group variance to the total variance, also known as an F-test. A slight variation of this method plots the curvature of the within group variance.\n", + "# Basically, number of clusters = the x-axis value of the point that is the corner of the \"elbow\"(the plot looks often looks like an elbow)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "_cell_guid": "06271223-bd32-48ac-a373-6c1e6bbf7c7b", + "_uuid": "c57d7277510a8c11fdc3d311e4d8a22539617ed9" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.cluster import KMeans\n", + "wcss = []\n", + "for i in range(1,11):\n", + " kmeans = KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)\n", + " kmeans.fit(X)\n", + " wcss.append(kmeans.inertia_)\n", + "plt.plot(range(1,11),wcss)\n", + "plt.title('The Elbow Method')\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('WCSS')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "ad4bc40a-9540-497d-95e3-3fee6088ea95", + "_uuid": "6450dd1c3d7a8114931dc358d2f09a0424b52fd7" + }, + "source": [ + "As the elbow corner coincides with x=2, we will have to form **2 clusters**. Personally, I would have liked to select 3 to 4 clusters. But trust me, only selecting 2 clusters can lead to best results.\n", + "Now, we apply k-means algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "_cell_guid": "eed3f672-e089-4dbb-aad8-b9618967abf3", + "_uuid": "d92d758ee7213ddcd84e9b8b2f61c9e260ed6ba2" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", + " after removing the cwd from sys.path.\n" + ] + } + ], + "source": [ + "kmeans = KMeans(n_clusters=2,init='k-means++',max_iter=300,n_init=10,random_state=0) \n", + "y_kmeans = kmeans.fit_predict(X)\n", + "\n", + "X = X.as_matrix(columns=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "ef07bd6d-679d-4375-b7b3-abeca3421e37", + "_uuid": "6f93a4bd3f17427f4b2dbe08af8e015a1e4a2f89" + }, + "source": [ + "Now, let's visualize the results." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "_cell_guid": "5a7fe139-13df-453b-8c16-891929bc595e", + "_uuid": "a57e0a38f4c0f0385be75fd9f71d4a2d8213aea3" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0,1],s=100,c='red',label='Others')\n", + "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1,1],s=100,c='blue',label='China(mainland),USA,India')\n", + "plt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],s=300,c='yellow',label='Centroids')\n", + "plt.title('Clusters of countries by Productivity')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "923d4536-2bce-4b99-b98a-33b801a56a8b", + "_uuid": "fe531e8c41eec0eb5dc52a9890871841f5d27211" + }, + "source": [ + "So, the blue cluster represents China(Mainland), USA and India while the red cluster represents all the other countries.\n", + "This result was highly probable. Just take a look at the plot of cell 3 above. See how China, USA and India stand out. That has been observed here in clustering too.\n", + "\n", + "You should try this algorithm for 3 or 4 clusters. Looking at the distribution, you will realise why 2 clusters is the best choice for the given data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "_cell_guid": "6dee7acb-0f08-4ae1-85b4-f4704026694a", + "_uuid": "179a1ede21ae330664a0b7c63e36574acdc0428c" + }, + "source": [ + "This is not the end! More is yet to come." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Now, lets try to predict the production using regression for 2020. We will predict the production for USA,India and Pakistan.**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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aDDxiZr8Ky+4L33If8FczW0zQQ7k4vM/ccObYvPA+V2nml0h+WL91B799cRF/fXM5HYoK6FBYwMZttZRpeCrvWfBHfzMXme0LfBvoS1Qgcvcr0lazNCkvL/eZM2dmuxoi7VJNXT3/eGsFd774ERura7jkmD788PSB7L1Hx2xXTZpgZrPcvTyRaxPtqfwL+DfwIsFCQxGRpMxYuIZfTZ7P4jWbOeHgvfn5WUMYfEDXbFdLUizRoNLZ3a9La01EpE1avGYzv5o8jxkL11IYJlCWrt3CgtWbFFTaoESDyjNmNtrdn01rbUSkzYjOmxQXGkUFRm19MNy+asO2Zg/dkvyU6OyvawgCyzYz2xR+bUxnxUQkP9XU1fPg68sYcfsMHnpjGRcd3ZvSkg47A0pEU4duSf5KqKfi7ns2f5WItHcvf7SWm56Zx+I1mzm+f5A3GdKzK/3emhzzep1V0vYkPKXYzM4BTgqfznD3Z9JTJRHJN4vXbObmyfOYvnAtB+7dmXsvHc7pQ/bfeYxvU4duSduSUFAxs4nA0cDfw6JrzOxEdx+XtpqJSM5bv3UHv5u2iL++sZyS4kJ+OnowY77Ql45Fu2/6qD242o9E16l8ABzp7vXh80Jgtrsfnub6pZzWqYi0Xm1dPf94ewV3TA3Wm1x0dB9+dMZA9gnXm8Tazh60B1e+Ssc6FYBSghXtEOy/JSLt0Cth3mRRmDf5xdlDOKTHrqnBke3sI72SyHb2E84fymvjTslWtSVDEg0qE4DZZjadYHfgk4DxaauViOScJWs3c/Pk+UxbsIYD9+7Mny8dzhlReZOIpk5bVM+k7Ut09tfDZjaDIK9iwHXu/kk6KyYiuWHD1hruemkRD76+rFHeJNYwV7wZXZrp1T40GVTMbLC7LzCzo8KiivB7TzPr6e7vprd6IpIttXX1PBzmTdZX13Dx0cE+XfvuuStvEmuYq7RzMeu21jS6n2Z6tQ/N9VR+SHBi4m9ivOYERwCLSBsTnTc5rn93fnHWoQzpufuWKvGGuToWFVBSXKiZXu1Uk0HF3ceGD0e5+7bo18ysU9pqJSJZ8fHazdySQN4E4g9nbaiu4c6LjtRMr3Yq0UT968BRCZSJSB6Kzpt0Ki5k/KjBXHbCrvUmsXInTS1o1GmL7VdzOZUDgDKgxMyGsetc+K5A5zTXTUTSrHHepDc/PH3QzrwJxM+dXDC8jCdmVWqYS3bTXE9lJHAZwfnvv2FXUNkI/DR91RKRdPv3oiBv8tGnQd7k52cN4dCejZegxcudTF+wlgnnD9Uwl+ymuZzKg8CDZnaBuz+RoTqJSBotWbuZW56dz4vz19Cne2f+9M3hjDw0dt4E4udOVq2v1jCXNJJoTmW4mU1z9/UAZtYN+JG7/3f6qiYiqbShuoa7pu3Km4wbNZjLT9h9n65kcyciDSW699dsdx/WoOxdd8+7RL32/pL2praunoffWckdLyxkfXUNF5X35tCeXfnTy0sa7c0Va9PHeLmTCecPVS+lnUjH3l+FZtbR3beHH1ACdGzmPSKSZa8u+oybnpnHwk83cWy/7vzi7CEs+nRzzMR7p+IC5U6k1RINKn8DppnZAwSLHq8AHkxbrUSkVZZ+toWbJ8/jxflr6N29hHu+cRRnHnYAZsbYh2bFDB4NyyKUO5FkJLr3161mNgc4lWAG2E3uPiWtNRORpG2oruH30xbx4BvL6FBYwHVnBnmTTsW78ibJ7sGl3IkkI+Gt7939OeC5NNZFRFqotq6eR2eu5DcvfMS6rTv42vDe/GjkQPbbs/HGF/ES76UlxWyvrde6E2mVRE9+3EQw7AXQASgGtrh71/jvEpFMeG1xkDdZ8MkmjunXnV+cNYTDyuIfeRTvFMYbzjkU0EFa0jqJDn/tGf3czM4DjklLjUQkIUs/28Itz85n6rxP6dVt97xJRKwpwpEgEa9cQURaI5mTH3dy96fMTOfTi2TBxm013P3SYh54bSkdCgv4yZmDuOKEfrvlTSD+9iqAEu+SNokOf50f9bQAKGfXcJiIZEBdvfPoOyv5zQsLqdq6gwuH9+LHIwfFzJuATmCU7Ei0p3J21ONaYBlwbsprIyIxvb74M24M8yb99+1CgRn/nFnBa4s/j5v30AmMkg2J5lQuT3dFRKSxZWHe5IUwb3LZF/ryyNsr2FZbDzQe0oqm7VUkG5rb+v73NDHM5e5Xp7xGIhI3b3Lqb17eGVAi4g1pxZvlpSnCkk7N9VQim2SdAAwBHg2fXwjMSlelRNqrmHmTMwaxX9cgb9LUkFasmV7aXkUyLdENJacDZ7h7Tfi8GHjB3U9u0YealQJ/AQ5j17YvCwmCVl+CnM3X3H2dBfMjfweMBrYCl7n7u+F9xgCRnZJ/FW7V3yRtKCm5Kjpvckzf4HyTob12X29ywsSXklq4qE0fJRWS2VCyIMF79gSi16rsEZa11O+A5919MHAEMB8YB0xz9wHAtPA5wChgQPg1FrgHwMy6A9cDxxKsmbk+3JJfJK8s+2wLYx+aydf/8habttXyh68fxaPfOa5RQIFgSKukwdThkuJCzIg700skkxKd/TURmB32WAC+BNzQkg80s67ASQQnSuLuO4AdZnYuMCK87EFgBnAdwSyzhzzoUr1pZqVm1iO8dqq7V4X3nQqcCTzcknqJZNrGbTX84aXF3P/aUooLC7h25CCuPLHxepNo8RYu/uDR92Jer5lekmmJzv56wMyeI+gVODDO3T9p4Wf2B9YCD5jZEQS5mWuA/d19dfh5q81sv/D6MmBl1PsrwrJ45Y2Y2ViCXg59+vRpYbVFUqNh3uSrR/Xi2pG78iYR8VbDx1q4eNuUhZrpJTkh0eEvCIaYvkjQyzi6FZ9ZBBwF3BMe/LWFXUNdscQ649SbKG9c6H6vu5e7e/m+++6bbH1FUuaNjz/nrN+/yk//bw799+3CpKtO5LYLj4gZUMY/OYfK9dU4u6YOPzW7MuZ94w2LaaaXZFpCQcXMJhL0JuaFX1eb2YQWfmYFUOHub4XPHycIMp+Gw1qE39dEXd876v29gFVNlIvknOWfb+E7f53JJf/zJhura7j768N47DvHx8ybQNOr4WM5b1gZE84fSllpCQaUlZYoSS9ZkWhOZTRwpLvXA5jZg8BsYHyyH+jun5jZSjMb5O4LCc5oiQSrMQT5mzHAv8K3TAK+Z2aPEAy/bQiHx6YAt0Ql589oSX1E0mnTthrunr6YB15dRlGhJZQ3gZathtd+XpILktlQshSoCh/H31c7Mf8P+LuZdQCWAJcT9JoeM7MrgRUEa2EAniUIaosJphRfDuDuVWZ2E/BOeN2NkaS9SLbV1Tv/nLmS219YyGebd/DV4UHeZP+usffpakir4SVfJRpUJrBr9pcR5FVa3Ctw9/cINqVs6NQY1zpwVZz73A/c39J6iKTDGx9/zk3PzGPe6o2UH9iN+y87msN7lca9PlZCXqvhJV81u/gxXHzYi2AjyaMJgspbrZj9lVVa/CjpsuLzrdzy7Hyen/sJZaUljBs1mLMO77Hb+SYNNdyeHnYtWgQdmCW5IZnFj4muqJ/l7sNbXbMcoKAiqbZpWw1/mP4x97+6lKJC479GHMR/fLF/s3kTiL9Cvqy0hNfGnZKO6ookLZmgkujw15tmdrS7v9P8pSLtQ1298/isldw25SM+27ydC47qxU/OTDxvAtqeXtqeRIPKycB3zWwZwboSI0h3HJ6uionksjeXBHmTuas2MvzAbtx/WXmTeROInTtRQl7amkSDyqi01kIkT6z4fCsTnpvPcx8GeZPfXzKs2bwJxD/a94LhZTwxq1IJeWkzmjtPpRPwXeBgYA5wn7vXZqJiIrlk8/Za/jB9Mff9eymFBcaPTh/It0+KnTeJ1SOJt5hx+oK12p5e2pQmE/Vm9ihQA/yboLey3N2vyVDd0kKJeklGXb3zxKwKbp2ykM82b+f8o8r4ycjBHLBXp5jBA4g5m6thQIkwYOnEL2eiKSItlspE/RB3Hxre9D7g7dZWTiRfvLXkc26MypvcN6acI3oHeZN4w1mdigti9kgKzaiL8QeccifS1jQXVGoiD9y9trlxY5G2YGVVkDd5ds4n9NyrE3ddMoyzG+RN4g1nxeuR1Lk36rEodyJtUXNB5Qgz2xg+NqAkfB6Z/dU1rbUTyaCGeZMfnj6Qb3+xPyUdGudNkp3yWxaVW1HuRNqyJoOKuze/ekskz9XXO49H502GlXHtmYPosdeuoamG+ZPSzsWs21rT6F7xjvWNPgtFpC1LZkNJkTbn7aVV/PLpucxdtZGj+pTylzHlHNl79/UmsfInxQVGcaFRU7crT1JSXMgN5xwKaHsVab8UVKRdWlm1lYnPLWDynNX03KsTv7v4SM45omfM9Sax8ic19U5pSTFdOhbFDB4KItJeKahIu7J5ey1/nL6Yv7y6lEIzfnDaQMaeFDtvEhEvf7Khuob3rj8jXVUVyUsKKtIu1Nc7j79bwW1TFrJ203a+MqyMnzTIm8SjrVREEqegIm3e20uruPGZuXxYuZFhfUq599LhDOvTrfk3hnS2iUjiFFSkzYrOm/RoJm/SlEh+RMl3keYpqEibs2V7LX+csZj/+fdSCgy+f9oAvnPSQU3mTZqj6cAiiVFQkTajvt554t1gvUmyeRMRSQ0FFWkT3llWxY1Pz2NO5QaO7F3Kny8dzlFJ5E0iYm0SqR6KSOIUVCSvVazbyoTnFjD5g9Uc0LUTv70oyJsUFDR/vklzOwxHNokErTsRSZSCiuSlLdtruWfGx9z77yU78yZjT+pP5w7N/0onu8PwbVMWKqiIJEhBRfJKfb3z5OxKbn1+AWs2bee8I3vykzMHJ7VmJNkdhnVevEjiFFQkb8xcVsWNz8zjg4oNHNG7lD+1MG+SbJDQIkeRxCmoSM6rWBesN3kmybxJPPFWyDe1w7CIJEZBRXLWlu21/Onlj7n3lSWYwTWnDqBnaSdum7KQHzz6XotnZ8VbIa8dhkVaT0FFck59vfN/syu5dcoCPt24nXOP7Ml1Zw7m7aVVcWdnQexg0NQU4XjlCiIiLWce49zstqy8vNxnzpyZ7WpIHLOWB+tN3g/zJr846xCGH9gdgBMmvpTUsNUFw8t4YlZlo/IJ5w9V4BBJgpnNcvfyRK5VT0VyQuX6aiY+t4Cn31/F/l078o1j+zB9wRq+es8bO3sS8RLs66sbn8BYXVPHw2+tpK7BH02aIiySXgoqklVbd9Typxkf8+dXlgBw9akDKCvtxA2T5jUa5op3hG88DQNKhKYIi6SPgopkRX2989R7lfz6+SBvcvYRPRk3ajBlpSWcMPGlmOtIOhYVUFJc2Gg4q1NxQcxgU2gWM7BoirBI+hRk64PNrNDMZpvZM+Hzfmb2lpktMrNHzaxDWN4xfL44fL1v1D3Gh+ULzWxkdloiyZq1vIqv/PE1fvjY+xzQtRNP/Ofx/P6SYZSF/9g3ddLihPOHUlZaggFlpSVMOH8o1599KCXFu+9AXFJcyCXH9o5ZrinCIumTzZ7KNcB8oGv4/NfAne7+iJn9CbgSuCf8vs7dDzazi8PrLjKzIcDFwKFAT+BFMxvo7rGXRUvWVa6v5tfPLWBSmDe542tHcN6RZY3WmzR10mJTW9DHms1VfmB3TREWyaCszP4ys17Ag8DNwA+Bs4G1wAHuXmtmxwM3uPtIM5sSPn7DzIqAT4B9gXEA7j4hvOfO65r6bM3+yryGeZOxJ/Xnu186iC4dY/9N03BvLtCsLZFsyofZX78FfgLsGT7fG1jv7rXh8wog8q9HGbASIAw4G8Lry4A3o+4Z/R7JAQ3zJsP6lLJqfTV3v7SYJ9+tjNtr0EmLIvkr40HFzM4C1rj7LDMbESmOcak381pT72n4mWOBsQB9+vRJqr7SMrOWr+PGZ+bx/sr1HN5rLy4+ug/3vrIkqYWLCiIi+ScbPZUTgHPMbDTQiSCn8lug1MyKwt5KL2BVeH0F0BuoCIe/9gKqosojot+zG3e/F7gXguGvlLdIdloVrjeZ9P4q9tuzI7+58Ai+MqyML946PeaMrhsmzd1t4aLOMBHJbxmf/eXu4929l7v3JUi0v+Tu3wCmA18NLxsD/Ct8PCl8Tvj6Sx4kgiYBF4ezw/oBA4AbYNt1AAAOsklEQVS3M9QMaWDrjlrumPoRp/xmBlPmfsL/O+Vgpv94BBcM70VBgTW5cDHeGSYikn9yaZ3KdcAjZvYrYDZwX1h+H/BXM1tM0EO5GMDd55rZY8A8oBa4SjO/Muup8FyTVRu2UWBQ73DW4T0YN2owvbp13u3aeDO64tECRZH8pL2/pEWeml3JdY9/wPa6+p1lHQoLuPWrhwONcyRAzBld8RYulpWW8Nq4U9LcChFJRDKzv7K2+FHy16ow7xEdUAB21NVzw6S5jH9yDpXrq3F2z5Eks3BRCxRF8lMuDX9JjopsH1+5vpo9OxaxrbaOmrrYPdx4mzveNmUhr407JamFiyKSfxRU2qmmzhlpeN24Jz5gW23QK9m0vZZCM7p2KmLjttpG18fTVI5E04dF2g4FlXao4Yr1yBDVzOVVTF+wdrdAc/Pk+TsDSkSdOwVmSW3uqE0cRdoH5VTaodumLIw5jffvb67YLRfyo8feZ+3m7THvkezmjsqRiLQP6qm0cbGGueINRTXMktS5YzHKoWWbO4pI26cpxW1YvI0Z4w1RxRNrmEubO4q0H5pSLED8YS53Gg1RxRMZ1mo4zKWAIiKxaPirjUhmmGtDdQ13XnQkE59bwCcbtwHQqbiAunrfbapwJBei2Vkikij1VNqAyDBXwwWHpZ2LY15/wF6dWP75VjZU19ChqID/GnEQM//7dG776hHqkYhIqyinkmdi9UgiCxMbKi0p3m0HYIDiQqNLxyLWb61h9NADGD/qEHp379zovSIiEflwSJe0QLz1JQ3zJhGRYa5I0OlQWMCOunrKSkv48zeHc2z/vTNZfRFpBxRUclS8HkmsxHuhGXUxepw9S0s4rv/eHNuvO0/OrmSvzsVcO3IQFxzVi8KCWGeciYi0joJKDkq2R1Ln3mjab6eiAob22ouTb59BnTv/OeIgrjr5YPaIcy68iEgq6F+YLEtFj6SsQW6lW+diHHj+w0+UNxGRjFJQyZBYwQNodY8ketpvv326cOMz85i1fB2H9uyqvImIZJyCSisks9NvrODRqbigxT2S6M88/qC9+eFj7/Hku5Xss0dHbr3gcC4YrryJiGSegkoLxQsUEdH/8G/dURszeLS0RxIJXNtq6vifV5Yw/sk51NUHeZP/GnEQe3aKvT5FRCTdFFQSkEze44ZJc3dbG5LMuewR8XokkWDi7jzzwWomPreAyvXVjDosyJv02Vt5ExHJLi1+bEa8TRnj9TKSEWtxYnObNb6/cv3OvMmQHl35xdlDOE55ExFJIy1+TKFkZ2IlqqS4kBvOOXTnZzSXl/l04zZ+/fyCMG/SgYnnD+XC8t7Km4hITlFQaUa8TRnj5T3ibStfWlJMl45FMYNHU/trbaup4y//XsIfZ3xMbZ3z3S8dxFUnK28iIrlJQaUZPUtLYuZF4uU9gJjDZTecc2hSmzM2zJuceegBjB89mAP37tL6RomIpImCSjOuHTkoZpBobkv41px8+EHFem58eh4zl6/jkB5duf3CIzj+IOVNRCT3Kag0IxIMkgkSLT1/5NON27htykIen1WhvImI5CUFlQSk+5CqhnmT73ypP987+WDlTUQk7yioZJG7M3nOaiY8q7yJiLQNCipZMqdiAzc+M5d3lgV5k9suPJwvHLRPtqslItIqCioZtiaSN3m3gu6dOzDh/KF8TXkTEWkjFFQyZFtNHfe9upQ/Tl9MTZ0z9qT+XHXywXRV3kRE2hAFlTRzd56d8wm3PDufyvXVnDFkf3725UOUNxGRNklBJY0+rNzAjU/P4+1lVQw+YE/+8e1jlTcRkTatINMfaGa9zWy6mc03s7lmdk1Y3t3MpprZovB7t7DczOwuM1tsZh+Y2VFR9xoTXr/IzMZkui3xrNm4jWv/+T5n3/0qH6/dzC1fGcrkq7+ogCIibV42eiq1wI/c/V0z2xOYZWZTgcuAae4+0czGAeOA64BRwIDw61jgHuBYM+sOXA+UAx7eZ5K7r8t4i0LReZMddfV8+4v9+d4pypuISPuR8aDi7quB1eHjTWY2HygDzgVGhJc9CMwgCCrnAg95sEf/m2ZWamY9wmununsVQBiYzgQezlhjQpG8yYTn5lOxrprTh+zPz0YfQt99lDcRkfYlqzkVM+sLDAPeAvYPAw7uvtrM9gsvKwNWRr2tIiyLVx7rc8YCYwH69OmTugbQOG/y9/84lhMO1jCXiLRPWQsqZrYH8ATwfXffaBZ3nUasF7yJ8saF7vcC90JwSFfytW1szaZt3D5lIf+cVUG3zh24+SuHcVF5b4oKM56mEhHJGVkJKmZWTBBQ/u7uT4bFn5pZj7CX0gNYE5ZXAL2j3t4LWBWWj2hQPiOd9YbGeZP/OLEf3ztlAHuVKG8iIpLxoGJBl+Q+YL673xH10iRgDDAx/P6vqPLvmdkjBIn6DWHgmQLcEpklBpwBjE9Xvd2d5z4M1ptUrKvmtEOC9Sb9lDcREdkpGz2VE4BLgTlm9l5Y9lOCYPKYmV0JrAAuDF97FhgNLAa2ApcDuHuVmd0EvBNed2MkaZ9q1TvqGPPA27y9tIpB++/J3648lhMHKG8iItJQNmZ/vUrsfAjAqTGud+CqOPe6H7g/dbWLraRDIf327sI5R/Tk4qOVNxERiUcr6hP0668enu0qiIjkPP3JLSIiKaOgIiIiKaOgIiIiKaOgIiIiKaOgIiIiKaOgIiIiKaOgIiIiKaOgIiIiKWPBgvX2w8zWAsubuWwf4LMMVCcXtJe2tpd2Qvtpa3tpJ2S/rQe6+76JXNjugkoizGymu5dnux6Z0F7a2l7aCe2nre2lnZBfbdXwl4iIpIyCioiIpIyCSmz3ZrsCGdRe2tpe2gntp63tpZ2QR21VTkVERFJGPRUREUkZBRUREUmZdhFUzOx+M1tjZh9GlR1hZm+Y2Rwze9rMuka9dnj42tzw9U5h+fDw+WIzu8vM4p1gmTXJtNXMvmFm70V91ZvZkeFrba2txWb2YFg+38zGR73nTDNbGLZ1XDba0pQk29nBzB4Iy983sxFR78npn6mZ9Taz6eHPZ66ZXROWdzezqWa2KPzeLSy3sB2LzewDMzsq6l5jwusXmdmYbLUpnha0dXD4895uZj9ucK/c+v119zb/BZwEHAV8GFX2DvCl8PEVwE3h4yLgA+CI8PneQGH4+G3geILjkJ8DRmW7ba1pa4P3DQWWRD1vU20Fvg48Ej7uDCwD+gKFwMdAf6AD8D4wJNtta0U7rwIeCB/vB8wCCvLhZwr0AI4KH+8JfAQMAW4FxoXl44Bfh49Hh+0w4DjgrbC8O7Ak/N4tfNwt2+1rZVv3A44GbgZ+HHWfnPv9bRc9FXd/BahqUDwIeCV8PBW4IHx8BvCBu78fvvdzd68zsx5AV3d/w4Of5kPAeemvfXKSbGu0S4CHAdpoWx3oYmZFQAmwA9gIHAMsdvcl7r4DeAQ4N911T0aS7RwCTAvftwZYD5Tnw8/U3Ve7+7vh403AfKCM4OfxYHjZg+yq97nAQx54EygN2zkSmOruVe6+juC/z5kZbEqzkm2ru69x93eAmga3yrnf33YRVOL4EDgnfHwh0Dt8PBBwM5tiZu+a2U/C8jKgIur9FWFZPojX1mgXEQYV2mZbHwe2AKuBFcDt7l5F0K6VUe/Pl7bGa+f7wLlmVmRm/YDh4Wt59TM1s77AMOAtYH93Xw3BP8YEf7VD/J9dXv1ME2xrPDnX1vYcVK4ArjKzWQTdzx1heRFwIvCN8PtXzOxUgi52Q/kyHzteWwEws2OBre4eGbNvi209BqgDegL9gB+ZWX/yt63x2nk/wT8sM4HfAq8DteRRO81sD+AJ4PvuvrGpS2OUeRPlOSeJtsa9RYyyrLa1KJsfnk3uvoBgqAszGwh8OXypAnjZ3T8LX3uWYDz7b0CvqFv0AlZlrMKt0ERbIy5mVy8Fgv8Gba2tXweed/caYI2ZvQaUE/yVF91zy4u2xmunu9cCP4hcZ2avA4uAdeTBz9TMign+kf27uz8ZFn9qZj3cfXU4vLUmLK8g9s+uAhjRoHxGOuvdEkm2NZ54/w2ypt32VMxsv/B7AfDfwJ/Cl6YAh5tZ53D8/UvAvLArusnMjgtnzXwL+FcWqp60JtoaKbuQYCwW2NntbmttXQGcEs4Y6kKQ2F1AkPAeYGb9zKwDQYCdlPmaJydeO8Pf2y7h49OBWnfPi9/fsF73AfPd/Y6olyYBkRlcY9hV70nAt8Kf6XHAhrCdU4AzzKxbOHvqjLAsZ7SgrfHk3u9vNmcJZOqL4K/w1QRJrgrgSuAaghkXHwETCXcXCK//JjCXYNz61qjy8rDsY+Du6PfkylcL2joCeDPGfdpUW4E9gH+GP9d5wLVR9xkdXv8x8LNst6uV7ewLLCRI/L5IsGV5XvxMCYabnWD25Xvh12iCGZjTCHpc04Du4fUG/CFszxygPOpeVwCLw6/Ls922FLT1gPBnv5Fg8kUFwcSLnPv91TYtIiKSMu12+EtERFJPQUVERFJGQUVERFJGQUVERFJGQUVERFJGQUUkjcI1FK+a2aiosq+Z2fPZrJdIumhKsUiamdlhBGtkhhHsKvsecKa7f9yKexZ5sHpeJKcoqIhkgJndSrChZRdgk7vfFJ7zcRXBluWvA99z93ozu5dga6AS4FF3vzG8RwXwZ4Idd3/r7v/MQlNEmtRu9/4SybBfAu8SbPxYHvZevgJ8wd1rw0ByMfAPgvM0qsJtgqab2ePuPi+8zxZ3PyEbDRBJhIKKSAa4+xYzexTY7O7bzew0gkOXZgbbQFHCri3MLzGzKwn+/+xJcEZKJKg8mtmaiyRHQUUkc+rDLwj2rbrf3X8efYGZDSDY1+sYd19vZn8DOkVdsiUjNRVpIc3+EsmOF4Gvmdk+AGa2t5n1AboCm4CNUacYiuQN9VREssDd55jZL4EXw+3ra4DvEhyuNY9gN+ElwGvZq6VI8jT7S0REUkbDXyIikjIKKiIikjIKKiIikjIKKiIikjIKKiIikjIKKiIikjIKKiIikjL/H2HG3kny6adeAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "Expected 2D array, got scalar array instead:\narray=2020.\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2020\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mArea\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'India'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mElement\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'Food'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Y1961'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0mReturns\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 212\u001b[0m \"\"\"\n\u001b[0;32m--> 213\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0m_preprocess_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstaticmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coef_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 197\u001b[0m return safe_sparse_dot(X, self.coef_.T,\n\u001b[1;32m 198\u001b[0m dense_output=True) + self.intercept_\n", + "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m 543\u001b[0m \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[0;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m \"if it contains a single sample.\".format(array))\n\u001b[0m\u001b[1;32m 546\u001b[0m \u001b[0;31m# If input is 1D raise error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 547\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got scalar array instead:\narray=2020.\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample." + ] + } + ], + "source": [ + "india_list=[]\n", + "year_list = list(df.iloc[:,10:].columns)\n", + "for i in year_list:\n", + " x=df[(df.Area=='India') & (df.Element=='Food')][i].mean()\n", + " india_list.append(x) \n", + "\n", + "reset=[]\n", + "for i in year_list:\n", + " reset.append(int(i[1:]))\n", + "\n", + "\n", + "reset=np.array(reset)\n", + "reset=reset.reshape(-1,1)\n", + "\n", + "\n", + "india_list=np.array(india_list)\n", + "india_list=india_list.reshape(-1,1)\n", + "\n", + "\n", + "reg = LinearRegression()\n", + "reg.fit(reset,india_list)\n", + "predictions = reg.predict(reset)\n", + "plt.title(\"India\")\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Production\")\n", + "plt.scatter(reset,india_list)\n", + "plt.plot(reset,predictions)\n", + "plt.show()\n", + "print(reg.predict(2020))\n", + "\n", + "df[(df.Area=='India') & (df.Element=='Food')]['Y1961'].mean()\n", + "\n", + "df[(df.Area=='Pakistan') & (df.Element=='Food')]\n", + "\n", + "Pak_list=[]\n", + "year_list = list(df.iloc[:,10:].columns)\n", + "for i in year_list:\n", + " yx=df[(df.Area=='Pakistan') & (df.Element=='Food')][i].mean()\n", + " Pak_list.append(yx) \n", + "\n", + "Pak_list=np.array(Pak_list)\n", + "Pak_list=Pak_list.reshape(-1,1)\n", + "Pak_list\n", + "reg = LinearRegression()\n", + "reg.fit(reset,Pak_list)\n", + "predictions = reg.predict(reset)\n", + "plt.title(\"Pakistan\")\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Production\")\n", + "plt.scatter(reset,Pak_list)\n", + "plt.plot(reset,predictions)\n", + "plt.show()\n", + "print(reg.predict(2020))\n", + "\n", + "\n", + "\n", + "usa_list=[]\n", + "year_list = list(df.iloc[:,10:].columns)\n", + "for i in year_list:\n", + " xu=df[(df.Area=='United States of America') & (df.Element=='Food')][i].mean()\n", + " usa_list.append(xu)\n", + "\n", + "usa_list=np.array(usa_list)\n", + "usa_list=india_list.reshape(-1,1)\n", + "\n", + "\n", + "reg = LinearRegression()\n", + "reg.fit(reset,usa_list)\n", + "predictions = reg.predict(reset)\n", + "plt.title(\"USA\")\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"Production\")\n", + "plt.scatter(reset,usa_list)\n", + "plt.plot(reset,predictions)\n", + "plt.show()\n", + "print(reg.predict(2020))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 628794d89de355e87ae51ed022cf242dac30ea47 Mon Sep 17 00:00:00 2001 From: obelisk0114 Date: Sat, 13 Jul 2019 22:45:54 -0700 Subject: [PATCH 073/193] Add combinations (#1015) * Update Bucket Sort time complexity analysis * Add combinations * Adding doctest * Fix doctest problem --- backtracking/all_combinations.py | 41 ++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 backtracking/all_combinations.py diff --git a/backtracking/all_combinations.py b/backtracking/all_combinations.py new file mode 100644 index 000000000000..63425aeabbd1 --- /dev/null +++ b/backtracking/all_combinations.py @@ -0,0 +1,41 @@ +# -*- coding: utf-8 -*- + +""" + In this problem, we want to determine all possible combinations of k + numbers out of 1 ... n. We use backtracking to solve this problem. + Time complexity: O(C(n,k)) which is O(n choose k) = O((n!/(k! * (n - k)!))) +""" + + +def generate_all_combinations(n: int, k: int) -> [[int]]: + """ + >>> generate_all_combinations(n=4, k=2) + [[1, 2], [1, 3], [1, 4], [2, 3], [2, 4], [3, 4]] + """ + + result = [] + create_all_state(1, n, k, [], result) + return result + + +def create_all_state(increment, total_number, level, current_list, total_list): + if level == 0: + total_list.append(current_list[:]) + return + + for i in range(increment, total_number - level + 2): + current_list.append(i) + create_all_state(i + 1, total_number, level - 1, current_list, total_list) + current_list.pop() + + +def print_all_state(total_list): + for i in total_list: + print(*i) + + +if __name__ == '__main__': + n = 4 + k = 2 + total_list = generate_all_combinations(n, k) + print_all_state(total_list) From 3b2738ed89a4ecb1cfb6f16aa96bb90701914796 Mon Sep 17 00:00:00 2001 From: obelisk0114 Date: Sun, 14 Jul 2019 23:48:35 -0700 Subject: [PATCH 074/193] Add rotate matrix problem (#1021) * Add rotate matrix problem * Fix doctest * Adding return matrix to enable doctest --- matrix/rotate_matrix.py | 99 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 99 insertions(+) create mode 100644 matrix/rotate_matrix.py diff --git a/matrix/rotate_matrix.py b/matrix/rotate_matrix.py new file mode 100644 index 000000000000..e3495e647954 --- /dev/null +++ b/matrix/rotate_matrix.py @@ -0,0 +1,99 @@ +# -*- coding: utf-8 -*- + +""" + In this problem, we want to rotate the matrix elements by 90, 180, 270 (counterclockwise) + Discussion in stackoverflow: + https://stackoverflow.com/questions/42519/how-do-you-rotate-a-two-dimensional-array +""" + + +def rotate_90(matrix: [[]]): + """ + >>> rotate_90([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) + [[4, 8, 12, 16], [3, 7, 11, 15], [2, 6, 10, 14], [1, 5, 9, 13]] + """ + + transpose(matrix) + reverse_row(matrix) + return matrix + + +def rotate_180(matrix: [[]]): + """ + >>> rotate_180([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) + [[16, 15, 14, 13], [12, 11, 10, 9], [8, 7, 6, 5], [4, 3, 2, 1]] + """ + + reverse_column(matrix) + reverse_row(matrix) + + """ + OR + + reverse_row(matrix) + reverse_column(matrix) + """ + + return matrix + + +def rotate_270(matrix: [[]]): + """ + >>> rotate_270([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) + [[13, 9, 5, 1], [14, 10, 6, 2], [15, 11, 7, 3], [16, 12, 8, 4]] + """ + + transpose(matrix) + reverse_column(matrix) + + """ + OR + + reverse_row(matrix) + transpose(matrix) + """ + + return matrix + + +def transpose(matrix: [[]]): + matrix[:] = [list(x) for x in zip(*matrix)] + return matrix + + +def reverse_row(matrix: [[]]): + matrix[:] = matrix[::-1] + return matrix + + +def reverse_column(matrix: [[]]): + matrix[:] = [x[::-1] for x in matrix] + return matrix + + +def print_matrix(matrix: [[]]): + for i in matrix: + print(*i) + + +if __name__ == '__main__': + matrix = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]] + print("\norigin:\n") + print_matrix(matrix) + rotate_90(matrix) + print("\nrotate 90 counterclockwise:\n") + print_matrix(matrix) + + matrix = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]] + print("\norigin:\n") + print_matrix(matrix) + rotate_180(matrix) + print("\nrotate 180:\n") + print_matrix(matrix) + + matrix = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]] + print("\norigin:\n") + print_matrix(matrix) + rotate_270(matrix) + print("\nrotate 270 counterclockwise:\n") + print_matrix(matrix) From 1e55bfd4da5a15d8e8536a1a87ad148c69b16a1e Mon Sep 17 00:00:00 2001 From: Sanders Lin <45224617+SandersLin@users.noreply.github.com> Date: Tue, 16 Jul 2019 00:17:41 +0800 Subject: [PATCH 075/193] Create climbing_stairs.py (#1002) a simple dp problem seen on LeetCode: https://leetcode.com/problems/climbing-stairs/ --- dynamic_programming/climbing_stairs.py | 27 ++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 dynamic_programming/climbing_stairs.py diff --git a/dynamic_programming/climbing_stairs.py b/dynamic_programming/climbing_stairs.py new file mode 100644 index 000000000000..8a6213b22323 --- /dev/null +++ b/dynamic_programming/climbing_stairs.py @@ -0,0 +1,27 @@ +def climb_stairs(n: int) -> int: + """ + LeetCdoe No.70: Climbing Stairs + Distinct ways to climb a n step staircase where + each time you can either climb 1 or 2 steps. + + Args: + n: number of steps of staircase + + Returns: + Distinct ways to climb a n step staircase + + Raises: + AssertionError: n not positive integer + + >>> climb_stairs(3) + 3 + >>> climb_stairs(1) + 1 + """ + assert isinstance(n,int) and n > 0, "n needs to be positive integer, your input {0}".format(0) + if n == 1: return 1 + dp = [0]*(n+1) + dp[0], dp[1] = (1, 1) + for i in range(2,n+1): + dp[i] = dp[i-1] + dp[i-2] + return dp[n] From 2fb3beeaf1215a1be4a7bc97097ef6a33fd65aeb Mon Sep 17 00:00:00 2001 From: cclauss Date: Tue, 16 Jul 2019 07:26:28 +0200 Subject: [PATCH 076/193] Fix error message and format with python/black (#1025) @SandersLin Your review please? --- dynamic_programming/climbing_stairs.py | 67 ++++++++++++++++---------- 1 file changed, 41 insertions(+), 26 deletions(-) diff --git a/dynamic_programming/climbing_stairs.py b/dynamic_programming/climbing_stairs.py index 8a6213b22323..79605261f981 100644 --- a/dynamic_programming/climbing_stairs.py +++ b/dynamic_programming/climbing_stairs.py @@ -1,27 +1,42 @@ +#!/usr/bin/env python3 + + def climb_stairs(n: int) -> int: - """ - LeetCdoe No.70: Climbing Stairs - Distinct ways to climb a n step staircase where - each time you can either climb 1 or 2 steps. - - Args: - n: number of steps of staircase - - Returns: - Distinct ways to climb a n step staircase - - Raises: - AssertionError: n not positive integer - - >>> climb_stairs(3) - 3 - >>> climb_stairs(1) - 1 - """ - assert isinstance(n,int) and n > 0, "n needs to be positive integer, your input {0}".format(0) - if n == 1: return 1 - dp = [0]*(n+1) - dp[0], dp[1] = (1, 1) - for i in range(2,n+1): - dp[i] = dp[i-1] + dp[i-2] - return dp[n] + """ + LeetCdoe No.70: Climbing Stairs + Distinct ways to climb a n step staircase where + each time you can either climb 1 or 2 steps. + + Args: + n: number of steps of staircase + + Returns: + Distinct ways to climb a n step staircase + + Raises: + AssertionError: n not positive integer + + >>> climb_stairs(3) + 3 + >>> climb_stairs(1) + 1 + >>> climb_stairs(-7) # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + AssertionError: n needs to be positive integer, your input -7 + """ + fmt = "n needs to be positive integer, your input {}" + assert isinstance(n, int) and n > 0, fmt.format(n) + if n == 1: + return 1 + dp = [0] * (n + 1) + dp[0], dp[1] = (1, 1) + for i in range(2, n + 1): + dp[i] = dp[i - 1] + dp[i - 2] + return dp[n] + + +if __name__ == "__main__": + import doctest + + doctest.testmod() From 267b5eff40409034322d046b3b0054ac3462cf9e Mon Sep 17 00:00:00 2001 From: Bruno Simas Hadlich Date: Tue, 16 Jul 2019 20:09:53 -0300 Subject: [PATCH 077/193] Added doctest and more explanation about Dijkstra execution. (#1014) * Added doctest and more explanation about Dijkstra execution. * tests were not passing with python2 due to missing __init__.py file at number_theory folder * Removed the dot at the beginning of the imported modules names because 'python3 -m doctest -v data_structures/hashing/*.py' and 'python3 -m doctest -v data_structures/stacks/*.py' were failing not finding hash_table.py and stack.py modules. * Moved global code to main scope and added doctest for project euler problems 1 to 14. * Added test case for negative input. * Changed N variable to do not use end of line scape because in case there is a space after it the script will break making it much more error prone. * Added problems description and doctests to the ones that were missing. Limited line length to 79 and executed python black over all scripts. * Changed the way files are loaded to support pytest call. * Added __init__.py to problems to make them modules and allow pytest execution. * Added project_euler folder to test units execution * Changed 'os.path.split(os.path.realpath(__file__))' to 'os.path.dirname()' --- .travis.yml | 1 + data_structures/hashing/double_hash.py | 2 +- .../hashing/hash_table_with_linked_list.py | 2 +- .../hashing/number_theory/__init__.py | 0 data_structures/hashing/quadratic_probing.py | 2 +- .../stacks/infix_to_postfix_conversion.py | 2 +- graphs/dijkstra.py | 117 +++- project_euler/problem_01/__init__.py | 0 project_euler/problem_01/sol1.py | 31 +- project_euler/problem_01/sol2.py | 43 +- project_euler/problem_01/sol3.py | 98 +-- project_euler/problem_01/sol4.py | 47 +- project_euler/problem_01/sol5.py | 30 +- project_euler/problem_01/sol6.py | 49 +- project_euler/problem_02/__init__.py | 0 project_euler/problem_02/sol1.py | 57 +- project_euler/problem_02/sol2.py | 46 +- project_euler/problem_02/sol3.py | 63 +- project_euler/problem_02/sol4.py | 48 +- project_euler/problem_03/__init__.py | 0 project_euler/problem_03/sol1.py | 76 ++- project_euler/problem_03/sol2.py | 52 +- project_euler/problem_04/__init__.py | 0 project_euler/problem_04/sol1.py | 61 +- project_euler/problem_04/sol2.py | 47 +- project_euler/problem_05/__init__.py | 0 project_euler/problem_05/sol1.py | 59 +- project_euler/problem_05/sol2.py | 60 +- project_euler/problem_06/__init__.py | 0 project_euler/problem_06/sol1.py | 52 +- project_euler/problem_06/sol2.py | 47 +- project_euler/problem_06/sol3.py | 47 +- project_euler/problem_07/__init__.py | 0 project_euler/problem_07/sol1.py | 73 ++- project_euler/problem_07/sol2.py | 67 +- project_euler/problem_07/sol3.py | 47 +- project_euler/problem_08/__init__.py | 0 project_euler/problem_08/sol1.py | 75 ++- project_euler/problem_08/sol2.py | 77 ++- project_euler/problem_09/__init__.py | 0 project_euler/problem_09/sol1.py | 49 +- project_euler/problem_09/sol2.py | 60 +- project_euler/problem_09/sol3.py | 43 +- project_euler/problem_10/__init__.py | 0 project_euler/problem_10/sol1.py | 78 ++- project_euler/problem_10/sol2.py | 49 +- project_euler/problem_11/__init__.py | 0 project_euler/problem_11/sol1.py | 97 ++- project_euler/problem_11/sol2.py | 129 ++-- project_euler/problem_12/__init__.py | 0 project_euler/problem_12/sol1.py | 73 ++- project_euler/problem_12/sol2.py | 59 +- project_euler/problem_13/__init__.py | 0 project_euler/problem_13/sol1.py | 38 +- project_euler/problem_13/sol2.py | 5 - project_euler/problem_14/__init__.py | 0 project_euler/problem_14/sol1.py | 92 ++- project_euler/problem_14/sol2.py | 83 ++- project_euler/problem_15/__init__.py | 0 project_euler/problem_15/sol1.py | 71 ++- project_euler/problem_16/__init__.py | 0 project_euler/problem_16/sol1.py | 37 +- project_euler/problem_16/sol2.py | 34 +- project_euler/problem_17/__init__.py | 0 project_euler/problem_17/sol1.py | 92 ++- project_euler/problem_19/__init__.py | 0 project_euler/problem_19/sol1.py | 75 ++- project_euler/problem_20/__init__.py | 0 project_euler/problem_20/sol1.py | 50 +- project_euler/problem_20/sol2.py | 36 +- project_euler/problem_21/__init__.py | 0 project_euler/problem_21/sol1.py | 69 ++- project_euler/problem_22/__init__.py | 0 project_euler/problem_22/sol1.py | 59 +- project_euler/problem_22/sol2.py | 572 ++---------------- project_euler/problem_234/__init__.py | 0 project_euler/problem_234/sol1.py | 52 +- project_euler/problem_24/__init__.py | 0 project_euler/problem_24/sol1.py | 30 +- project_euler/problem_25/__init__.py | 0 project_euler/problem_25/sol1.py | 84 ++- project_euler/problem_25/sol2.py | 67 +- project_euler/problem_28/__init__.py | 0 project_euler/problem_28/sol1.py | 77 ++- project_euler/problem_29/__init__.py | 0 project_euler/problem_29/solution.py | 64 +- project_euler/problem_31/__init__.py | 0 project_euler/problem_31/sol1.py | 34 +- project_euler/problem_36/__init__.py | 0 project_euler/problem_36/sol1.py | 61 +- project_euler/problem_40/__init__.py | 0 project_euler/problem_40/sol1.py | 47 +- project_euler/problem_48/__init__.py | 0 project_euler/problem_48/sol1.py | 26 +- project_euler/problem_52/__init__.py | 0 project_euler/problem_52/sol1.py | 46 +- project_euler/problem_53/__init__.py | 0 project_euler/problem_53/sol1.py | 46 +- project_euler/problem_76/__init__.py | 0 project_euler/problem_76/sol1.py | 57 +- 100 files changed, 2651 insertions(+), 1468 deletions(-) create mode 100644 data_structures/hashing/number_theory/__init__.py create mode 100644 project_euler/problem_01/__init__.py create mode 100644 project_euler/problem_02/__init__.py create mode 100644 project_euler/problem_03/__init__.py create mode 100644 project_euler/problem_04/__init__.py create mode 100644 project_euler/problem_05/__init__.py create mode 100644 project_euler/problem_06/__init__.py create mode 100644 project_euler/problem_07/__init__.py create mode 100644 project_euler/problem_08/__init__.py create mode 100644 project_euler/problem_09/__init__.py create mode 100644 project_euler/problem_10/__init__.py create mode 100644 project_euler/problem_11/__init__.py create mode 100644 project_euler/problem_12/__init__.py create mode 100644 project_euler/problem_13/__init__.py delete mode 100644 project_euler/problem_13/sol2.py create mode 100644 project_euler/problem_14/__init__.py create mode 100644 project_euler/problem_15/__init__.py create mode 100644 project_euler/problem_16/__init__.py create mode 100644 project_euler/problem_17/__init__.py create mode 100644 project_euler/problem_19/__init__.py create mode 100644 project_euler/problem_20/__init__.py create mode 100644 project_euler/problem_21/__init__.py create mode 100644 project_euler/problem_22/__init__.py create mode 100644 project_euler/problem_234/__init__.py create mode 100644 project_euler/problem_24/__init__.py create mode 100644 project_euler/problem_25/__init__.py create mode 100644 project_euler/problem_28/__init__.py create mode 100644 project_euler/problem_29/__init__.py create mode 100644 project_euler/problem_31/__init__.py create mode 100644 project_euler/problem_36/__init__.py create mode 100644 project_euler/problem_40/__init__.py create mode 100644 project_euler/problem_48/__init__.py create mode 100644 project_euler/problem_52/__init__.py create mode 100644 project_euler/problem_53/__init__.py create mode 100644 project_euler/problem_76/__init__.py diff --git a/.travis.yml b/.travis.yml index 9afc0c93a037..3b55045ac33f 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,6 +26,7 @@ script: matrix networking_flow other + project_euler searches sorts strings diff --git a/data_structures/hashing/double_hash.py b/data_structures/hashing/double_hash.py index 60098cda0ce1..7a0ce0b3a67b 100644 --- a/data_structures/hashing/double_hash.py +++ b/data_structures/hashing/double_hash.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 -from .hash_table import HashTable +from hash_table import HashTable from number_theory.prime_numbers import next_prime, check_prime diff --git a/data_structures/hashing/hash_table_with_linked_list.py b/data_structures/hashing/hash_table_with_linked_list.py index 9689e4fc9fcf..a45876df49bd 100644 --- a/data_structures/hashing/hash_table_with_linked_list.py +++ b/data_structures/hashing/hash_table_with_linked_list.py @@ -1,4 +1,4 @@ -from .hash_table import HashTable +from hash_table import HashTable from collections import deque diff --git a/data_structures/hashing/number_theory/__init__.py b/data_structures/hashing/number_theory/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/data_structures/hashing/quadratic_probing.py b/data_structures/hashing/quadratic_probing.py index f7a9ac1ae347..1e61100a81fa 100644 --- a/data_structures/hashing/quadratic_probing.py +++ b/data_structures/hashing/quadratic_probing.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 -from .hash_table import HashTable +from hash_table import HashTable class QuadraticProbing(HashTable): diff --git a/data_structures/stacks/infix_to_postfix_conversion.py b/data_structures/stacks/infix_to_postfix_conversion.py index 75211fed258d..e71dccf1f45c 100644 --- a/data_structures/stacks/infix_to_postfix_conversion.py +++ b/data_structures/stacks/infix_to_postfix_conversion.py @@ -2,7 +2,7 @@ from __future__ import absolute_import import string -from .Stack import Stack +from stack import Stack __author__ = 'Omkar Pathak' diff --git a/graphs/dijkstra.py b/graphs/dijkstra.py index 4b6bc347b061..52354b5c916b 100644 --- a/graphs/dijkstra.py +++ b/graphs/dijkstra.py @@ -1,24 +1,50 @@ """pseudo-code""" """ -DIJKSTRA(graph G, start vertex s,destination vertex d): -// all nodes initially unexplored -let H = min heap data structure, initialized with 0 and s [here 0 indicates the distance from start vertex] -while H is non-empty: - remove the first node and cost of H, call it U and cost - if U is not explored - mark U as explored - if U is d: - return cost // total cost from start to destination vertex - for each edge(U, V): c=cost of edge(u,V) // for V in graph[U] - if V unexplored: - next=cost+c - add next,V to H (at the end) +DIJKSTRA(graph G, start vertex s, destination vertex d): + +//all nodes initially unexplored + +1 - let H = min heap data structure, initialized with 0 and s [here 0 indicates + the distance from start vertex s] +2 - while H is non-empty: +3 - remove the first node and cost of H, call it U and cost +4 - if U has been previously explored: +5 - go to the while loop, line 2 //Once a node is explored there is no need + to make it again +6 - mark U as explored +7 - if U is d: +8 - return cost // total cost from start to destination vertex +9 - for each edge(U, V): c=cost of edge(U,V) // for V in graph[U] +10 - if V explored: +11 - go to next V in line 9 +12 - total_cost = cost + c +13 - add (total_cost,V) to H + +You can think at cost as a distance where Dijkstra finds the shortest distance +between vertexes s and v in a graph G. The use of a min heap as H guarantees +that if a vertex has already been explored there will be no other path with +shortest distance, that happens because heapq.heappop will always return the +next vertex with the shortest distance, considering that the heap stores not +only the distance between previous vertex and current vertex but the entire +distance between each vertex that makes up the path from start vertex to target +vertex. """ + import heapq def dijkstra(graph, start, end): + """Return the cost of the shortest path between vertexes start and end. + + >>> dijkstra(G, "E", "C") + 6 + >>> dijkstra(G2, "E", "F") + 3 + >>> dijkstra(G3, "E", "F") + 3 + """ + heap = [(0, start)] # cost from start node,end node visited = set() while heap: @@ -28,20 +54,65 @@ def dijkstra(graph, start, end): visited.add(u) if u == end: return cost - for v, c in G[u]: + for v, c in graph[u]: if v in visited: continue next = cost + c heapq.heappush(heap, (next, v)) - return (-1, -1) + return -1 + + +G = { + "A": [["B", 2], ["C", 5]], + "B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]], + "C": [["A", 5], ["F", 3]], + "D": [["B", 3]], + "E": [["B", 4], ["F", 3]], + "F": [["C", 3], ["E", 3]], +} + +""" +Layout of G2: + +E -- 1 --> B -- 1 --> C -- 1 --> D -- 1 --> F + \ /\ + \ || + ----------------- 3 -------------------- +""" +G2 = { + "B": [["C", 1]], + "C": [["D", 1]], + "D": [["F", 1]], + "E": [["B", 1], ["F", 3]], + "F": [], +} + +""" +Layout of G3: + +E -- 1 --> B -- 1 --> C -- 1 --> D -- 1 --> F + \ /\ + \ || + -------- 2 ---------> G ------- 1 ------ +""" +G3 = { + "B": [["C", 1]], + "C": [["D", 1]], + "D": [["F", 1]], + "E": [["B", 1], ["G", 2]], + "F": [], + "G": [["F", 1]], +} + +shortDistance = dijkstra(G, "E", "C") +print(shortDistance) # E -- 3 --> F -- 3 --> C == 6 +shortDistance = dijkstra(G2, "E", "F") +print(shortDistance) # E -- 3 --> F == 3 -G = {'A': [['B', 2], ['C', 5]], - 'B': [['A', 2], ['D', 3], ['E', 1]], - 'C': [['A', 5], ['F', 3]], - 'D': [['B', 3]], - 'E': [['B', 1], ['F', 3]], - 'F': [['C', 3], ['E', 3]]} +shortDistance = dijkstra(G3, "E", "F") +print(shortDistance) # E -- 2 --> G -- 1 --> F == 3 -shortDistance = dijkstra(G, 'E', 'C') -print(shortDistance) +if __name__ == "__main__": + import doctest + doctest.testmod() diff --git a/project_euler/problem_01/__init__.py b/project_euler/problem_01/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_01/sol1.py b/project_euler/problem_01/sol1.py index c9a8c0f1ebeb..1433129af303 100644 --- a/project_euler/problem_01/sol1.py +++ b/project_euler/problem_01/sol1.py @@ -1,13 +1,34 @@ -''' +""" Problem Statement: If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. -''' +""" from __future__ import print_function + try: - raw_input # Python 2 + raw_input # Python 2 except NameError: raw_input = input # Python 3 -n = int(raw_input().strip()) -print(sum([e for e in range(3, n) if e % 3 == 0 or e % 5 == 0])) + + +def solution(n): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution(3) + 0 + >>> solution(4) + 3 + >>> solution(10) + 23 + >>> solution(600) + 83700 + >>> solution(-7) + 0 + """ + + return sum([e for e in range(3, n) if e % 3 == 0 or e % 5 == 0]) + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_01/sol2.py b/project_euler/problem_01/sol2.py index 2b7760e0bfff..e58fb03a8fb0 100644 --- a/project_euler/problem_01/sol2.py +++ b/project_euler/problem_01/sol2.py @@ -1,20 +1,39 @@ -''' +""" Problem Statement: If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. -''' +""" from __future__ import print_function + try: - raw_input # Python 2 + raw_input # Python 2 except NameError: raw_input = input # Python 3 -n = int(raw_input().strip()) -sum = 0 -terms = (n-1)//3 -sum+= ((terms)*(6+(terms-1)*3))//2 #sum of an A.P. -terms = (n-1)//5 -sum+= ((terms)*(10+(terms-1)*5))//2 -terms = (n-1)//15 -sum-= ((terms)*(30+(terms-1)*15))//2 -print(sum) + + +def solution(n): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution(3) + 0 + >>> solution(4) + 3 + >>> solution(10) + 23 + >>> solution(600) + 83700 + """ + + sum = 0 + terms = (n - 1) // 3 + sum += ((terms) * (6 + (terms - 1) * 3)) // 2 # sum of an A.P. + terms = (n - 1) // 5 + sum += ((terms) * (10 + (terms - 1) * 5)) // 2 + terms = (n - 1) // 15 + sum -= ((terms) * (30 + (terms - 1) * 15)) // 2 + return sum + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_01/sol3.py b/project_euler/problem_01/sol3.py index f4f3aefcc5de..013ce5e54fdf 100644 --- a/project_euler/problem_01/sol3.py +++ b/project_euler/problem_01/sol3.py @@ -1,50 +1,66 @@ -from __future__ import print_function - -''' +""" Problem Statement: If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. -''' -''' -This solution is based on the pattern that the successive numbers in the series follow: 0+3,+2,+1,+3,+1,+2,+3. -''' +""" +from __future__ import print_function try: - raw_input # Python 2 + raw_input # Python 2 except NameError: raw_input = input # Python 3 -n = int(raw_input().strip()) -sum=0 -num=0 -while(1): - num+=3 - if(num>=n): - break - sum+=num - num+=2 - if(num>=n): - break - sum+=num - num+=1 - if(num>=n): - break - sum+=num - num+=3 - if(num>=n): - break - sum+=num - num+=1 - if(num>=n): - break - sum+=num - num+=2 - if(num>=n): - break - sum+=num - num+=3 - if(num>=n): - break - sum+=num -print(sum); + +def solution(n): + """ + This solution is based on the pattern that the successive numbers in the + series follow: 0+3,+2,+1,+3,+1,+2,+3. + Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution(3) + 0 + >>> solution(4) + 3 + >>> solution(10) + 23 + >>> solution(600) + 83700 + """ + + sum = 0 + num = 0 + while 1: + num += 3 + if num >= n: + break + sum += num + num += 2 + if num >= n: + break + sum += num + num += 1 + if num >= n: + break + sum += num + num += 3 + if num >= n: + break + sum += num + num += 1 + if num >= n: + break + sum += num + num += 2 + if num >= n: + break + sum += num + num += 3 + if num >= n: + break + sum += num + return sum + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_01/sol4.py b/project_euler/problem_01/sol4.py index 7941f5fcd3fe..90403c3bd6a3 100644 --- a/project_euler/problem_01/sol4.py +++ b/project_euler/problem_01/sol4.py @@ -1,4 +1,30 @@ -def mulitples(limit): +""" +Problem Statement: +If we list all the natural numbers below 10 that are multiples of 3 or 5, +we get 3,5,6 and 9. The sum of these multiples is 23. +Find the sum of all the multiples of 3 or 5 below N. +""" +from __future__ import print_function + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + +def solution(n): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution(3) + 0 + >>> solution(4) + 3 + >>> solution(10) + 23 + >>> solution(600) + 83700 + """ + xmulti = [] zmulti = [] z = 3 @@ -6,7 +32,7 @@ def mulitples(limit): temp = 1 while True: result = z * temp - if (result < limit): + if result < n: zmulti.append(result) temp += 1 else: @@ -14,17 +40,14 @@ def mulitples(limit): break while True: result = x * temp - if (result < limit): + if result < n: xmulti.append(result) temp += 1 else: break - collection = list(set(xmulti+zmulti)) - return (sum(collection)) - - - - - - -print (mulitples(1000)) + collection = list(set(xmulti + zmulti)) + return sum(collection) + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_01/sol5.py b/project_euler/problem_01/sol5.py index e261cc8fc729..302fe44f8bfa 100644 --- a/project_euler/problem_01/sol5.py +++ b/project_euler/problem_01/sol5.py @@ -1,16 +1,34 @@ -''' +""" Problem Statement: If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. -''' +""" from __future__ import print_function + try: - input = raw_input #python3 + raw_input # Python 2 except NameError: - pass #python 2 + raw_input = input # Python 3 """A straightforward pythonic solution using list comprehension""" -n = int(input().strip()) -print(sum([i for i in range(n) if i%3==0 or i%5==0])) + +def solution(n): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution(3) + 0 + >>> solution(4) + 3 + >>> solution(10) + 23 + >>> solution(600) + 83700 + """ + + return sum([i for i in range(n) if i % 3 == 0 or i % 5 == 0]) + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_01/sol6.py b/project_euler/problem_01/sol6.py index 54c3073f3897..cf6e751d4c05 100644 --- a/project_euler/problem_01/sol6.py +++ b/project_euler/problem_01/sol6.py @@ -1,9 +1,40 @@ -a = 3 -result = 0 -while a < 1000: - if(a % 3 == 0 or a % 5 == 0): - result += a - elif(a % 15 == 0): - result -= a - a += 1 -print(result) +""" +Problem Statement: +If we list all the natural numbers below 10 that are multiples of 3 or 5, +we get 3,5,6 and 9. The sum of these multiples is 23. +Find the sum of all the multiples of 3 or 5 below N. +""" +from __future__ import print_function + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + +def solution(n): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution(3) + 0 + >>> solution(4) + 3 + >>> solution(10) + 23 + >>> solution(600) + 83700 + """ + + a = 3 + result = 0 + while a < n: + if a % 3 == 0 or a % 5 == 0: + result += a + elif a % 15 == 0: + result -= a + a += 1 + return result + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_02/__init__.py b/project_euler/problem_02/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_02/sol1.py b/project_euler/problem_02/sol1.py index 44ea980f2df0..f61d04e3dfce 100644 --- a/project_euler/problem_02/sol1.py +++ b/project_euler/problem_02/sol1.py @@ -1,24 +1,47 @@ -''' +""" Problem: -Each new term in the Fibonacci sequence is generated by adding the previous two terms. By starting with 1 and 2, -the first 10 terms will be: - 1,2,3,5,8,13,21,34,55,89,.. -By considering the terms in the Fibonacci sequence whose values do not exceed n, find the sum of the even-valued terms. -e.g. for n=10, we have {2,8}, sum is 10. -''' +Each new term in the Fibonacci sequence is generated by adding the previous two +terms. By starting with 1 and 2, the first 10 terms will be: + + 1,2,3,5,8,13,21,34,55,89,.. + +By considering the terms in the Fibonacci sequence whose values do not exceed +n, find the sum of the even-valued terms. e.g. for n=10, we have {2,8}, sum is +10. +""" from __future__ import print_function try: - raw_input # Python 2 + raw_input # Python 2 except NameError: raw_input = input # Python 3 -n = int(raw_input().strip()) -i=1 -j=2 -sum=0 -while(j<=n): - if j%2 == 0: - sum+=j - i , j = j, i+j -print(sum) + +def solution(n): + """Returns the sum of all fibonacci sequence even elements that are lower + or equals to n. + + >>> solution(10) + 10 + >>> solution(15) + 10 + >>> solution(2) + 2 + >>> solution(1) + 0 + >>> solution(34) + 44 + """ + i = 1 + j = 2 + sum = 0 + while j <= n: + if j % 2 == 0: + sum += j + i, j = j, i + j + + return sum + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_02/sol2.py b/project_euler/problem_02/sol2.py index a2772697bb79..3e103a6a4373 100644 --- a/project_euler/problem_02/sol2.py +++ b/project_euler/problem_02/sol2.py @@ -1,15 +1,45 @@ -def fib(n): - """ - Returns a list of all the even terms in the Fibonacci sequence that are less than n. +""" +Problem: +Each new term in the Fibonacci sequence is generated by adding the previous two +terms. By starting with 1 and 2, the first 10 terms will be: + + 1,2,3,5,8,13,21,34,55,89,.. + +By considering the terms in the Fibonacci sequence whose values do not exceed +n, find the sum of the even-valued terms. e.g. for n=10, we have {2,8}, sum is +10. +""" +from __future__ import print_function + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + +def solution(n): + """Returns the sum of all fibonacci sequence even elements that are lower + or equals to n. + + >>> solution(10) + [2, 8] + >>> solution(15) + [2, 8] + >>> solution(2) + [2] + >>> solution(1) + [] + >>> solution(34) + [2, 8, 34] """ ls = [] a, b = 0, 1 - while b < n: + while b <= n: if b % 2 == 0: ls.append(b) - a, b = b, a+b + a, b = b, a + b return ls -if __name__ == '__main__': - n = int(input("Enter max number: ").strip()) - print(sum(fib(n))) + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_02/sol3.py b/project_euler/problem_02/sol3.py index 0eb46d879704..abd9d6c753b8 100644 --- a/project_euler/problem_02/sol3.py +++ b/project_euler/problem_02/sol3.py @@ -1,18 +1,47 @@ -''' +""" Problem: -Each new term in the Fibonacci sequence is generated by adding the previous two terms. - 0,1,1,2,3,5,8,13,21,34,55,89,.. -Every third term from 0 is even So using this I have written a simple code -By considering the terms in the Fibonacci sequence whose values do not exceed n, find the sum of the even-valued terms. -e.g. for n=10, we have {2,8}, sum is 10. -''' -"""Python 3""" -n = int(input()) -a=0 -b=2 -count=0 -while 4*b+a>> solution(10) + 10 + >>> solution(15) + 10 + >>> solution(2) + 2 + >>> solution(1) + 0 + >>> solution(34) + 44 + """ + if n <= 1: + return 0 + a = 0 + b = 2 + count = 0 + while 4 * b + a <= n: + a, b = b, 4 * b + a + count += a + return count + b + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_02/sol4.py b/project_euler/problem_02/sol4.py index 64bae65f49b4..ba13b12a15e9 100644 --- a/project_euler/problem_02/sol4.py +++ b/project_euler/problem_02/sol4.py @@ -1,13 +1,47 @@ +""" +Problem: +Each new term in the Fibonacci sequence is generated by adding the previous two +terms. By starting with 1 and 2, the first 10 terms will be: + + 1,2,3,5,8,13,21,34,55,89,.. + +By considering the terms in the Fibonacci sequence whose values do not exceed +n, find the sum of the even-valued terms. e.g. for n=10, we have {2,8}, sum is +10. +""" +from __future__ import print_function import math from decimal import * -getcontext().prec = 100 -phi = (Decimal(5) ** Decimal(0.5) + 1) / Decimal(2) +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + +def solution(n): + """Returns the sum of all fibonacci sequence even elements that are lower + or equals to n. + + >>> solution(10) + 10 + >>> solution(15) + 10 + >>> solution(2) + 2 + >>> solution(1) + 0 + >>> solution(34) + 44 + """ + getcontext().prec = 100 + phi = (Decimal(5) ** Decimal(0.5) + 1) / Decimal(2) -n = Decimal(int(input()) - 1) + index = (math.floor(math.log(n * (phi + 2), phi) - 1) // 3) * 3 + 2 + num = Decimal(round(phi ** Decimal(index + 1))) / (phi + 2) + sum = num // 2 + return int(sum) -index = (math.floor(math.log(n * (phi + 2), phi) - 1) // 3) * 3 + 2 -num = round(phi ** Decimal(index + 1)) / (phi + 2) -sum = num // 2 -print(int(sum)) +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_03/__init__.py b/project_euler/problem_03/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_03/sol1.py b/project_euler/problem_03/sol1.py index bb9f8ca9ad12..c2e601bd0040 100644 --- a/project_euler/problem_03/sol1.py +++ b/project_euler/problem_03/sol1.py @@ -1,39 +1,61 @@ -''' +""" Problem: -The prime factors of 13195 are 5,7,13 and 29. What is the largest prime factor of a given number N? +The prime factors of 13195 are 5,7,13 and 29. What is the largest prime factor +of a given number N? + e.g. for 10, largest prime factor = 5. For 17, largest prime factor = 17. -''' +""" from __future__ import print_function, division - import math +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + def isprime(no): - if(no==2): + if no == 2: return True - elif (no%2==0): + elif no % 2 == 0: return False - sq = int(math.sqrt(no))+1 - for i in range(3,sq,2): - if(no%i==0): + sq = int(math.sqrt(no)) + 1 + for i in range(3, sq, 2): + if no % i == 0: return False return True -maxNumber = 0 -n=int(input()) -if(isprime(n)): - print(n) -else: - while (n%2==0): - n=n/2 - if(isprime(n)): - print(n) + +def solution(n): + """Returns the largest prime factor of a given number n. + + >>> solution(13195) + 29 + >>> solution(10) + 5 + >>> solution(17) + 17 + """ + maxNumber = 0 + if isprime(n): + return n else: - n1 = int(math.sqrt(n))+1 - for i in range(3,n1,2): - if(n%i==0): - if(isprime(n/i)): - maxNumber = n/i - break - elif(isprime(i)): - maxNumber = i - print(maxNumber) + while n % 2 == 0: + n = n / 2 + if isprime(n): + return int(n) + else: + n1 = int(math.sqrt(n)) + 1 + for i in range(3, n1, 2): + if n % i == 0: + if isprime(n / i): + maxNumber = n / i + break + elif isprime(i): + maxNumber = i + return maxNumber + return int(sum) + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_03/sol2.py b/project_euler/problem_03/sol2.py index 44f9c63dfb6a..497db3965cc3 100644 --- a/project_euler/problem_03/sol2.py +++ b/project_euler/problem_03/sol2.py @@ -1,18 +1,40 @@ -''' +""" Problem: -The prime factors of 13195 are 5,7,13 and 29. What is the largest prime factor of a given number N? +The prime factors of 13195 are 5,7,13 and 29. What is the largest prime factor +of a given number N? + e.g. for 10, largest prime factor = 5. For 17, largest prime factor = 17. -''' +""" +from __future__ import print_function, division +import math + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + +def solution(n): + """Returns the largest prime factor of a given number n. + + >>> solution(13195) + 29 + >>> solution(10) + 5 + >>> solution(17) + 17 + """ + prime = 1 + i = 2 + while i * i <= n: + while n % i == 0: + prime = i + n //= i + i += 1 + if n > 1: + prime = n + return int(prime) + -from __future__ import print_function -n=int(input()) -prime=1 -i=2 -while(i*i<=n): - while(n%i==0): - prime=i - n//=i - i+=1 -if(n>1): - prime=n -print(prime) +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_04/__init__.py b/project_euler/problem_04/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_04/sol1.py b/project_euler/problem_04/sol1.py index 05fdd9ebab55..7a255f7308e6 100644 --- a/project_euler/problem_04/sol1.py +++ b/project_euler/problem_04/sol1.py @@ -1,29 +1,50 @@ -''' +""" Problem: -A palindromic number reads the same both ways. The largest palindrome made from the product of two 2-digit numbers is 9009 = 91 x 99. -Find the largest palindrome made from the product of two 3-digit numbers which is less than N. -''' +A palindromic number reads the same both ways. The largest palindrome made from +the product of two 2-digit numbers is 9009 = 91 x 99. + +Find the largest palindrome made from the product of two 3-digit numbers which +is less than N. +""" from __future__ import print_function -limit = int(input("limit? ")) -# fetchs the next number -for number in range(limit-1,10000,-1): +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + +def solution(n): + """Returns the largest palindrome made from the product of two 3-digit + numbers which is less than n. + + >>> solution(20000) + 19591 + >>> solution(30000) + 29992 + >>> solution(40000) + 39893 + """ + # fetchs the next number + for number in range(n - 1, 10000, -1): - # converts number into string. - strNumber = str(number) + # converts number into string. + strNumber = str(number) - # checks whether 'strNumber' is a palindrome. - if(strNumber == strNumber[::-1]): + # checks whether 'strNumber' is a palindrome. + if strNumber == strNumber[::-1]: - divisor = 999 + divisor = 999 - # if 'number' is a product of two 3-digit numbers - # then number is the answer otherwise fetch next number. - while(divisor != 99): - - if((number % divisor == 0) and (len(str(number / divisor)) == 3)): + # if 'number' is a product of two 3-digit numbers + # then number is the answer otherwise fetch next number. + while divisor != 99: + if (number % divisor == 0) and ( + len(str(int(number / divisor))) == 3 + ): + return number + divisor -= 1 - print(number) - exit(0) - divisor -=1 +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_04/sol2.py b/project_euler/problem_04/sol2.py index 70810c38986f..45c6b256daf8 100644 --- a/project_euler/problem_04/sol2.py +++ b/project_euler/problem_04/sol2.py @@ -1,17 +1,38 @@ -''' +""" Problem: -A palindromic number reads the same both ways. The largest palindrome made from the product of two 2-digit numbers is 9009 = 91 x 99. -Find the largest palindrome made from the product of two 3-digit numbers which is less than N. -''' +A palindromic number reads the same both ways. The largest palindrome made from +the product of two 2-digit numbers is 9009 = 91 x 99. + +Find the largest palindrome made from the product of two 3-digit numbers which +is less than N. +""" from __future__ import print_function -n = int(input().strip()) -answer = 0 -for i in range(999,99,-1): #3 digit nimbers range from 999 down to 100 - for j in range(999,99,-1): - t = str(i*j) - if t == t[::-1] and i*j < n: - answer = max(answer,i*j) -print(answer) -exit(0) + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + +def solution(n): + """Returns the largest palindrome made from the product of two 3-digit + numbers which is less than n. + + >>> solution(20000) + 19591 + >>> solution(30000) + 29992 + >>> solution(40000) + 39893 + """ + answer = 0 + for i in range(999, 99, -1): # 3 digit nimbers range from 999 down to 100 + for j in range(999, 99, -1): + t = str(i * j) + if t == t[::-1] and i * j < n: + answer = max(answer, i * j) + return answer +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_05/__init__.py b/project_euler/problem_05/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_05/sol1.py b/project_euler/problem_05/sol1.py index 7896d75e3456..609f02102a08 100644 --- a/project_euler/problem_05/sol1.py +++ b/project_euler/problem_05/sol1.py @@ -1,21 +1,46 @@ -''' +""" Problem: -2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder. -What is the smallest positive number that is evenly divisible(divisible with no remainder) by all of the numbers from 1 to N? -''' +2520 is the smallest number that can be divided by each of the numbers from 1 +to 10 without any remainder. + +What is the smallest positive number that is evenly divisible(divisible with no +remainder) by all of the numbers from 1 to N? +""" from __future__ import print_function -n = int(input()) -i = 0 -while 1: - i+=n*(n-1) - nfound=0 - for j in range(2,n): - if (i%j != 0): - nfound=1 +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + +def solution(n): + """Returns the smallest positive number that is evenly divisible(divisible + with no remainder) by all of the numbers from 1 to n. + + >>> solution(10) + 2520 + >>> solution(15) + 360360 + >>> solution(20) + 232792560 + >>> solution(22) + 232792560 + """ + i = 0 + while 1: + i += n * (n - 1) + nfound = 0 + for j in range(2, n): + if i % j != 0: + nfound = 1 + break + if nfound == 0: + if i == 0: + i = 1 + return i break - if(nfound==0): - if(i==0): - i=1 - print(i) - break + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_05/sol2.py b/project_euler/problem_05/sol2.py index cd11437f30db..293dd96f2294 100644 --- a/project_euler/problem_05/sol2.py +++ b/project_euler/problem_05/sol2.py @@ -1,20 +1,50 @@ -#!/bin/python3 -''' +""" Problem: -2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder. -What is the smallest positive number that is evenly divisible(divisible with no remainder) by all of the numbers from 1 to N? -''' +2520 is the smallest number that can be divided by each of the numbers from 1 +to 10 without any remainder. + +What is the smallest positive number that is evenly divisible(divisible with no +remainder) by all of the numbers from 1 to N? +""" +from __future__ import print_function + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 """ Euclidean GCD Algorithm """ -def gcd(x,y): - return x if y==0 else gcd(y,x%y) + + +def gcd(x, y): + return x if y == 0 else gcd(y, x % y) + """ Using the property lcm*gcd of two numbers = product of them """ -def lcm(x,y): - return (x*y)//gcd(x,y) - -n = int(input()) -g=1 -for i in range(1,n+1): - g=lcm(g,i) -print(g) + + +def lcm(x, y): + return (x * y) // gcd(x, y) + + +def solution(n): + """Returns the smallest positive number that is evenly divisible(divisible + with no remainder) by all of the numbers from 1 to n. + + >>> solution(10) + 2520 + >>> solution(15) + 360360 + >>> solution(20) + 232792560 + >>> solution(22) + 232792560 + """ + g = 1 + for i in range(1, n + 1): + g = lcm(g, i) + return g + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_06/__init__.py b/project_euler/problem_06/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_06/sol1.py b/project_euler/problem_06/sol1.py index 852d4e2f9fc4..728701e167c3 100644 --- a/project_euler/problem_06/sol1.py +++ b/project_euler/problem_06/sol1.py @@ -1,20 +1,48 @@ # -*- coding: utf-8 -*- -''' +""" Problem: + The sum of the squares of the first ten natural numbers is, 1^2 + 2^2 + ... + 10^2 = 385 + The square of the sum of the first ten natural numbers is, (1 + 2 + ... + 10)^2 = 552 = 3025 -Hence the difference between the sum of the squares of the first ten natural numbers and the square of the sum is 3025 − 385 = 2640. -Find the difference between the sum of the squares of the first N natural numbers and the square of the sum. -''' + +Hence the difference between the sum of the squares of the first ten natural +numbers and the square of the sum is 3025 − 385 = 2640. + +Find the difference between the sum of the squares of the first N natural +numbers and the square of the sum. +""" from __future__ import print_function -suma = 0 -sumb = 0 -n = int(input()) -for i in range(1,n+1): - suma += i**2 - sumb += i -sum = sumb**2 - suma -print(sum) +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + +def solution(n): + """Returns the difference between the sum of the squares of the first n + natural numbers and the square of the sum. + + >>> solution(10) + 2640 + >>> solution(15) + 13160 + >>> solution(20) + 41230 + >>> solution(50) + 1582700 + """ + suma = 0 + sumb = 0 + for i in range(1, n + 1): + suma += i ** 2 + sumb += i + sum = sumb ** 2 - suma + return sum + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_06/sol2.py b/project_euler/problem_06/sol2.py index aa8aea58fd7b..2c64812d56f8 100644 --- a/project_euler/problem_06/sol2.py +++ b/project_euler/problem_06/sol2.py @@ -1,16 +1,45 @@ # -*- coding: utf-8 -*- -''' +""" Problem: + The sum of the squares of the first ten natural numbers is, 1^2 + 2^2 + ... + 10^2 = 385 + The square of the sum of the first ten natural numbers is, (1 + 2 + ... + 10)^2 = 552 = 3025 -Hence the difference between the sum of the squares of the first ten natural numbers and the square of the sum is 3025 − 385 = 2640. -Find the difference between the sum of the squares of the first N natural numbers and the square of the sum. -''' + +Hence the difference between the sum of the squares of the first ten natural +numbers and the square of the sum is 3025 − 385 = 2640. + +Find the difference between the sum of the squares of the first N natural +numbers and the square of the sum. +""" from __future__ import print_function -n = int(input()) -suma = n*(n+1)/2 -suma **= 2 -sumb = n*(n+1)*(2*n+1)/6 -print(suma-sumb) + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + +def solution(n): + """Returns the difference between the sum of the squares of the first n + natural numbers and the square of the sum. + + >>> solution(10) + 2640 + >>> solution(15) + 13160 + >>> solution(20) + 41230 + >>> solution(50) + 1582700 + """ + suma = n * (n + 1) / 2 + suma **= 2 + sumb = n * (n + 1) * (2 * n + 1) / 6 + return int(suma - sumb) + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_06/sol3.py b/project_euler/problem_06/sol3.py index b2d9f444d9a9..7d94b1e2254f 100644 --- a/project_euler/problem_06/sol3.py +++ b/project_euler/problem_06/sol3.py @@ -1,20 +1,45 @@ -''' +# -*- coding: utf-8 -*- +""" Problem: + The sum of the squares of the first ten natural numbers is, 1^2 + 2^2 + ... + 10^2 = 385 + The square of the sum of the first ten natural numbers is, (1 + 2 + ... + 10)^2 = 552 = 3025 -Hence the difference between the sum of the squares of the first ten natural numbers and the square of the sum is 3025 − 385 = 2640. -Find the difference between the sum of the squares of the first N natural numbers and the square of the sum. -''' + +Hence the difference between the sum of the squares of the first ten natural +numbers and the square of the sum is 3025 − 385 = 2640. + +Find the difference between the sum of the squares of the first N natural +numbers and the square of the sum. +""" from __future__ import print_function import math -def problem6(number=100): - sum_of_squares = sum([i*i for i in range(1,number+1)]) - square_of_sum = int(math.pow(sum(range(1,number+1)),2)) + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + +def solution(n): + """Returns the difference between the sum of the squares of the first n + natural numbers and the square of the sum. + + >>> solution(10) + 2640 + >>> solution(15) + 13160 + >>> solution(20) + 41230 + >>> solution(50) + 1582700 + """ + sum_of_squares = sum([i * i for i in range(1, n + 1)]) + square_of_sum = int(math.pow(sum(range(1, n + 1)), 2)) return square_of_sum - sum_of_squares -def main(): - print(problem6()) -if __name__ == '__main__': - main() \ No newline at end of file + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_07/__init__.py b/project_euler/problem_07/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_07/sol1.py b/project_euler/problem_07/sol1.py index ea31d0b2bb2c..403ded568dda 100644 --- a/project_euler/problem_07/sol1.py +++ b/project_euler/problem_07/sol1.py @@ -1,30 +1,61 @@ -''' +# -*- coding: utf-8 -*- +""" By listing the first six prime numbers: -2, 3, 5, 7, 11, and 13, we can see that the 6th prime is 13. -What is the Nth prime number? -''' + + 2, 3, 5, 7, 11, and 13 + +We can see that the 6th prime is 13. What is the Nth prime number? +""" from __future__ import print_function from math import sqrt + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + def isprime(n): - if (n==2): + if n == 2: return True - elif (n%2==0): + elif n % 2 == 0: return False else: - sq = int(sqrt(n))+1 - for i in range(3,sq,2): - if(n%i==0): + sq = int(sqrt(n)) + 1 + for i in range(3, sq, 2): + if n % i == 0: return False return True -n = int(input()) -i=0 -j=1 -while(i!=n and j<3): - j+=1 - if (isprime(j)): - i+=1 -while(i!=n): - j+=2 - if(isprime(j)): - i+=1 -print(j) + + +def solution(n): + """Returns the n-th prime number. + + >>> solution(6) + 13 + >>> solution(1) + 2 + >>> solution(3) + 5 + >>> solution(20) + 71 + >>> solution(50) + 229 + >>> solution(100) + 541 + """ + i = 0 + j = 1 + while i != n and j < 3: + j += 1 + if isprime(j): + i += 1 + while i != n: + j += 2 + if isprime(j): + i += 1 + return j + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_07/sol2.py b/project_euler/problem_07/sol2.py index fdf39cbc4d26..630e5196796d 100644 --- a/project_euler/problem_07/sol2.py +++ b/project_euler/problem_07/sol2.py @@ -1,16 +1,53 @@ -# By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we can see that the 6th prime is 13. What is the Nth prime number? +# -*- coding: utf-8 -*- +""" +By listing the first six prime numbers: + + 2, 3, 5, 7, 11, and 13 + +We can see that the 6th prime is 13. What is the Nth prime number? +""" +from __future__ import print_function +from math import sqrt + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + def isprime(number): - for i in range(2,int(number**0.5)+1): - if number%i==0: - return False - return True -n = int(input('Enter The N\'th Prime Number You Want To Get: ')) # Ask For The N'th Prime Number Wanted -primes = [] -num = 2 -while len(primes) < n: - if isprime(num): - primes.append(num) - num += 1 - else: - num += 1 -print(primes[len(primes) - 1]) + for i in range(2, int(number ** 0.5) + 1): + if number % i == 0: + return False + return True + + +def solution(n): + """Returns the n-th prime number. + + >>> solution(6) + 13 + >>> solution(1) + 2 + >>> solution(3) + 5 + >>> solution(20) + 71 + >>> solution(50) + 229 + >>> solution(100) + 541 + """ + primes = [] + num = 2 + while len(primes) < n: + if isprime(num): + primes.append(num) + num += 1 + else: + num += 1 + return primes[len(primes) - 1] + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_07/sol3.py b/project_euler/problem_07/sol3.py index 0001e4318cc9..bc94762604b3 100644 --- a/project_euler/problem_07/sol3.py +++ b/project_euler/problem_07/sol3.py @@ -1,28 +1,53 @@ -''' +# -*- coding: utf-8 -*- +""" By listing the first six prime numbers: -2, 3, 5, 7, 11, and 13, we can see that the 6th prime is 13. -What is the Nth prime number? -''' + + 2, 3, 5, 7, 11, and 13 + +We can see that the 6th prime is 13. What is the Nth prime number? +""" from __future__ import print_function -# from Python.Math import PrimeCheck import math import itertools + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + def primeCheck(number): if number % 2 == 0 and number > 2: return False return all(number % i for i in range(3, int(math.sqrt(number)) + 1, 2)) + def prime_generator(): num = 2 while True: if primeCheck(num): yield num - num+=1 + num += 1 + -def main(): - n = int(input('Enter The N\'th Prime Number You Want To Get: ')) # Ask For The N'th Prime Number Wanted - print(next(itertools.islice(prime_generator(),n-1,n))) +def solution(n): + """Returns the n-th prime number. + + >>> solution(6) + 13 + >>> solution(1) + 2 + >>> solution(3) + 5 + >>> solution(20) + 71 + >>> solution(50) + 229 + >>> solution(100) + 541 + """ + return next(itertools.islice(prime_generator(), n - 1, n)) -if __name__ == '__main__': - main() \ No newline at end of file +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_08/__init__.py b/project_euler/problem_08/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_08/sol1.py b/project_euler/problem_08/sol1.py index 817fd3f87507..6752fae3de60 100644 --- a/project_euler/problem_08/sol1.py +++ b/project_euler/problem_08/sol1.py @@ -1,15 +1,72 @@ +# -*- coding: utf-8 -*- +""" +The four adjacent digits in the 1000-digit number that have the greatest +product are 9 × 9 × 8 × 9 = 5832. + +73167176531330624919225119674426574742355349194934 +96983520312774506326239578318016984801869478851843 +85861560789112949495459501737958331952853208805511 +12540698747158523863050715693290963295227443043557 +66896648950445244523161731856403098711121722383113 +62229893423380308135336276614282806444486645238749 +30358907296290491560440772390713810515859307960866 +70172427121883998797908792274921901699720888093776 +65727333001053367881220235421809751254540594752243 +52584907711670556013604839586446706324415722155397 +53697817977846174064955149290862569321978468622482 +83972241375657056057490261407972968652414535100474 +82166370484403199890008895243450658541227588666881 +16427171479924442928230863465674813919123162824586 +17866458359124566529476545682848912883142607690042 +24219022671055626321111109370544217506941658960408 +07198403850962455444362981230987879927244284909188 +84580156166097919133875499200524063689912560717606 +05886116467109405077541002256983155200055935729725 +71636269561882670428252483600823257530420752963450 + +Find the thirteen adjacent digits in the 1000-digit number that have the +greatest product. What is the value of this product? +""" import sys -def main(): - LargestProduct = -sys.maxsize-1 - number=input().strip() - for i in range(len(number)-12): - product=1 + +N = """73167176531330624919225119674426574742355349194934\ +96983520312774506326239578318016984801869478851843\ +85861560789112949495459501737958331952853208805511\ +12540698747158523863050715693290963295227443043557\ +66896648950445244523161731856403098711121722383113\ +62229893423380308135336276614282806444486645238749\ +30358907296290491560440772390713810515859307960866\ +70172427121883998797908792274921901699720888093776\ +65727333001053367881220235421809751254540594752243\ +52584907711670556013604839586446706324415722155397\ +53697817977846174064955149290862569321978468622482\ +83972241375657056057490261407972968652414535100474\ +82166370484403199890008895243450658541227588666881\ +16427171479924442928230863465674813919123162824586\ +17866458359124566529476545682848912883142607690042\ +24219022671055626321111109370544217506941658960408\ +07198403850962455444362981230987879927244284909188\ +84580156166097919133875499200524063689912560717606\ +05886116467109405077541002256983155200055935729725\ +71636269561882670428252483600823257530420752963450""" + + +def solution(n): + """Find the thirteen adjacent digits in the 1000-digit number n that have + the greatest product and returns it. + + >>> solution(N) + 23514624000 + """ + LargestProduct = -sys.maxsize - 1 + for i in range(len(n) - 12): + product = 1 for j in range(13): - product *= int(number[i+j]) + product *= int(n[i + j]) if product > LargestProduct: LargestProduct = product - print(LargestProduct) + return LargestProduct -if __name__ == '__main__': - main() +if __name__ == "__main__": + print(solution(N)) diff --git a/project_euler/problem_08/sol2.py b/project_euler/problem_08/sol2.py index ae03f3ad0aa6..bae96e373d6c 100644 --- a/project_euler/problem_08/sol2.py +++ b/project_euler/problem_08/sol2.py @@ -1,8 +1,73 @@ +# -*- coding: utf-8 -*- +""" +The four adjacent digits in the 1000-digit number that have the greatest +product are 9 × 9 × 8 × 9 = 5832. + +73167176531330624919225119674426574742355349194934 +96983520312774506326239578318016984801869478851843 +85861560789112949495459501737958331952853208805511 +12540698747158523863050715693290963295227443043557 +66896648950445244523161731856403098711121722383113 +62229893423380308135336276614282806444486645238749 +30358907296290491560440772390713810515859307960866 +70172427121883998797908792274921901699720888093776 +65727333001053367881220235421809751254540594752243 +52584907711670556013604839586446706324415722155397 +53697817977846174064955149290862569321978468622482 +83972241375657056057490261407972968652414535100474 +82166370484403199890008895243450658541227588666881 +16427171479924442928230863465674813919123162824586 +17866458359124566529476545682848912883142607690042 +24219022671055626321111109370544217506941658960408 +07198403850962455444362981230987879927244284909188 +84580156166097919133875499200524063689912560717606 +05886116467109405077541002256983155200055935729725 +71636269561882670428252483600823257530420752963450 + +Find the thirteen adjacent digits in the 1000-digit number that have the +greatest product. What is the value of this product? +""" + from functools import reduce -def main(): - number=input().strip() - print(max([reduce(lambda x,y: int(x)*int(y),number[i:i+13]) for i in range(len(number)-12)])) - -if __name__ == '__main__': - main() +N = ( + "73167176531330624919225119674426574742355349194934" + "96983520312774506326239578318016984801869478851843" + "85861560789112949495459501737958331952853208805511" + "12540698747158523863050715693290963295227443043557" + "66896648950445244523161731856403098711121722383113" + "62229893423380308135336276614282806444486645238749" + "30358907296290491560440772390713810515859307960866" + "70172427121883998797908792274921901699720888093776" + "65727333001053367881220235421809751254540594752243" + "52584907711670556013604839586446706324415722155397" + "53697817977846174064955149290862569321978468622482" + "83972241375657056057490261407972968652414535100474" + "82166370484403199890008895243450658541227588666881" + "16427171479924442928230863465674813919123162824586" + "17866458359124566529476545682848912883142607690042" + "24219022671055626321111109370544217506941658960408" + "07198403850962455444362981230987879927244284909188" + "84580156166097919133875499200524063689912560717606" + "05886116467109405077541002256983155200055935729725" + "71636269561882670428252483600823257530420752963450" +) + + +def solution(n): + """Find the thirteen adjacent digits in the 1000-digit number n that have + the greatest product and returns it. + + >>> solution(N) + 23514624000 + """ + return max( + [ + reduce(lambda x, y: int(x) * int(y), n[i : i + 13]) + for i in range(len(n) - 12) + ] + ) + + +if __name__ == "__main__": + print(solution(str(N))) diff --git a/project_euler/problem_09/__init__.py b/project_euler/problem_09/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_09/sol1.py b/project_euler/problem_09/sol1.py index e54c543b4721..0f368e48d2e3 100644 --- a/project_euler/problem_09/sol1.py +++ b/project_euler/problem_09/sol1.py @@ -1,15 +1,36 @@ +""" +Problem Statement: +A Pythagorean triplet is a set of three natural numbers, a < b < c, for which, + a^2 + b^2 = c^2 +For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2. + +There exists exactly one Pythagorean triplet for which a + b + c = 1000. +Find the product abc. +""" from __future__ import print_function -# Program to find the product of a,b,c which are Pythagorean Triplet that satisfice the following: -# 1. a < b < c -# 2. a**2 + b**2 = c**2 -# 3. a + b + c = 1000 - -print("Please Wait...") -for a in range(300): - for b in range(400): - for c in range(500): - if(a < b < c): - if((a**2) + (b**2) == (c**2)): - if((a+b+c) == 1000): - print(("Product of",a,"*",b,"*",c,"=",(a*b*c))) - break + + +def solution(): + """ + Returns the product of a,b,c which are Pythagorean Triplet that satisfies + the following: + 1. a < b < c + 2. a**2 + b**2 = c**2 + 3. a + b + c = 1000 + + >>> solution() + 31875000 + """ + for a in range(300): + for b in range(400): + for c in range(500): + if a < b < c: + if (a ** 2) + (b ** 2) == (c ** 2): + if (a + b + c) == 1000: + return a * b * c + break + + +if __name__ == "__main__": + print("Please Wait...") + print(solution()) diff --git a/project_euler/problem_09/sol2.py b/project_euler/problem_09/sol2.py index 933f5c557d71..674daae9ec8e 100644 --- a/project_euler/problem_09/sol2.py +++ b/project_euler/problem_09/sol2.py @@ -1,18 +1,44 @@ -"""A Pythagorean triplet is a set of three natural numbers, for which, -a^2+b^2=c^2 -Given N, Check if there exists any Pythagorean triplet for which a+b+c=N -Find maximum possible value of product of a,b,c among all such Pythagorean triplets, If there is no such Pythagorean triplet print -1.""" -#!/bin/python3 +""" +Problem Statement: +A Pythagorean triplet is a set of three natural numbers, a < b < c, for which, + a^2 + b^2 = c^2 +For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2. -product=-1 -d=0 -N = int(input()) -for a in range(1,N//3): - """Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c """ - b=(N*N-2*a*N)//(2*N-2*a) - c=N-a-b - if c*c==(a*a+b*b): - d=(a*b*c) - if d>=product: - product=d -print(product) +There exists exactly one Pythagorean triplet for which a + b + c = 1000. +Find the product abc. +""" +from __future__ import print_function + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + +def solution(n): + """ + Return the product of a,b,c which are Pythagorean Triplet that satisfies + the following: + 1. a < b < c + 2. a**2 + b**2 = c**2 + 3. a + b + c = 1000 + + >>> solution(1000) + 31875000 + """ + product = -1 + d = 0 + for a in range(1, n // 3): + """Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c + """ + b = (n * n - 2 * a * n) // (2 * n - 2 * a) + c = n - a - b + if c * c == (a * a + b * b): + d = a * b * c + if d >= product: + product = d + return product + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_09/sol3.py b/project_euler/problem_09/sol3.py index 5ebf38e76e1a..f749b8a61f11 100644 --- a/project_euler/problem_09/sol3.py +++ b/project_euler/problem_09/sol3.py @@ -1,6 +1,37 @@ -def main(): - print([a*b*c for a in range(1,999) for b in range(a,999) for c in range(b,999) - if (a*a+b*b==c*c) and (a+b+c==1000 ) ][0]) - -if __name__ == '__main__': - main() +""" +Problem Statement: + +A Pythagorean triplet is a set of three natural numbers, a < b < c, for which, + + a^2 + b^2 = c^2 + +For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2. + +There exists exactly one Pythagorean triplet for which a + b + c = 1000. +Find the product abc. +""" +from __future__ import print_function + + +def solution(): + """ + Returns the product of a,b,c which are Pythagorean Triplet that satisfies + the following: + + 1. a**2 + b**2 = c**2 + 2. a + b + c = 1000 + + >>> solution() + 31875000 + """ + return [ + a * b * c + for a in range(1, 999) + for b in range(a, 999) + for c in range(b, 999) + if (a * a + b * b == c * c) and (a + b + c == 1000) + ][0] + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_10/__init__.py b/project_euler/problem_10/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_10/sol1.py b/project_euler/problem_10/sol1.py index 94e5b7362114..038da96e6352 100644 --- a/project_euler/problem_10/sol1.py +++ b/project_euler/problem_10/sol1.py @@ -1,38 +1,60 @@ +""" +Problem Statement: +The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17. + +Find the sum of all the primes below two million. +""" from __future__ import print_function from math import sqrt try: - xrange #Python 2 + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + +try: + xrange # Python 2 except NameError: - xrange = range #Python 3 + xrange = range # Python 3 + def is_prime(n): - for i in xrange(2, int(sqrt(n))+1): - if n%i == 0: - return False + for i in xrange(2, int(sqrt(n)) + 1): + if n % i == 0: + return False + + return True - return True def sum_of_primes(n): - if n > 2: - sumOfPrimes = 2 - else: - return 0 - - for i in xrange(3, n, 2): - if is_prime(i): - sumOfPrimes += i - - return sumOfPrimes - -if __name__ == '__main__': - import sys - - if len(sys.argv) == 1: - print(sum_of_primes(2000000)) - else: - try: - n = int(sys.argv[1]) - print(sum_of_primes(n)) - except ValueError: - print('Invalid entry - please enter a number.') + if n > 2: + sumOfPrimes = 2 + else: + return 0 + + for i in xrange(3, n, 2): + if is_prime(i): + sumOfPrimes += i + + return sumOfPrimes + + +def solution(n): + """Returns the sum of all the primes below n. + + >>> solution(2000000) + 142913828922 + >>> solution(1000) + 76127 + >>> solution(5000) + 1548136 + >>> solution(10000) + 5736396 + >>> solution(7) + 10 + """ + return sum_of_primes(n) + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_10/sol2.py b/project_euler/problem_10/sol2.py index 22df95c063e2..9e51d61b8749 100644 --- a/project_euler/problem_10/sol2.py +++ b/project_euler/problem_10/sol2.py @@ -1,22 +1,49 @@ -#from Python.Math import prime_generator -import math -from itertools import takewhile +""" +Problem Statement: +The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17. + +Find the sum of all the primes below two million. +""" +from __future__ import print_function +import math +from itertools import takewhile + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + def primeCheck(number): if number % 2 == 0 and number > 2: return False return all(number % i for i in range(3, int(math.sqrt(number)) + 1, 2)) - + + def prime_generator(): num = 2 while True: if primeCheck(num): yield num - num+=1 - -def main(): - n = int(input('Enter The upper limit of prime numbers: ')) - print(sum(takewhile(lambda x: x < n,prime_generator()))) + num += 1 + + +def solution(n): + """Returns the sum of all the primes below n. -if __name__ == '__main__': - main() + >>> solution(2000000) + 142913828922 + >>> solution(1000) + 76127 + >>> solution(5000) + 1548136 + >>> solution(10000) + 5736396 + >>> solution(7) + 10 + """ + return sum(takewhile(lambda x: x < n, prime_generator())) + + +if __name__ == "__main__": + print(solution(int(raw_input().strip()))) diff --git a/project_euler/problem_11/__init__.py b/project_euler/problem_11/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_11/sol1.py b/project_euler/problem_11/sol1.py index b882dc449156..3bdddc89d917 100644 --- a/project_euler/problem_11/sol1.py +++ b/project_euler/problem_11/sol1.py @@ -1,6 +1,6 @@ -from __future__ import print_function -''' -What is the greatest product of four adjacent numbers (horizontally, vertically, or diagonally) in this 20x20 array? +""" +What is the greatest product of four adjacent numbers (horizontally, +vertically, or diagonally) in this 20x20 array? 08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08 49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00 @@ -22,47 +22,78 @@ 20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16 20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54 01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48 -''' +""" + +from __future__ import print_function +import os try: - xrange #Python 2 + xrange # Python 2 except NameError: - xrange = range #Python 2 + xrange = range # Python 2 + def largest_product(grid): - nColumns = len(grid[0]) - nRows = len(grid) + nColumns = len(grid[0]) + nRows = len(grid) + + largest = 0 + lrDiagProduct = 0 + rlDiagProduct = 0 + + # Check vertically, horizontally, diagonally at the same time (only works + # for nxn grid) + for i in xrange(nColumns): + for j in xrange(nRows - 3): + vertProduct = ( + grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i] + ) + horzProduct = ( + grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3] + ) + + # Left-to-right diagonal (\) product + if i < nColumns - 3: + lrDiagProduct = ( + grid[i][j] + * grid[i + 1][j + 1] + * grid[i + 2][j + 2] + * grid[i + 3][j + 3] + ) - largest = 0 - lrDiagProduct = 0 - rlDiagProduct = 0 + # Right-to-left diagonal(/) product + if i > 2: + rlDiagProduct = ( + grid[i][j] + * grid[i - 1][j + 1] + * grid[i - 2][j + 2] + * grid[i - 3][j + 3] + ) - #Check vertically, horizontally, diagonally at the same time (only works for nxn grid) - for i in xrange(nColumns): - for j in xrange(nRows-3): - vertProduct = grid[j][i]*grid[j+1][i]*grid[j+2][i]*grid[j+3][i] - horzProduct = grid[i][j]*grid[i][j+1]*grid[i][j+2]*grid[i][j+3] + maxProduct = max( + vertProduct, horzProduct, lrDiagProduct, rlDiagProduct + ) + if maxProduct > largest: + largest = maxProduct - #Left-to-right diagonal (\) product - if (i < nColumns-3): - lrDiagProduct = grid[i][j]*grid[i+1][j+1]*grid[i+2][j+2]*grid[i+3][j+3] + return largest - #Right-to-left diagonal(/) product - if (i > 2): - rlDiagProduct = grid[i][j]*grid[i-1][j+1]*grid[i-2][j+2]*grid[i-3][j+3] - maxProduct = max(vertProduct, horzProduct, lrDiagProduct, rlDiagProduct) - if maxProduct > largest: - largest = maxProduct +def solution(): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution() + 70600674 + """ + grid = [] + with open(os.path.dirname(__file__) + "/grid.txt") as file: + for line in file: + grid.append(line.strip("\n").split(" ")) - return largest + grid = [[int(i) for i in grid[j]] for j in xrange(len(grid))] -if __name__ == '__main__': - grid = [] - with open('grid.txt') as file: - for line in file: - grid.append(line.strip('\n').split(' ')) + return largest_product(grid) - grid = [[int(i) for i in grid[j]] for j in xrange(len(grid))] - print(largest_product(grid)) \ No newline at end of file +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_11/sol2.py b/project_euler/problem_11/sol2.py index b03395f01697..0a5785b42b2c 100644 --- a/project_euler/problem_11/sol2.py +++ b/project_euler/problem_11/sol2.py @@ -1,39 +1,90 @@ -def main(): - with open ("grid.txt", "r") as f: - l = [] - for i in range(20): - l.append([int(x) for x in f.readline().split()]) - - maximum = 0 - - # right - for i in range(20): - for j in range(17): - temp = l[i][j] * l[i][j+1] * l[i][j+2] * l[i][j+3] - if temp > maximum: - maximum = temp - - # down - for i in range(17): - for j in range(20): - temp = l[i][j] * l[i+1][j] * l[i+2][j] * l[i+3][j] - if temp > maximum: - maximum = temp - - #diagonal 1 - for i in range(17): - for j in range(17): - temp = l[i][j] * l[i+1][j+1] * l[i+2][j+2] * l[i+3][j+3] - if temp > maximum: - maximum = temp - - #diagonal 2 - for i in range(17): - for j in range(3, 20): - temp = l[i][j] * l[i+1][j-1] * l[i+2][j-2] * l[i+3][j-3] - if temp > maximum: - maximum = temp - print(maximum) - -if __name__ == '__main__': - main() \ No newline at end of file +""" +What is the greatest product of four adjacent numbers (horizontally, +vertically, or diagonally) in this 20x20 array? + +08 02 22 97 38 15 00 40 00 75 04 05 07 78 52 12 50 77 91 08 +49 49 99 40 17 81 18 57 60 87 17 40 98 43 69 48 04 56 62 00 +81 49 31 73 55 79 14 29 93 71 40 67 53 88 30 03 49 13 36 65 +52 70 95 23 04 60 11 42 69 24 68 56 01 32 56 71 37 02 36 91 +22 31 16 71 51 67 63 89 41 92 36 54 22 40 40 28 66 33 13 80 +24 47 32 60 99 03 45 02 44 75 33 53 78 36 84 20 35 17 12 50 +32 98 81 28 64 23 67 10 26 38 40 67 59 54 70 66 18 38 64 70 +67 26 20 68 02 62 12 20 95 63 94 39 63 08 40 91 66 49 94 21 +24 55 58 05 66 73 99 26 97 17 78 78 96 83 14 88 34 89 63 72 +21 36 23 09 75 00 76 44 20 45 35 14 00 61 33 97 34 31 33 95 +78 17 53 28 22 75 31 67 15 94 03 80 04 62 16 14 09 53 56 92 +16 39 05 42 96 35 31 47 55 58 88 24 00 17 54 24 36 29 85 57 +86 56 00 48 35 71 89 07 05 44 44 37 44 60 21 58 51 54 17 58 +19 80 81 68 05 94 47 69 28 73 92 13 86 52 17 77 04 89 55 40 +04 52 08 83 97 35 99 16 07 97 57 32 16 26 26 79 33 27 98 66 +88 36 68 87 57 62 20 72 03 46 33 67 46 55 12 32 63 93 53 69 +04 42 16 73 38 25 39 11 24 94 72 18 08 46 29 32 40 62 76 36 +20 69 36 41 72 30 23 88 34 62 99 69 82 67 59 85 74 04 36 16 +20 73 35 29 78 31 90 01 74 31 49 71 48 86 81 16 23 57 05 54 +01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48 +""" + +from __future__ import print_function +import os + +try: + xrange # Python 2 +except NameError: + xrange = range # Python 2 + + +def solution(): + """Returns the sum of all the multiples of 3 or 5 below n. + + >>> solution() + 70600674 + """ + with open(os.path.dirname(__file__) + "/grid.txt") as f: + l = [] + for i in xrange(20): + l.append([int(x) for x in f.readline().split()]) + + maximum = 0 + + # right + for i in xrange(20): + for j in xrange(17): + temp = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3] + if temp > maximum: + maximum = temp + + # down + for i in xrange(17): + for j in xrange(20): + temp = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j] + if temp > maximum: + maximum = temp + + # diagonal 1 + for i in xrange(17): + for j in xrange(17): + temp = ( + l[i][j] + * l[i + 1][j + 1] + * l[i + 2][j + 2] + * l[i + 3][j + 3] + ) + if temp > maximum: + maximum = temp + + # diagonal 2 + for i in xrange(17): + for j in xrange(3, 20): + temp = ( + l[i][j] + * l[i + 1][j - 1] + * l[i + 2][j - 2] + * l[i + 3][j - 3] + ) + if temp > maximum: + maximum = temp + return maximum + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_12/__init__.py b/project_euler/problem_12/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_12/sol1.py b/project_euler/problem_12/sol1.py index 73d48a2ec897..baf9babab686 100644 --- a/project_euler/problem_12/sol1.py +++ b/project_euler/problem_12/sol1.py @@ -1,9 +1,9 @@ -from __future__ import print_function -from math import sqrt -''' +""" Highly divisible triangular numbers Problem 12 -The sequence of triangle numbers is generated by adding the natural numbers. So the 7th triangle number would be 1 + 2 + 3 + 4 + 5 + 6 + 7 = 28. The first ten terms would be: +The sequence of triangle numbers is generated by adding the natural numbers. So +the 7th triangle number would be 1 + 2 + 3 + 4 + 5 + 6 + 7 = 28. The first ten +terms would be: 1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ... @@ -18,31 +18,48 @@ 28: 1,2,4,7,14,28 We can see that 28 is the first triangle number to have over five divisors. -What is the value of the first triangle number to have over five hundred divisors? -''' +What is the value of the first triangle number to have over five hundred +divisors? +""" +from __future__ import print_function +from math import sqrt + try: - xrange #Python 2 + xrange # Python 2 except NameError: - xrange = range #Python 3 + xrange = range # Python 3 + def count_divisors(n): - nDivisors = 0 - for i in xrange(1, int(sqrt(n))+1): - if n%i == 0: - nDivisors += 2 - #check if n is perfect square - if n**0.5 == int(n**0.5): - nDivisors -= 1 - return nDivisors - -tNum = 1 -i = 1 - -while True: - i += 1 - tNum += i - - if count_divisors(tNum) > 500: - break - -print(tNum) + nDivisors = 0 + for i in xrange(1, int(sqrt(n)) + 1): + if n % i == 0: + nDivisors += 2 + # check if n is perfect square + if n ** 0.5 == int(n ** 0.5): + nDivisors -= 1 + return nDivisors + + +def solution(): + """Returns the value of the first triangle number to have over five hundred + divisors. + + >>> solution() + 76576500 + """ + tNum = 1 + i = 1 + + while True: + i += 1 + tNum += i + + if count_divisors(tNum) > 500: + break + + return tNum + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_12/sol2.py b/project_euler/problem_12/sol2.py index 479ab2b900cb..071d7516ac0f 100644 --- a/project_euler/problem_12/sol2.py +++ b/project_euler/problem_12/sol2.py @@ -1,8 +1,51 @@ -def triangle_number_generator(): - for n in range(1,1000000): - yield n*(n+1)//2 - -def count_divisors(n): - return sum([2 for i in range(1,int(n**0.5)+1) if n%i==0 and i*i != n]) - -print(next(i for i in triangle_number_generator() if count_divisors(i) > 500)) +""" +Highly divisible triangular numbers +Problem 12 +The sequence of triangle numbers is generated by adding the natural numbers. So +the 7th triangle number would be 1 + 2 + 3 + 4 + 5 + 6 + 7 = 28. The first ten +terms would be: + +1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ... + +Let us list the factors of the first seven triangle numbers: + + 1: 1 + 3: 1,3 + 6: 1,2,3,6 +10: 1,2,5,10 +15: 1,3,5,15 +21: 1,3,7,21 +28: 1,2,4,7,14,28 +We can see that 28 is the first triangle number to have over five divisors. + +What is the value of the first triangle number to have over five hundred +divisors? +""" +from __future__ import print_function + + +def triangle_number_generator(): + for n in range(1, 1000000): + yield n * (n + 1) // 2 + + +def count_divisors(n): + return sum( + [2 for i in range(1, int(n ** 0.5) + 1) if n % i == 0 and i * i != n] + ) + + +def solution(): + """Returns the value of the first triangle number to have over five hundred + divisors. + + >>> solution() + 76576500 + """ + return next( + i for i in triangle_number_generator() if count_divisors(i) > 500 + ) + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_13/__init__.py b/project_euler/problem_13/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_13/sol1.py b/project_euler/problem_13/sol1.py index faaaad5e88c1..983347675b3f 100644 --- a/project_euler/problem_13/sol1.py +++ b/project_euler/problem_13/sol1.py @@ -1,14 +1,36 @@ -''' +""" Problem Statement: -Work out the first ten digits of the sum of the N 50-digit numbers. -''' +Work out the first ten digits of the sum of the following one-hundred 50-digit +numbers. +""" from __future__ import print_function +import os -n = int(input().strip()) +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 -array = [] -for i in range(n): - array.append(int(input().strip())) -print(str(sum(array))[:10]) +def solution(array): + """Returns the first ten digits of the sum of the array elements. + + >>> sum = 0 + >>> array = [] + >>> with open(os.path.dirname(__file__) + "/num.txt","r") as f: + ... for line in f: + ... array.append(int(line)) + ... + >>> solution(array) + '5537376230' + """ + return str(sum(array))[:10] + +if __name__ == "__main__": + n = int(input().strip()) + + array = [] + for i in range(n): + array.append(int(input().strip())) + print(solution(array)) diff --git a/project_euler/problem_13/sol2.py b/project_euler/problem_13/sol2.py deleted file mode 100644 index c1416bcd6e7d..000000000000 --- a/project_euler/problem_13/sol2.py +++ /dev/null @@ -1,5 +0,0 @@ -sum = 0 -with open("num.txt",'r') as f: - for line in f: - sum += int(line) -print(str(sum)[:10]) diff --git a/project_euler/problem_14/__init__.py b/project_euler/problem_14/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_14/sol1.py b/project_euler/problem_14/sol1.py index 9037f6eb8bd5..6b80cd7cb24b 100644 --- a/project_euler/problem_14/sol1.py +++ b/project_euler/problem_14/sol1.py @@ -1,21 +1,73 @@ +# -*- coding: utf-8 -*- +""" +Problem Statement: +The following iterative sequence is defined for the set of positive integers: + + n → n/2 (n is even) + n → 3n + 1 (n is odd) + +Using the rule above and starting with 13, we generate the following sequence: + + 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1 + +It can be seen that this sequence (starting at 13 and finishing at 1) contains +10 terms. Although it has not been proved yet (Collatz Problem), it is thought +that all starting numbers finish at 1. + +Which starting number, under one million, produces the longest chain? +""" from __future__ import print_function -largest_number = 0 -pre_counter = 0 - -for input1 in range(750000,1000000): - counter = 1 - number = input1 - - while number > 1: - if number % 2 == 0: - number /=2 - counter += 1 - else: - number = (3*number)+1 - counter += 1 - - if counter > pre_counter: - largest_number = input1 - pre_counter = counter - -print(('Largest Number:',largest_number,'->',pre_counter,'digits')) + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + +def solution(n): + """Returns the number under n that generates the longest sequence using the + formula: + n → n/2 (n is even) + n → 3n + 1 (n is odd) + + >>> solution(1000000) + {'counter': 525, 'largest_number': 837799} + >>> solution(200) + {'counter': 125, 'largest_number': 171} + >>> solution(5000) + {'counter': 238, 'largest_number': 3711} + >>> solution(15000) + {'counter': 276, 'largest_number': 13255} + """ + largest_number = 0 + pre_counter = 0 + + for input1 in range(n): + counter = 1 + number = input1 + + while number > 1: + if number % 2 == 0: + number /= 2 + counter += 1 + else: + number = (3 * number) + 1 + counter += 1 + + if counter > pre_counter: + largest_number = input1 + pre_counter = counter + return {"counter": pre_counter, "largest_number": largest_number} + + +if __name__ == "__main__": + result = solution(int(raw_input().strip())) + print( + ( + "Largest Number:", + result["largest_number"], + "->", + result["counter"], + "digits", + ) + ) diff --git a/project_euler/problem_14/sol2.py b/project_euler/problem_14/sol2.py index b9de42be1108..59fa79515148 100644 --- a/project_euler/problem_14/sol2.py +++ b/project_euler/problem_14/sol2.py @@ -1,16 +1,69 @@ +# -*- coding: utf-8 -*- +""" +Collatz conjecture: start with any positive integer n. Next term obtained from +the previous term as follows: + +If the previous term is even, the next term is one half the previous term. +If the previous term is odd, the next term is 3 times the previous term plus 1. +The conjecture states the sequence will always reach 1 regardless of starting +n. + +Problem Statement: +The following iterative sequence is defined for the set of positive integers: + + n → n/2 (n is even) + n → 3n + 1 (n is odd) + +Using the rule above and starting with 13, we generate the following sequence: + + 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1 + +It can be seen that this sequence (starting at 13 and finishing at 1) contains +10 terms. Although it has not been proved yet (Collatz Problem), it is thought +that all starting numbers finish at 1. + +Which starting number, under one million, produces the longest chain? +""" +from __future__ import print_function + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 + + def collatz_sequence(n): - """Collatz conjecture: start with any positive integer n.Next termis obtained from the previous term as follows: - if the previous term is even, the next term is one half the previous term. - If the previous term is odd, the next term is 3 times the previous term plus 1. - The conjecture states the sequence will always reach 1 regaardess of starting n.""" - sequence = [n] - while n != 1: - if n % 2 == 0:# even - n //= 2 - else: - n = 3*n +1 - sequence.append(n) - return sequence - -answer = max([(len(collatz_sequence(i)), i) for i in range(1,1000000)]) -print("Longest Collatz sequence under one million is %d with length %d" % (answer[1],answer[0])) \ No newline at end of file + """Returns the Collatz sequence for n.""" + sequence = [n] + while n != 1: + if n % 2 == 0: + n //= 2 + else: + n = 3 * n + 1 + sequence.append(n) + return sequence + + +def solution(n): + """Returns the number under n that generates the longest Collatz sequence. + + >>> solution(1000000) + {'counter': 525, 'largest_number': 837799} + >>> solution(200) + {'counter': 125, 'largest_number': 171} + >>> solution(5000) + {'counter': 238, 'largest_number': 3711} + >>> solution(15000) + {'counter': 276, 'largest_number': 13255} + """ + + result = max([(len(collatz_sequence(i)), i) for i in range(1, n)]) + return {"counter": result[0], "largest_number": result[1]} + + +if __name__ == "__main__": + result = solution(int(raw_input().strip())) + print( + "Longest Collatz sequence under one million is %d with length %d" + % (result["largest_number"], result["counter"]) + ) diff --git a/project_euler/problem_15/__init__.py b/project_euler/problem_15/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_15/sol1.py b/project_euler/problem_15/sol1.py index d24748011ef9..de58bb436d68 100644 --- a/project_euler/problem_15/sol1.py +++ b/project_euler/problem_15/sol1.py @@ -1,20 +1,57 @@ -from __future__ import print_function +""" +Starting in the top left corner of a 2×2 grid, and only being able to move to +the right and down, there are exactly 6 routes to the bottom right corner. +How many such routes are there through a 20×20 grid? +""" from math import factorial + def lattice_paths(n): - n = 2*n #middle entry of odd rows starting at row 3 is the solution for n = 1, 2, 3,... - k = n/2 - - return factorial(n)/(factorial(k)*factorial(n-k)) - -if __name__ == '__main__': - import sys - - if len(sys.argv) == 1: - print(lattice_paths(20)) - else: - try: - n = int(sys.argv[1]) - print(lattice_paths(n)) - except ValueError: - print('Invalid entry - please enter a number.') + """ + Returns the number of paths possible in a n x n grid starting at top left + corner going to bottom right corner and being able to move right and down + only. + +bruno@bruno-laptop:~/git/Python/project_euler/problem_15$ python3 sol1.py 50 +1.008913445455642e+29 +bruno@bruno-laptop:~/git/Python/project_euler/problem_15$ python3 sol1.py 25 +126410606437752.0 +bruno@bruno-laptop:~/git/Python/project_euler/problem_15$ python3 sol1.py 23 +8233430727600.0 +bruno@bruno-laptop:~/git/Python/project_euler/problem_15$ python3 sol1.py 15 +155117520.0 +bruno@bruno-laptop:~/git/Python/project_euler/problem_15$ python3 sol1.py 1 +2.0 + + >>> lattice_paths(25) + 126410606437752 + >>> lattice_paths(23) + 8233430727600 + >>> lattice_paths(20) + 137846528820 + >>> lattice_paths(15) + 155117520 + >>> lattice_paths(1) + 2 + + """ + n = ( + 2 * n + ) # middle entry of odd rows starting at row 3 is the solution for n = 1, + # 2, 3,... + k = n / 2 + + return int(factorial(n) / (factorial(k) * factorial(n - k))) + + +if __name__ == "__main__": + import sys + + if len(sys.argv) == 1: + print(lattice_paths(20)) + else: + try: + n = int(sys.argv[1]) + print(lattice_paths(n)) + except ValueError: + print("Invalid entry - please enter a number.") diff --git a/project_euler/problem_16/__init__.py b/project_euler/problem_16/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_16/sol1.py b/project_euler/problem_16/sol1.py index 05c7916bd10a..67c50ac87876 100644 --- a/project_euler/problem_16/sol1.py +++ b/project_euler/problem_16/sol1.py @@ -1,15 +1,34 @@ -power = int(input("Enter the power of 2: ")) -num = 2**power +""" +2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26. -string_num = str(num) +What is the sum of the digits of the number 2^1000? +""" -list_num = list(string_num) -sum_of_num = 0 +def solution(power): + """Returns the sum of the digits of the number 2^power. + >>> solution(1000) + 1366 + >>> solution(50) + 76 + >>> solution(20) + 31 + >>> solution(15) + 26 + """ + num = 2 ** power + string_num = str(num) + list_num = list(string_num) + sum_of_num = 0 -print("2 ^",power,"=",num) + for i in list_num: + sum_of_num += int(i) -for i in list_num: - sum_of_num += int(i) + return sum_of_num -print("Sum of the digits are:",sum_of_num) + +if __name__ == "__main__": + power = int(input("Enter the power of 2: ").strip()) + print("2 ^ ", power, " = ", 2 ** power) + result = solution(power) + print("Sum of the digits is: ", result) diff --git a/project_euler/problem_16/sol2.py b/project_euler/problem_16/sol2.py index cce3d2354bb1..88672e9a9e54 100644 --- a/project_euler/problem_16/sol2.py +++ b/project_euler/problem_16/sol2.py @@ -1,6 +1,28 @@ -from __future__ import print_function -n = 2**1000 -r = 0 -while n: - r, n = r + n % 10, n // 10 -print(r) +""" +2^15 = 32768 and the sum of its digits is 3 + 2 + 7 + 6 + 8 = 26. + +What is the sum of the digits of the number 2^1000? +""" + + +def solution(power): + """Returns the sum of the digits of the number 2^power. + + >>> solution(1000) + 1366 + >>> solution(50) + 76 + >>> solution(20) + 31 + >>> solution(15) + 26 + """ + n = 2 ** power + r = 0 + while n: + r, n = r + n % 10, n // 10 + return r + + +if __name__ == "__main__": + print(solution(int(str(input()).strip()))) diff --git a/project_euler/problem_17/__init__.py b/project_euler/problem_17/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_17/sol1.py b/project_euler/problem_17/sol1.py index 8dd6f1af2093..d585d81a0825 100644 --- a/project_euler/problem_17/sol1.py +++ b/project_euler/problem_17/sol1.py @@ -1,35 +1,63 @@ -from __future__ import print_function -''' +""" Number letter counts Problem 17 -If the numbers 1 to 5 are written out in words: one, two, three, four, five, then there are 3 + 3 + 5 + 4 + 4 = 19 letters used in total. - -If all the numbers from 1 to 1000 (one thousand) inclusive were written out in words, how many letters would be used? - - -NOTE: Do not count spaces or hyphens. For example, 342 (three hundred and forty-two) contains 23 letters and 115 (one hundred and fifteen) -contains 20 letters. The use of "and" when writing out numbers is in compliance with British usage. -''' - -ones_counts = [0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8] #number of letters in zero, one, two, ..., nineteen (0 for zero since it's never said aloud) -tens_counts = [0, 0, 6, 6, 5, 5, 5, 7, 6, 6] #number of letters in twenty, thirty, ..., ninety (0 for numbers less than 20 due to inconsistency in teens) - -count = 0 - -for i in range(1, 1001): - if i < 1000: - if i >= 100: - count += ones_counts[i/100] + 7 #add number of letters for "n hundred" - - if i%100 != 0: - count += 3 #add number of letters for "and" if number is not multiple of 100 - - if 0 < i%100 < 20: - count += ones_counts[i%100] #add number of letters for one, two, three, ..., nineteen (could be combined with below if not for inconsistency in teens) - else: - count += ones_counts[i%10] + tens_counts[(i%100-i%10)/10] #add number of letters for twenty, twenty one, ..., ninety nine - else: - count += ones_counts[i/1000] + 8 - -print(count) +If the numbers 1 to 5 are written out in words: one, two, three, four, five, +then there are 3 + 3 + 5 + 4 + 4 = 19 letters used in total. + +If all the numbers from 1 to 1000 (one thousand) inclusive were written out in +words, how many letters would be used? + + +NOTE: Do not count spaces or hyphens. For example, 342 (three hundred and +forty-two) contains 23 letters and 115 (one hundred and fifteen) contains 20 +letters. The use of "and" when writing out numbers is in compliance withBritish +usage. +""" + + +def solution(n): + """Returns the number of letters used to write all numbers from 1 to n. + where n is lower or equals to 1000. + >>> solution(1000) + 21124 + >>> solution(5) + 19 + """ + # number of letters in zero, one, two, ..., nineteen (0 for zero since it's + # never said aloud) + ones_counts = [0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8] + # number of letters in twenty, thirty, ..., ninety (0 for numbers less than + # 20 due to inconsistency in teens) + tens_counts = [0, 0, 6, 6, 5, 5, 5, 7, 6, 6] + + count = 0 + + for i in range(1, n + 1): + if i < 1000: + if i >= 100: + # add number of letters for "n hundred" + count += ones_counts[i // 100] + 7 + + if i % 100 != 0: + # add number of letters for "and" if number is not multiple + # of 100 + count += 3 + + if 0 < i % 100 < 20: + # add number of letters for one, two, three, ..., nineteen + # (could be combined with below if not for inconsistency in + # teens) + count += ones_counts[i % 100] + else: + # add number of letters for twenty, twenty one, ..., ninety + # nine + count += ones_counts[i % 10] + count += tens_counts[(i % 100 - i % 10) // 10] + else: + count += ones_counts[i // 1000] + 8 + return count + + +if __name__ == "__main__": + print(solution(int(input().strip()))) diff --git a/project_euler/problem_19/__init__.py b/project_euler/problem_19/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_19/sol1.py b/project_euler/problem_19/sol1.py index 13e520ca76e4..6e4e29ec19c6 100644 --- a/project_euler/problem_19/sol1.py +++ b/project_euler/problem_19/sol1.py @@ -1,9 +1,9 @@ -from __future__ import print_function -''' +""" Counting Sundays Problem 19 -You are given the following information, but you may prefer to do some research for yourself. +You are given the following information, but you may prefer to do some research +for yourself. 1 Jan 1900 was a Monday. Thirty days has September, @@ -13,39 +13,52 @@ Which has twenty-eight, rain or shine. And on leap years, twenty-nine. -A leap year occurs on any year evenly divisible by 4, but not on a century unless it is divisible by 400. +A leap year occurs on any year evenly divisible by 4, but not on a century +unless it is divisible by 400. -How many Sundays fell on the first of the month during the twentieth century (1 Jan 1901 to 31 Dec 2000)? -''' +How many Sundays fell on the first of the month during the twentieth century +(1 Jan 1901 to 31 Dec 2000)? +""" -days_per_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] -day = 6 -month = 1 -year = 1901 +def solution(): + """Returns the number of mondays that fall on the first of the month during + the twentieth century (1 Jan 1901 to 31 Dec 2000)? -sundays = 0 + >>> solution() + 171 + """ + days_per_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] -while year < 2001: - day += 7 + day = 6 + month = 1 + year = 1901 - if (year%4 == 0 and not year%100 == 0) or (year%400 == 0): - if day > days_per_month[month-1] and month != 2: - month += 1 - day = day-days_per_month[month-2] - elif day > 29 and month == 2: - month += 1 - day = day-29 - else: - if day > days_per_month[month-1]: - month += 1 - day = day-days_per_month[month-2] - - if month > 12: - year += 1 - month = 1 + sundays = 0 - if year < 2001 and day == 1: - sundays += 1 + while year < 2001: + day += 7 -print(sundays) + if (year % 4 == 0 and not year % 100 == 0) or (year % 400 == 0): + if day > days_per_month[month - 1] and month != 2: + month += 1 + day = day - days_per_month[month - 2] + elif day > 29 and month == 2: + month += 1 + day = day - 29 + else: + if day > days_per_month[month - 1]: + month += 1 + day = day - days_per_month[month - 2] + + if month > 12: + year += 1 + month = 1 + + if year < 2001 and day == 1: + sundays += 1 + return sundays + + +if __name__ == "__main__": + print(solution(171)) diff --git a/project_euler/problem_20/__init__.py b/project_euler/problem_20/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_20/sol1.py b/project_euler/problem_20/sol1.py index 73e41d5cc8fa..13b3c987f046 100644 --- a/project_euler/problem_20/sol1.py +++ b/project_euler/problem_20/sol1.py @@ -1,27 +1,51 @@ -# Finding the factorial. +""" +n! means n × (n − 1) × ... × 3 × 2 × 1 + +For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800, +and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27. + +Find the sum of the digits in the number 100! +""" + + def factorial(n): fact = 1 - for i in range(1,n+1): + for i in range(1, n + 1): fact *= i return fact -# Spliting the digits and adding it. + def split_and_add(number): + """Split number digits and add them.""" sum_of_digits = 0 - while(number>0): + while number > 0: last_digit = number % 10 sum_of_digits += last_digit - number = int(number/10) # Removing the last_digit from the given number. + number = number // 10 # Removing the last_digit from the given number return sum_of_digits -# Taking the user input. -number = int(input("Enter the Number: ")) -# Assigning the factorial from the factorial function. -factorial = factorial(number) +def solution(n): + """Returns the sum of the digits in the number 100! + >>> solution(100) + 648 + >>> solution(50) + 216 + >>> solution(10) + 27 + >>> solution(5) + 3 + >>> solution(3) + 6 + >>> solution(2) + 2 + >>> solution(1) + 1 + """ + f = factorial(n) + result = split_and_add(f) + return result -# Spliting and adding the factorial into answer. -answer = split_and_add(factorial) -# Printing the answer. -print(answer) +if __name__ == "__main__": + print(solution(int(input("Enter the Number: ").strip()))) diff --git a/project_euler/problem_20/sol2.py b/project_euler/problem_20/sol2.py index bca9af9cb9ef..14e591795292 100644 --- a/project_euler/problem_20/sol2.py +++ b/project_euler/problem_20/sol2.py @@ -1,5 +1,33 @@ +""" +n! means n × (n − 1) × ... × 3 × 2 × 1 + +For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800, +and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27. + +Find the sum of the digits in the number 100! +""" from math import factorial -def main(): - print(sum([int(x) for x in str(factorial(100))])) -if __name__ == '__main__': - main() \ No newline at end of file + + +def solution(n): + """Returns the sum of the digits in the number 100! + >>> solution(100) + 648 + >>> solution(50) + 216 + >>> solution(10) + 27 + >>> solution(5) + 3 + >>> solution(3) + 6 + >>> solution(2) + 2 + >>> solution(1) + 1 + """ + return sum([int(x) for x in str(factorial(n))]) + + +if __name__ == "__main__": + print(solution(int(input("Enter the Number: ").strip()))) diff --git a/project_euler/problem_21/__init__.py b/project_euler/problem_21/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_21/sol1.py b/project_euler/problem_21/sol1.py index da29a5c7b631..9cf2a64cf2a9 100644 --- a/project_euler/problem_21/sol1.py +++ b/project_euler/problem_21/sol1.py @@ -1,30 +1,61 @@ -#-.- coding: latin-1 -.- -from __future__ import print_function +# -.- coding: latin-1 -.- from math import sqrt -''' + +""" Amicable Numbers Problem 21 -Let d(n) be defined as the sum of proper divisors of n (numbers less than n which divide evenly into n). -If d(a) = b and d(b) = a, where a ≠ b, then a and b are an amicable pair and each of a and b are called amicable numbers. +Let d(n) be defined as the sum of proper divisors of n (numbers less than n +which divide evenly into n). +If d(a) = b and d(b) = a, where a ≠ b, then a and b are an amicable pair and +each of a and b are called amicable numbers. -For example, the proper divisors of 220 are 1, 2, 4, 5, 10, 11, 20, 22, 44, 55 and 110; therefore d(220) = 284. The proper divisors of 284 are 1, 2, 4, 71 and 142; so d(284) = 220. +For example, the proper divisors of 220 are 1, 2, 4, 5, 10, 11, 20, 22, 44, 55 +and 110; therefore d(220) = 284. The proper divisors of 284 are 1, 2, 4, 71 and +142; so d(284) = 220. Evaluate the sum of all the amicable numbers under 10000. -''' +""" try: - xrange #Python 2 + xrange # Python 2 except NameError: - xrange = range #Python 3 + xrange = range # Python 3 + def sum_of_divisors(n): - total = 0 - for i in xrange(1, int(sqrt(n)+1)): - if n%i == 0 and i != sqrt(n): - total += i + n//i - elif i == sqrt(n): - total += i - return total-n - -total = [i for i in range(1,10000) if sum_of_divisors(sum_of_divisors(i)) == i and sum_of_divisors(i) != i] -print(sum(total)) + total = 0 + for i in xrange(1, int(sqrt(n) + 1)): + if n % i == 0 and i != sqrt(n): + total += i + n // i + elif i == sqrt(n): + total += i + return total - n + + +def solution(n): + """Returns the sum of all the amicable numbers under n. + + >>> solution(10000) + 31626 + >>> solution(5000) + 8442 + >>> solution(1000) + 504 + >>> solution(100) + 0 + >>> solution(50) + 0 + """ + total = sum( + [ + i + for i in range(1, n) + if sum_of_divisors(sum_of_divisors(i)) == i + and sum_of_divisors(i) != i + ] + ) + return total + + +if __name__ == "__main__": + print(solution(int(str(input()).strip()))) diff --git a/project_euler/problem_22/__init__.py b/project_euler/problem_22/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_22/sol1.py b/project_euler/problem_22/sol1.py index 7754306583dc..aa779f222eaa 100644 --- a/project_euler/problem_22/sol1.py +++ b/project_euler/problem_22/sol1.py @@ -1,37 +1,52 @@ # -*- coding: latin-1 -*- -from __future__ import print_function -''' +""" Name scores Problem 22 -Using names.txt (right click and 'Save Link/Target As...'), a 46K text file containing over five-thousand first names, begin by sorting it -into alphabetical order. Then working out the alphabetical value for each name, multiply this value by its alphabetical position in the list -to obtain a name score. +Using names.txt (right click and 'Save Link/Target As...'), a 46K text file +containing over five-thousand first names, begin by sorting it into +alphabetical order. Then working out the alphabetical value for each name, +multiply this value by its alphabetical position in the list to obtain a name +score. -For example, when the list is sorted into alphabetical order, COLIN, which is worth 3 + 15 + 12 + 9 + 14 = 53, is the 938th name in the list. -So, COLIN would obtain a score of 938 × 53 = 49714. +For example, when the list is sorted into alphabetical order, COLIN, which is +worth 3 + 15 + 12 + 9 + 14 = 53, is the 938th name in the list. So, COLIN would +obtain a score of 938 × 53 = 49714. What is the total of all the name scores in the file? -''' +""" +import os + + try: - xrange #Python 2 + xrange # Python 2 except NameError: - xrange = range #Python 3 + xrange = range # Python 3 + + +def solution(): + """Returns the total of all the name scores in the file. + + >>> solution() + 871198282 + """ + with open(os.path.dirname(__file__) + "/p022_names.txt") as file: + names = str(file.readlines()[0]) + names = names.replace('"', "").split(",") -with open('p022_names.txt') as file: - names = str(file.readlines()[0]) - names = names.replace('"', '').split(',') + names.sort() -names.sort() + name_score = 0 + total_score = 0 -name_score = 0 -total_score = 0 + for i, name in enumerate(names): + for letter in name: + name_score += ord(letter) - 64 -for i, name in enumerate(names): - for letter in name: - name_score += ord(letter) - 64 + total_score += (i + 1) * name_score + name_score = 0 + return total_score - total_score += (i+1)*name_score - name_score = 0 -print(total_score) \ No newline at end of file +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_22/sol2.py b/project_euler/problem_22/sol2.py index d7f9abf09d49..69acd2fb8ef3 100644 --- a/project_euler/problem_22/sol2.py +++ b/project_euler/problem_22/sol2.py @@ -1,533 +1,43 @@ -def main(): - name = [ - "MARY", "PATRICIA", "LINDA", "BARBARA", "ELIZABETH", "JENNIFER", "MARIA", "SUSAN", "MARGARET", "DOROTHY", - "LISA", "NANCY", "KAREN", "BETTY", "HELEN", "SANDRA", "DONNA", "CAROL", "RUTH", "SHARON", - "MICHELLE", "LAURA", "SARAH", "KIMBERLY", "DEBORAH", "JESSICA", "SHIRLEY", "CYNTHIA", "ANGELA", "MELISSA", - "BRENDA", "AMY", "ANNA", "REBECCA", "VIRGINIA", "KATHLEEN", "PAMELA", "MARTHA", "DEBRA", "AMANDA", - "STEPHANIE", "CAROLYN", "CHRISTINE", "MARIE", "JANET", "CATHERINE", "FRANCES", "ANN", "JOYCE", "DIANE", - "ALICE", "JULIE", "HEATHER", "TERESA", "DORIS", "GLORIA", "EVELYN", "JEAN", "CHERYL", "MILDRED", - "KATHERINE", "JOAN", "ASHLEY", "JUDITH", "ROSE", "JANICE", "KELLY", "NICOLE", "JUDY", "CHRISTINA", - "KATHY", "THERESA", "BEVERLY", "DENISE", "TAMMY", "IRENE", "JANE", "LORI", "RACHEL", "MARILYN", - "ANDREA", "KATHRYN", "LOUISE", "SARA", "ANNE", "JACQUELINE", "WANDA", "BONNIE", "JULIA", "RUBY", - "LOIS", "TINA", "PHYLLIS", "NORMA", "PAULA", "DIANA", "ANNIE", "LILLIAN", "EMILY", "ROBIN", - "PEGGY", "CRYSTAL", "GLADYS", "RITA", "DAWN", "CONNIE", "FLORENCE", "TRACY", "EDNA", "TIFFANY", - "CARMEN", "ROSA", "CINDY", "GRACE", "WENDY", "VICTORIA", "EDITH", "KIM", "SHERRY", "SYLVIA", - "JOSEPHINE", "THELMA", "SHANNON", "SHEILA", "ETHEL", "ELLEN", "ELAINE", "MARJORIE", "CARRIE", "CHARLOTTE", - "MONICA", "ESTHER", "PAULINE", "EMMA", "JUANITA", "ANITA", "RHONDA", "HAZEL", "AMBER", "EVA", - "DEBBIE", "APRIL", "LESLIE", "CLARA", "LUCILLE", "JAMIE", "JOANNE", "ELEANOR", "VALERIE", "DANIELLE", - "MEGAN", "ALICIA", "SUZANNE", "MICHELE", "GAIL", "BERTHA", "DARLENE", "VERONICA", "JILL", "ERIN", - "GERALDINE", "LAUREN", "CATHY", "JOANN", "LORRAINE", "LYNN", "SALLY", "REGINA", "ERICA", "BEATRICE", - "DOLORES", "BERNICE", "AUDREY", "YVONNE", "ANNETTE", "JUNE", "SAMANTHA", "MARION", "DANA", "STACY", - "ANA", "RENEE", "IDA", "VIVIAN", "ROBERTA", "HOLLY", "BRITTANY", "MELANIE", "LORETTA", "YOLANDA", - "JEANETTE", "LAURIE", "KATIE", "KRISTEN", "VANESSA", "ALMA", "SUE", "ELSIE", "BETH", "JEANNE", - "VICKI", "CARLA", "TARA", "ROSEMARY", "EILEEN", "TERRI", "GERTRUDE", "LUCY", "TONYA", "ELLA", - "STACEY", "WILMA", "GINA", "KRISTIN", "JESSIE", "NATALIE", "AGNES", "VERA", "WILLIE", "CHARLENE", - "BESSIE", "DELORES", "MELINDA", "PEARL", "ARLENE", "MAUREEN", "COLLEEN", "ALLISON", "TAMARA", "JOY", - "GEORGIA", "CONSTANCE", "LILLIE", "CLAUDIA", "JACKIE", "MARCIA", "TANYA", "NELLIE", "MINNIE", "MARLENE", - "HEIDI", "GLENDA", "LYDIA", "VIOLA", "COURTNEY", "MARIAN", "STELLA", "CAROLINE", "DORA", "JO", - "VICKIE", "MATTIE", "TERRY", "MAXINE", "IRMA", "MABEL", "MARSHA", "MYRTLE", "LENA", "CHRISTY", - "DEANNA", "PATSY", "HILDA", "GWENDOLYN", "JENNIE", "NORA", "MARGIE", "NINA", "CASSANDRA", "LEAH", - "PENNY", "KAY", "PRISCILLA", "NAOMI", "CAROLE", "BRANDY", "OLGA", "BILLIE", "DIANNE", "TRACEY", - "LEONA", "JENNY", "FELICIA", "SONIA", "MIRIAM", "VELMA", "BECKY", "BOBBIE", "VIOLET", "KRISTINA", - "TONI", "MISTY", "MAE", "SHELLY", "DAISY", "RAMONA", "SHERRI", "ERIKA", "KATRINA", "CLAIRE", - "LINDSEY", "LINDSAY", "GENEVA", "GUADALUPE", "BELINDA", "MARGARITA", "SHERYL", "CORA", "FAYE", "ADA", - "NATASHA", "SABRINA", "ISABEL", "MARGUERITE", "HATTIE", "HARRIET", "MOLLY", "CECILIA", "KRISTI", "BRANDI", - "BLANCHE", "SANDY", "ROSIE", "JOANNA", "IRIS", "EUNICE", "ANGIE", "INEZ", "LYNDA", "MADELINE", - "AMELIA", "ALBERTA", "GENEVIEVE", "MONIQUE", "JODI", "JANIE", "MAGGIE", "KAYLA", "SONYA", "JAN", - "LEE", "KRISTINE", "CANDACE", "FANNIE", "MARYANN", "OPAL", "ALISON", "YVETTE", "MELODY", "LUZ", - "SUSIE", "OLIVIA", "FLORA", "SHELLEY", "KRISTY", "MAMIE", "LULA", "LOLA", "VERNA", "BEULAH", - "ANTOINETTE", "CANDICE", "JUANA", "JEANNETTE", "PAM", "KELLI", "HANNAH", "WHITNEY", "BRIDGET", "KARLA", - "CELIA", "LATOYA", "PATTY", "SHELIA", "GAYLE", "DELLA", "VICKY", "LYNNE", "SHERI", "MARIANNE", - "KARA", "JACQUELYN", "ERMA", "BLANCA", "MYRA", "LETICIA", "PAT", "KRISTA", "ROXANNE", "ANGELICA", - "JOHNNIE", "ROBYN", "FRANCIS", "ADRIENNE", "ROSALIE", "ALEXANDRA", "BROOKE", "BETHANY", "SADIE", "BERNADETTE", - "TRACI", "JODY", "KENDRA", "JASMINE", "NICHOLE", "RACHAEL", "CHELSEA", "MABLE", "ERNESTINE", "MURIEL", - "MARCELLA", "ELENA", "KRYSTAL", "ANGELINA", "NADINE", "KARI", "ESTELLE", "DIANNA", "PAULETTE", "LORA", - "MONA", "DOREEN", "ROSEMARIE", "ANGEL", "DESIREE", "ANTONIA", "HOPE", "GINGER", "JANIS", "BETSY", - "CHRISTIE", "FREDA", "MERCEDES", "MEREDITH", "LYNETTE", "TERI", "CRISTINA", "EULA", "LEIGH", "MEGHAN", - "SOPHIA", "ELOISE", "ROCHELLE", "GRETCHEN", "CECELIA", "RAQUEL", "HENRIETTA", "ALYSSA", "JANA", "KELLEY", - "GWEN", "KERRY", "JENNA", "TRICIA", "LAVERNE", "OLIVE", "ALEXIS", "TASHA", "SILVIA", "ELVIRA", - "CASEY", "DELIA", "SOPHIE", "KATE", "PATTI", "LORENA", "KELLIE", "SONJA", "LILA", "LANA", - "DARLA", "MAY", "MINDY", "ESSIE", "MANDY", "LORENE", "ELSA", "JOSEFINA", "JEANNIE", "MIRANDA", - "DIXIE", "LUCIA", "MARTA", "FAITH", "LELA", "JOHANNA", "SHARI", "CAMILLE", "TAMI", "SHAWNA", - "ELISA", "EBONY", "MELBA", "ORA", "NETTIE", "TABITHA", "OLLIE", "JAIME", "WINIFRED", "KRISTIE", - "MARINA", "ALISHA", "AIMEE", "RENA", "MYRNA", "MARLA", "TAMMIE", "LATASHA", "BONITA", "PATRICE", - "RONDA", "SHERRIE", "ADDIE", "FRANCINE", "DELORIS", "STACIE", "ADRIANA", "CHERI", "SHELBY", "ABIGAIL", - "CELESTE", "JEWEL", "CARA", "ADELE", "REBEKAH", "LUCINDA", "DORTHY", "CHRIS", "EFFIE", "TRINA", - "REBA", "SHAWN", "SALLIE", "AURORA", "LENORA", "ETTA", "LOTTIE", "KERRI", "TRISHA", "NIKKI", - "ESTELLA", "FRANCISCA", "JOSIE", "TRACIE", "MARISSA", "KARIN", "BRITTNEY", "JANELLE", "LOURDES", "LAUREL", - "HELENE", "FERN", "ELVA", "CORINNE", "KELSEY", "INA", "BETTIE", "ELISABETH", "AIDA", "CAITLIN", - "INGRID", "IVA", "EUGENIA", "CHRISTA", "GOLDIE", "CASSIE", "MAUDE", "JENIFER", "THERESE", "FRANKIE", - "DENA", "LORNA", "JANETTE", "LATONYA", "CANDY", "MORGAN", "CONSUELO", "TAMIKA", "ROSETTA", "DEBORA", - "CHERIE", "POLLY", "DINA", "JEWELL", "FAY", "JILLIAN", "DOROTHEA", "NELL", "TRUDY", "ESPERANZA", - "PATRICA", "KIMBERLEY", "SHANNA", "HELENA", "CAROLINA", "CLEO", "STEFANIE", "ROSARIO", "OLA", "JANINE", - "MOLLIE", "LUPE", "ALISA", "LOU", "MARIBEL", "SUSANNE", "BETTE", "SUSANA", "ELISE", "CECILE", - "ISABELLE", "LESLEY", "JOCELYN", "PAIGE", "JONI", "RACHELLE", "LEOLA", "DAPHNE", "ALTA", "ESTER", - "PETRA", "GRACIELA", "IMOGENE", "JOLENE", "KEISHA", "LACEY", "GLENNA", "GABRIELA", "KERI", "URSULA", - "LIZZIE", "KIRSTEN", "SHANA", "ADELINE", "MAYRA", "JAYNE", "JACLYN", "GRACIE", "SONDRA", "CARMELA", - "MARISA", "ROSALIND", "CHARITY", "TONIA", "BEATRIZ", "MARISOL", "CLARICE", "JEANINE", "SHEENA", "ANGELINE", - "FRIEDA", "LILY", "ROBBIE", "SHAUNA", "MILLIE", "CLAUDETTE", "CATHLEEN", "ANGELIA", "GABRIELLE", "AUTUMN", - "KATHARINE", "SUMMER", "JODIE", "STACI", "LEA", "CHRISTI", "JIMMIE", "JUSTINE", "ELMA", "LUELLA", - "MARGRET", "DOMINIQUE", "SOCORRO", "RENE", "MARTINA", "MARGO", "MAVIS", "CALLIE", "BOBBI", "MARITZA", - "LUCILE", "LEANNE", "JEANNINE", "DEANA", "AILEEN", "LORIE", "LADONNA", "WILLA", "MANUELA", "GALE", - "SELMA", "DOLLY", "SYBIL", "ABBY", "LARA", "DALE", "IVY", "DEE", "WINNIE", "MARCY", - "LUISA", "JERI", "MAGDALENA", "OFELIA", "MEAGAN", "AUDRA", "MATILDA", "LEILA", "CORNELIA", "BIANCA", - "SIMONE", "BETTYE", "RANDI", "VIRGIE", "LATISHA", "BARBRA", "GEORGINA", "ELIZA", "LEANN", "BRIDGETTE", - "RHODA", "HALEY", "ADELA", "NOLA", "BERNADINE", "FLOSSIE", "ILA", "GRETA", "RUTHIE", "NELDA", - "MINERVA", "LILLY", "TERRIE", "LETHA", "HILARY", "ESTELA", "VALARIE", "BRIANNA", "ROSALYN", "EARLINE", - "CATALINA", "AVA", "MIA", "CLARISSA", "LIDIA", "CORRINE", "ALEXANDRIA", "CONCEPCION", "TIA", "SHARRON", - "RAE", "DONA", "ERICKA", "JAMI", "ELNORA", "CHANDRA", "LENORE", "NEVA", "MARYLOU", "MELISA", - "TABATHA", "SERENA", "AVIS", "ALLIE", "SOFIA", "JEANIE", "ODESSA", "NANNIE", "HARRIETT", "LORAINE", - "PENELOPE", "MILAGROS", "EMILIA", "BENITA", "ALLYSON", "ASHLEE", "TANIA", "TOMMIE", "ESMERALDA", "KARINA", - "EVE", "PEARLIE", "ZELMA", "MALINDA", "NOREEN", "TAMEKA", "SAUNDRA", "HILLARY", "AMIE", "ALTHEA", - "ROSALINDA", "JORDAN", "LILIA", "ALANA", "GAY", "CLARE", "ALEJANDRA", "ELINOR", "MICHAEL", "LORRIE", - "JERRI", "DARCY", "EARNESTINE", "CARMELLA", "TAYLOR", "NOEMI", "MARCIE", "LIZA", "ANNABELLE", "LOUISA", - "EARLENE", "MALLORY", "CARLENE", "NITA", "SELENA", "TANISHA", "KATY", "JULIANNE", "JOHN", "LAKISHA", - "EDWINA", "MARICELA", "MARGERY", "KENYA", "DOLLIE", "ROXIE", "ROSLYN", "KATHRINE", "NANETTE", "CHARMAINE", - "LAVONNE", "ILENE", "KRIS", "TAMMI", "SUZETTE", "CORINE", "KAYE", "JERRY", "MERLE", "CHRYSTAL", - "LINA", "DEANNE", "LILIAN", "JULIANA", "ALINE", "LUANN", "KASEY", "MARYANNE", "EVANGELINE", "COLETTE", - "MELVA", "LAWANDA", "YESENIA", "NADIA", "MADGE", "KATHIE", "EDDIE", "OPHELIA", "VALERIA", "NONA", - "MITZI", "MARI", "GEORGETTE", "CLAUDINE", "FRAN", "ALISSA", "ROSEANN", "LAKEISHA", "SUSANNA", "REVA", - "DEIDRE", "CHASITY", "SHEREE", "CARLY", "JAMES", "ELVIA", "ALYCE", "DEIRDRE", "GENA", "BRIANA", - "ARACELI", "KATELYN", "ROSANNE", "WENDI", "TESSA", "BERTA", "MARVA", "IMELDA", "MARIETTA", "MARCI", - "LEONOR", "ARLINE", "SASHA", "MADELYN", "JANNA", "JULIETTE", "DEENA", "AURELIA", "JOSEFA", "AUGUSTA", - "LILIANA", "YOUNG", "CHRISTIAN", "LESSIE", "AMALIA", "SAVANNAH", "ANASTASIA", "VILMA", "NATALIA", "ROSELLA", - "LYNNETTE", "CORINA", "ALFREDA", "LEANNA", "CAREY", "AMPARO", "COLEEN", "TAMRA", "AISHA", "WILDA", - "KARYN", "CHERRY", "QUEEN", "MAURA", "MAI", "EVANGELINA", "ROSANNA", "HALLIE", "ERNA", "ENID", - "MARIANA", "LACY", "JULIET", "JACKLYN", "FREIDA", "MADELEINE", "MARA", "HESTER", "CATHRYN", "LELIA", - "CASANDRA", "BRIDGETT", "ANGELITA", "JANNIE", "DIONNE", "ANNMARIE", "KATINA", "BERYL", "PHOEBE", "MILLICENT", - "KATHERYN", "DIANN", "CARISSA", "MARYELLEN", "LIZ", "LAURI", "HELGA", "GILDA", "ADRIAN", "RHEA", - "MARQUITA", "HOLLIE", "TISHA", "TAMERA", "ANGELIQUE", "FRANCESCA", "BRITNEY", "KAITLIN", "LOLITA", "FLORINE", - "ROWENA", "REYNA", "TWILA", "FANNY", "JANELL", "INES", "CONCETTA", "BERTIE", "ALBA", "BRIGITTE", - "ALYSON", "VONDA", "PANSY", "ELBA", "NOELLE", "LETITIA", "KITTY", "DEANN", "BRANDIE", "LOUELLA", - "LETA", "FELECIA", "SHARLENE", "LESA", "BEVERLEY", "ROBERT", "ISABELLA", "HERMINIA", "TERRA", "CELINA", - "TORI", "OCTAVIA", "JADE", "DENICE", "GERMAINE", "SIERRA", "MICHELL", "CORTNEY", "NELLY", "DORETHA", - "SYDNEY", "DEIDRA", "MONIKA", "LASHONDA", "JUDI", "CHELSEY", "ANTIONETTE", "MARGOT", "BOBBY", "ADELAIDE", - "NAN", "LEEANN", "ELISHA", "DESSIE", "LIBBY", "KATHI", "GAYLA", "LATANYA", "MINA", "MELLISA", - "KIMBERLEE", "JASMIN", "RENAE", "ZELDA", "ELDA", "MA", "JUSTINA", "GUSSIE", "EMILIE", "CAMILLA", - "ABBIE", "ROCIO", "KAITLYN", "JESSE", "EDYTHE", "ASHLEIGH", "SELINA", "LAKESHA", "GERI", "ALLENE", - "PAMALA", "MICHAELA", "DAYNA", "CARYN", "ROSALIA", "SUN", "JACQULINE", "REBECA", "MARYBETH", "KRYSTLE", - "IOLA", "DOTTIE", "BENNIE", "BELLE", "AUBREY", "GRISELDA", "ERNESTINA", "ELIDA", "ADRIANNE", "DEMETRIA", - "DELMA", "CHONG", "JAQUELINE", "DESTINY", "ARLEEN", "VIRGINA", "RETHA", "FATIMA", "TILLIE", "ELEANORE", - "CARI", "TREVA", "BIRDIE", "WILHELMINA", "ROSALEE", "MAURINE", "LATRICE", "YONG", "JENA", "TARYN", - "ELIA", "DEBBY", "MAUDIE", "JEANNA", "DELILAH", "CATRINA", "SHONDA", "HORTENCIA", "THEODORA", "TERESITA", - "ROBBIN", "DANETTE", "MARYJANE", "FREDDIE", "DELPHINE", "BRIANNE", "NILDA", "DANNA", "CINDI", "BESS", - "IONA", "HANNA", "ARIEL", "WINONA", "VIDA", "ROSITA", "MARIANNA", "WILLIAM", "RACHEAL", "GUILLERMINA", - "ELOISA", "CELESTINE", "CAREN", "MALISSA", "LONA", "CHANTEL", "SHELLIE", "MARISELA", "LEORA", "AGATHA", - "SOLEDAD", "MIGDALIA", "IVETTE", "CHRISTEN", "ATHENA", "JANEL", "CHLOE", "VEDA", "PATTIE", "TESSIE", - "TERA", "MARILYNN", "LUCRETIA", "KARRIE", "DINAH", "DANIELA", "ALECIA", "ADELINA", "VERNICE", "SHIELA", - "PORTIA", "MERRY", "LASHAWN", "DEVON", "DARA", "TAWANA", "OMA", "VERDA", "CHRISTIN", "ALENE", - "ZELLA", "SANDI", "RAFAELA", "MAYA", "KIRA", "CANDIDA", "ALVINA", "SUZAN", "SHAYLA", "LYN", - "LETTIE", "ALVA", "SAMATHA", "ORALIA", "MATILDE", "MADONNA", "LARISSA", "VESTA", "RENITA", "INDIA", - "DELOIS", "SHANDA", "PHILLIS", "LORRI", "ERLINDA", "CRUZ", "CATHRINE", "BARB", "ZOE", "ISABELL", - "IONE", "GISELA", "CHARLIE", "VALENCIA", "ROXANNA", "MAYME", "KISHA", "ELLIE", "MELLISSA", "DORRIS", - "DALIA", "BELLA", "ANNETTA", "ZOILA", "RETA", "REINA", "LAURETTA", "KYLIE", "CHRISTAL", "PILAR", - "CHARLA", "ELISSA", "TIFFANI", "TANA", "PAULINA", "LEOTA", "BREANNA", "JAYME", "CARMEL", "VERNELL", - "TOMASA", "MANDI", "DOMINGA", "SANTA", "MELODIE", "LURA", "ALEXA", "TAMELA", "RYAN", "MIRNA", - "KERRIE", "VENUS", "NOEL", "FELICITA", "CRISTY", "CARMELITA", "BERNIECE", "ANNEMARIE", "TIARA", "ROSEANNE", - "MISSY", "CORI", "ROXANA", "PRICILLA", "KRISTAL", "JUNG", "ELYSE", "HAYDEE", "ALETHA", "BETTINA", - "MARGE", "GILLIAN", "FILOMENA", "CHARLES", "ZENAIDA", "HARRIETTE", "CARIDAD", "VADA", "UNA", "ARETHA", - "PEARLINE", "MARJORY", "MARCELA", "FLOR", "EVETTE", "ELOUISE", "ALINA", "TRINIDAD", "DAVID", "DAMARIS", - "CATHARINE", "CARROLL", "BELVA", "NAKIA", "MARLENA", "LUANNE", "LORINE", "KARON", "DORENE", "DANITA", - "BRENNA", "TATIANA", "SAMMIE", "LOUANN", "LOREN", "JULIANNA", "ANDRIA", "PHILOMENA", "LUCILA", "LEONORA", - "DOVIE", "ROMONA", "MIMI", "JACQUELIN", "GAYE", "TONJA", "MISTI", "JOE", "GENE", "CHASTITY", - "STACIA", "ROXANN", "MICAELA", "NIKITA", "MEI", "VELDA", "MARLYS", "JOHNNA", "AURA", "LAVERN", - "IVONNE", "HAYLEY", "NICKI", "MAJORIE", "HERLINDA", "GEORGE", "ALPHA", "YADIRA", "PERLA", "GREGORIA", - "DANIEL", "ANTONETTE", "SHELLI", "MOZELLE", "MARIAH", "JOELLE", "CORDELIA", "JOSETTE", "CHIQUITA", "TRISTA", - "LOUIS", "LAQUITA", "GEORGIANA", "CANDI", "SHANON", "LONNIE", "HILDEGARD", "CECIL", "VALENTINA", "STEPHANY", - "MAGDA", "KAROL", "GERRY", "GABRIELLA", "TIANA", "ROMA", "RICHELLE", "RAY", "PRINCESS", "OLETA", - "JACQUE", "IDELLA", "ALAINA", "SUZANNA", "JOVITA", "BLAIR", "TOSHA", "RAVEN", "NEREIDA", "MARLYN", - "KYLA", "JOSEPH", "DELFINA", "TENA", "STEPHENIE", "SABINA", "NATHALIE", "MARCELLE", "GERTIE", "DARLEEN", - "THEA", "SHARONDA", "SHANTEL", "BELEN", "VENESSA", "ROSALINA", "ONA", "GENOVEVA", "COREY", "CLEMENTINE", - "ROSALBA", "RENATE", "RENATA", "MI", "IVORY", "GEORGIANNA", "FLOY", "DORCAS", "ARIANA", "TYRA", - "THEDA", "MARIAM", "JULI", "JESICA", "DONNIE", "VIKKI", "VERLA", "ROSELYN", "MELVINA", "JANNETTE", - "GINNY", "DEBRAH", "CORRIE", "ASIA", "VIOLETA", "MYRTIS", "LATRICIA", "COLLETTE", "CHARLEEN", "ANISSA", - "VIVIANA", "TWYLA", "PRECIOUS", "NEDRA", "LATONIA", "LAN", "HELLEN", "FABIOLA", "ANNAMARIE", "ADELL", - "SHARYN", "CHANTAL", "NIKI", "MAUD", "LIZETTE", "LINDY", "KIA", "KESHA", "JEANA", "DANELLE", - "CHARLINE", "CHANEL", "CARROL", "VALORIE", "LIA", "DORTHA", "CRISTAL", "SUNNY", "LEONE", "LEILANI", - "GERRI", "DEBI", "ANDRA", "KESHIA", "IMA", "EULALIA", "EASTER", "DULCE", "NATIVIDAD", "LINNIE", - "KAMI", "GEORGIE", "CATINA", "BROOK", "ALDA", "WINNIFRED", "SHARLA", "RUTHANN", "MEAGHAN", "MAGDALENE", - "LISSETTE", "ADELAIDA", "VENITA", "TRENA", "SHIRLENE", "SHAMEKA", "ELIZEBETH", "DIAN", "SHANTA", "MICKEY", - "LATOSHA", "CARLOTTA", "WINDY", "SOON", "ROSINA", "MARIANN", "LEISA", "JONNIE", "DAWNA", "CATHIE", - "BILLY", "ASTRID", "SIDNEY", "LAUREEN", "JANEEN", "HOLLI", "FAWN", "VICKEY", "TERESSA", "SHANTE", - "RUBYE", "MARCELINA", "CHANDA", "CARY", "TERESE", "SCARLETT", "MARTY", "MARNIE", "LULU", "LISETTE", - "JENIFFER", "ELENOR", "DORINDA", "DONITA", "CARMAN", "BERNITA", "ALTAGRACIA", "ALETA", "ADRIANNA", "ZORAIDA", - "RONNIE", "NICOLA", "LYNDSEY", "KENDALL", "JANINA", "CHRISSY", "AMI", "STARLA", "PHYLIS", "PHUONG", - "KYRA", "CHARISSE", "BLANCH", "SANJUANITA", "RONA", "NANCI", "MARILEE", "MARANDA", "CORY", "BRIGETTE", - "SANJUANA", "MARITA", "KASSANDRA", "JOYCELYN", "IRA", "FELIPA", "CHELSIE", "BONNY", "MIREYA", "LORENZA", - "KYONG", "ILEANA", "CANDELARIA", "TONY", "TOBY", "SHERIE", "OK", "MARK", "LUCIE", "LEATRICE", - "LAKESHIA", "GERDA", "EDIE", "BAMBI", "MARYLIN", "LAVON", "HORTENSE", "GARNET", "EVIE", "TRESSA", - "SHAYNA", "LAVINA", "KYUNG", "JEANETTA", "SHERRILL", "SHARA", "PHYLISS", "MITTIE", "ANABEL", "ALESIA", - "THUY", "TAWANDA", "RICHARD", "JOANIE", "TIFFANIE", "LASHANDA", "KARISSA", "ENRIQUETA", "DARIA", "DANIELLA", - "CORINNA", "ALANNA", "ABBEY", "ROXANE", "ROSEANNA", "MAGNOLIA", "LIDA", "KYLE", "JOELLEN", "ERA", - "CORAL", "CARLEEN", "TRESA", "PEGGIE", "NOVELLA", "NILA", "MAYBELLE", "JENELLE", "CARINA", "NOVA", - "MELINA", "MARQUERITE", "MARGARETTE", "JOSEPHINA", "EVONNE", "DEVIN", "CINTHIA", "ALBINA", "TOYA", "TAWNYA", - "SHERITA", "SANTOS", "MYRIAM", "LIZABETH", "LISE", "KEELY", "JENNI", "GISELLE", "CHERYLE", "ARDITH", - "ARDIS", "ALESHA", "ADRIANE", "SHAINA", "LINNEA", "KAROLYN", "HONG", "FLORIDA", "FELISHA", "DORI", - "DARCI", "ARTIE", "ARMIDA", "ZOLA", "XIOMARA", "VERGIE", "SHAMIKA", "NENA", "NANNETTE", "MAXIE", - "LOVIE", "JEANE", "JAIMIE", "INGE", "FARRAH", "ELAINA", "CAITLYN", "STARR", "FELICITAS", "CHERLY", - "CARYL", "YOLONDA", "YASMIN", "TEENA", "PRUDENCE", "PENNIE", "NYDIA", "MACKENZIE", "ORPHA", "MARVEL", - "LIZBETH", "LAURETTE", "JERRIE", "HERMELINDA", "CAROLEE", "TIERRA", "MIRIAN", "META", "MELONY", "KORI", - "JENNETTE", "JAMILA", "ENA", "ANH", "YOSHIKO", "SUSANNAH", "SALINA", "RHIANNON", "JOLEEN", "CRISTINE", - "ASHTON", "ARACELY", "TOMEKA", "SHALONDA", "MARTI", "LACIE", "KALA", "JADA", "ILSE", "HAILEY", - "BRITTANI", "ZONA", "SYBLE", "SHERRYL", "RANDY", "NIDIA", "MARLO", "KANDICE", "KANDI", "DEB", - "DEAN", "AMERICA", "ALYCIA", "TOMMY", "RONNA", "NORENE", "MERCY", "JOSE", "INGEBORG", "GIOVANNA", - "GEMMA", "CHRISTEL", "AUDRY", "ZORA", "VITA", "VAN", "TRISH", "STEPHAINE", "SHIRLEE", "SHANIKA", - "MELONIE", "MAZIE", "JAZMIN", "INGA", "HOA", "HETTIE", "GERALYN", "FONDA", "ESTRELLA", "ADELLA", - "SU", "SARITA", "RINA", "MILISSA", "MARIBETH", "GOLDA", "EVON", "ETHELYN", "ENEDINA", "CHERISE", - "CHANA", "VELVA", "TAWANNA", "SADE", "MIRTA", "LI", "KARIE", "JACINTA", "ELNA", "DAVINA", - "CIERRA", "ASHLIE", "ALBERTHA", "TANESHA", "STEPHANI", "NELLE", "MINDI", "LU", "LORINDA", "LARUE", - "FLORENE", "DEMETRA", "DEDRA", "CIARA", "CHANTELLE", "ASHLY", "SUZY", "ROSALVA", "NOELIA", "LYDA", - "LEATHA", "KRYSTYNA", "KRISTAN", "KARRI", "DARLINE", "DARCIE", "CINDA", "CHEYENNE", "CHERRIE", "AWILDA", - "ALMEDA", "ROLANDA", "LANETTE", "JERILYN", "GISELE", "EVALYN", "CYNDI", "CLETA", "CARIN", "ZINA", - "ZENA", "VELIA", "TANIKA", "PAUL", "CHARISSA", "THOMAS", "TALIA", "MARGARETE", "LAVONDA", "KAYLEE", - "KATHLENE", "JONNA", "IRENA", "ILONA", "IDALIA", "CANDIS", "CANDANCE", "BRANDEE", "ANITRA", "ALIDA", - "SIGRID", "NICOLETTE", "MARYJO", "LINETTE", "HEDWIG", "CHRISTIANA", "CASSIDY", "ALEXIA", "TRESSIE", "MODESTA", - "LUPITA", "LITA", "GLADIS", "EVELIA", "DAVIDA", "CHERRI", "CECILY", "ASHELY", "ANNABEL", "AGUSTINA", - "WANITA", "SHIRLY", "ROSAURA", "HULDA", "EUN", "BAILEY", "YETTA", "VERONA", "THOMASINA", "SIBYL", - "SHANNAN", "MECHELLE", "LUE", "LEANDRA", "LANI", "KYLEE", "KANDY", "JOLYNN", "FERNE", "EBONI", - "CORENE", "ALYSIA", "ZULA", "NADA", "MOIRA", "LYNDSAY", "LORRETTA", "JUAN", "JAMMIE", "HORTENSIA", - "GAYNELL", "CAMERON", "ADRIA", "VINA", "VICENTA", "TANGELA", "STEPHINE", "NORINE", "NELLA", "LIANA", - "LESLEE", "KIMBERELY", "ILIANA", "GLORY", "FELICA", "EMOGENE", "ELFRIEDE", "EDEN", "EARTHA", "CARMA", - "BEA", "OCIE", "MARRY", "LENNIE", "KIARA", "JACALYN", "CARLOTA", "ARIELLE", "YU", "STAR", - "OTILIA", "KIRSTIN", "KACEY", "JOHNETTA", "JOEY", "JOETTA", "JERALDINE", "JAUNITA", "ELANA", "DORTHEA", - "CAMI", "AMADA", "ADELIA", "VERNITA", "TAMAR", "SIOBHAN", "RENEA", "RASHIDA", "OUIDA", "ODELL", - "NILSA", "MERYL", "KRISTYN", "JULIETA", "DANICA", "BREANNE", "AUREA", "ANGLEA", "SHERRON", "ODETTE", - "MALIA", "LORELEI", "LIN", "LEESA", "KENNA", "KATHLYN", "FIONA", "CHARLETTE", "SUZIE", "SHANTELL", - "SABRA", "RACQUEL", "MYONG", "MIRA", "MARTINE", "LUCIENNE", "LAVADA", "JULIANN", "JOHNIE", "ELVERA", - "DELPHIA", "CLAIR", "CHRISTIANE", "CHAROLETTE", "CARRI", "AUGUSTINE", "ASHA", "ANGELLA", "PAOLA", "NINFA", - "LEDA", "LAI", "EDA", "SUNSHINE", "STEFANI", "SHANELL", "PALMA", "MACHELLE", "LISSA", "KECIA", - "KATHRYNE", "KARLENE", "JULISSA", "JETTIE", "JENNIFFER", "HUI", "CORRINA", "CHRISTOPHER", "CAROLANN", "ALENA", - "TESS", "ROSARIA", "MYRTICE", "MARYLEE", "LIANE", "KENYATTA", "JUDIE", "JANEY", "IN", "ELMIRA", - "ELDORA", "DENNA", "CRISTI", "CATHI", "ZAIDA", "VONNIE", "VIVA", "VERNIE", "ROSALINE", "MARIELA", - "LUCIANA", "LESLI", "KARAN", "FELICE", "DENEEN", "ADINA", "WYNONA", "TARSHA", "SHERON", "SHASTA", - "SHANITA", "SHANI", "SHANDRA", "RANDA", "PINKIE", "PARIS", "NELIDA", "MARILOU", "LYLA", "LAURENE", - "LACI", "JOI", "JANENE", "DOROTHA", "DANIELE", "DANI", "CAROLYNN", "CARLYN", "BERENICE", "AYESHA", - "ANNELIESE", "ALETHEA", "THERSA", "TAMIKO", "RUFINA", "OLIVA", "MOZELL", "MARYLYN", "MADISON", "KRISTIAN", - "KATHYRN", "KASANDRA", "KANDACE", "JANAE", "GABRIEL", "DOMENICA", "DEBBRA", "DANNIELLE", "CHUN", "BUFFY", - "BARBIE", "ARCELIA", "AJA", "ZENOBIA", "SHAREN", "SHAREE", "PATRICK", "PAGE", "MY", "LAVINIA", - "KUM", "KACIE", "JACKELINE", "HUONG", "FELISA", "EMELIA", "ELEANORA", "CYTHIA", "CRISTIN", "CLYDE", - "CLARIBEL", "CARON", "ANASTACIA", "ZULMA", "ZANDRA", "YOKO", "TENISHA", "SUSANN", "SHERILYN", "SHAY", - "SHAWANDA", "SABINE", "ROMANA", "MATHILDA", "LINSEY", "KEIKO", "JOANA", "ISELA", "GRETTA", "GEORGETTA", - "EUGENIE", "DUSTY", "DESIRAE", "DELORA", "CORAZON", "ANTONINA", "ANIKA", "WILLENE", "TRACEE", "TAMATHA", - "REGAN", "NICHELLE", "MICKIE", "MAEGAN", "LUANA", "LANITA", "KELSIE", "EDELMIRA", "BREE", "AFTON", - "TEODORA", "TAMIE", "SHENA", "MEG", "LINH", "KELI", "KACI", "DANYELLE", "BRITT", "ARLETTE", - "ALBERTINE", "ADELLE", "TIFFINY", "STORMY", "SIMONA", "NUMBERS", "NICOLASA", "NICHOL", "NIA", "NAKISHA", - "MEE", "MAIRA", "LOREEN", "KIZZY", "JOHNNY", "JAY", "FALLON", "CHRISTENE", "BOBBYE", "ANTHONY", - "YING", "VINCENZA", "TANJA", "RUBIE", "RONI", "QUEENIE", "MARGARETT", "KIMBERLI", "IRMGARD", "IDELL", - "HILMA", "EVELINA", "ESTA", "EMILEE", "DENNISE", "DANIA", "CARL", "CARIE", "ANTONIO", "WAI", - "SANG", "RISA", "RIKKI", "PARTICIA", "MUI", "MASAKO", "MARIO", "LUVENIA", "LOREE", "LONI", - "LIEN", "KEVIN", "GIGI", "FLORENCIA", "DORIAN", "DENITA", "DALLAS", "CHI", "BILLYE", "ALEXANDER", - "TOMIKA", "SHARITA", "RANA", "NIKOLE", "NEOMA", "MARGARITE", "MADALYN", "LUCINA", "LAILA", "KALI", - "JENETTE", "GABRIELE", "EVELYNE", "ELENORA", "CLEMENTINA", "ALEJANDRINA", "ZULEMA", "VIOLETTE", "VANNESSA", "THRESA", - "RETTA", "PIA", "PATIENCE", "NOELLA", "NICKIE", "JONELL", "DELTA", "CHUNG", "CHAYA", "CAMELIA", - "BETHEL", "ANYA", "ANDREW", "THANH", "SUZANN", "SPRING", "SHU", "MILA", "LILLA", "LAVERNA", - "KEESHA", "KATTIE", "GIA", "GEORGENE", "EVELINE", "ESTELL", "ELIZBETH", "VIVIENNE", "VALLIE", "TRUDIE", - "STEPHANE", "MICHEL", "MAGALY", "MADIE", "KENYETTA", "KARREN", "JANETTA", "HERMINE", "HARMONY", "DRUCILLA", - "DEBBI", "CELESTINA", "CANDIE", "BRITNI", "BECKIE", "AMINA", "ZITA", "YUN", "YOLANDE", "VIVIEN", - "VERNETTA", "TRUDI", "SOMMER", "PEARLE", "PATRINA", "OSSIE", "NICOLLE", "LOYCE", "LETTY", "LARISA", - "KATHARINA", "JOSELYN", "JONELLE", "JENELL", "IESHA", "HEIDE", "FLORINDA", "FLORENTINA", "FLO", "ELODIA", - "DORINE", "BRUNILDA", "BRIGID", "ASHLI", "ARDELLA", "TWANA", "THU", "TARAH", "SUNG", "SHEA", - "SHAVON", "SHANE", "SERINA", "RAYNA", "RAMONITA", "NGA", "MARGURITE", "LUCRECIA", "KOURTNEY", "KATI", - "JESUS", "JESENIA", "DIAMOND", "CRISTA", "AYANA", "ALICA", "ALIA", "VINNIE", "SUELLEN", "ROMELIA", - "RACHELL", "PIPER", "OLYMPIA", "MICHIKO", "KATHALEEN", "JOLIE", "JESSI", "JANESSA", "HANA", "HA", - "ELEASE", "CARLETTA", "BRITANY", "SHONA", "SALOME", "ROSAMOND", "REGENA", "RAINA", "NGOC", "NELIA", - "LOUVENIA", "LESIA", "LATRINA", "LATICIA", "LARHONDA", "JINA", "JACKI", "HOLLIS", "HOLLEY", "EMMY", - "DEEANN", "CORETTA", "ARNETTA", "VELVET", "THALIA", "SHANICE", "NETA", "MIKKI", "MICKI", "LONNA", - "LEANA", "LASHUNDA", "KILEY", "JOYE", "JACQULYN", "IGNACIA", "HYUN", "HIROKO", "HENRY", "HENRIETTE", - "ELAYNE", "DELINDA", "DARNELL", "DAHLIA", "COREEN", "CONSUELA", "CONCHITA", "CELINE", "BABETTE", "AYANNA", - "ANETTE", "ALBERTINA", "SKYE", "SHAWNEE", "SHANEKA", "QUIANA", "PAMELIA", "MIN", "MERRI", "MERLENE", - "MARGIT", "KIESHA", "KIERA", "KAYLENE", "JODEE", "JENISE", "ERLENE", "EMMIE", "ELSE", "DARYL", - "DALILA", "DAISEY", "CODY", "CASIE", "BELIA", "BABARA", "VERSIE", "VANESA", "SHELBA", "SHAWNDA", - "SAM", "NORMAN", "NIKIA", "NAOMA", "MARNA", "MARGERET", "MADALINE", "LAWANA", "KINDRA", "JUTTA", - "JAZMINE", "JANETT", "HANNELORE", "GLENDORA", "GERTRUD", "GARNETT", "FREEDA", "FREDERICA", "FLORANCE", "FLAVIA", - "DENNIS", "CARLINE", "BEVERLEE", "ANJANETTE", "VALDA", "TRINITY", "TAMALA", "STEVIE", "SHONNA", "SHA", - "SARINA", "ONEIDA", "MICAH", "MERILYN", "MARLEEN", "LURLINE", "LENNA", "KATHERIN", "JIN", "JENI", - "HAE", "GRACIA", "GLADY", "FARAH", "ERIC", "ENOLA", "EMA", "DOMINQUE", "DEVONA", "DELANA", - "CECILA", "CAPRICE", "ALYSHA", "ALI", "ALETHIA", "VENA", "THERESIA", "TAWNY", "SONG", "SHAKIRA", - "SAMARA", "SACHIKO", "RACHELE", "PAMELLA", "NICKY", "MARNI", "MARIEL", "MAREN", "MALISA", "LIGIA", - "LERA", "LATORIA", "LARAE", "KIMBER", "KATHERN", "KAREY", "JENNEFER", "JANETH", "HALINA", "FREDIA", - "DELISA", "DEBROAH", "CIERA", "CHIN", "ANGELIKA", "ANDREE", "ALTHA", "YEN", "VIVAN", "TERRESA", - "TANNA", "SUK", "SUDIE", "SOO", "SIGNE", "SALENA", "RONNI", "REBBECCA", "MYRTIE", "MCKENZIE", - "MALIKA", "MAIDA", "LOAN", "LEONARDA", "KAYLEIGH", "FRANCE", "ETHYL", "ELLYN", "DAYLE", "CAMMIE", - "BRITTNI", "BIRGIT", "AVELINA", "ASUNCION", "ARIANNA", "AKIKO", "VENICE", "TYESHA", "TONIE", "TIESHA", - "TAKISHA", "STEFFANIE", "SINDY", "SANTANA", "MEGHANN", "MANDA", "MACIE", "LADY", "KELLYE", "KELLEE", - "JOSLYN", "JASON", "INGER", "INDIRA", "GLINDA", "GLENNIS", "FERNANDA", "FAUSTINA", "ENEIDA", "ELICIA", - "DOT", "DIGNA", "DELL", "ARLETTA", "ANDRE", "WILLIA", "TAMMARA", "TABETHA", "SHERRELL", "SARI", - "REFUGIO", "REBBECA", "PAULETTA", "NIEVES", "NATOSHA", "NAKITA", "MAMMIE", "KENISHA", "KAZUKO", "KASSIE", - "GARY", "EARLEAN", "DAPHINE", "CORLISS", "CLOTILDE", "CAROLYNE", "BERNETTA", "AUGUSTINA", "AUDREA", "ANNIS", - "ANNABELL", "YAN", "TENNILLE", "TAMICA", "SELENE", "SEAN", "ROSANA", "REGENIA", "QIANA", "MARKITA", - "MACY", "LEEANNE", "LAURINE", "KYM", "JESSENIA", "JANITA", "GEORGINE", "GENIE", "EMIKO", "ELVIE", - "DEANDRA", "DAGMAR", "CORIE", "COLLEN", "CHERISH", "ROMAINE", "PORSHA", "PEARLENE", "MICHELINE", "MERNA", - "MARGORIE", "MARGARETTA", "LORE", "KENNETH", "JENINE", "HERMINA", "FREDERICKA", "ELKE", "DRUSILLA", "DORATHY", - "DIONE", "DESIRE", "CELENA", "BRIGIDA", "ANGELES", "ALLEGRA", "THEO", "TAMEKIA", "SYNTHIA", "STEPHEN", - "SOOK", "SLYVIA", "ROSANN", "REATHA", "RAYE", "MARQUETTA", "MARGART", "LING", "LAYLA", "KYMBERLY", - "KIANA", "KAYLEEN", "KATLYN", "KARMEN", "JOELLA", "IRINA", "EMELDA", "ELENI", "DETRA", "CLEMMIE", - "CHERYLL", "CHANTELL", "CATHEY", "ARNITA", "ARLA", "ANGLE", "ANGELIC", "ALYSE", "ZOFIA", "THOMASINE", - "TENNIE", "SON", "SHERLY", "SHERLEY", "SHARYL", "REMEDIOS", "PETRINA", "NICKOLE", "MYUNG", "MYRLE", - "MOZELLA", "LOUANNE", "LISHA", "LATIA", "LANE", "KRYSTA", "JULIENNE", "JOEL", "JEANENE", "JACQUALINE", - "ISAURA", "GWENDA", "EARLEEN", "DONALD", "CLEOPATRA", "CARLIE", "AUDIE", "ANTONIETTA", "ALISE", "ALEX", - "VERDELL", "VAL", "TYLER", "TOMOKO", "THAO", "TALISHA", "STEVEN", "SO", "SHEMIKA", "SHAUN", - "SCARLET", "SAVANNA", "SANTINA", "ROSIA", "RAEANN", "ODILIA", "NANA", "MINNA", "MAGAN", "LYNELLE", - "LE", "KARMA", "JOEANN", "IVANA", "INELL", "ILANA", "HYE", "HONEY", "HEE", "GUDRUN", - "FRANK", "DREAMA", "CRISSY", "CHANTE", "CARMELINA", "ARVILLA", "ARTHUR", "ANNAMAE", "ALVERA", "ALEIDA", - "AARON", "YEE", "YANIRA", "VANDA", "TIANNA", "TAM", "STEFANIA", "SHIRA", "PERRY", "NICOL", - "NANCIE", "MONSERRATE", "MINH", "MELYNDA", "MELANY", "MATTHEW", "LOVELLA", "LAURE", "KIRBY", "KACY", - "JACQUELYNN", "HYON", "GERTHA", "FRANCISCO", "ELIANA", "CHRISTENA", "CHRISTEEN", "CHARISE", "CATERINA", "CARLEY", - "CANDYCE", "ARLENA", "AMMIE", "YANG", "WILLETTE", "VANITA", "TUYET", "TINY", "SYREETA", "SILVA", - "SCOTT", "RONALD", "PENNEY", "NYLA", "MICHAL", "MAURICE", "MARYAM", "MARYA", "MAGEN", "LUDIE", - "LOMA", "LIVIA", "LANELL", "KIMBERLIE", "JULEE", "DONETTA", "DIEDRA", "DENISHA", "DEANE", "DAWNE", - "CLARINE", "CHERRYL", "BRONWYN", "BRANDON", "ALLA", "VALERY", "TONDA", "SUEANN", "SORAYA", "SHOSHANA", - "SHELA", "SHARLEEN", "SHANELLE", "NERISSA", "MICHEAL", "MERIDITH", "MELLIE", "MAYE", "MAPLE", "MAGARET", - "LUIS", "LILI", "LEONILA", "LEONIE", "LEEANNA", "LAVONIA", "LAVERA", "KRISTEL", "KATHEY", "KATHE", - "JUSTIN", "JULIAN", "JIMMY", "JANN", "ILDA", "HILDRED", "HILDEGARDE", "GENIA", "FUMIKO", "EVELIN", - "ERMELINDA", "ELLY", "DUNG", "DOLORIS", "DIONNA", "DANAE", "BERNEICE", "ANNICE", "ALIX", "VERENA", - "VERDIE", "TRISTAN", "SHAWNNA", "SHAWANA", "SHAUNNA", "ROZELLA", "RANDEE", "RANAE", "MILAGRO", "LYNELL", - "LUISE", "LOUIE", "LOIDA", "LISBETH", "KARLEEN", "JUNITA", "JONA", "ISIS", "HYACINTH", "HEDY", - "GWENN", "ETHELENE", "ERLINE", "EDWARD", "DONYA", "DOMONIQUE", "DELICIA", "DANNETTE", "CICELY", "BRANDA", - "BLYTHE", "BETHANN", "ASHLYN", "ANNALEE", "ALLINE", "YUKO", "VELLA", "TRANG", "TOWANDA", "TESHA", - "SHERLYN", "NARCISA", "MIGUELINA", "MERI", "MAYBELL", "MARLANA", "MARGUERITA", "MADLYN", "LUNA", "LORY", - "LORIANN", "LIBERTY", "LEONORE", "LEIGHANN", "LAURICE", "LATESHA", "LARONDA", "KATRICE", "KASIE", "KARL", - "KALEY", "JADWIGA", "GLENNIE", "GEARLDINE", "FRANCINA", "EPIFANIA", "DYAN", "DORIE", "DIEDRE", "DENESE", - "DEMETRICE", "DELENA", "DARBY", "CRISTIE", "CLEORA", "CATARINA", "CARISA", "BERNIE", "BARBERA", "ALMETA", - "TRULA", "TEREASA", "SOLANGE", "SHEILAH", "SHAVONNE", "SANORA", "ROCHELL", "MATHILDE", "MARGARETA", "MAIA", - "LYNSEY", "LAWANNA", "LAUNA", "KENA", "KEENA", "KATIA", "JAMEY", "GLYNDA", "GAYLENE", "ELVINA", - "ELANOR", "DANUTA", "DANIKA", "CRISTEN", "CORDIE", "COLETTA", "CLARITA", "CARMON", "BRYNN", "AZUCENA", - "AUNDREA", "ANGELE", "YI", "WALTER", "VERLIE", "VERLENE", "TAMESHA", "SILVANA", "SEBRINA", "SAMIRA", - "REDA", "RAYLENE", "PENNI", "PANDORA", "NORAH", "NOMA", "MIREILLE", "MELISSIA", "MARYALICE", "LARAINE", - "KIMBERY", "KARYL", "KARINE", "KAM", "JOLANDA", "JOHANA", "JESUSA", "JALEESA", "JAE", "JACQUELYNE", - "IRISH", "ILUMINADA", "HILARIA", "HANH", "GENNIE", "FRANCIE", "FLORETTA", "EXIE", "EDDA", "DREMA", - "DELPHA", "BEV", "BARBAR", "ASSUNTA", "ARDELL", "ANNALISA", "ALISIA", "YUKIKO", "YOLANDO", "WONDA", - "WEI", "WALTRAUD", "VETA", "TEQUILA", "TEMEKA", "TAMEIKA", "SHIRLEEN", "SHENITA", "PIEDAD", "OZELLA", - "MIRTHA", "MARILU", "KIMIKO", "JULIANE", "JENICE", "JEN", "JANAY", "JACQUILINE", "HILDE", "FE", - "FAE", "EVAN", "EUGENE", "ELOIS", "ECHO", "DEVORAH", "CHAU", "BRINDA", "BETSEY", "ARMINDA", - "ARACELIS", "APRYL", "ANNETT", "ALISHIA", "VEOLA", "USHA", "TOSHIKO", "THEOLA", "TASHIA", "TALITHA", - "SHERY", "RUDY", "RENETTA", "REIKO", "RASHEEDA", "OMEGA", "OBDULIA", "MIKA", "MELAINE", "MEGGAN", - "MARTIN", "MARLEN", "MARGET", "MARCELINE", "MANA", "MAGDALEN", "LIBRADA", "LEZLIE", "LEXIE", "LATASHIA", - "LASANDRA", "KELLE", "ISIDRA", "ISA", "INOCENCIA", "GWYN", "FRANCOISE", "ERMINIA", "ERINN", "DIMPLE", - "DEVORA", "CRISELDA", "ARMANDA", "ARIE", "ARIANE", "ANGELO", "ANGELENA", "ALLEN", "ALIZA", "ADRIENE", - "ADALINE", "XOCHITL", "TWANNA", "TRAN", "TOMIKO", "TAMISHA", "TAISHA", "SUSY", "SIU", "RUTHA", - "ROXY", "RHONA", "RAYMOND", "OTHA", "NORIKO", "NATASHIA", "MERRIE", "MELVIN", "MARINDA", "MARIKO", - "MARGERT", "LORIS", "LIZZETTE", "LEISHA", "KAILA", "KA", "JOANNIE", "JERRICA", "JENE", "JANNET", - "JANEE", "JACINDA", "HERTA", "ELENORE", "DORETTA", "DELAINE", "DANIELL", "CLAUDIE", "CHINA", "BRITTA", - "APOLONIA", "AMBERLY", "ALEASE", "YURI", "YUK", "WEN", "WANETA", "UTE", "TOMI", "SHARRI", - "SANDIE", "ROSELLE", "REYNALDA", "RAGUEL", "PHYLICIA", "PATRIA", "OLIMPIA", "ODELIA", "MITZIE", "MITCHELL", - "MISS", "MINDA", "MIGNON", "MICA", "MENDY", "MARIVEL", "MAILE", "LYNETTA", "LAVETTE", "LAURYN", - "LATRISHA", "LAKIESHA", "KIERSTEN", "KARY", "JOSPHINE", "JOLYN", "JETTA", "JANISE", "JACQUIE", "IVELISSE", - "GLYNIS", "GIANNA", "GAYNELLE", "EMERALD", "DEMETRIUS", "DANYELL", "DANILLE", "DACIA", "CORALEE", "CHER", - "CEOLA", "BRETT", "BELL", "ARIANNE", "ALESHIA", "YUNG", "WILLIEMAE", "TROY", "TRINH", "THORA", - "TAI", "SVETLANA", "SHERIKA", "SHEMEKA", "SHAUNDA", "ROSELINE", "RICKI", "MELDA", "MALLIE", "LAVONNA", - "LATINA", "LARRY", "LAQUANDA", "LALA", "LACHELLE", "KLARA", "KANDIS", "JOHNA", "JEANMARIE", "JAYE", - "HANG", "GRAYCE", "GERTUDE", "EMERITA", "EBONIE", "CLORINDA", "CHING", "CHERY", "CAROLA", "BREANN", - "BLOSSOM", "BERNARDINE", "BECKI", "ARLETHA", "ARGELIA", "ARA", "ALITA", "YULANDA", "YON", "YESSENIA", - "TOBI", "TASIA", "SYLVIE", "SHIRL", "SHIRELY", "SHERIDAN", "SHELLA", "SHANTELLE", "SACHA", "ROYCE", - "REBECKA", "REAGAN", "PROVIDENCIA", "PAULENE", "MISHA", "MIKI", "MARLINE", "MARICA", "LORITA", "LATOYIA", - "LASONYA", "KERSTIN", "KENDA", "KEITHA", "KATHRIN", "JAYMIE", "JACK", "GRICELDA", "GINETTE", "ERYN", - "ELINA", "ELFRIEDA", "DANYEL", "CHEREE", "CHANELLE", "BARRIE", "AVERY", "AURORE", "ANNAMARIA", "ALLEEN", - "AILENE", "AIDE", "YASMINE", "VASHTI", "VALENTINE", "TREASA", "TORY", "TIFFANEY", "SHERYLL", "SHARIE", - "SHANAE", "SAU", "RAISA", "PA", "NEDA", "MITSUKO", "MIRELLA", "MILDA", "MARYANNA", "MARAGRET", - "MABELLE", "LUETTA", "LORINA", "LETISHA", "LATARSHA", "LANELLE", "LAJUANA", "KRISSY", "KARLY", "KARENA", - "JON", "JESSIKA", "JERICA", "JEANELLE", "JANUARY", "JALISA", "JACELYN", "IZOLA", "IVEY", "GREGORY", - "EUNA", "ETHA", "DREW", "DOMITILA", "DOMINICA", "DAINA", "CREOLA", "CARLI", "CAMIE", "BUNNY", - "BRITTNY", "ASHANTI", "ANISHA", "ALEEN", "ADAH", "YASUKO", "WINTER", "VIKI", "VALRIE", "TONA", - "TINISHA", "THI", "TERISA", "TATUM", "TANEKA", "SIMONNE", "SHALANDA", "SERITA", "RESSIE", "REFUGIA", - "PAZ", "OLENE", "NA", "MERRILL", "MARGHERITA", "MANDIE", "MAN", "MAIRE", "LYNDIA", "LUCI", - "LORRIANE", "LORETA", "LEONIA", "LAVONA", "LASHAWNDA", "LAKIA", "KYOKO", "KRYSTINA", "KRYSTEN", "KENIA", - "KELSI", "JUDE", "JEANICE", "ISOBEL", "GEORGIANN", "GENNY", "FELICIDAD", "EILENE", "DEON", "DELOISE", - "DEEDEE", "DANNIE", "CONCEPTION", "CLORA", "CHERILYN", "CHANG", "CALANDRA", "BERRY", "ARMANDINA", "ANISA", - "ULA", "TIMOTHY", "TIERA", "THERESSA", "STEPHANIA", "SIMA", "SHYLA", "SHONTA", "SHERA", "SHAQUITA", - "SHALA", "SAMMY", "ROSSANA", "NOHEMI", "NERY", "MORIAH", "MELITA", "MELIDA", "MELANI", "MARYLYNN", - "MARISHA", "MARIETTE", "MALORIE", "MADELENE", "LUDIVINA", "LORIA", "LORETTE", "LORALEE", "LIANNE", "LEON", - "LAVENIA", "LAURINDA", "LASHON", "KIT", "KIMI", "KEILA", "KATELYNN", "KAI", "JONE", "JOANE", - "JI", "JAYNA", "JANELLA", "JA", "HUE", "HERTHA", "FRANCENE", "ELINORE", "DESPINA", "DELSIE", - "DEEDRA", "CLEMENCIA", "CARRY", "CAROLIN", "CARLOS", "BULAH", "BRITTANIE", "BOK", "BLONDELL", "BIBI", - "BEAULAH", "BEATA", "ANNITA", "AGRIPINA", "VIRGEN", "VALENE", "UN", "TWANDA", "TOMMYE", "TOI", - "TARRA", "TARI", "TAMMERA", "SHAKIA", "SADYE", "RUTHANNE", "ROCHEL", "RIVKA", "PURA", "NENITA", - "NATISHA", "MING", "MERRILEE", "MELODEE", "MARVIS", "LUCILLA", "LEENA", "LAVETA", "LARITA", "LANIE", - "KEREN", "ILEEN", "GEORGEANN", "GENNA", "GENESIS", "FRIDA", "EWA", "EUFEMIA", "EMELY", "ELA", - "EDYTH", "DEONNA", "DEADRA", "DARLENA", "CHANELL", "CHAN", "CATHERN", "CASSONDRA", "CASSAUNDRA", "BERNARDA", - "BERNA", "ARLINDA", "ANAMARIA", "ALBERT", "WESLEY", "VERTIE", "VALERI", "TORRI", "TATYANA", "STASIA", - "SHERISE", "SHERILL", "SEASON", "SCOTTIE", "SANDA", "RUTHE", "ROSY", "ROBERTO", "ROBBI", "RANEE", - "QUYEN", "PEARLY", "PALMIRA", "ONITA", "NISHA", "NIESHA", "NIDA", "NEVADA", "NAM", "MERLYN", - "MAYOLA", "MARYLOUISE", "MARYLAND", "MARX", "MARTH", "MARGENE", "MADELAINE", "LONDA", "LEONTINE", "LEOMA", - "LEIA", "LAWRENCE", "LAURALEE", "LANORA", "LAKITA", "KIYOKO", "KETURAH", "KATELIN", "KAREEN", "JONIE", - "JOHNETTE", "JENEE", "JEANETT", "IZETTA", "HIEDI", "HEIKE", "HASSIE", "HAROLD", "GIUSEPPINA", "GEORGANN", - "FIDELA", "FERNANDE", "ELWANDA", "ELLAMAE", "ELIZ", "DUSTI", "DOTTY", "CYNDY", "CORALIE", "CELESTA", - "ARGENTINA", "ALVERTA", "XENIA", "WAVA", "VANETTA", "TORRIE", "TASHINA", "TANDY", "TAMBRA", "TAMA", - "STEPANIE", "SHILA", "SHAUNTA", "SHARAN", "SHANIQUA", "SHAE", "SETSUKO", "SERAFINA", "SANDEE", "ROSAMARIA", - "PRISCILA", "OLINDA", "NADENE", "MUOI", "MICHELINA", "MERCEDEZ", "MARYROSE", "MARIN", "MARCENE", "MAO", - "MAGALI", "MAFALDA", "LOGAN", "LINN", "LANNIE", "KAYCE", "KAROLINE", "KAMILAH", "KAMALA", "JUSTA", - "JOLINE", "JENNINE", "JACQUETTA", "IRAIDA", "GERALD", "GEORGEANNA", "FRANCHESCA", "FAIRY", "EMELINE", "ELANE", - "EHTEL", "EARLIE", "DULCIE", "DALENE", "CRIS", "CLASSIE", "CHERE", "CHARIS", "CAROYLN", "CARMINA", - "CARITA", "BRIAN", "BETHANIE", "AYAKO", "ARICA", "AN", "ALYSA", "ALESSANDRA", "AKILAH", "ADRIEN", - "ZETTA", "YOULANDA", "YELENA", "YAHAIRA", "XUAN", "WENDOLYN", "VICTOR", "TIJUANA", "TERRELL", "TERINA", - "TERESIA", "SUZI", "SUNDAY", "SHERELL", "SHAVONDA", "SHAUNTE", "SHARDA", "SHAKITA", "SENA", "RYANN", - "RUBI", "RIVA", "REGINIA", "REA", "RACHAL", "PARTHENIA", "PAMULA", "MONNIE", "MONET", "MICHAELE", - "MELIA", "MARINE", "MALKA", "MAISHA", "LISANDRA", "LEO", "LEKISHA", "LEAN", "LAURENCE", "LAKENDRA", - "KRYSTIN", "KORTNEY", "KIZZIE", "KITTIE", "KERA", "KENDAL", "KEMBERLY", "KANISHA", "JULENE", "JULE", - "JOSHUA", "JOHANNE", "JEFFREY", "JAMEE", "HAN", "HALLEY", "GIDGET", "GALINA", "FREDRICKA", "FLETA", - "FATIMAH", "EUSEBIA", "ELZA", "ELEONORE", "DORTHEY", "DORIA", "DONELLA", "DINORAH", "DELORSE", "CLARETHA", - "CHRISTINIA", "CHARLYN", "BONG", "BELKIS", "AZZIE", "ANDERA", "AIKO", "ADENA", "YER", "YAJAIRA", - "WAN", "VANIA", "ULRIKE", "TOSHIA", "TIFANY", "STEFANY", "SHIZUE", "SHENIKA", "SHAWANNA", "SHAROLYN", - "SHARILYN", "SHAQUANA", "SHANTAY", "SEE", "ROZANNE", "ROSELEE", "RICKIE", "REMONA", "REANNA", "RAELENE", - "QUINN", "PHUNG", "PETRONILA", "NATACHA", "NANCEY", "MYRL", "MIYOKO", "MIESHA", "MERIDETH", "MARVELLA", - "MARQUITTA", "MARHTA", "MARCHELLE", "LIZETH", "LIBBIE", "LAHOMA", "LADAWN", "KINA", "KATHELEEN", "KATHARYN", - "KARISA", "KALEIGH", "JUNIE", "JULIEANN", "JOHNSIE", "JANEAN", "JAIMEE", "JACKQUELINE", "HISAKO", "HERMA", - "HELAINE", "GWYNETH", "GLENN", "GITA", "EUSTOLIA", "EMELINA", "ELIN", "EDRIS", "DONNETTE", "DONNETTA", - "DIERDRE", "DENAE", "DARCEL", "CLAUDE", "CLARISA", "CINDERELLA", "CHIA", "CHARLESETTA", "CHARITA", "CELSA", - "CASSY", "CASSI", "CARLEE", "BRUNA", "BRITTANEY", "BRANDE", "BILLI", "BAO", "ANTONETTA", "ANGLA", - "ANGELYN", "ANALISA", "ALANE", "WENONA", "WENDIE", "VERONIQUE", "VANNESA", "TOBIE", "TEMPIE", "SUMIKO", - "SULEMA", "SPARKLE", "SOMER", "SHEBA", "SHAYNE", "SHARICE", "SHANEL", "SHALON", "SAGE", "ROY", - "ROSIO", "ROSELIA", "RENAY", "REMA", "REENA", "PORSCHE", "PING", "PEG", "OZIE", "ORETHA", - "ORALEE", "ODA", "NU", "NGAN", "NAKESHA", "MILLY", "MARYBELLE", "MARLIN", "MARIS", "MARGRETT", - "MARAGARET", "MANIE", "LURLENE", "LILLIA", "LIESELOTTE", "LAVELLE", "LASHAUNDA", "LAKEESHA", "KEITH", "KAYCEE", - "KALYN", "JOYA", "JOETTE", "JENAE", "JANIECE", "ILLA", "GRISEL", "GLAYDS", "GENEVIE", "GALA", - "FREDDA", "FRED", "ELMER", "ELEONOR", "DEBERA", "DEANDREA", "DAN", "CORRINNE", "CORDIA", "CONTESSA", - "COLENE", "CLEOTILDE", "CHARLOTT", "CHANTAY", "CECILLE", "BEATRIS", "AZALEE", "ARLEAN", "ARDATH", "ANJELICA", - "ANJA", "ALFREDIA", "ALEISHA", "ADAM", "ZADA", "YUONNE", "XIAO", "WILLODEAN", "WHITLEY", "VENNIE", - "VANNA", "TYISHA", "TOVA", "TORIE", "TONISHA", "TILDA", "TIEN", "TEMPLE", "SIRENA", "SHERRIL", - "SHANTI", "SHAN", "SENAIDA", "SAMELLA", "ROBBYN", "RENDA", "REITA", "PHEBE", "PAULITA", "NOBUKO", - "NGUYET", "NEOMI", "MOON", "MIKAELA", "MELANIA", "MAXIMINA", "MARG", "MAISIE", "LYNNA", "LILLI", - "LAYNE", "LASHAUN", "LAKENYA", "LAEL", "KIRSTIE", "KATHLINE", "KASHA", "KARLYN", "KARIMA", "JOVAN", - "JOSEFINE", "JENNELL", "JACQUI", "JACKELYN", "HYO", "HIEN", "GRAZYNA", "FLORRIE", "FLORIA", "ELEONORA", - "DWANA", "DORLA", "DONG", "DELMY", "DEJA", "DEDE", "DANN", "CRYSTA", "CLELIA", "CLARIS", - "CLARENCE", "CHIEKO", "CHERLYN", "CHERELLE", "CHARMAIN", "CHARA", "CAMMY", "BEE", "ARNETTE", "ARDELLE", - "ANNIKA", "AMIEE", "AMEE", "ALLENA", "YVONE", "YUKI", "YOSHIE", "YEVETTE", "YAEL", "WILLETTA", - "VONCILE", "VENETTA", "TULA", "TONETTE", "TIMIKA", "TEMIKA", "TELMA", "TEISHA", "TAREN", "TA", - "STACEE", "SHIN", "SHAWNTA", "SATURNINA", "RICARDA", "POK", "PASTY", "ONIE", "NUBIA", "MORA", - "MIKE", "MARIELLE", "MARIELLA", "MARIANELA", "MARDELL", "MANY", "LUANNA", "LOISE", "LISABETH", "LINDSY", - "LILLIANA", "LILLIAM", "LELAH", "LEIGHA", "LEANORA", "LANG", "KRISTEEN", "KHALILAH", "KEELEY", "KANDRA", - "JUNKO", "JOAQUINA", "JERLENE", "JANI", "JAMIKA", "JAME", "HSIU", "HERMILA", "GOLDEN", "GENEVIVE", - "EVIA", "EUGENA", "EMMALINE", "ELFREDA", "ELENE", "DONETTE", "DELCIE", "DEEANNA", "DARCEY", "CUC", - "CLARINDA", "CIRA", "CHAE", "CELINDA", "CATHERYN", "CATHERIN", "CASIMIRA", "CARMELIA", "CAMELLIA", "BREANA", - "BOBETTE", "BERNARDINA", "BEBE", "BASILIA", "ARLYNE", "AMAL", "ALAYNA", "ZONIA", "ZENIA", "YURIKO", - "YAEKO", "WYNELL", "WILLOW", "WILLENA", "VERNIA", "TU", "TRAVIS", "TORA", "TERRILYN", "TERICA", - "TENESHA", "TAWNA", "TAJUANA", "TAINA", "STEPHNIE", "SONA", "SOL", "SINA", "SHONDRA", "SHIZUKO", - "SHERLENE", "SHERICE", "SHARIKA", "ROSSIE", "ROSENA", "RORY", "RIMA", "RIA", "RHEBA", "RENNA", - "PETER", "NATALYA", "NANCEE", "MELODI", "MEDA", "MAXIMA", "MATHA", "MARKETTA", "MARICRUZ", "MARCELENE", - "MALVINA", "LUBA", "LOUETTA", "LEIDA", "LECIA", "LAURAN", "LASHAWNA", "LAINE", "KHADIJAH", "KATERINE", - "KASI", "KALLIE", "JULIETTA", "JESUSITA", "JESTINE", "JESSIA", "JEREMY", "JEFFIE", "JANYCE", "ISADORA", - "GEORGIANNE", "FIDELIA", "EVITA", "EURA", "EULAH", "ESTEFANA", "ELSY", "ELIZABET", "ELADIA", "DODIE", - "DION", "DIA", "DENISSE", "DELORAS", "DELILA", "DAYSI", "DAKOTA", "CURTIS", "CRYSTLE", "CONCHA", - "COLBY", "CLARETTA", "CHU", "CHRISTIA", "CHARLSIE", "CHARLENA", "CARYLON", "BETTYANN", "ASLEY", "ASHLEA", - "AMIRA", "AI", "AGUEDA", "AGNUS", "YUETTE", "VINITA", "VICTORINA", "TYNISHA", "TREENA", "TOCCARA", - "TISH", "THOMASENA", "TEGAN", "SOILA", "SHILOH", "SHENNA", "SHARMAINE", "SHANTAE", "SHANDI", "SEPTEMBER", - "SARAN", "SARAI", "SANA", "SAMUEL", "SALLEY", "ROSETTE", "ROLANDE", "REGINE", "OTELIA", "OSCAR", - "OLEVIA", "NICHOLLE", "NECOLE", "NAIDA", "MYRTA", "MYESHA", "MITSUE", "MINTA", "MERTIE", "MARGY", - "MAHALIA", "MADALENE", "LOVE", "LOURA", "LOREAN", "LEWIS", "LESHA", "LEONIDA", "LENITA", "LAVONE", - "LASHELL", "LASHANDRA", "LAMONICA", "KIMBRA", "KATHERINA", "KARRY", "KANESHA", "JULIO", "JONG", "JENEVA", - "JAQUELYN", "HWA", "GILMA", "GHISLAINE", "GERTRUDIS", "FRANSISCA", "FERMINA", "ETTIE", "ETSUKO", "ELLIS", - "ELLAN", "ELIDIA", "EDRA", "DORETHEA", "DOREATHA", "DENYSE", "DENNY", "DEETTA", "DAINE", "CYRSTAL", - "CORRIN", "CAYLA", "CARLITA", "CAMILA", "BURMA", "BULA", "BUENA", "BLAKE", "BARABARA", "AVRIL", - "AUSTIN", "ALAINE", "ZANA", "WILHEMINA", "WANETTA", "VIRGIL", "VI", "VERONIKA", "VERNON", "VERLINE", - "VASILIKI", "TONITA", "TISA", "TEOFILA", "TAYNA", "TAUNYA", "TANDRA", "TAKAKO", "SUNNI", "SUANNE", - "SIXTA", "SHARELL", "SEEMA", "RUSSELL", "ROSENDA", "ROBENA", "RAYMONDE", "PEI", "PAMILA", "OZELL", - "NEIDA", "NEELY", "MISTIE", "MICHA", "MERISSA", "MAURITA", "MARYLN", "MARYETTA", "MARSHALL", "MARCELL", - "MALENA", "MAKEDA", "MADDIE", "LOVETTA", "LOURIE", "LORRINE", "LORILEE", "LESTER", "LAURENA", "LASHAY", - "LARRAINE", "LAREE", "LACRESHA", "KRISTLE", "KRISHNA", "KEVA", "KEIRA", "KAROLE", "JOIE", "JINNY", - "JEANNETTA", "JAMA", "HEIDY", "GILBERTE", "GEMA", "FAVIOLA", "EVELYNN", "ENDA", "ELLI", "ELLENA", - "DIVINA", "DAGNY", "COLLENE", "CODI", "CINDIE", "CHASSIDY", "CHASIDY", "CATRICE", "CATHERINA", "CASSEY", - "CAROLL", "CARLENA", "CANDRA", "CALISTA", "BRYANNA", "BRITTENY", "BEULA", "BARI", "AUDRIE", "AUDRIA", - "ARDELIA", "ANNELLE", "ANGILA", "ALONA", "ALLYN", "DOUGLAS", "ROGER", "JONATHAN", "RALPH", "NICHOLAS", - "BENJAMIN", "BRUCE", "HARRY", "WAYNE", "STEVE", "HOWARD", "ERNEST", "PHILLIP", "TODD", "CRAIG", - "ALAN", "PHILIP", "EARL", "DANNY", "BRYAN", "STANLEY", "LEONARD", "NATHAN", "MANUEL", "RODNEY", - "MARVIN", "VINCENT", "JEFFERY", "JEFF", "CHAD", "JACOB", "ALFRED", "BRADLEY", "HERBERT", "FREDERICK", - "EDWIN", "DON", "RICKY", "RANDALL", "BARRY", "BERNARD", "LEROY", "MARCUS", "THEODORE", "CLIFFORD", - "MIGUEL", "JIM", "TOM", "CALVIN", "BILL", "LLOYD", "DEREK", "WARREN", "DARRELL", "JEROME", - "FLOYD", "ALVIN", "TIM", "GORDON", "GREG", "JORGE", "DUSTIN", "PEDRO", "DERRICK", "ZACHARY", - "HERMAN", "GLEN", "HECTOR", "RICARDO", "RICK", "BRENT", "RAMON", "GILBERT", "MARC", "REGINALD", - "RUBEN", "NATHANIEL", "RAFAEL", "EDGAR", "MILTON", "RAUL", "BEN", "CHESTER", "DUANE", "FRANKLIN", - "BRAD", "RON", "ROLAND", "ARNOLD", "HARVEY", "JARED", "ERIK", "DARRYL", "NEIL", "JAVIER", - "FERNANDO", "CLINTON", "TED", "MATHEW", "TYRONE", "DARREN", "LANCE", "KURT", "ALLAN", "NELSON", - "GUY", "CLAYTON", "HUGH", "MAX", "DWAYNE", "DWIGHT", "ARMANDO", "FELIX", "EVERETT", "IAN", - "WALLACE", "KEN", "BOB", "ALFREDO", "ALBERTO", "DAVE", "IVAN", "BYRON", "ISAAC", "MORRIS", - "CLIFTON", "WILLARD", "ROSS", "ANDY", "SALVADOR", "KIRK", "SERGIO", "SETH", "KENT", "TERRANCE", - "EDUARDO", "TERRENCE", "ENRIQUE", "WADE", "STUART", "FREDRICK", "ARTURO", "ALEJANDRO", "NICK", "LUTHER", - "WENDELL", "JEREMIAH", "JULIUS", "OTIS", "TREVOR", "OLIVER", "LUKE", "HOMER", "GERARD", "DOUG", - "KENNY", "HUBERT", "LYLE", "MATT", "ALFONSO", "ORLANDO", "REX", "CARLTON", "ERNESTO", "NEAL", - "PABLO", "LORENZO", "OMAR", "WILBUR", "GRANT", "HORACE", "RODERICK", "ABRAHAM", "WILLIS", "RICKEY", - "ANDRES", "CESAR", "JOHNATHAN", "MALCOLM", "RUDOLPH", "DAMON", "KELVIN", "PRESTON", "ALTON", "ARCHIE", - "MARCO", "WM", "PETE", "RANDOLPH", "GARRY", "GEOFFREY", "JONATHON", "FELIPE", "GERARDO", "ED", - "DOMINIC", "DELBERT", "COLIN", "GUILLERMO", "EARNEST", "LUCAS", "BENNY", "SPENCER", "RODOLFO", "MYRON", - "EDMUND", "GARRETT", "SALVATORE", "CEDRIC", "LOWELL", "GREGG", "SHERMAN", "WILSON", "SYLVESTER", "ROOSEVELT", - "ISRAEL", "JERMAINE", "FORREST", "WILBERT", "LELAND", "SIMON", "CLARK", "IRVING", "BRYANT", "OWEN", - "RUFUS", "WOODROW", "KRISTOPHER", "MACK", "LEVI", "MARCOS", "GUSTAVO", "JAKE", "LIONEL", "GILBERTO", - "CLINT", "NICOLAS", "ISMAEL", "ORVILLE", "ERVIN", "DEWEY", "AL", "WILFRED", "JOSH", "HUGO", - "IGNACIO", "CALEB", "TOMAS", "SHELDON", "ERICK", "STEWART", "DOYLE", "DARREL", "ROGELIO", "TERENCE", - "SANTIAGO", "ALONZO", "ELIAS", "BERT", "ELBERT", "RAMIRO", "CONRAD", "NOAH", "GRADY", "PHIL", - "CORNELIUS", "LAMAR", "ROLANDO", "CLAY", "PERCY", "DEXTER", "BRADFORD", "DARIN", "AMOS", "MOSES", - "IRVIN", "SAUL", "ROMAN", "RANDAL", "TIMMY", "DARRIN", "WINSTON", "BRENDAN", "ABEL", "DOMINICK", - "BOYD", "EMILIO", "ELIJAH", "DOMINGO", "EMMETT", "MARLON", "EMANUEL", "JERALD", "EDMOND", "EMIL", - "DEWAYNE", "WILL", "OTTO", "TEDDY", "REYNALDO", "BRET", "JESS", "TRENT", "HUMBERTO", "EMMANUEL", - "STEPHAN", "VICENTE", "LAMONT", "GARLAND", "MILES", "EFRAIN", "HEATH", "RODGER", "HARLEY", "ETHAN", - "ELDON", "ROCKY", "PIERRE", "JUNIOR", "FREDDY", "ELI", "BRYCE", "ANTOINE", "STERLING", "CHASE", - "GROVER", "ELTON", "CLEVELAND", "DYLAN", "CHUCK", "DAMIAN", "REUBEN", "STAN", "AUGUST", "LEONARDO", - "JASPER", "RUSSEL", "ERWIN", "BENITO", "HANS", "MONTE", "BLAINE", "ERNIE", "CURT", "QUENTIN", - "AGUSTIN", "MURRAY", "JAMAL", "ADOLFO", "HARRISON", "TYSON", "BURTON", "BRADY", "ELLIOTT", "WILFREDO", - "BART", "JARROD", "VANCE", "DENIS", "DAMIEN", "JOAQUIN", "HARLAN", "DESMOND", "ELLIOT", "DARWIN", - "GREGORIO", "BUDDY", "XAVIER", "KERMIT", "ROSCOE", "ESTEBAN", "ANTON", "SOLOMON", "SCOTTY", "NORBERT", - "ELVIN", "WILLIAMS", "NOLAN", "ROD", "QUINTON", "HAL", "BRAIN", "ROB", "ELWOOD", "KENDRICK", - "DARIUS", "MOISES", "FIDEL", "THADDEUS", "CLIFF", "MARCEL", "JACKSON", "RAPHAEL", "BRYON", "ARMAND", - "ALVARO", "JEFFRY", "DANE", "JOESPH", "THURMAN", "NED", "RUSTY", "MONTY", "FABIAN", "REGGIE", - "MASON", "GRAHAM", "ISAIAH", "VAUGHN", "GUS", "LOYD", "DIEGO", "ADOLPH", "NORRIS", "MILLARD", - "ROCCO", "GONZALO", "DERICK", "RODRIGO", "WILEY", "RIGOBERTO", "ALPHONSO", "TY", "NOE", "VERN", - "REED", "JEFFERSON", "ELVIS", "BERNARDO", "MAURICIO", "HIRAM", "DONOVAN", "BASIL", "RILEY", "NICKOLAS", - "MAYNARD", "SCOT", "VINCE", "QUINCY", "EDDY", "SEBASTIAN", "FEDERICO", "ULYSSES", "HERIBERTO", "DONNELL", - "COLE", "DAVIS", "GAVIN", "EMERY", "WARD", "ROMEO", "JAYSON", "DANTE", "CLEMENT", "COY", - "MAXWELL", "JARVIS", "BRUNO", "ISSAC", "DUDLEY", "BROCK", "SANFORD", "CARMELO", "BARNEY", "NESTOR", - "STEFAN", "DONNY", "ART", "LINWOOD", "BEAU", "WELDON", "GALEN", "ISIDRO", "TRUMAN", "DELMAR", - "JOHNATHON", "SILAS", "FREDERIC", "DICK", "IRWIN", "MERLIN", "CHARLEY", "MARCELINO", "HARRIS", "CARLO", - "TRENTON", "KURTIS", "HUNTER", "AURELIO", "WINFRED", "VITO", "COLLIN", "DENVER", "CARTER", "LEONEL", - "EMORY", "PASQUALE", "MOHAMMAD", "MARIANO", "DANIAL", "LANDON", "DIRK", "BRANDEN", "ADAN", "BUFORD", - "GERMAN", "WILMER", "EMERSON", "ZACHERY", "FLETCHER", "JACQUES", "ERROL", "DALTON", "MONROE", "JOSUE", - "EDWARDO", "BOOKER", "WILFORD", "SONNY", "SHELTON", "CARSON", "THERON", "RAYMUNDO", "DAREN", "HOUSTON", - "ROBBY", "LINCOLN", "GENARO", "BENNETT", "OCTAVIO", "CORNELL", "HUNG", "ARRON", "ANTONY", "HERSCHEL", - "GIOVANNI", "GARTH", "CYRUS", "CYRIL", "RONNY", "LON", "FREEMAN", "DUNCAN", "KENNITH", "CARMINE", - "ERICH", "CHADWICK", "WILBURN", "RUSS", "REID", "MYLES", "ANDERSON", "MORTON", "JONAS", "FOREST", - "MITCHEL", "MERVIN", "ZANE", "RICH", "JAMEL", "LAZARO", "ALPHONSE", "RANDELL", "MAJOR", "JARRETT", - "BROOKS", "ABDUL", "LUCIANO", "SEYMOUR", "EUGENIO", "MOHAMMED", "VALENTIN", "CHANCE", "ARNULFO", "LUCIEN", - "FERDINAND", "THAD", "EZRA", "ALDO", "RUBIN", "ROYAL", "MITCH", "EARLE", "ABE", "WYATT", - "MARQUIS", "LANNY", "KAREEM", "JAMAR", "BORIS", "ISIAH", "EMILE", "ELMO", "ARON", "LEOPOLDO", - "EVERETTE", "JOSEF", "ELOY", "RODRICK", "REINALDO", "LUCIO", "JERROD", "WESTON", "HERSHEL", "BARTON", - "PARKER", "LEMUEL", "BURT", "JULES", "GIL", "ELISEO", "AHMAD", "NIGEL", "EFREN", "ANTWAN", - "ALDEN", "MARGARITO", "COLEMAN", "DINO", "OSVALDO", "LES", "DEANDRE", "NORMAND", "KIETH", "TREY", - "NORBERTO", "NAPOLEON", "JEROLD", "FRITZ", "ROSENDO", "MILFORD", "CHRISTOPER", "ALFONZO", "LYMAN", "JOSIAH", - "BRANT", "WILTON", "RICO", "JAMAAL", "DEWITT", "BRENTON", "OLIN", "FOSTER", "FAUSTINO", "CLAUDIO", - "JUDSON", "GINO", "EDGARDO", "ALEC", "TANNER", "JARRED", "DONN", "TAD", "PRINCE", "PORFIRIO", - "ODIS", "LENARD", "CHAUNCEY", "TOD", "MEL", "MARCELO", "KORY", "AUGUSTUS", "KEVEN", "HILARIO", - "BUD", "SAL", "ORVAL", "MAURO", "ZACHARIAH", "OLEN", "ANIBAL", "MILO", "JED", "DILLON", - "AMADO", "NEWTON", "LENNY", "RICHIE", "HORACIO", "BRICE", "MOHAMED", "DELMER", "DARIO", "REYES", - "MAC", "JONAH", "JERROLD", "ROBT", "HANK", "RUPERT", "ROLLAND", "KENTON", "DAMION", "ANTONE", - "WALDO", "FREDRIC", "BRADLY", "KIP", "BURL", "WALKER", "TYREE", "JEFFEREY", "AHMED", "WILLY", - "STANFORD", "OREN", "NOBLE", "MOSHE", "MIKEL", "ENOCH", "BRENDON", "QUINTIN", "JAMISON", "FLORENCIO", - "DARRICK", "TOBIAS", "HASSAN", "GIUSEPPE", "DEMARCUS", "CLETUS", "TYRELL", "LYNDON", "KEENAN", "WERNER", - "GERALDO", "COLUMBUS", "CHET", "BERTRAM", "MARKUS", "HUEY", "HILTON", "DWAIN", "DONTE", "TYRON", - "OMER", "ISAIAS", "HIPOLITO", "FERMIN", "ADALBERTO", "BO", "BARRETT", "TEODORO", "MCKINLEY", "MAXIMO", - "GARFIELD", "RALEIGH", "LAWERENCE", "ABRAM", "RASHAD", "KING", "EMMITT", "DARON", "SAMUAL", "MIQUEL", - "EUSEBIO", "DOMENIC", "DARRON", "BUSTER", "WILBER", "RENATO", "JC", "HOYT", "HAYWOOD", "EZEKIEL", - "CHAS", "FLORENTINO", "ELROY", "CLEMENTE", "ARDEN", "NEVILLE", "EDISON", "DESHAWN", "NATHANIAL", "JORDON", - "DANILO", "CLAUD", "SHERWOOD", "RAYMON", "RAYFORD", "CRISTOBAL", "AMBROSE", "TITUS", "HYMAN", "FELTON", - "EZEQUIEL", "ERASMO", "STANTON", "LONNY", "LEN", "IKE", "MILAN", "LINO", "JAROD", "HERB", - "ANDREAS", "WALTON", "RHETT", "PALMER", "DOUGLASS", "CORDELL", "OSWALDO", "ELLSWORTH", "VIRGILIO", "TONEY", - "NATHANAEL", "DEL", "BENEDICT", "MOSE", "JOHNSON", "ISREAL", "GARRET", "FAUSTO", "ASA", "ARLEN", - "ZACK", "WARNER", "MODESTO", "FRANCESCO", "MANUAL", "GAYLORD", "GASTON", "FILIBERTO", "DEANGELO", "MICHALE", - "GRANVILLE", "WES", "MALIK", "ZACKARY", "TUAN", "ELDRIDGE", "CRISTOPHER", "CORTEZ", "ANTIONE", "MALCOM", - "LONG", "KOREY", "JOSPEH", "COLTON", "WAYLON", "VON", "HOSEA", "SHAD", "SANTO", "RUDOLF", - "ROLF", "REY", "RENALDO", "MARCELLUS", "LUCIUS", "KRISTOFER", "BOYCE", "BENTON", "HAYDEN", "HARLAND", - "ARNOLDO", "RUEBEN", "LEANDRO", "KRAIG", "JERRELL", "JEROMY", "HOBERT", "CEDRICK", "ARLIE", "WINFORD", - "WALLY", "LUIGI", "KENETH", "JACINTO", "GRAIG", "FRANKLYN", "EDMUNDO", "SID", "PORTER", "LEIF", - "JERAMY", "BUCK", "WILLIAN", "VINCENZO", "SHON", "LYNWOOD", "JERE", "HAI", "ELDEN", "DORSEY", - "DARELL", "BRODERICK", "ALONSO" - ] - total_sum = 0 - temp_sum = 0 - name.sort() - for i in range(len(name)): - for j in name[i]: - temp_sum += ord(j) - ord('A') + 1 - total_sum += (i + 1) * temp_sum - temp_sum = 0 - print(total_sum) +# -*- coding: latin-1 -*- +""" +Name scores +Problem 22 +Using names.txt (right click and 'Save Link/Target As...'), a 46K text file +containing over five-thousand first names, begin by sorting it into +alphabetical order. Then working out the alphabetical value for each name, +multiply this value by its alphabetical position in the list to obtain a name +score. -if __name__ == '__main__': - main() +For example, when the list is sorted into alphabetical order, COLIN, which is +worth 3 + 15 + 12 + 9 + 14 = 53, is the 938th name in the list. So, COLIN would +obtain a score of 938 × 53 = 49714. + +What is the total of all the name scores in the file? +""" +import os + + +def solution(): + """Returns the total of all the name scores in the file. + + >>> solution() + 871198282 + """ + total_sum = 0 + temp_sum = 0 + with open(os.path.dirname(__file__) + "/p022_names.txt") as file: + name = str(file.readlines()[0]) + name = name.replace('"', "").split(",") + + name.sort() + for i in range(len(name)): + for j in name[i]: + temp_sum += ord(j) - ord("A") + 1 + total_sum += (i + 1) * temp_sum + temp_sum = 0 + return total_sum + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_234/__init__.py b/project_euler/problem_234/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_234/sol1.py b/project_euler/problem_234/sol1.py index c7a6bd97d66b..8298a7f8cce3 100644 --- a/project_euler/problem_234/sol1.py +++ b/project_euler/problem_234/sol1.py @@ -1,4 +1,22 @@ -# https://projecteuler.net/problem=234 +""" +https://projecteuler.net/problem=234 + +For an integer n ≥ 4, we define the lower prime square root of n, denoted by +lps(n), as the largest prime ≤ √n and the upper prime square root of n, ups(n), +as the smallest prime ≥ √n. + +So, for example, lps(4) = 2 = ups(4), lps(1000) = 31, ups(1000) = 37. Let us +call an integer n ≥ 4 semidivisible, if one of lps(n) and ups(n) divides n, +but not both. + +The sum of the semidivisible numbers not exceeding 15 is 30, the numbers are 8, +10 and 12. 15 is not semidivisible because it is a multiple of both lps(15) = 3 +and ups(15) = 5. As a further example, the sum of the 92 semidivisible numbers +up to 1000 is 34825. + +What is the sum of all semidivisible numbers not exceeding 999966663333 ? +""" + def fib(a, b, n): if n==1: @@ -17,16 +35,22 @@ def fib(a, b, n): return c -q=int(input()) -for x in range(q): - l=[i for i in input().split()] - c1=0 - c2=1 - while(1): - - if len(fib(l[0],l[1],c2))>> solution() + '2783915460' + """ + result = list(map("".join, permutations("0123456789"))) + return result[999999] + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_25/__init__.py b/project_euler/problem_25/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_25/sol1.py b/project_euler/problem_25/sol1.py index f8cea3093dcf..be3b4d9b2d7d 100644 --- a/project_euler/problem_25/sol1.py +++ b/project_euler/problem_25/sol1.py @@ -1,31 +1,75 @@ -from __future__ import print_function +# -*- coding: utf-8 -*- +""" +The Fibonacci sequence is defined by the recurrence relation: + + Fn = Fn−1 + Fn−2, where F1 = 1 and F2 = 1. + +Hence the first 12 terms will be: + + F1 = 1 + F2 = 1 + F3 = 2 + F4 = 3 + F5 = 5 + F6 = 8 + F7 = 13 + F8 = 21 + F9 = 34 + F10 = 55 + F11 = 89 + F12 = 144 + +The 12th term, F12, is the first term to contain three digits. + +What is the index of the first term in the Fibonacci sequence to contain 1000 +digits? +""" try: - xrange #Python 2 + xrange # Python 2 except NameError: - xrange = range #Python 3 + xrange = range # Python 3 + def fibonacci(n): - if n == 1 or type(n) is not int: - return 0 - elif n == 2: - return 1 - else: - sequence = [0, 1] - for i in xrange(2, n+1): - sequence.append(sequence[i-1] + sequence[i-2]) + if n == 1 or type(n) is not int: + return 0 + elif n == 2: + return 1 + else: + sequence = [0, 1] + for i in xrange(2, n + 1): + sequence.append(sequence[i - 1] + sequence[i - 2]) + + return sequence[n] - return sequence[n] def fibonacci_digits_index(n): - digits = 0 - index = 2 + digits = 0 + index = 2 + + while digits < n: + index += 1 + digits = len(str(fibonacci(index))) + + return index + + +def solution(n): + """Returns the index of the first term in the Fibonacci sequence to contain + n digits. - while digits < n: - index += 1 - digits = len(str(fibonacci(index))) + >>> solution(1000) + 4782 + >>> solution(100) + 476 + >>> solution(50) + 237 + >>> solution(3) + 12 + """ + return fibonacci_digits_index(n) - return index -if __name__ == '__main__': - print(fibonacci_digits_index(1000)) \ No newline at end of file +if __name__ == "__main__": + print(solution(int(str(input()).strip()))) diff --git a/project_euler/problem_25/sol2.py b/project_euler/problem_25/sol2.py index 35147a9bfb14..d754e2ddd722 100644 --- a/project_euler/problem_25/sol2.py +++ b/project_euler/problem_25/sol2.py @@ -1,10 +1,57 @@ -def fibonacci_genrator(): - a, b = 0,1 - while True: - a,b = b,a+b - yield b -answer = 1 -gen = fibonacci_genrator() -while len(str(next(gen))) < 1000: - answer += 1 -assert answer+1 == 4782 +# -*- coding: utf-8 -*- +""" +The Fibonacci sequence is defined by the recurrence relation: + + Fn = Fn−1 + Fn−2, where F1 = 1 and F2 = 1. + +Hence the first 12 terms will be: + + F1 = 1 + F2 = 1 + F3 = 2 + F4 = 3 + F5 = 5 + F6 = 8 + F7 = 13 + F8 = 21 + F9 = 34 + F10 = 55 + F11 = 89 + F12 = 144 + +The 12th term, F12, is the first term to contain three digits. + +What is the index of the first term in the Fibonacci sequence to contain 1000 +digits? +""" + + +def fibonacci_generator(): + a, b = 0, 1 + while True: + a, b = b, a + b + yield b + + +def solution(n): + """Returns the index of the first term in the Fibonacci sequence to contain + n digits. + + >>> solution(1000) + 4782 + >>> solution(100) + 476 + >>> solution(50) + 237 + >>> solution(3) + 12 + """ + answer = 1 + gen = fibonacci_generator() + while len(str(next(gen))) < n: + answer += 1 + return answer + 1 + + +if __name__ == "__main__": + print(solution(int(str(input()).strip()))) diff --git a/project_euler/problem_28/__init__.py b/project_euler/problem_28/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_28/sol1.py b/project_euler/problem_28/sol1.py index 4942115ce537..63386ce3058c 100644 --- a/project_euler/problem_28/sol1.py +++ b/project_euler/problem_28/sol1.py @@ -1,29 +1,60 @@ -from __future__ import print_function +""" +Starting with the number 1 and moving to the right in a clockwise direction a 5 +by 5 spiral is formed as follows: + + 21 22 23 24 25 + 20 7 8 9 10 + 19 6 1 2 11 + 18 5 4 3 12 + 17 16 15 14 13 + +It can be verified that the sum of the numbers on the diagonals is 101. + +What is the sum of the numbers on the diagonals in a 1001 by 1001 spiral formed +in the same way? +""" + from math import ceil try: - xrange #Python 2 + xrange # Python 2 except NameError: - xrange = range #Python 3 + xrange = range # Python 3 + def diagonal_sum(n): - total = 1 - - for i in xrange(1, int(ceil(n/2.0))): - odd = 2*i+1 - even = 2*i - total = total + 4*odd**2 - 6*even - - return total - -if __name__ == '__main__': - import sys - - if len(sys.argv) == 1: - print(diagonal_sum(1001)) - else: - try: - n = int(sys.argv[1]) - diagonal_sum(n) - except ValueError: - print('Invalid entry - please enter a number') \ No newline at end of file + """Returns the sum of the numbers on the diagonals in a n by n spiral + formed in the same way. + + >>> diagonal_sum(1001) + 669171001 + >>> diagonal_sum(500) + 82959497 + >>> diagonal_sum(100) + 651897 + >>> diagonal_sum(50) + 79697 + >>> diagonal_sum(10) + 537 + """ + total = 1 + + for i in xrange(1, int(ceil(n / 2.0))): + odd = 2 * i + 1 + even = 2 * i + total = total + 4 * odd ** 2 - 6 * even + + return total + + +if __name__ == "__main__": + import sys + + if len(sys.argv) == 1: + print(diagonal_sum(1001)) + else: + try: + n = int(sys.argv[1]) + print(diagonal_sum(n)) + except ValueError: + print("Invalid entry - please enter a number") diff --git a/project_euler/problem_29/__init__.py b/project_euler/problem_29/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_29/solution.py b/project_euler/problem_29/solution.py index 64d35c84d9ca..e67dafe4639d 100644 --- a/project_euler/problem_29/solution.py +++ b/project_euler/problem_29/solution.py @@ -1,33 +1,51 @@ -def main(): +""" +Consider all integer combinations of ab for 2 <= a <= 5 and 2 <= b <= 5: + +2^2=4, 2^3=8, 2^4=16, 2^5=32 +3^2=9, 3^3=27, 3^4=81, 3^5=243 +4^2=16, 4^3=64, 4^4=256, 4^5=1024 +5^2=25, 5^3=125, 5^4=625, 5^5=3125 + +If they are then placed in numerical order, with any repeats removed, we get +the following sequence of 15 distinct terms: + +4, 8, 9, 16, 25, 27, 32, 64, 81, 125, 243, 256, 625, 1024, 3125 + +How many distinct terms are in the sequence generated by ab +for 2 <= a <= 100 and 2 <= b <= 100? +""" +from __future__ import print_function + + +def solution(n): + """Returns the number of distinct terms in the sequence generated by a^b + for 2 <= a <= 100 and 2 <= b <= 100. + + >>> solution(100) + 9183 + >>> solution(50) + 2184 + >>> solution(20) + 324 + >>> solution(5) + 15 + >>> solution(2) + 1 + >>> solution(1) + 0 """ - Consider all integer combinations of ab for 2 <= a <= 5 and 2 <= b <= 5: - - 22=4, 23=8, 24=16, 25=32 - 32=9, 33=27, 34=81, 35=243 - 42=16, 43=64, 44=256, 45=1024 - 52=25, 53=125, 54=625, 55=3125 - If they are then placed in numerical order, with any repeats removed, - we get the following sequence of 15 distinct terms: - - 4, 8, 9, 16, 25, 27, 32, 64, 81, 125, 243, 256, 625, 1024, 3125 - - How many distinct terms are in the sequence generated by ab - for 2 <= a <= 100 and 2 <= b <= 100? - """ - collectPowers = set() currentPow = 0 - N = 101 # maximum limit + N = n + 1 # maximum limit for a in range(2, N): for b in range(2, N): - currentPow = a**b # calculates the current power - collectPowers.add(currentPow) # adds the result to the set - - print("Number of terms ", len(collectPowers)) + currentPow = a ** b # calculates the current power + collectPowers.add(currentPow) # adds the result to the set + return len(collectPowers) -if __name__ == '__main__': - main() +if __name__ == "__main__": + print("Number of terms ", solution(int(str(input()).strip()))) diff --git a/project_euler/problem_31/__init__.py b/project_euler/problem_31/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_31/sol1.py b/project_euler/problem_31/sol1.py index 33653722f890..e2a209e5df5a 100644 --- a/project_euler/problem_31/sol1.py +++ b/project_euler/problem_31/sol1.py @@ -1,10 +1,5 @@ # -*- coding: utf-8 -*- -from __future__ import print_function -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 -''' +""" Coin sums Problem 31 In England the currency is made up of pound, £, and pence, p, and there are @@ -15,7 +10,13 @@ 1×£1 + 1×50p + 2×20p + 1×5p + 1×2p + 3×1p How many different ways can £2 be made using any number of coins? -''' +""" +from __future__ import print_function + +try: + raw_input # Python 2 +except NameError: + raw_input = input # Python 3 def one_pence(): @@ -50,4 +51,21 @@ def two_pound(x): return 0 if x < 0 else two_pound(x - 200) + one_pound(x) -print(two_pound(200)) +def solution(n): + """Returns the number of different ways can £n be made using any number of + coins? + + >>> solution(500) + 6295434 + >>> solution(200) + 73682 + >>> solution(50) + 451 + >>> solution(10) + 11 + """ + return two_pound(n) + + +if __name__ == "__main__": + print(solution(int(str(input()).strip()))) diff --git a/project_euler/problem_36/__init__.py b/project_euler/problem_36/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_36/sol1.py b/project_euler/problem_36/sol1.py index d78e7e59f210..38b60420992b 100644 --- a/project_euler/problem_36/sol1.py +++ b/project_euler/problem_36/sol1.py @@ -1,30 +1,57 @@ -from __future__ import print_function -''' +""" Double-base palindromes Problem 36 The decimal number, 585 = 10010010012 (binary), is palindromic in both bases. -Find the sum of all numbers, less than one million, which are palindromic in base 10 and base 2. +Find the sum of all numbers, less than one million, which are palindromic in +base 10 and base 2. -(Please note that the palindromic number, in either base, may not include leading zeros.) -''' +(Please note that the palindromic number, in either base, may not include +leading zeros.) +""" try: - xrange #Python 2 + xrange # Python 2 except NameError: - xrange = range #Python 3 + xrange = range # Python 3 + def is_palindrome(n): - n = str(n) + n = str(n) + + if n == n[::-1]: + return True + else: + return False + + +def solution(n): + """Return the sum of all numbers, less than n , which are palindromic in + base 10 and base 2. - if n == n[::-1]: - return True - else: - return False + >>> solution(1000000) + 872187 + >>> solution(500000) + 286602 + >>> solution(100000) + 286602 + >>> solution(1000) + 1772 + >>> solution(100) + 157 + >>> solution(10) + 25 + >>> solution(2) + 1 + >>> solution(1) + 0 + """ + total = 0 -total = 0 + for i in xrange(1, n): + if is_palindrome(i) and is_palindrome(bin(i).split("b")[1]): + total += i + return total -for i in xrange(1, 1000000): - if is_palindrome(i) and is_palindrome(bin(i).split('b')[1]): - total += i -print(total) \ No newline at end of file +if __name__ == "__main__": + print(solution(int(str(input().strip())))) diff --git a/project_euler/problem_40/__init__.py b/project_euler/problem_40/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_40/sol1.py b/project_euler/problem_40/sol1.py index ab4017512a1a..accd7125354c 100644 --- a/project_euler/problem_40/sol1.py +++ b/project_euler/problem_40/sol1.py @@ -1,26 +1,47 @@ -#-.- coding: latin-1 -.- -from __future__ import print_function -''' +# -.- coding: latin-1 -.- +""" Champernowne's constant Problem 40 -An irrational decimal fraction is created by concatenating the positive integers: +An irrational decimal fraction is created by concatenating the positive +integers: 0.123456789101112131415161718192021... It can be seen that the 12th digit of the fractional part is 1. -If dn represents the nth digit of the fractional part, find the value of the following expression. +If dn represents the nth digit of the fractional part, find the value of the +following expression. d1 × d10 × d100 × d1000 × d10000 × d100000 × d1000000 -''' +""" +from __future__ import print_function + + +def solution(): + """Returns + + >>> solution() + 210 + """ + constant = [] + i = 1 + + while len(constant) < 1e6: + constant.append(str(i)) + i += 1 -constant = [] -i = 1 + constant = "".join(constant) -while len(constant) < 1e6: - constant.append(str(i)) - i += 1 + return ( + int(constant[0]) + * int(constant[9]) + * int(constant[99]) + * int(constant[999]) + * int(constant[9999]) + * int(constant[99999]) + * int(constant[999999]) + ) -constant = ''.join(constant) -print(int(constant[0])*int(constant[9])*int(constant[99])*int(constant[999])*int(constant[9999])*int(constant[99999])*int(constant[999999])) \ No newline at end of file +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_48/__init__.py b/project_euler/problem_48/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_48/sol1.py b/project_euler/problem_48/sol1.py index 5c4bdb0f6384..95af951c0e8a 100644 --- a/project_euler/problem_48/sol1.py +++ b/project_euler/problem_48/sol1.py @@ -1,21 +1,29 @@ -from __future__ import print_function -''' +""" Self Powers Problem 48 The series, 11 + 22 + 33 + ... + 1010 = 10405071317. Find the last ten digits of the series, 11 + 22 + 33 + ... + 10001000. -''' +""" try: - xrange + xrange except NameError: - xrange = range + xrange = range -total = 0 -for i in xrange(1, 1001): - total += i**i +def solution(): + """Returns the last 10 digits of the series, 11 + 22 + 33 + ... + 10001000. -print(str(total)[-10:]) \ No newline at end of file + >>> solution() + '9110846700' + """ + total = 0 + for i in xrange(1, 1001): + total += i ** i + return str(total)[-10:] + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_52/__init__.py b/project_euler/problem_52/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_52/sol1.py b/project_euler/problem_52/sol1.py index 376b4cfa1d63..df5c46ae05d1 100644 --- a/project_euler/problem_52/sol1.py +++ b/project_euler/problem_52/sol1.py @@ -1,23 +1,37 @@ -from __future__ import print_function -''' +""" Permuted multiples Problem 52 -It can be seen that the number, 125874, and its double, 251748, contain exactly the same digits, but in a different order. +It can be seen that the number, 125874, and its double, 251748, contain exactly +the same digits, but in a different order. -Find the smallest positive integer, x, such that 2x, 3x, 4x, 5x, and 6x, contain the same digits. -''' -i = 1 +Find the smallest positive integer, x, such that 2x, 3x, 4x, 5x, and 6x, +contain the same digits. +""" -while True: - if sorted(list(str(i))) == \ - sorted(list(str(2*i))) == \ - sorted(list(str(3*i))) == \ - sorted(list(str(4*i))) == \ - sorted(list(str(5*i))) == \ - sorted(list(str(6*i))): - break - i += 1 +def solution(): + """Returns the smallest positive integer, x, such that 2x, 3x, 4x, 5x, and + 6x, contain the same digits. -print(i) \ No newline at end of file + >>> solution() + 142857 + """ + i = 1 + + while True: + if ( + sorted(list(str(i))) + == sorted(list(str(2 * i))) + == sorted(list(str(3 * i))) + == sorted(list(str(4 * i))) + == sorted(list(str(5 * i))) + == sorted(list(str(6 * i))) + ): + return i + + i += 1 + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_53/__init__.py b/project_euler/problem_53/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_53/sol1.py b/project_euler/problem_53/sol1.py index ed6d5329eb4e..c72e0b993a34 100644 --- a/project_euler/problem_53/sol1.py +++ b/project_euler/problem_53/sol1.py @@ -1,13 +1,11 @@ -#-.- coding: latin-1 -.- -from __future__ import print_function -from math import factorial -''' +# -.- coding: latin-1 -.- +""" Combinatoric selections Problem 53 There are exactly ten ways of selecting three from five, 12345: -123, 124, 125, 134, 135, 145, 234, 235, 245, and 345 + 123, 124, 125, 134, 135, 145, 234, 235, 245, and 345 In combinatorics, we use the notation, 5C3 = 10. @@ -16,21 +14,37 @@ nCr = n!/(r!(n−r)!),where r ≤ n, n! = n×(n−1)×...×3×2×1, and 0! = 1. It is not until n = 23, that a value exceeds one-million: 23C10 = 1144066. -How many, not necessarily distinct, values of nCr, for 1 ≤ n ≤ 100, are greater than one-million? -''' +How many, not necessarily distinct, values of nCr, for 1 ≤ n ≤ 100, are greater +than one-million? +""" +from __future__ import print_function +from math import factorial + try: - xrange #Python 2 + xrange # Python 2 except NameError: - xrange = range #Python 3 + xrange = range # Python 3 + def combinations(n, r): - return factorial(n)/(factorial(r)*factorial(n-r)) + return factorial(n) / (factorial(r) * factorial(n - r)) + + +def solution(): + """Returns the number of values of nCr, for 1 ≤ n ≤ 100, are greater than + one-million + + >>> solution() + 4075 + """ + total = 0 -total = 0 + for i in xrange(1, 101): + for j in xrange(1, i + 1): + if combinations(i, j) > 1e6: + total += 1 + return total -for i in xrange(1, 101): - for j in xrange(1, i+1): - if combinations(i, j) > 1e6: - total += 1 -print(total) \ No newline at end of file +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_76/__init__.py b/project_euler/problem_76/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_76/sol1.py b/project_euler/problem_76/sol1.py index 2832f6d7afb6..c9e3c452fbc4 100644 --- a/project_euler/problem_76/sol1.py +++ b/project_euler/problem_76/sol1.py @@ -1,5 +1,4 @@ -from __future__ import print_function -''' +""" Counting Summations Problem 76 @@ -12,24 +11,50 @@ 2 + 1 + 1 + 1 1 + 1 + 1 + 1 + 1 -How many different ways can one hundred be written as a sum of at least two positive integers? -''' +How many different ways can one hundred be written as a sum of at least two +positive integers? +""" +from __future__ import print_function + try: - xrange #Python 2 + xrange # Python 2 except NameError: - xrange = range #Python 3 + xrange = range # Python 3 + def partition(m): - memo = [[0 for _ in xrange(m)] for _ in xrange(m+1)] - for i in xrange(m+1): - memo[i][0] = 1 + """Returns the number of different ways one hundred can be written as a sum + of at least two positive integers. + + >>> partition(100) + 190569291 + >>> partition(50) + 204225 + >>> partition(30) + 5603 + >>> partition(10) + 41 + >>> partition(5) + 6 + >>> partition(3) + 2 + >>> partition(2) + 1 + >>> partition(1) + 0 + """ + memo = [[0 for _ in xrange(m)] for _ in xrange(m + 1)] + for i in xrange(m + 1): + memo[i][0] = 1 + + for n in xrange(m + 1): + for k in xrange(1, m): + memo[n][k] += memo[n][k - 1] + if n > k: + memo[n][k] += memo[n - k - 1][k] - for n in xrange(m+1): - for k in xrange(1, m): - memo[n][k] += memo[n][k-1] - if n > k: - memo[n][k] += memo[n-k-1][k] + return memo[m][m - 1] - 1 - return (memo[m][m-1] - 1) -print(partition(100)) \ No newline at end of file +if __name__ == "__main__": + print(partition(int(str(input()).strip()))) From 7cdda931fd6e272529f25a4cd2dbd7c6785d467c Mon Sep 17 00:00:00 2001 From: cclauss Date: Wed, 17 Jul 2019 06:07:25 +0200 Subject: [PATCH 078/193] Travis CI: Add pytest --doctest-modules graphs (#1018) --- .travis.yml | 1 + graphs/basic_graphs.py | 75 ++++++++++--------- graphs/bellman_ford.py | 40 +++++----- graphs/dijkstra_2.py | 40 +++++----- ...d_warshall.py => graphs_floyd_warshall.py} | 0 graphs/minimum_spanning_tree_kruskal.py | 48 ++++++------ graphs/minimum_spanning_tree_prims.py | 19 ++--- graphs/multi_hueristic_astar.py | 25 ++++--- graphs/scc_kosaraju.py | 35 +++++---- 9 files changed, 148 insertions(+), 135 deletions(-) rename graphs/{floyd_warshall.py => graphs_floyd_warshall.py} (100%) diff --git a/.travis.yml b/.travis.yml index 3b55045ac33f..55ea2c7ddc24 100644 --- a/.travis.yml +++ b/.travis.yml @@ -21,6 +21,7 @@ script: digital_image_processing divide_and_conquer dynamic_programming + graphs hashes linear_algebra_python matrix diff --git a/graphs/basic_graphs.py b/graphs/basic_graphs.py index 3b3abeb1720d..ee63ca995de6 100644 --- a/graphs/basic_graphs.py +++ b/graphs/basic_graphs.py @@ -10,42 +10,44 @@ except NameError: xrange = range # Python 3 -# Accept No. of Nodes and edges -n, m = map(int, raw_input().split(" ")) -# Initialising Dictionary of edges -g = {} -for i in xrange(n): - g[i + 1] = [] - -""" --------------------------------------------------------------------------------- - Accepting edges of Unweighted Directed Graphs --------------------------------------------------------------------------------- -""" -for _ in xrange(m): - x, y = map(int, raw_input().split(" ")) - g[x].append(y) - -""" --------------------------------------------------------------------------------- - Accepting edges of Unweighted Undirected Graphs --------------------------------------------------------------------------------- -""" -for _ in xrange(m): - x, y = map(int, raw_input().split(" ")) - g[x].append(y) - g[y].append(x) +if __name__ == "__main__": + # Accept No. of Nodes and edges + n, m = map(int, raw_input().split(" ")) -""" --------------------------------------------------------------------------------- - Accepting edges of Weighted Undirected Graphs --------------------------------------------------------------------------------- -""" -for _ in xrange(m): - x, y, r = map(int, raw_input().split(" ")) - g[x].append([y, r]) - g[y].append([x, r]) + # Initialising Dictionary of edges + g = {} + for i in xrange(n): + g[i + 1] = [] + + """ + ---------------------------------------------------------------------------- + Accepting edges of Unweighted Directed Graphs + ---------------------------------------------------------------------------- + """ + for _ in xrange(m): + x, y = map(int, raw_input().strip().split(" ")) + g[x].append(y) + + """ + ---------------------------------------------------------------------------- + Accepting edges of Unweighted Undirected Graphs + ---------------------------------------------------------------------------- + """ + for _ in xrange(m): + x, y = map(int, raw_input().strip().split(" ")) + g[x].append(y) + g[y].append(x) + + """ + ---------------------------------------------------------------------------- + Accepting edges of Weighted Undirected Graphs + ---------------------------------------------------------------------------- + """ + for _ in xrange(m): + x, y, r = map(int, raw_input().strip().split(" ")) + g[x].append([y, r]) + g[y].append([x, r]) """ -------------------------------------------------------------------------------- @@ -168,9 +170,10 @@ def topo(G, ind=None, Q=[1]): def adjm(): - n, a = raw_input(), [] + n = raw_input().strip() + a = [] for i in xrange(n): - a.append(map(int, raw_input().split())) + a.append(map(int, raw_input().strip().split())) return a, n diff --git a/graphs/bellman_ford.py b/graphs/bellman_ford.py index 82db80546b94..f49157230054 100644 --- a/graphs/bellman_ford.py +++ b/graphs/bellman_ford.py @@ -12,7 +12,7 @@ def printDist(dist, V): def BellmanFord(graph, V, E, src): mdist=[float('inf') for i in range(V)] mdist[src] = 0.0 - + for i in range(V-1): for j in range(V): u = graph[j]["src"] @@ -20,7 +20,7 @@ def BellmanFord(graph, V, E, src): w = graph[j]["weight"] if mdist[u] != float('inf') and mdist[u] + w < mdist[v]: - mdist[v] = mdist[u] + w + mdist[v] = mdist[u] + w for j in range(V): u = graph[j]["src"] v = graph[j]["dst"] @@ -29,26 +29,26 @@ def BellmanFord(graph, V, E, src): if mdist[u] != float('inf') and mdist[u] + w < mdist[v]: print("Negative cycle found. Solution not possible.") return - - printDist(mdist, V) - + printDist(mdist, V) + + + +if __name__ == "__main__": + V = int(input("Enter number of vertices: ").strip()) + E = int(input("Enter number of edges: ").strip()) -#MAIN -V = int(input("Enter number of vertices: ")) -E = int(input("Enter number of edges: ")) + graph = [dict() for j in range(E)] -graph = [dict() for j in range(E)] + for i in range(V): + graph[i][i] = 0.0 -for i in range(V): - graph[i][i] = 0.0 + for i in range(E): + print("\nEdge ",i+1) + src = int(input("Enter source:").strip()) + dst = int(input("Enter destination:").strip()) + weight = float(input("Enter weight:").strip()) + graph[i] = {"src": src,"dst": dst, "weight": weight} -for i in range(E): - print("\nEdge ",i+1) - src = int(input("Enter source:")) - dst = int(input("Enter destination:")) - weight = float(input("Enter weight:")) - graph[i] = {"src": src,"dst": dst, "weight": weight} - -gsrc = int(input("\nEnter shortest path source:")) -BellmanFord(graph, V, E, gsrc) + gsrc = int(input("\nEnter shortest path source:").strip()) + BellmanFord(graph, V, E, gsrc) diff --git a/graphs/dijkstra_2.py b/graphs/dijkstra_2.py index a6c340e8a68d..8f39aec41906 100644 --- a/graphs/dijkstra_2.py +++ b/graphs/dijkstra_2.py @@ -22,36 +22,36 @@ def Dijkstra(graph, V, src): mdist=[float('inf') for i in range(V)] vset = [False for i in range(V)] mdist[src] = 0.0 - + for i in range(V-1): u = minDist(mdist, vset, V) vset[u] = True - + for v in range(V): if (not vset[v]) and graph[u][v]!=float('inf') and mdist[u] + graph[u][v] < mdist[v]: - mdist[v] = mdist[u] + graph[u][v] + mdist[v] = mdist[u] + graph[u][v] + + - + printDist(mdist, V) - printDist(mdist, V) - -#MAIN -V = int(input("Enter number of vertices: ")) -E = int(input("Enter number of edges: ")) +if __name__ == "__main__": + V = int(input("Enter number of vertices: ").strip()) + E = int(input("Enter number of edges: ").strip()) -graph = [[float('inf') for i in range(V)] for j in range(V)] + graph = [[float('inf') for i in range(V)] for j in range(V)] -for i in range(V): - graph[i][i] = 0.0 + for i in range(V): + graph[i][i] = 0.0 -for i in range(E): - print("\nEdge ",i+1) - src = int(input("Enter source:")) - dst = int(input("Enter destination:")) - weight = float(input("Enter weight:")) - graph[src][dst] = weight + for i in range(E): + print("\nEdge ",i+1) + src = int(input("Enter source:").strip()) + dst = int(input("Enter destination:").strip()) + weight = float(input("Enter weight:").strip()) + graph[src][dst] = weight -gsrc = int(input("\nEnter shortest path source:")) -Dijkstra(graph, V, gsrc) + gsrc = int(input("\nEnter shortest path source:").strip()) + Dijkstra(graph, V, gsrc) diff --git a/graphs/floyd_warshall.py b/graphs/graphs_floyd_warshall.py similarity index 100% rename from graphs/floyd_warshall.py rename to graphs/graphs_floyd_warshall.py diff --git a/graphs/minimum_spanning_tree_kruskal.py b/graphs/minimum_spanning_tree_kruskal.py index 81d64f421a31..975151c90ede 100644 --- a/graphs/minimum_spanning_tree_kruskal.py +++ b/graphs/minimum_spanning_tree_kruskal.py @@ -1,32 +1,34 @@ from __future__ import print_function -num_nodes, num_edges = list(map(int,input().split())) -edges = [] +if __name__ == "__main__": + num_nodes, num_edges = list(map(int, input().strip().split())) -for i in range(num_edges): - node1, node2, cost = list(map(int,input().split())) - edges.append((i,node1,node2,cost)) + edges = [] -edges = sorted(edges, key=lambda edge: edge[3]) + for i in range(num_edges): + node1, node2, cost = list(map(int, input().strip().split())) + edges.append((i,node1,node2,cost)) -parent = [i for i in range(num_nodes)] + edges = sorted(edges, key=lambda edge: edge[3]) -def find_parent(i): - if(i != parent[i]): - parent[i] = find_parent(parent[i]) - return parent[i] + parent = list(range(num_nodes)) -minimum_spanning_tree_cost = 0 -minimum_spanning_tree = [] + def find_parent(i): + if i != parent[i]: + parent[i] = find_parent(parent[i]) + return parent[i] -for edge in edges: - parent_a = find_parent(edge[1]) - parent_b = find_parent(edge[2]) - if(parent_a != parent_b): - minimum_spanning_tree_cost += edge[3] - minimum_spanning_tree.append(edge) - parent[parent_a] = parent_b + minimum_spanning_tree_cost = 0 + minimum_spanning_tree = [] -print(minimum_spanning_tree_cost) -for edge in minimum_spanning_tree: - print(edge) + for edge in edges: + parent_a = find_parent(edge[1]) + parent_b = find_parent(edge[2]) + if parent_a != parent_b: + minimum_spanning_tree_cost += edge[3] + minimum_spanning_tree.append(edge) + parent[parent_a] = parent_b + + print(minimum_spanning_tree_cost) + for edge in minimum_spanning_tree: + print(edge) diff --git a/graphs/minimum_spanning_tree_prims.py b/graphs/minimum_spanning_tree_prims.py index 7b1ad0e743f7..0f21b8f494e4 100644 --- a/graphs/minimum_spanning_tree_prims.py +++ b/graphs/minimum_spanning_tree_prims.py @@ -100,12 +100,13 @@ def deleteMinimum(heap, positions): Nbr_TV[ v[0] ] = vertex return TreeEdges -# < --------- Prims Algorithm --------- > -n = int(input("Enter number of vertices: ")) -e = int(input("Enter number of edges: ")) -adjlist = defaultdict(list) -for x in range(e): - l = [int(x) for x in input().split()] - adjlist[l[0]].append([ l[1], l[2] ]) - adjlist[l[1]].append([ l[0], l[2] ]) -print(PrimsAlgorithm(adjlist)) +if __name__ == "__main__": + # < --------- Prims Algorithm --------- > + n = int(input("Enter number of vertices: ").strip()) + e = int(input("Enter number of edges: ").strip()) + adjlist = defaultdict(list) + for x in range(e): + l = [int(x) for x in input().strip().split()] + adjlist[l[0]].append([ l[1], l[2] ]) + adjlist[l[1]].append([ l[0], l[2] ]) + print(PrimsAlgorithm(adjlist)) diff --git a/graphs/multi_hueristic_astar.py b/graphs/multi_hueristic_astar.py index 1acd098f327d..1c01fe9aa6d3 100644 --- a/graphs/multi_hueristic_astar.py +++ b/graphs/multi_hueristic_astar.py @@ -18,7 +18,7 @@ def minkey(self): return self.elements[0][0] else: return float('inf') - + def empty(self): return len(self.elements) == 0 @@ -48,10 +48,10 @@ def remove_element(self, item): (pro, x) = heapq.heappop(self.elements) for (prito, yyy) in temp: heapq.heappush(self.elements, (prito, yyy)) - + def top_show(self): return self.elements[0][1] - + def get(self): (priority, item) = heapq.heappop(self.elements) self.set.remove(item) @@ -65,7 +65,7 @@ def consistent_hueristic(P, goal): def hueristic_2(P, goal): # integer division by time variable - return consistent_hueristic(P, goal) // t + return consistent_hueristic(P, goal) // t def hueristic_1(P, goal): # manhattan distance @@ -74,13 +74,13 @@ def hueristic_1(P, goal): def key(start, i, goal, g_function): ans = g_function[start] + W1 * hueristics[i](start, goal) return ans - + def do_something(back_pointer, goal, start): grid = np.chararray((n, n)) for i in range(n): for j in range(n): grid[i][j] = '*' - + for i in range(n): for j in range(n): if (j, (n-1)-i) in blocks: @@ -94,7 +94,7 @@ def do_something(back_pointer, goal, start): grid[(n-1)-y_c][x_c] = "-" x = back_pointer[x] grid[(n-1)][0] = "-" - + for i in xrange(n): for j in range(n): @@ -112,7 +112,7 @@ def do_something(back_pointer, goal, start): print("PATH TAKEN BY THE ALGORITHM IS:-") x = back_pointer[goal] while x != start: - print(x, end=' ') + print(x, end=' ') x = back_pointer[x] print(x) quit() @@ -153,7 +153,7 @@ def expand_state(s, j, visited, g_function, close_list_anchor, close_list_inad, if key(neighbours, var, goal, g_function) <= W2 * key(neighbours, 0, goal, g_function): # print("why not plssssssssss") open_list[j].put(neighbours, key(neighbours, var, goal, g_function)) - + # print @@ -212,7 +212,7 @@ def multi_a_star(start, goal, n_hueristic): for i in range(n_hueristic): open_list.append(PriorityQueue()) open_list[i].put(start, key(start, i, goal, g_function)) - + close_list_anchor = [] close_list_inad = [] while open_list[0].minkey() < float('inf'): @@ -263,4 +263,7 @@ def multi_a_star(start, goal, n_hueristic): print() print("# is an obstacle") print("- is the path taken by algorithm") -multi_a_star(start, goal, n_hueristic) + + +if __name__ == "__main__": + multi_a_star(start, goal, n_hueristic) diff --git a/graphs/scc_kosaraju.py b/graphs/scc_kosaraju.py index 1f13ebaba36b..0d0375203b6d 100644 --- a/graphs/scc_kosaraju.py +++ b/graphs/scc_kosaraju.py @@ -1,19 +1,5 @@ from __future__ import print_function -# n - no of nodes, m - no of edges -n, m = list(map(int,input().split())) - -g = [[] for i in range(n)] #graph -r = [[] for i in range(n)] #reversed graph -# input graph data (edges) -for i in range(m): - u, v = list(map(int,input().split())) - g[u].append(v) - r[v].append(u) - -stack = [] -visit = [False]*n -scc = [] -component = [] + def dfs(u): global g, r, scc, component, visit, stack @@ -43,4 +29,21 @@ def kosaraju(): scc.append(component) return scc -print(kosaraju()) + +if __name__ == "__main__": + # n - no of nodes, m - no of edges + n, m = list(map(int,input().strip().split())) + + g = [[] for i in range(n)] #graph + r = [[] for i in range(n)] #reversed graph + # input graph data (edges) + for i in range(m): + u, v = list(map(int,input().strip().split())) + g[u].append(v) + r[v].append(u) + + stack = [] + visit = [False]*n + scc = [] + component = [] + print(kosaraju()) From f195d9251cc64adb64549f79f4e774e220a668ab Mon Sep 17 00:00:00 2001 From: Jigyasa G <33327397+jpg-130@users.noreply.github.com> Date: Wed, 17 Jul 2019 11:52:09 +0530 Subject: [PATCH 079/193] adding factorial (#930) * adding factorial * adding doctest * Update factorial.py --- dynamic_programming/factorial.py | 34 ++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 dynamic_programming/factorial.py diff --git a/dynamic_programming/factorial.py b/dynamic_programming/factorial.py new file mode 100644 index 000000000000..7c6541ee2a74 --- /dev/null +++ b/dynamic_programming/factorial.py @@ -0,0 +1,34 @@ +#Factorial of a number using memoization +result=[-1]*10 +result[0]=result[1]=1 +def factorial(num): + """ + >>> factorial(7) + 5040 + >>> factorial(-1) + 'Number should not be negative.' + >>> [factorial(i) for i in range(5)] + [1, 1, 2, 6, 24] + """ + + if num<0: + return "Number should not be negative." + if result[num]!=-1: + return result[num] + else: + result[num]=num*factorial(num-1) + #uncomment the following to see how recalculations are avoided + #print(result) + return result[num] + +#factorial of num +#uncomment the following to see how recalculations are avoided +##result=[-1]*10 +##result[0]=result[1]=1 +##print(factorial(5)) +# print(factorial(3)) +# print(factorial(7)) + +if __name__ == "__main__": + import doctest + doctest.testmod() From f64b6029389019a2f2599b88078b940054e8be6c Mon Sep 17 00:00:00 2001 From: Sanders Lin <45224617+SandersLin@users.noreply.github.com> Date: Thu, 18 Jul 2019 00:12:24 +0800 Subject: [PATCH 080/193] Update max_sub_array.py (#1000) * Update max_sub_array.py added another method of computing maximum sum subarray * Update max_sub_array.py * Update max_sub_array.py --- dynamic_programming/max_sub_array.py | 24 ++++++++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/dynamic_programming/max_sub_array.py b/dynamic_programming/max_sub_array.py index 5d48882427c0..56983b7d22c2 100644 --- a/dynamic_programming/max_sub_array.py +++ b/dynamic_programming/max_sub_array.py @@ -2,7 +2,7 @@ author : Mayank Kumar Jha (mk9440) """ from __future__ import print_function - +from typing import List import time import matplotlib.pyplot as plt from random import randint @@ -37,7 +37,27 @@ def find_max_cross_sum(A,low,mid,high): right_sum=summ max_right=i return max_left,max_right,(left_sum+right_sum) - + +def max_sub_array(nums: List[int]) -> int: + """ + Finds the contiguous subarray (can be empty array) + which has the largest sum and return its sum. + + >>> max_sub_array([-2,1,-3,4,-1,2,1,-5,4]) + 6 + >>> max_sub_array([]) + 0 + >>> max_sub_array([-1,-2,-3]) + 0 + """ + best = 0 + current = 0 + for i in nums: + current += i + if current < 0: + current = 0 + best = max(best, current) + return best if __name__=='__main__': inputs=[10,100,1000,10000,50000,100000,200000,300000,400000,500000] From e662a5aaefb6f236021c89e9d410519c2013efa0 Mon Sep 17 00:00:00 2001 From: Bruno Simas Hadlich Date: Wed, 17 Jul 2019 15:32:04 -0300 Subject: [PATCH 081/193] Added Burrows-Wheeler transform algorithm. (#1029) * Added doctest and more explanation about Dijkstra execution. * tests were not passing with python2 due to missing __init__.py file at number_theory folder * Removed the dot at the beginning of the imported modules names because 'python3 -m doctest -v data_structures/hashing/*.py' and 'python3 -m doctest -v data_structures/stacks/*.py' were failing not finding hash_table.py and stack.py modules. * Moved global code to main scope and added doctest for project euler problems 1 to 14. * Added test case for negative input. * Changed N variable to do not use end of line scape because in case there is a space after it the script will break making it much more error prone. * Added problems description and doctests to the ones that were missing. Limited line length to 79 and executed python black over all scripts. * Changed the way files are loaded to support pytest call. * Added __init__.py to problems to make them modules and allow pytest execution. * Added project_euler folder to test units execution * Changed 'os.path.split(os.path.realpath(__file__))' to 'os.path.dirname()' * Added Burrows-Wheeler transform algorithm. * Added changes suggested by cclauss --- compression/burrows_wheeler.py | 176 +++++++++++++++++++++++++++++++++ 1 file changed, 176 insertions(+) create mode 100644 compression/burrows_wheeler.py diff --git a/compression/burrows_wheeler.py b/compression/burrows_wheeler.py new file mode 100644 index 000000000000..fabeab39adf8 --- /dev/null +++ b/compression/burrows_wheeler.py @@ -0,0 +1,176 @@ +""" +https://en.wikipedia.org/wiki/Burrows%E2%80%93Wheeler_transform + +The Burrows–Wheeler transform (BWT, also called block-sorting compression) +rearranges a character string into runs of similar characters. This is useful +for compression, since it tends to be easy to compress a string that has runs +of repeated characters by techniques such as move-to-front transform and +run-length encoding. More importantly, the transformation is reversible, +without needing to store any additional data except the position of the first +original character. The BWT is thus a "free" method of improving the efficiency +of text compression algorithms, costing only some extra computation. +""" +from typing import List, Dict + + +def all_rotations(s: str) -> List[str]: + """ + :param s: The string that will be rotated len(s) times. + :return: A list with the rotations. + :raises TypeError: If s is not an instance of str. + Examples: + + >>> all_rotations("^BANANA|") # doctest: +NORMALIZE_WHITESPACE + ['^BANANA|', 'BANANA|^', 'ANANA|^B', 'NANA|^BA', 'ANA|^BAN', 'NA|^BANA', + 'A|^BANAN', '|^BANANA'] + >>> all_rotations("a_asa_da_casa") # doctest: +NORMALIZE_WHITESPACE + ['a_asa_da_casa', '_asa_da_casaa', 'asa_da_casaa_', 'sa_da_casaa_a', + 'a_da_casaa_as', '_da_casaa_asa', 'da_casaa_asa_', 'a_casaa_asa_d', + '_casaa_asa_da', 'casaa_asa_da_', 'asaa_asa_da_c', 'saa_asa_da_ca', + 'aa_asa_da_cas'] + >>> all_rotations("panamabanana") # doctest: +NORMALIZE_WHITESPACE + ['panamabanana', 'anamabananap', 'namabananapa', 'amabananapan', + 'mabananapana', 'abananapanam', 'bananapanama', 'ananapanamab', + 'nanapanamaba', 'anapanamaban', 'napanamabana', 'apanamabanan'] + >>> all_rotations(5) + Traceback (most recent call last): + ... + TypeError: The parameter s type must be str. + """ + if not isinstance(s, str): + raise TypeError("The parameter s type must be str.") + + return [s[i:] + s[:i] for i in range(len(s))] + + +def bwt_transform(s: str) -> Dict: + """ + :param s: The string that will be used at bwt algorithm + :return: the string composed of the last char of each row of the ordered + rotations and the index of the original string at ordered rotations list + :raises TypeError: If the s parameter type is not str + :raises ValueError: If the s parameter is empty + Examples: + + >>> bwt_transform("^BANANA") + {'bwt_string': 'BNN^AAA', 'idx_original_string': 6} + >>> bwt_transform("a_asa_da_casa") + {'bwt_string': 'aaaadss_c__aa', 'idx_original_string': 3} + >>> bwt_transform("panamabanana") + {'bwt_string': 'mnpbnnaaaaaa', 'idx_original_string': 11} + >>> bwt_transform(4) + Traceback (most recent call last): + ... + TypeError: The parameter s type must be str. + >>> bwt_transform('') + Traceback (most recent call last): + ... + ValueError: The parameter s must not be empty. + """ + if not isinstance(s, str): + raise TypeError("The parameter s type must be str.") + if not s: + raise ValueError("The parameter s must not be empty.") + + rotations = all_rotations(s) + rotations.sort() # sort the list of rotations in alphabetically order + # make a string composed of the last char of each rotation + return { + "bwt_string": "".join([word[-1] for word in rotations]), + "idx_original_string": rotations.index(s), + } + + +def reverse_bwt(bwt_string: str, idx_original_string: int) -> str: + """ + :param bwt_string: The string returned from bwt algorithm execution + :param idx_original_string: A 0-based index of the string that was used to + generate bwt_string at ordered rotations list + :return: The string used to generate bwt_string when bwt was executed + :raises TypeError: If the bwt_string parameter type is not str + :raises ValueError: If the bwt_string parameter is empty + :raises TypeError: If the idx_original_string type is not int or if not + possible to cast it to int + :raises ValueError: If the idx_original_string value is lower than 0 or + greater than len(bwt_string) - 1 + + >>> reverse_bwt("BNN^AAA", 6) + '^BANANA' + >>> reverse_bwt("aaaadss_c__aa", 3) + 'a_asa_da_casa' + >>> reverse_bwt("mnpbnnaaaaaa", 11) + 'panamabanana' + >>> reverse_bwt(4, 11) + Traceback (most recent call last): + ... + TypeError: The parameter bwt_string type must be str. + >>> reverse_bwt("", 11) + Traceback (most recent call last): + ... + ValueError: The parameter bwt_string must not be empty. + >>> reverse_bwt("mnpbnnaaaaaa", "asd") # doctest: +NORMALIZE_WHITESPACE + Traceback (most recent call last): + ... + TypeError: The parameter idx_original_string type must be int or passive + of cast to int. + >>> reverse_bwt("mnpbnnaaaaaa", -1) + Traceback (most recent call last): + ... + ValueError: The parameter idx_original_string must not be lower than 0. + >>> reverse_bwt("mnpbnnaaaaaa", 12) # doctest: +NORMALIZE_WHITESPACE + Traceback (most recent call last): + ... + ValueError: The parameter idx_original_string must be lower than + len(bwt_string). + >>> reverse_bwt("mnpbnnaaaaaa", 11.0) + 'panamabanana' + >>> reverse_bwt("mnpbnnaaaaaa", 11.4) + 'panamabanana' + """ + if not isinstance(bwt_string, str): + raise TypeError("The parameter bwt_string type must be str.") + if not bwt_string: + raise ValueError("The parameter bwt_string must not be empty.") + try: + idx_original_string = int(idx_original_string) + except ValueError: + raise TypeError( + ( + "The parameter idx_original_string type must be int or passive" + " of cast to int." + ) + ) + if idx_original_string < 0: + raise ValueError( + "The parameter idx_original_string must not be lower than 0." + ) + if idx_original_string >= len(bwt_string): + raise ValueError( + ( + "The parameter idx_original_string must be lower than" + " len(bwt_string)." + ) + ) + + ordered_rotations = [""] * len(bwt_string) + for x in range(len(bwt_string)): + for i in range(len(bwt_string)): + ordered_rotations[i] = bwt_string[i] + ordered_rotations[i] + ordered_rotations.sort() + return ordered_rotations[idx_original_string] + + +if __name__ == "__main__": + entry_msg = "Provide a string that I will generate its BWT transform: " + s = input(entry_msg).strip() + result = bwt_transform(s) + bwt_output_msg = "Burrows Wheeler tranform for string '{}' results in '{}'" + print(bwt_output_msg.format(s, result["bwt_string"])) + original_string = reverse_bwt( + result["bwt_string"], result["idx_original_string"] + ) + fmt = ( + "Reversing Burrows Wheeler tranform for entry '{}' we get original" + " string '{}'" + ) + print(fmt.format(result["bwt_string"], original_string)) From 4658f4a49e2bf9b13d806bd7dfc3df522056ad1c Mon Sep 17 00:00:00 2001 From: Jigyasa G <33327397+jpg-130@users.noreply.github.com> Date: Thu, 18 Jul 2019 16:17:15 +0530 Subject: [PATCH 082/193] lgtm fixes (#1032) * adding sum of subsets * lgtm fixes --- project_euler/problem_07/sol2.py | 1 - 1 file changed, 1 deletion(-) diff --git a/project_euler/problem_07/sol2.py b/project_euler/problem_07/sol2.py index 630e5196796d..3dc0b1343eb7 100644 --- a/project_euler/problem_07/sol2.py +++ b/project_euler/problem_07/sol2.py @@ -7,7 +7,6 @@ We can see that the 6th prime is 13. What is the Nth prime number? """ from __future__ import print_function -from math import sqrt try: raw_input # Python 2 From c2e8582abdf64c837c61c08cecdd5cbea222e290 Mon Sep 17 00:00:00 2001 From: cclauss Date: Thu, 18 Jul 2019 13:10:52 +0200 Subject: [PATCH 083/193] Travis CI: Add pytest --doctest-modules neural_network (#1028) * neural_network/perceptron.py: Add if __name__ == '__main__': * Remove tab indentation * Add neural_network to the pytests --- .travis.yml | 1 + neural_network/perceptron.py | 27 ++++++++++++++------------- 2 files changed, 15 insertions(+), 13 deletions(-) diff --git a/.travis.yml b/.travis.yml index 55ea2c7ddc24..6ac3010c5396 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,6 +26,7 @@ script: linear_algebra_python matrix networking_flow + neural_network other project_euler searches diff --git a/neural_network/perceptron.py b/neural_network/perceptron.py index eb8b04e855d3..787ea8f73bf1 100644 --- a/neural_network/perceptron.py +++ b/neural_network/perceptron.py @@ -1,12 +1,12 @@ ''' - Perceptron - w = w + N * (d(k) - y) * x(k) + Perceptron + w = w + N * (d(k) - y) * x(k) - Using perceptron network for oil analysis, - with Measuring of 3 parameters that represent chemical characteristics we can classify the oil, in p1 or p2 - p1 = -1 - p2 = 1 + Using perceptron network for oil analysis, + with Measuring of 3 parameters that represent chemical characteristics we can classify the oil, in p1 or p2 + p1 = -1 + p2 = 1 ''' from __future__ import print_function @@ -113,12 +113,13 @@ def sign(self, u): exit = [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1] -network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1) +if __name__ == '__main__': + network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1) -network.training() + network.training() -while True: - sample = [] - for i in range(3): - sample.insert(i, float(input('value: '))) - network.sort(sample) + while True: + sample = [] + for i in range(3): + sample.insert(i, float(input('value: ').strip())) + network.sort(sample) From 9a55f2b36a569f27a0893a79f0ba9cee23819557 Mon Sep 17 00:00:00 2001 From: cclauss Date: Thu, 18 Jul 2019 18:15:54 +0200 Subject: [PATCH 084/193] Remove the space: lucas series.py --> lucas_series.py (#1036) --- maths/{lucas series.py => lucas_series.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename maths/{lucas series.py => lucas_series.py} (100%) diff --git a/maths/lucas series.py b/maths/lucas_series.py similarity index 100% rename from maths/lucas series.py rename to maths/lucas_series.py From f438440ac54bbaad1557d5d8d2caa3331231f99b Mon Sep 17 00:00:00 2001 From: Bruno Simas Hadlich Date: Thu, 18 Jul 2019 14:05:14 -0300 Subject: [PATCH 085/193] Fixes for issue "Fix the LGTM issues #1024" (#1034) * Added doctest and more explanation about Dijkstra execution. * tests were not passing with python2 due to missing __init__.py file at number_theory folder * Removed the dot at the beginning of the imported modules names because 'python3 -m doctest -v data_structures/hashing/*.py' and 'python3 -m doctest -v data_structures/stacks/*.py' were failing not finding hash_table.py and stack.py modules. * Moved global code to main scope and added doctest for project euler problems 1 to 14. * Added test case for negative input. * Changed N variable to do not use end of line scape because in case there is a space after it the script will break making it much more error prone. * Added problems description and doctests to the ones that were missing. Limited line length to 79 and executed python black over all scripts. * Changed the way files are loaded to support pytest call. * Added __init__.py to problems to make them modules and allow pytest execution. * Added project_euler folder to test units execution * Changed 'os.path.split(os.path.realpath(__file__))' to 'os.path.dirname()' * Added Burrows-Wheeler transform algorithm. * Added changes suggested by cclauss * Fixes for issue 'Fix the LGTM issues #1024'. * Added doctest for different parameter types and negative values. * Fixed doctest issue added at last commit. --- project_euler/problem_02/sol4.py | 26 +++++++++++++++++++++++++- project_euler/problem_03/sol1.py | 27 +++++++++++++++++++++++++-- project_euler/problem_03/sol2.py | 27 +++++++++++++++++++++++++-- project_euler/problem_05/sol1.py | 27 +++++++++++++++++++++++++-- project_euler/problem_07/sol2.py | 24 ++++++++++++++++++++++++ project_euler/problem_09/sol1.py | 1 - project_euler/problem_19/sol1.py | 2 +- project_euler/problem_234/sol1.py | 1 - 8 files changed, 125 insertions(+), 10 deletions(-) diff --git a/project_euler/problem_02/sol4.py b/project_euler/problem_02/sol4.py index ba13b12a15e9..5e8c04899f3d 100644 --- a/project_euler/problem_02/sol4.py +++ b/project_euler/problem_02/sol4.py @@ -11,7 +11,7 @@ """ from __future__ import print_function import math -from decimal import * +from decimal import Decimal, getcontext try: raw_input # Python 2 @@ -33,7 +33,31 @@ def solution(n): 0 >>> solution(34) 44 + >>> solution(3.4) + 2 + >>> solution(0) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution(-17) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution([]) + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + >>> solution("asd") + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. """ + try: + n = int(n) + except (TypeError, ValueError) as e: + raise TypeError("Parameter n must be int or passive of cast to int.") + if n <= 0: + raise ValueError("Parameter n must be greater or equal to one.") getcontext().prec = 100 phi = (Decimal(5) ** Decimal(0.5) + 1) / Decimal(2) diff --git a/project_euler/problem_03/sol1.py b/project_euler/problem_03/sol1.py index c2e601bd0040..ab19d8b30457 100644 --- a/project_euler/problem_03/sol1.py +++ b/project_euler/problem_03/sol1.py @@ -28,14 +28,38 @@ def isprime(no): def solution(n): """Returns the largest prime factor of a given number n. - + >>> solution(13195) 29 >>> solution(10) 5 >>> solution(17) 17 + >>> solution(3.4) + 3 + >>> solution(0) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution(-17) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution([]) + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + >>> solution("asd") + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. """ + try: + n = int(n) + except (TypeError, ValueError) as e: + raise TypeError("Parameter n must be int or passive of cast to int.") + if n <= 0: + raise ValueError("Parameter n must be greater or equal to one.") maxNumber = 0 if isprime(n): return n @@ -54,7 +78,6 @@ def solution(n): elif isprime(i): maxNumber = i return maxNumber - return int(sum) if __name__ == "__main__": diff --git a/project_euler/problem_03/sol2.py b/project_euler/problem_03/sol2.py index 497db3965cc3..f93a0b75f4e0 100644 --- a/project_euler/problem_03/sol2.py +++ b/project_euler/problem_03/sol2.py @@ -6,7 +6,6 @@ e.g. for 10, largest prime factor = 5. For 17, largest prime factor = 17. """ from __future__ import print_function, division -import math try: raw_input # Python 2 @@ -16,14 +15,38 @@ def solution(n): """Returns the largest prime factor of a given number n. - + >>> solution(13195) 29 >>> solution(10) 5 >>> solution(17) 17 + >>> solution(3.4) + 3 + >>> solution(0) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution(-17) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution([]) + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + >>> solution("asd") + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. """ + try: + n = int(n) + except (TypeError, ValueError) as e: + raise TypeError("Parameter n must be int or passive of cast to int.") + if n <= 0: + raise ValueError("Parameter n must be greater or equal to one.") prime = 1 i = 2 while i * i <= n: diff --git a/project_euler/problem_05/sol1.py b/project_euler/problem_05/sol1.py index 609f02102a08..e2deb91fb6aa 100644 --- a/project_euler/problem_05/sol1.py +++ b/project_euler/problem_05/sol1.py @@ -17,7 +17,7 @@ def solution(n): """Returns the smallest positive number that is evenly divisible(divisible with no remainder) by all of the numbers from 1 to n. - + >>> solution(10) 2520 >>> solution(15) @@ -26,7 +26,31 @@ def solution(n): 232792560 >>> solution(22) 232792560 + >>> solution(3.4) + 6 + >>> solution(0) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution(-17) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution([]) + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + >>> solution("asd") + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. """ + try: + n = int(n) + except (TypeError, ValueError) as e: + raise TypeError("Parameter n must be int or passive of cast to int.") + if n <= 0: + raise ValueError("Parameter n must be greater or equal to one.") i = 0 while 1: i += n * (n - 1) @@ -39,7 +63,6 @@ def solution(n): if i == 0: i = 1 return i - break if __name__ == "__main__": diff --git a/project_euler/problem_07/sol2.py b/project_euler/problem_07/sol2.py index 3dc0b1343eb7..67336f7c1c96 100644 --- a/project_euler/problem_07/sol2.py +++ b/project_euler/problem_07/sol2.py @@ -36,7 +36,31 @@ def solution(n): 229 >>> solution(100) 541 + >>> solution(3.4) + 5 + >>> solution(0) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution(-17) + Traceback (most recent call last): + ... + ValueError: Parameter n must be greater or equal to one. + >>> solution([]) + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. + >>> solution("asd") + Traceback (most recent call last): + ... + TypeError: Parameter n must be int or passive of cast to int. """ + try: + n = int(n) + except (TypeError, ValueError) as e: + raise TypeError("Parameter n must be int or passive of cast to int.") + if n <= 0: + raise ValueError("Parameter n must be greater or equal to one.") primes = [] num = 2 while len(primes) < n: diff --git a/project_euler/problem_09/sol1.py b/project_euler/problem_09/sol1.py index 0f368e48d2e3..20dedb84bc0e 100644 --- a/project_euler/problem_09/sol1.py +++ b/project_euler/problem_09/sol1.py @@ -28,7 +28,6 @@ def solution(): if (a ** 2) + (b ** 2) == (c ** 2): if (a + b + c) == 1000: return a * b * c - break if __name__ == "__main__": diff --git a/project_euler/problem_19/sol1.py b/project_euler/problem_19/sol1.py index 6e4e29ec19c6..ab59365843b2 100644 --- a/project_euler/problem_19/sol1.py +++ b/project_euler/problem_19/sol1.py @@ -61,4 +61,4 @@ def solution(): if __name__ == "__main__": - print(solution(171)) + print(solution()) diff --git a/project_euler/problem_234/sol1.py b/project_euler/problem_234/sol1.py index 8298a7f8cce3..c0d2949285e9 100644 --- a/project_euler/problem_234/sol1.py +++ b/project_euler/problem_234/sol1.py @@ -40,7 +40,6 @@ def solution(n): semidivisible = [] for x in range(n): l=[i for i in input().split()] - c1=0 c2=1 while(1): if len(fib(l[0],l[1],c2)) Date: Fri, 19 Jul 2019 03:10:51 +0530 Subject: [PATCH 086/193] Update find_lcm.py (#1019) * Update find_lcm.py Improved code quality and added comments. * Make the doctests work --- maths/find_lcm.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/maths/find_lcm.py b/maths/find_lcm.py index 9062d462b8b3..f7ac958070b5 100644 --- a/maths/find_lcm.py +++ b/maths/find_lcm.py @@ -4,8 +4,17 @@ def find_lcm(num_1, num_2): - """Find the LCM of two numbers.""" - max_num = num_1 if num_1 > num_2 else num_2 + """Find the least common multiple of two numbers. + >>> find_lcm(5,2) + 10 + >>> find_lcm(12,76) + 228 + """ + if num_1>=num_2: + max_num=num_1 + else: + max_num=num_2 + lcm = max_num while True: if ((lcm % num_1 == 0) and (lcm % num_2 == 0)): @@ -16,8 +25,8 @@ def find_lcm(num_1, num_2): def main(): """Use test numbers to run the find_lcm algorithm.""" - num_1 = 12 - num_2 = 76 + num_1 = int(input().strip()) + num_2 = int(input().strip()) print(find_lcm(num_1, num_2)) From f7ac8b5ed054198bdb254635e8f06c6f219c2f75 Mon Sep 17 00:00:00 2001 From: Bruno Simas Hadlich Date: Thu, 18 Jul 2019 19:34:29 -0300 Subject: [PATCH 087/193] Commented doctests that were causing slowness at Travis. (#1039) * Added doctest and more explanation about Dijkstra execution. * tests were not passing with python2 due to missing __init__.py file at number_theory folder * Removed the dot at the beginning of the imported modules names because 'python3 -m doctest -v data_structures/hashing/*.py' and 'python3 -m doctest -v data_structures/stacks/*.py' were failing not finding hash_table.py and stack.py modules. * Moved global code to main scope and added doctest for project euler problems 1 to 14. * Added test case for negative input. * Changed N variable to do not use end of line scape because in case there is a space after it the script will break making it much more error prone. * Added problems description and doctests to the ones that were missing. Limited line length to 79 and executed python black over all scripts. * Changed the way files are loaded to support pytest call. * Added __init__.py to problems to make them modules and allow pytest execution. * Added project_euler folder to test units execution * Changed 'os.path.split(os.path.realpath(__file__))' to 'os.path.dirname()' * Added Burrows-Wheeler transform algorithm. * Added changes suggested by cclauss * Fixes for issue 'Fix the LGTM issues #1024'. * Added doctest for different parameter types and negative values. * Fixed doctest issue added at last commit. * Commented doctest that were causing slowness at Travis. * Added comment with the reason for some doctest commented. * pytest --ignore --- .travis.yml | 38 +++++++++++--------------------- project_euler/problem_09/sol1.py | 5 +++-- project_euler/problem_09/sol3.py | 4 ++-- project_euler/problem_10/sol1.py | 5 +++-- project_euler/problem_10/sol2.py | 5 +++-- project_euler/problem_12/sol1.py | 5 +++-- project_euler/problem_12/sol2.py | 5 +++-- project_euler/problem_14/sol1.py | 5 +++-- project_euler/problem_14/sol2.py | 5 +++-- 9 files changed, 36 insertions(+), 41 deletions(-) diff --git a/.travis.yml b/.travis.yml index 6ac3010c5396..a3ff22fb09b7 100644 --- a/.travis.yml +++ b/.travis.yml @@ -9,31 +9,19 @@ before_script: - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics script: - mypy --ignore-missing-imports . - #- IGNORE="data_structures,file_transfer_protocol,graphs,machine_learning,maths,neural_network,project_euler" - #- pytest . --doctest-modules --ignore=${IGNORE} - - pytest --doctest-modules - arithmetic_analysis - backtracking - boolean_algebra - ciphers - compression - conversions - digital_image_processing - divide_and_conquer - dynamic_programming - graphs - hashes - linear_algebra_python - matrix - networking_flow - neural_network - other - project_euler - searches - sorts - strings - traversals - + - pytest . --doctest-modules + --ignore=data_structures/stacks/balanced_parentheses.py + --ignore=data_structures/stacks/infix_to_postfix_conversion.py + --ignore=file_transfer_protocol/ftp_send_receive.py + --ignore=file_transfer_protocol/ftp_client_server.py + --ignore=machine_learning/linear_regression.py + --ignore=machine_learning/perceptron.py + --ignore=machine_learning/random_forest_classification/random_forest_classification.py + --ignore=machine_learning/random_forest_regression/random_forest_regression.py + --ignore=maths/abs_min.py + --ignore=maths/binary_exponentiation.py + --ignore=maths/lucas_series.py + --ignore=maths/sieve_of_eratosthenes.py after_success: - python scripts/build_directory_md.py - cat DIRECTORY.md diff --git a/project_euler/problem_09/sol1.py b/project_euler/problem_09/sol1.py index 20dedb84bc0e..d9ebe8760861 100644 --- a/project_euler/problem_09/sol1.py +++ b/project_euler/problem_09/sol1.py @@ -18,8 +18,9 @@ def solution(): 2. a**2 + b**2 = c**2 3. a + b + c = 1000 - >>> solution() - 31875000 + # The code below has been commented due to slow execution affecting Travis. + # >>> solution() + # 31875000 """ for a in range(300): for b in range(400): diff --git a/project_euler/problem_09/sol3.py b/project_euler/problem_09/sol3.py index f749b8a61f11..829ba84c4a77 100644 --- a/project_euler/problem_09/sol3.py +++ b/project_euler/problem_09/sol3.py @@ -21,8 +21,8 @@ def solution(): 1. a**2 + b**2 = c**2 2. a + b + c = 1000 - >>> solution() - 31875000 + #>>> solution() + #31875000 """ return [ a * b * c diff --git a/project_euler/problem_10/sol1.py b/project_euler/problem_10/sol1.py index 038da96e6352..49384d7c78f0 100644 --- a/project_euler/problem_10/sol1.py +++ b/project_euler/problem_10/sol1.py @@ -42,8 +42,9 @@ def sum_of_primes(n): def solution(n): """Returns the sum of all the primes below n. - >>> solution(2000000) - 142913828922 + # The code below has been commented due to slow execution affecting Travis. + # >>> solution(2000000) + # 142913828922 >>> solution(1000) 76127 >>> solution(5000) diff --git a/project_euler/problem_10/sol2.py b/project_euler/problem_10/sol2.py index 9e51d61b8749..451a4ae5e8f3 100644 --- a/project_euler/problem_10/sol2.py +++ b/project_euler/problem_10/sol2.py @@ -31,8 +31,9 @@ def prime_generator(): def solution(n): """Returns the sum of all the primes below n. - >>> solution(2000000) - 142913828922 + # The code below has been commented due to slow execution affecting Travis. + # >>> solution(2000000) + # 142913828922 >>> solution(1000) 76127 >>> solution(5000) diff --git a/project_euler/problem_12/sol1.py b/project_euler/problem_12/sol1.py index baf9babab686..54476110b503 100644 --- a/project_euler/problem_12/sol1.py +++ b/project_euler/problem_12/sol1.py @@ -45,8 +45,9 @@ def solution(): """Returns the value of the first triangle number to have over five hundred divisors. - >>> solution() - 76576500 + # The code below has been commented due to slow execution affecting Travis. + # >>> solution() + # 76576500 """ tNum = 1 i = 1 diff --git a/project_euler/problem_12/sol2.py b/project_euler/problem_12/sol2.py index 071d7516ac0f..0d1502830bee 100644 --- a/project_euler/problem_12/sol2.py +++ b/project_euler/problem_12/sol2.py @@ -39,8 +39,9 @@ def solution(): """Returns the value of the first triangle number to have over five hundred divisors. - >>> solution() - 76576500 + # The code below has been commented due to slow execution affecting Travis. + # >>> solution() + # 76576500 """ return next( i for i in triangle_number_generator() if count_divisors(i) > 500 diff --git a/project_euler/problem_14/sol1.py b/project_euler/problem_14/sol1.py index 6b80cd7cb24b..8d3efbc59eb5 100644 --- a/project_euler/problem_14/sol1.py +++ b/project_euler/problem_14/sol1.py @@ -30,8 +30,9 @@ def solution(n): n → n/2 (n is even) n → 3n + 1 (n is odd) - >>> solution(1000000) - {'counter': 525, 'largest_number': 837799} + # The code below has been commented due to slow execution affecting Travis. + # >>> solution(1000000) + # {'counter': 525, 'largest_number': 837799} >>> solution(200) {'counter': 125, 'largest_number': 171} >>> solution(5000) diff --git a/project_euler/problem_14/sol2.py b/project_euler/problem_14/sol2.py index 59fa79515148..0ec80e221f09 100644 --- a/project_euler/problem_14/sol2.py +++ b/project_euler/problem_14/sol2.py @@ -47,8 +47,9 @@ def collatz_sequence(n): def solution(n): """Returns the number under n that generates the longest Collatz sequence. - >>> solution(1000000) - {'counter': 525, 'largest_number': 837799} + # The code below has been commented due to slow execution affecting Travis. + # >>> solution(1000000) + # {'counter': 525, 'largest_number': 837799} >>> solution(200) {'counter': 125, 'largest_number': 171} >>> solution(5000) From 9fcfe6a02bca94664189a656cf76bd425bbf3417 Mon Sep 17 00:00:00 2001 From: Bruno Simas Hadlich Date: Fri, 19 Jul 2019 01:33:28 -0300 Subject: [PATCH 088/193] Commented doctests that were causing slowness at Travis. #2 (#1041) * Added doctest and more explanation about Dijkstra execution. * tests were not passing with python2 due to missing __init__.py file at number_theory folder * Removed the dot at the beginning of the imported modules names because 'python3 -m doctest -v data_structures/hashing/*.py' and 'python3 -m doctest -v data_structures/stacks/*.py' were failing not finding hash_table.py and stack.py modules. * Moved global code to main scope and added doctest for project euler problems 1 to 14. * Added test case for negative input. * Changed N variable to do not use end of line scape because in case there is a space after it the script will break making it much more error prone. * Added problems description and doctests to the ones that were missing. Limited line length to 79 and executed python black over all scripts. * Changed the way files are loaded to support pytest call. * Added __init__.py to problems to make them modules and allow pytest execution. * Added project_euler folder to test units execution * Changed 'os.path.split(os.path.realpath(__file__))' to 'os.path.dirname()' * Added Burrows-Wheeler transform algorithm. * Added changes suggested by cclauss * Fixes for issue 'Fix the LGTM issues #1024'. * Added doctest for different parameter types and negative values. * Fixed doctest issue added at last commit. * Commented doctest that were causing slowness at Travis. * Added comment with the reason for some doctest commented. * pytest --ignore * Added tests execution again. * Had forgotten to add comment to file project_euler/problem_09/sol3.py --- project_euler/problem_09/sol3.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/project_euler/problem_09/sol3.py b/project_euler/problem_09/sol3.py index 829ba84c4a77..006029c8a30d 100644 --- a/project_euler/problem_09/sol3.py +++ b/project_euler/problem_09/sol3.py @@ -21,8 +21,9 @@ def solution(): 1. a**2 + b**2 = c**2 2. a + b + c = 1000 - #>>> solution() - #31875000 + # The code below has been commented due to slow execution affecting Travis. + # >>> solution() + # 31875000 """ return [ a * b * c From 60c608d85a41f85f795233b5ff913ca35f8a2f92 Mon Sep 17 00:00:00 2001 From: "Md. Mahbubur Rahman" Date: Fri, 19 Jul 2019 16:41:37 +0900 Subject: [PATCH 089/193] Added matrix exponentiation approach for finding fibonacci number. (#1042) * Added matrix exponentiation approach for finding fibonacci number. * Implemented the way of finding nth fibonacci. * Complexity is about O(log(n)*8) * Updated the matrix exponentiation approach of finding nth fibonacci. - Removed some extra spaces - Added the complexity of bruteforce algorithm - Removed unused function called zerro() - Added some docktest based on request * Updated the matrix exponentiation approach of finding nth fibonacci. - Removed some extra spaces - Added the complexity of bruteforce algorithm - Removed unused function called zerro() - Added some docktest based on request * Tighten up main() and add comments on performance --- ...h_fibonacci_using_matrix_exponentiation.py | 88 +++++++++++++++++++ 1 file changed, 88 insertions(+) create mode 100644 matrix/nth_fibonacci_using_matrix_exponentiation.py diff --git a/matrix/nth_fibonacci_using_matrix_exponentiation.py b/matrix/nth_fibonacci_using_matrix_exponentiation.py new file mode 100644 index 000000000000..cee6b21c81eb --- /dev/null +++ b/matrix/nth_fibonacci_using_matrix_exponentiation.py @@ -0,0 +1,88 @@ +""" +Implementation of finding nth fibonacci number using matrix exponentiation. +Time Complexity is about O(log(n)*8), where 8 is the complexity of matrix multiplication of size 2 by 2. +And on the other hand complexity of bruteforce solution is O(n). +As we know + f[n] = f[n-1] + f[n-1] +Converting to matrix, + [f(n),f(n-1)] = [[1,1],[1,0]] * [f(n-1),f(n-2)] +-> [f(n),f(n-1)] = [[1,1],[1,0]]^2 * [f(n-2),f(n-3)] + ... + ... +-> [f(n),f(n-1)] = [[1,1],[1,0]]^(n-1) * [f(1),f(0)] +So we just need the n times multiplication of the matrix [1,1],[1,0]]. +We can decrease the n times multiplication by following the divide and conquer approach. +""" +from __future__ import print_function + + +def multiply(matrix_a, matrix_b): + matrix_c = [] + n = len(matrix_a) + for i in range(n): + list_1 = [] + for j in range(n): + val = 0 + for k in range(n): + val = val + matrix_a[i][k] * matrix_b[k][j] + list_1.append(val) + matrix_c.append(list_1) + return matrix_c + + +def identity(n): + return [[int(row == column) for column in range(n)] for row in range(n)] + + +def nth_fibonacci_matrix(n): + """ + >>> nth_fibonacci_matrix(100) + 354224848179261915075 + >>> nth_fibonacci_matrix(-100) + -100 + """ + if n <= 1: + return n + res_matrix = identity(2) + fibonacci_matrix = [[1, 1], [1, 0]] + n = n - 1 + while n > 0: + if n % 2 == 1: + res_matrix = multiply(res_matrix, fibonacci_matrix) + fibonacci_matrix = multiply(fibonacci_matrix, fibonacci_matrix) + n = int(n / 2) + return res_matrix[0][0] + + +def nth_fibonacci_bruteforce(n): + """ + >>> nth_fibonacci_bruteforce(100) + 354224848179261915075 + >>> nth_fibonacci_bruteforce(-100) + -100 + """ + if n <= 1: + return n + fib0 = 0 + fib1 = 1 + for i in range(2, n + 1): + fib0, fib1 = fib1, fib0 + fib1 + return fib1 + + +def main(): + fmt = "{} fibonacci number using matrix exponentiation is {} and using bruteforce is {}\n" + for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split(): + n = int("".join(c for c in ordinal if c in "0123456789")) # 1000th --> 1000 + print(fmt.format(ordinal, nth_fibonacci(n), nth_fibonacci_test(n))) + # from timeit import timeit + # print(timeit("nth_fibonacci_matrix(1000000)", + # "from main import nth_fibonacci_matrix", number=5)) + # print(timeit("nth_fibonacci_bruteforce(1000000)", + # "from main import nth_fibonacci_bruteforce", number=5)) + # 2.3342058970001744 + # 57.256506615000035 + + +if __name__ == "__main__": + main() From dc1de946eabe20053b811b50e0e1d50697bd46fa Mon Sep 17 00:00:00 2001 From: cclauss Date: Fri, 19 Jul 2019 10:55:45 +0200 Subject: [PATCH 090/193] Use correct function names in nth_fibonacci_using_matrix_exponentiation.py (#1045) @AnupKumarPanwar @ParthS007 @poyea Could I please get a quick review on this one because I made a mistake here that breaks the build for new pull requests. --- matrix/nth_fibonacci_using_matrix_exponentiation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/matrix/nth_fibonacci_using_matrix_exponentiation.py b/matrix/nth_fibonacci_using_matrix_exponentiation.py index cee6b21c81eb..7491abcae031 100644 --- a/matrix/nth_fibonacci_using_matrix_exponentiation.py +++ b/matrix/nth_fibonacci_using_matrix_exponentiation.py @@ -74,7 +74,7 @@ def main(): fmt = "{} fibonacci number using matrix exponentiation is {} and using bruteforce is {}\n" for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split(): n = int("".join(c for c in ordinal if c in "0123456789")) # 1000th --> 1000 - print(fmt.format(ordinal, nth_fibonacci(n), nth_fibonacci_test(n))) + print(fmt.format(ordinal, nth_fibonacci_matrix(n), nth_fibonacci_bruteforce(n))) # from timeit import timeit # print(timeit("nth_fibonacci_matrix(1000000)", # "from main import nth_fibonacci_matrix", number=5)) From 4e0717c3cfb336aa86f6720f2f49adf58f0e95d7 Mon Sep 17 00:00:00 2001 From: Stephen Gemin <45926479+StephenGemin@users.noreply.github.com> Date: Fri, 19 Jul 2019 23:06:29 -0400 Subject: [PATCH 091/193] Add error & test checks for matrix_operations.py (#925) * Update matrix_operation.py 1. Adding error checks for integer inputs 2. Adding error checks for matrix operations where size requirements do not match up 3. Added matrix subtraction function 4. included error check so only integer is passed into identity function * Create test_matrix_operation.py * Update matrix_ops and Add Test Cases 1. Included error checks in matrix operation. There were some cases where the functions would not work correctly. 2. PEP8 changes to matrix_operations.py 3. added test cases for matrix operations using pytest. * Update pytest.ini Add carriage return to end of file --- matrix/matrix_operation.py | 136 +++++++++++++++++++------- matrix/tests/pytest.ini | 3 + matrix/tests/test_matrix_operation.py | 112 +++++++++++++++++++++ 3 files changed, 217 insertions(+), 34 deletions(-) create mode 100644 matrix/tests/pytest.ini create mode 100644 matrix/tests/test_matrix_operation.py diff --git a/matrix/matrix_operation.py b/matrix/matrix_operation.py index dd7c01582681..b32a4dcf7af3 100644 --- a/matrix/matrix_operation.py +++ b/matrix/matrix_operation.py @@ -1,64 +1,131 @@ -from __future__ import print_function +""" +function based version of matrix operations, which are just 2D arrays +""" + def add(matrix_a, matrix_b): - rows = len(matrix_a) - columns = len(matrix_a[0]) - matrix_c = [] - for i in range(rows): - list_1 = [] - for j in range(columns): - val = matrix_a[i][j] + matrix_b[i][j] - list_1.append(val) - matrix_c.append(list_1) - return matrix_c - -def scalarMultiply(matrix , n): + if _check_not_integer(matrix_a) and _check_not_integer(matrix_b): + rows, cols = _verify_matrix_sizes(matrix_a, matrix_b) + matrix_c = [] + for i in range(rows[0]): + list_1 = [] + for j in range(cols[0]): + val = matrix_a[i][j] + matrix_b[i][j] + list_1.append(val) + matrix_c.append(list_1) + return matrix_c + + +def subtract(matrix_a, matrix_b): + if _check_not_integer(matrix_a) and _check_not_integer(matrix_b): + rows, cols = _verify_matrix_sizes(matrix_a, matrix_b) + matrix_c = [] + for i in range(rows[0]): + list_1 = [] + for j in range(cols[0]): + val = matrix_a[i][j] - matrix_b[i][j] + list_1.append(val) + matrix_c.append(list_1) + return matrix_c + + +def scalar_multiply(matrix, n): return [[x * n for x in row] for row in matrix] + def multiply(matrix_a, matrix_b): - matrix_c = [] - n = len(matrix_a) - for i in range(n): - list_1 = [] - for j in range(n): - val = 0 - for k in range(n): - val = val + matrix_a[i][k] * matrix_b[k][j] - list_1.append(val) - matrix_c.append(list_1) - return matrix_c + if _check_not_integer(matrix_a) and _check_not_integer(matrix_b): + matrix_c = [] + rows, cols = _verify_matrix_sizes(matrix_a, matrix_b) + + if cols[0] != rows[1]: + raise ValueError(f'Cannot multiply matrix of dimensions ({rows[0]},{cols[0]}) ' + f'and ({rows[1]},{cols[1]})') + for i in range(rows[0]): + list_1 = [] + for j in range(cols[1]): + val = 0 + for k in range(cols[1]): + val = val + matrix_a[i][k] * matrix_b[k][j] + list_1.append(val) + matrix_c.append(list_1) + return matrix_c + def identity(n): + """ + :param n: dimension for nxn matrix + :type n: int + :return: Identity matrix of shape [n, n] + """ + n = int(n) return [[int(row == column) for column in range(n)] for row in range(n)] -def transpose(matrix): - return map(list , zip(*matrix)) + +def transpose(matrix, return_map=True): + if _check_not_integer(matrix): + if return_map: + return map(list, zip(*matrix)) + else: + # mt = [] + # for i in range(len(matrix[0])): + # mt.append([row[i] for row in matrix]) + # return mt + return [[row[i] for row in matrix] for i in range(len(matrix[0]))] + def minor(matrix, row, column): minor = matrix[:row] + matrix[row + 1:] minor = [row[:column] + row[column + 1:] for row in minor] return minor + def determinant(matrix): - if len(matrix) == 1: return matrix[0][0] + if len(matrix) == 1: + return matrix[0][0] res = 0 for x in range(len(matrix)): - res += matrix[0][x] * determinant(minor(matrix , 0 , x)) * (-1) ** x + res += matrix[0][x] * determinant(minor(matrix, 0, x)) * (-1) ** x return res + def inverse(matrix): det = determinant(matrix) - if det == 0: return None + if det == 0: + return None - matrixMinor = [[] for _ in range(len(matrix))] + matrix_minor = [[] for _ in range(len(matrix))] for i in range(len(matrix)): for j in range(len(matrix)): - matrixMinor[i].append(determinant(minor(matrix , i , j))) + matrix_minor[i].append(determinant(minor(matrix, i, j))) - cofactors = [[x * (-1) ** (row + col) for col, x in enumerate(matrixMinor[row])] for row in range(len(matrix))] + cofactors = [[x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row])] for row in range(len(matrix))] adjugate = transpose(cofactors) - return scalarMultiply(adjugate , 1/det) + return scalar_multiply(adjugate, 1/det) + + +def _check_not_integer(matrix): + try: + rows = len(matrix) + cols = len(matrix[0]) + return True + except TypeError: + raise TypeError("Cannot input an integer value, it must be a matrix") + + +def _shape(matrix): + return list((len(matrix), len(matrix[0]))) + + +def _verify_matrix_sizes(matrix_a, matrix_b): + shape = _shape(matrix_a) + shape += _shape(matrix_b) + if shape[0] != shape[2] or shape[1] != shape[3]: + raise ValueError(f"operands could not be broadcast together with shape " + f"({shape[0], shape[1]}), ({shape[2], shape[3]})") + return [shape[0], shape[2]], [shape[1], shape[3]] + def main(): matrix_a = [[12, 10], [3, 9]] @@ -68,9 +135,10 @@ def main(): print('Add Operation, %s + %s = %s \n' %(matrix_a, matrix_b, (add(matrix_a, matrix_b)))) print('Multiply Operation, %s * %s = %s \n' %(matrix_a, matrix_b, multiply(matrix_a, matrix_b))) print('Identity: %s \n' %identity(5)) - print('Minor of %s = %s \n' %(matrix_c, minor(matrix_c , 1 , 2))) + print('Minor of %s = %s \n' %(matrix_c, minor(matrix_c, 1, 2))) print('Determinant of %s = %s \n' %(matrix_b, determinant(matrix_b))) print('Inverse of %s = %s\n'%(matrix_d, inverse(matrix_d))) + if __name__ == '__main__': main() diff --git a/matrix/tests/pytest.ini b/matrix/tests/pytest.ini new file mode 100644 index 000000000000..8a978b56ef8b --- /dev/null +++ b/matrix/tests/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + mat_ops: tests for matrix operations diff --git a/matrix/tests/test_matrix_operation.py b/matrix/tests/test_matrix_operation.py new file mode 100644 index 000000000000..8b81b65d0fc8 --- /dev/null +++ b/matrix/tests/test_matrix_operation.py @@ -0,0 +1,112 @@ +""" +Testing here assumes that numpy and linalg is ALWAYS correct!!!! + +If running from PyCharm you can place the following line in "Additional Arguments" for the pytest run configuration +-vv -m mat_ops -p no:cacheprovider +""" + +# standard libraries +import sys +import numpy as np +import pytest +import logging + +# Custom/local libraries +from matrix import matrix_operation as matop + +mat_a = [[12, 10], [3, 9]] +mat_b = [[3, 4], [7, 4]] +mat_c = [[3, 0, 2], [2, 0, -2], [0, 1, 1]] +mat_d = [[3, 0, -2], [2, 0, 2], [0, 1, 1]] +mat_e = [[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]] +mat_f = [1] +mat_h = [2] + +logger = logging.getLogger() +logger.level = logging.DEBUG +stream_handler = logging.StreamHandler(sys.stdout) +logger.addHandler(stream_handler) + + +@pytest.mark.mat_ops +@pytest.mark.parametrize(('mat1', 'mat2'), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), + (mat_f, mat_h)]) +def test_addition(mat1, mat2): + if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2): + with pytest.raises(TypeError): + logger.info(f"\n\t{test_addition.__name__} returned integer") + matop.add(mat1, mat2) + elif (np.array(mat1)).shape == (np.array(mat2)).shape: + logger.info(f"\n\t{test_addition.__name__} with same matrix dims") + act = (np.array(mat1) + np.array(mat2)).tolist() + theo = matop.add(mat1, mat2) + assert theo == act + else: + with pytest.raises(ValueError): + logger.info(f"\n\t{test_addition.__name__} with different matrix dims") + matop.add(mat1, mat2) + + +@pytest.mark.mat_ops +@pytest.mark.parametrize(('mat1', 'mat2'), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), + (mat_f, mat_h)]) +def test_subtraction(mat1, mat2): + if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2): + with pytest.raises(TypeError): + logger.info(f"\n\t{test_subtraction.__name__} returned integer") + matop.subtract(mat1, mat2) + elif (np.array(mat1)).shape == (np.array(mat2)).shape: + logger.info(f"\n\t{test_subtraction.__name__} with same matrix dims") + act = (np.array(mat1) - np.array(mat2)).tolist() + theo = matop.subtract(mat1, mat2) + assert theo == act + else: + with pytest.raises(ValueError): + logger.info(f"\n\t{test_subtraction.__name__} with different matrix dims") + assert matop.subtract(mat1, mat2) + + +@pytest.mark.mat_ops +@pytest.mark.parametrize(('mat1', 'mat2'), [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), + (mat_f, mat_h)]) +def test_multiplication(mat1, mat2): + if (np.array(mat1)).shape < (2, 2) or (np.array(mat2)).shape < (2, 2): + logger.info(f"\n\t{test_multiplication.__name__} returned integer") + with pytest.raises(TypeError): + matop.add(mat1, mat2) + elif (np.array(mat1)).shape == (np.array(mat2)).shape: + logger.info(f"\n\t{test_multiplication.__name__} meets dim requirements") + act = (np.matmul(mat1, mat2)).tolist() + theo = matop.multiply(mat1, mat2) + assert theo == act + else: + with pytest.raises(ValueError): + logger.info(f"\n\t{test_multiplication.__name__} does not meet dim requirements") + assert matop.subtract(mat1, mat2) + + +@pytest.mark.mat_ops +def test_scalar_multiply(): + act = (3.5 * np.array(mat_a)).tolist() + theo = matop.scalar_multiply(mat_a, 3.5) + assert theo == act + + +@pytest.mark.mat_ops +def test_identity(): + act = (np.identity(5)).tolist() + theo = matop.identity(5) + assert theo == act + + +@pytest.mark.mat_ops +@pytest.mark.parametrize('mat', [mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]) +def test_transpose(mat): + if (np.array(mat)).shape < (2, 2): + with pytest.raises(TypeError): + logger.info(f"\n\t{test_transpose.__name__} returned integer") + matop.transpose(mat) + else: + act = (np.transpose(mat)).tolist() + theo = matop.transpose(mat, return_map=False) + assert theo == act From f5e6d4e8cddb920439ed9a94b79f2af9a4fa2ac6 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Sat, 20 Jul 2019 09:36:55 +0200 Subject: [PATCH 092/193] Update DIRECTORY.md (#1046) * Update DIRECTORY.md * Remove blank lines --- DIRECTORY.md | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/DIRECTORY.md b/DIRECTORY.md index 66128228abc3..fc06a8cd1548 100644 --- a/DIRECTORY.md +++ b/DIRECTORY.md @@ -5,6 +5,7 @@ * [newton method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_method.py) * [newton raphson method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_raphson_method.py) ## Backtracking + * [all combinations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_combinations.py) * [all permutations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_permutations.py) * [all subsequences](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_subsequences.py) * [minimax](https://github.com/TheAlgorithms/Python/blob/master/backtracking/minimax.py) @@ -27,7 +28,6 @@ * [morse code implementation](https://github.com/TheAlgorithms/Python/blob/master/ciphers/morse_code_implementation.py) * [onepad cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/onepad_cipher.py) * [playfair cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/playfair_cipher.py) - * [prehistoric men](https://github.com/TheAlgorithms/Python/blob/master/ciphers/prehistoric_men.txt) * [rabin miller](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rabin_miller.py) * [rot13](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rot13.py) * [rsa cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_cipher.py) @@ -39,6 +39,7 @@ * [vigenere cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/vigenere_cipher.py) * [xor cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/xor_cipher.py) ## Compression + * [burrows wheeler](https://github.com/TheAlgorithms/Python/blob/master/compression/burrows_wheeler.py) * [huffman](https://github.com/TheAlgorithms/Python/blob/master/compression/huffman.py) * [peak signal to noise ratio](https://github.com/TheAlgorithms/Python/blob/master/compression/peak_signal_to_noise_ratio.py) * Image Data @@ -100,8 +101,10 @@ ## Dynamic Programming * [abbreviation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/abbreviation.py) * [bitmask](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/bitmask.py) + * [climbing stairs](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/climbing_stairs.py) * [coin change](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/coin_change.py) * [edit distance](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/edit_distance.py) + * [factorial](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/factorial.py) * [fast fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fast_fibonacci.py) * [fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fibonacci.py) * [floyd warshall](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/floyd_warshall.py) @@ -142,9 +145,9 @@ * [eulerian path and circuit for undirected graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/eulerian_path_and_circuit_for_undirected_graph.py) * [even tree](https://github.com/TheAlgorithms/Python/blob/master/graphs/even_tree.py) * [finding bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py) - * [floyd warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/floyd_warshall.py) * [graph list](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py) * [graph matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py) + * [graphs floyd warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/graphs_floyd_warshall.py) * [kahns algorithm long](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_long.py) * [kahns algorithm topo](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_topo.py) * [minimum spanning tree kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal.py) @@ -170,13 +173,17 @@ * [knn sklearn](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/knn_sklearn.py) * [linear regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_regression.py) * [logistic regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/logistic_regression.py) + * [NaiveBayes](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/NaiveBayes.ipynb) * [perceptron](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/perceptron.py) + * [reuters one vs rest classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/reuters_one_vs_rest_classifier.ipynb) * [scoring functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/scoring_functions.py) * Random Forest Classification * [random forest classification](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classification/random_forest_classification.py) + * [random forest classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classification/random_forest_classifier.ipynb) * [Social Network Ads](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classification/Social_Network_Ads.csv) * Random Forest Regression * [Position Salaries](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression/Position_Salaries.csv) + * [random forest regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression/random_forest_regression.ipynb) * [random forest regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression/random_forest_regression.py) ## Maths * [3n+1](https://github.com/TheAlgorithms/Python/blob/master/maths/3n+1.py) @@ -197,7 +204,7 @@ * [find max](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max.py) * [find min](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min.py) * [greater common divisor](https://github.com/TheAlgorithms/Python/blob/master/maths/greater_common_divisor.py) - * [lucas series](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas%20series.py) + * [lucas series](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_series.py) * [modular exponential](https://github.com/TheAlgorithms/Python/blob/master/maths/modular_exponential.py) * [newton raphson](https://github.com/TheAlgorithms/Python/blob/master/maths/newton_raphson.py) * [prime check](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_check.py) @@ -208,6 +215,8 @@ * [volume](https://github.com/TheAlgorithms/Python/blob/master/maths/volume.py) ## Matrix * [matrix operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_operation.py) + * [nth fibonacci using matrix exponentiation](https://github.com/TheAlgorithms/Python/blob/master/matrix/nth_fibonacci_using_matrix_exponentiation.py) + * [rotate matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/rotate_matrix.py) * [searching in sorted matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/searching_in_sorted_matrix.py) * [spiral print](https://github.com/TheAlgorithms/Python/blob/master/matrix/spiral_print.py) ## Networking Flow @@ -216,16 +225,17 @@ ## Neural Network * [back propagation neural network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/back_propagation_neural_network.py) * [convolution neural network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/convolution_neural_network.py) + * [fully connected neural network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/fully_connected_neural_network.ipynb) * [perceptron](https://github.com/TheAlgorithms/Python/blob/master/neural_network/perceptron.py) ## Other * [anagrams](https://github.com/TheAlgorithms/Python/blob/master/other/anagrams.py) * [binary exponentiation](https://github.com/TheAlgorithms/Python/blob/master/other/binary_exponentiation.py) * [binary exponentiation 2](https://github.com/TheAlgorithms/Python/blob/master/other/binary_exponentiation_2.py) * [detecting english programmatically](https://github.com/TheAlgorithms/Python/blob/master/other/detecting_english_programmatically.py) - * [dictionary](https://github.com/TheAlgorithms/Python/blob/master/other/dictionary.txt) * [euclidean gcd](https://github.com/TheAlgorithms/Python/blob/master/other/euclidean_gcd.py) * [finding primes](https://github.com/TheAlgorithms/Python/blob/master/other/finding_primes.py) * [fischer yates shuffle](https://github.com/TheAlgorithms/Python/blob/master/other/fischer_yates_shuffle.py) + * [Food wastage analysis from 1961-2013 (FAO)](https://github.com/TheAlgorithms/Python/blob/master/other/Food%20wastage%20analysis%20from%201961-2013%20(FAO).ipynb) * [frequency finder](https://github.com/TheAlgorithms/Python/blob/master/other/frequency_finder.py) * [game of life](https://github.com/TheAlgorithms/Python/blob/master/other/game_of_life.py) * [linear congruential generator](https://github.com/TheAlgorithms/Python/blob/master/other/linear_congruential_generator.py) @@ -238,8 +248,6 @@ * [two sum](https://github.com/TheAlgorithms/Python/blob/master/other/two_sum.py) * [word patterns](https://github.com/TheAlgorithms/Python/blob/master/other/word_patterns.py) * [words](https://github.com/TheAlgorithms/Python/blob/master/other/words) - * Pycache - * [password generator.cpython-37](https://github.com/TheAlgorithms/Python/blob/master/other/__pycache__/password_generator.cpython-37.pyc) ## Project Euler * Problem 01 * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol1.py) @@ -281,16 +289,13 @@ * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_10/sol1.py) * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_10/sol2.py) * Problem 11 - * [grid](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_11/grid.txt) * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_11/sol1.py) * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_11/sol2.py) * Problem 12 * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_12/sol1.py) * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_12/sol2.py) * Problem 13 - * [num](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_13/num.txt) * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_13/sol1.py) - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_13/sol2.py) * Problem 14 * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_14/sol1.py) * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_14/sol2.py) @@ -309,7 +314,6 @@ * Problem 21 * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_21/sol1.py) * Problem 22 - * [p022 names](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/p022_names.txt) * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/sol1.py) * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/sol2.py) * Problem 234 @@ -337,8 +341,6 @@ * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_53/sol1.py) * Problem 76 * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_76/sol1.py) -## Scripts - * [build directory md](https://github.com/TheAlgorithms/Python/blob/master/scripts/build_directory_md.py) ## Searches * [binary search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py) * [interpolation search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py) @@ -347,7 +349,6 @@ * [quick select](https://github.com/TheAlgorithms/Python/blob/master/searches/quick_select.py) * [sentinel linear search](https://github.com/TheAlgorithms/Python/blob/master/searches/sentinel_linear_search.py) * [tabu search](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_search.py) - * [tabu test data](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_test_data.txt) * [ternary search](https://github.com/TheAlgorithms/Python/blob/master/searches/ternary_search.py) ## Sorts * [bitonic sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bitonic_sort.py) From 61fec83242bb00766141ffa2571414bbba68ab6e Mon Sep 17 00:00:00 2001 From: QuantumNovice <43876848+QuantumNovice@users.noreply.github.com> Date: Sat, 20 Jul 2019 17:32:40 +0500 Subject: [PATCH 093/193] Adds Gaussian Function in maths section (#1054) * Create gaussian.py * Update gaussian.py * Update gaussian.py * Create gaussian.png * Add files via upload * Create prime_factors.py * Update prime_factors.py * Update prime_factors.py --- maths/gaussian.py | 61 ++++++++++++++++++++++++++++++++++++++ maths/images/gaussian.png | Bin 0 -> 53511 bytes maths/prime_factors.py | 52 ++++++++++++++++++++++++++++++++ 3 files changed, 113 insertions(+) create mode 100644 maths/gaussian.py create mode 100644 maths/images/gaussian.png create mode 100644 maths/prime_factors.py diff --git a/maths/gaussian.py b/maths/gaussian.py new file mode 100644 index 000000000000..f3a47a3f6a1b --- /dev/null +++ b/maths/gaussian.py @@ -0,0 +1,61 @@ + +""" +Reference: https://en.wikipedia.org/wiki/Gaussian_function + +python/black : True +python : 3.7.3 + +""" +from numpy import pi, sqrt, exp + + + +def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int: + """ + >>> gaussian(1) + 0.24197072451914337 + + >>> gaussian(24) + 3.342714441794458e-126 + + Supports NumPy Arrays + Use numpy.meshgrid with this to generate gaussian blur on images. + >>> import numpy as np + >>> x = np.arange(15) + >>> gaussian(x) + array([3.98942280e-01, 2.41970725e-01, 5.39909665e-02, 4.43184841e-03, + 1.33830226e-04, 1.48671951e-06, 6.07588285e-09, 9.13472041e-12, + 5.05227108e-15, 1.02797736e-18, 7.69459863e-23, 2.11881925e-27, + 2.14638374e-32, 7.99882776e-38, 1.09660656e-43]) + + >>> gaussian(15) + 5.530709549844416e-50 + + >>> gaussian([1,2, 'string']) + Traceback (most recent call last): + ... + TypeError: unsupported operand type(s) for -: 'list' and 'float' + + >>> gaussian('hello world') + Traceback (most recent call last): + ... + TypeError: unsupported operand type(s) for -: 'str' and 'float' + + >>> gaussian(10**234) # doctest: +IGNORE_EXCEPTION_DETAIL + Traceback (most recent call last): + ... + OverflowError: (34, 'Result too large') + + >>> gaussian(10**-326) + 0.3989422804014327 + + >>> gaussian(2523, mu=234234, sigma=3425) + 0.0 + """ + return 1 / sqrt(2 * pi * sigma ** 2) * exp(-(x - mu) ** 2 / 2 * sigma ** 2) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/maths/images/gaussian.png b/maths/images/gaussian.png new file mode 100644 index 0000000000000000000000000000000000000000..eb007c7e21b2296c17a11ebf4dee30cfa2e2f6b1 GIT 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zoakeE9*4&`V`=D@C{&K`8Zmg2cKSN4JTLy%k(cmZ^C~BN;jyFnoR c|G*9_Xra@hu!OYn0~{O?VNu8VM-vPG0{QMTvj6}9 literal 0 HcmV?d00001 diff --git a/maths/prime_factors.py b/maths/prime_factors.py new file mode 100644 index 000000000000..eb3de00de6a7 --- /dev/null +++ b/maths/prime_factors.py @@ -0,0 +1,52 @@ +""" +python/black : True +""" +from typing import List + + +def prime_factors(n: int) -> List[int]: + """ + Returns prime factors of n as a list. + + >>> prime_factors(0) + [] + >>> prime_factors(100) + [2, 2, 5, 5] + >>> prime_factors(2560) + [2, 2, 2, 2, 2, 2, 2, 2, 2, 5] + >>> prime_factors(10**-2) + [] + >>> prime_factors(0.02) + [] + >>> x = prime_factors(10**241) # doctest: +NORMALIZE_WHITESPACE + >>> x == [2]*241 + [5]*241 + True + >>> prime_factors(10**-354) + [] + >>> prime_factors('hello') + Traceback (most recent call last): + ... + TypeError: '<=' not supported between instances of 'int' and 'str' + >>> prime_factors([1,2,'hello']) + Traceback (most recent call last): + ... + TypeError: '<=' not supported between instances of 'int' and 'list' + + """ + i = 2 + factors = [] + while i * i <= n: + if n % i: + i += 1 + else: + n //= i + factors.append(i) + if n > 1: + factors.append(n) + return factors + + +if __name__ == "__main__": + import doctest + + doctest.testmod() From 5b5beb61d63b97562ba0b5d161b9a2e23fb8bcf5 Mon Sep 17 00:00:00 2001 From: QuantumNovice <43876848+QuantumNovice@users.noreply.github.com> Date: Sat, 20 Jul 2019 20:33:04 +0500 Subject: [PATCH 094/193] Update newton_raphson_method.py (#1057) --- arithmetic_analysis/newton_raphson_method.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/arithmetic_analysis/newton_raphson_method.py b/arithmetic_analysis/newton_raphson_method.py index 5e7e2f930abc..569f96476afc 100644 --- a/arithmetic_analysis/newton_raphson_method.py +++ b/arithmetic_analysis/newton_raphson_method.py @@ -1,5 +1,5 @@ # Implementing Newton Raphson method in Python -# Author: Haseeb +# Author: Syed Haseeb Shah (github.com/QuantumNovice) from sympy import diff from decimal import Decimal @@ -30,7 +30,3 @@ def NewtonRaphson(func, a): # Exponential Roots print ('exp(x) - 1 = 0', NewtonRaphson('exp(x) - 1', 0)) - - - - From 0f0953070750f9f9813ea599e74d04a9c34e5dd2 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Sat, 20 Jul 2019 18:31:08 +0200 Subject: [PATCH 095/193] dijkstra.py: Use r"strings" to fix two pylint warnings (#1052) ``` =============================== warnings summary =============================== graphs/dijkstra.py:81 /home/travis/build/TheAlgorithms/Python/graphs/dijkstra.py:81: DeprecationWarning: invalid escape sequence \ """ graphs/dijkstra.py:97 /home/travis/build/TheAlgorithms/Python/graphs/dijkstra.py:97: DeprecationWarning: invalid escape sequence \ """ -- Docs: https://docs.pytest.org/en/latest/warnings.html =================== 126 passed, 7 warnings in 19.35 seconds ==================== ``` --- graphs/dijkstra.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/graphs/dijkstra.py b/graphs/dijkstra.py index 52354b5c916b..5f09a45cf2c4 100644 --- a/graphs/dijkstra.py +++ b/graphs/dijkstra.py @@ -71,7 +71,7 @@ def dijkstra(graph, start, end): "F": [["C", 3], ["E", 3]], } -""" +r""" Layout of G2: E -- 1 --> B -- 1 --> C -- 1 --> D -- 1 --> F @@ -87,7 +87,7 @@ def dijkstra(graph, start, end): "F": [], } -""" +r""" Layout of G3: E -- 1 --> B -- 1 --> C -- 1 --> D -- 1 --> F From b35f5d971b45ed0740dc6f7a4a8b3a05f516a258 Mon Sep 17 00:00:00 2001 From: John Law Date: Sun, 21 Jul 2019 00:40:00 +0800 Subject: [PATCH 096/193] Update CONTRIBUTING.md (#1059) --- CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 02235ee89973..3202b817f1c5 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -69,7 +69,7 @@ We want your work to be readable by others; therefore, we encourage you to note """ This function sums two integers a and b Return: a + b - """ + """ return a + b ``` From 93fdc9f2a1d64c08e6f1ef5ebbb1470e4ee5c4e2 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Sun, 21 Jul 2019 08:16:28 +0200 Subject: [PATCH 097/193] Travis CI: Add pytest --doctest-modules maths (#1020) * Travis CI: Add pytest --doctest-modules maths * Update lucas_series.py * Update lucas_series.py --- .travis.yml | 4 ---- maths/abs_min.py | 9 ++++++--- maths/binary_exponentiation.py | 17 +++++++++-------- maths/lucas_series.py | 28 ++++++++++++++++++---------- maths/sieve_of_eratosthenes.py | 6 ++---- 5 files changed, 35 insertions(+), 29 deletions(-) diff --git a/.travis.yml b/.travis.yml index a3ff22fb09b7..bea512264c19 100644 --- a/.travis.yml +++ b/.travis.yml @@ -18,10 +18,6 @@ script: --ignore=machine_learning/perceptron.py --ignore=machine_learning/random_forest_classification/random_forest_classification.py --ignore=machine_learning/random_forest_regression/random_forest_regression.py - --ignore=maths/abs_min.py - --ignore=maths/binary_exponentiation.py - --ignore=maths/lucas_series.py - --ignore=maths/sieve_of_eratosthenes.py after_success: - python scripts/build_directory_md.py - cat DIRECTORY.md diff --git a/maths/abs_min.py b/maths/abs_min.py index d546196aa1b5..abb0c9051b7d 100644 --- a/maths/abs_min.py +++ b/maths/abs_min.py @@ -1,10 +1,11 @@ -from abs import abs_val +from .abs import abs_val + def absMin(x): """ - # >>>absMin([0,5,1,11]) + >>> absMin([0,5,1,11]) 0 - # >>absMin([3,-10,-2]) + >>> absMin([3,-10,-2]) -2 """ j = x[0] @@ -13,9 +14,11 @@ def absMin(x): j = i return j + def main(): a = [-3,-1,2,-11] print(absMin(a)) # = -1 + if __name__ == '__main__': main() \ No newline at end of file diff --git a/maths/binary_exponentiation.py b/maths/binary_exponentiation.py index cf789afc6f22..a8d736adfea0 100644 --- a/maths/binary_exponentiation.py +++ b/maths/binary_exponentiation.py @@ -17,11 +17,12 @@ def binary_exponentiation(a, n): return b * b -try: - BASE = int(input('Enter Base : ')) - POWER = int(input("Enter Power : ")) -except ValueError: - print("Invalid literal for integer") - -RESULT = binary_exponentiation(BASE, POWER) -print("{}^({}) : {}".format(BASE, POWER, RESULT)) +if __name__ == "__main__": + try: + BASE = int(input("Enter Base : ").strip()) + POWER = int(input("Enter Power : ").strip()) + except ValueError: + print("Invalid literal for integer") + + RESULT = binary_exponentiation(BASE, POWER) + print("{}^({}) : {}".format(BASE, POWER, RESULT)) diff --git a/maths/lucas_series.py b/maths/lucas_series.py index 91ea1ba72a56..9ae437dc9f54 100644 --- a/maths/lucas_series.py +++ b/maths/lucas_series.py @@ -1,13 +1,21 @@ # Lucas Sequence Using Recursion def recur_luc(n): - if n == 1: - return n - if n == 0: - return 2 - return (recur_luc(n-1) + recur_luc(n-2)) - -limit = int(input("How many terms to include in Lucas series:")) -print("Lucas series:") -for i in range(limit): - print(recur_luc(i)) + """ + >>> recur_luc(1) + 1 + >>> recur_luc(0) + 2 + """ + if n == 1: + return n + if n == 0: + return 2 + return recur_luc(n - 1) + recur_luc(n - 2) + + +if __name__ == "__main__": + limit = int(input("How many terms to include in Lucas series:")) + print("Lucas series:") + for i in range(limit): + print(recur_luc(i)) diff --git a/maths/sieve_of_eratosthenes.py b/maths/sieve_of_eratosthenes.py index 11c123693694..cedd04f92aa0 100644 --- a/maths/sieve_of_eratosthenes.py +++ b/maths/sieve_of_eratosthenes.py @@ -2,9 +2,6 @@ import math -N = int(input("Enter n: ")) - - def sieve(n): """Sieve of Eratosthones.""" l = [True] * (n + 1) @@ -26,4 +23,5 @@ def sieve(n): return prime -print(sieve(N)) +if __name__ == "__main__": + print(sieve(int(input("Enter n: ").strip()))) From c964d743b6275e31548f605d5b2d583656960de0 Mon Sep 17 00:00:00 2001 From: QuantumNovice <43876848+QuantumNovice@users.noreply.github.com> Date: Sun, 21 Jul 2019 13:35:42 +0500 Subject: [PATCH 098/193] Added Mobius Function (#1058) * Add files via upload * Update mobius_function.py * Add files via upload * Add files via upload * Add files via upload * Add files via upload * Add files via upload * Add files via upload * Update mobius_function.py * Delete mobius_function.py * Add files via upload --- maths/is_square_free.py | 39 ++++++++++++++++++++++++++++++++++++ maths/mobius_function.py | 43 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 82 insertions(+) create mode 100644 maths/is_square_free.py create mode 100644 maths/mobius_function.py diff --git a/maths/is_square_free.py b/maths/is_square_free.py new file mode 100644 index 000000000000..acc13fa5f833 --- /dev/null +++ b/maths/is_square_free.py @@ -0,0 +1,39 @@ +""" +References: wikipedia:square free number +python/black : True +flake8 : True +""" +from typing import List + + +def is_square_free(factors: List[int]) -> bool: + """ + # doctest: +NORMALIZE_WHITESPACE + This functions takes a list of prime factors as input. + returns True if the factors are square free. + >>> is_square_free([1, 1, 2, 3, 4]) + False + + These are wrong but should return some value + it simply checks for repition in the numbers. + >>> is_square_free([1, 3, 4, 'sd', 0.0]) + True + + >>> is_square_free([1, 0.5, 2, 0.0]) + True + >>> is_square_free([1, 2, 2, 5]) + False + >>> is_square_free('asd') + True + >>> is_square_free(24) + Traceback (most recent call last): + ... + TypeError: 'int' object is not iterable + """ + return len(set(factors)) == len(factors) + + +if __name__ == "__main__": + import doctest + + doctest.testmod() diff --git a/maths/mobius_function.py b/maths/mobius_function.py new file mode 100644 index 000000000000..15fb3d4380f4 --- /dev/null +++ b/maths/mobius_function.py @@ -0,0 +1,43 @@ +""" +Refrences: https://en.wikipedia.org/wiki/M%C3%B6bius_function +References: wikipedia:square free number +python/black : True +flake8 : True +""" + +from maths.prime_factors import prime_factors +from maths.is_square_free import is_square_free + + +def mobius(n: int) -> int: + """ + Mobius function + >>> mobius(24) + 0 + >>> mobius(-1) + 1 + >>> mobius('asd') + Traceback (most recent call last): + ... + TypeError: '<=' not supported between instances of 'int' and 'str' + >>> mobius(10**400) + 0 + >>> mobius(10**-400) + 1 + >>> mobius(-1424) + 1 + >>> mobius([1, '2', 2.0]) + Traceback (most recent call last): + ... + TypeError: '<=' not supported between instances of 'int' and 'list' + """ + factors = prime_factors(n) + if is_square_free(factors): + return -1 if len(factors) % 2 else 1 + return 0 + + +if __name__ == "__main__": + import doctest + + doctest.testmod() From 05e567c2f92dc746969fe859cd806d5928067f52 Mon Sep 17 00:00:00 2001 From: QuantumNovice <43876848+QuantumNovice@users.noreply.github.com> Date: Sun, 21 Jul 2019 16:03:39 +0500 Subject: [PATCH 099/193] Code to change contrast (#1060) * Add files via upload * Update requirements.txt * Add files via upload * Add files via upload * Add files via upload * Add files via upload --- digital_image_processing/change_contrast.py | 35 +++++++++++++++++++++ requirements.txt | 1 + 2 files changed, 36 insertions(+) create mode 100644 digital_image_processing/change_contrast.py diff --git a/digital_image_processing/change_contrast.py b/digital_image_processing/change_contrast.py new file mode 100644 index 000000000000..76f1a3e1fcd8 --- /dev/null +++ b/digital_image_processing/change_contrast.py @@ -0,0 +1,35 @@ +""" +Changing contrast with PIL + +This algorithm is used in +https://noivce.pythonanywhere.com/ python web app. + +python/black: True +flake8 : True +""" + +from PIL import Image + + +def change_contrast(img: Image, level: float) -> Image: + """ + Function to change contrast + """ + factor = (259 * (level + 255)) / (255 * (259 - level)) + + def contrast(c: int) -> float: + """ + Fundamental Transformation/Operation that'll be performed on + every bit. + """ + return 128 + factor * (c - 128) + + return img.point(contrast) + + +if __name__ == "__main__": + # Load image + with Image.open("image_data/lena.jpg") as img: + # Change contrast to 170 + cont_img = change_contrast(img, 170) + cont_img.save("image_data/lena_high_contrast.png", format="png") diff --git a/requirements.txt b/requirements.txt index 91d3df33323d..a3e62cf968f7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,6 +5,7 @@ mypy numpy opencv-python pandas +pillow pytest sklearn sympy From b2ed8d443c03bd9fa8cc523bcefcca4eeff04c2e Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Tue, 23 Jul 2019 00:07:09 +0200 Subject: [PATCH 100/193] rotate_matrix.py: Add type hints for return values (#1023) * rotate_matrix.py: Add type hints for return values @obelisk0114 Your review please? * Fix typo * Run the code thru python/black https://github.com/python/black * Fix 270 comment * Simplify with get_data() and test the alternatives * ) * 3 * Update rotate_matrix.py * Update rotate_matrix.py --- matrix/rotate_matrix.py | 115 ++++++++++++++++++++-------------------- 1 file changed, 58 insertions(+), 57 deletions(-) diff --git a/matrix/rotate_matrix.py b/matrix/rotate_matrix.py index e3495e647954..822851826121 100644 --- a/matrix/rotate_matrix.py +++ b/matrix/rotate_matrix.py @@ -1,99 +1,100 @@ # -*- coding: utf-8 -*- """ - In this problem, we want to rotate the matrix elements by 90, 180, 270 (counterclockwise) - Discussion in stackoverflow: - https://stackoverflow.com/questions/42519/how-do-you-rotate-a-two-dimensional-array +In this problem, we want to rotate the matrix elements by 90, 180, 270 (counterclockwise) +Discussion in stackoverflow: +https://stackoverflow.com/questions/42519/how-do-you-rotate-a-two-dimensional-array """ -def rotate_90(matrix: [[]]): +def make_matrix(row_size: int = 4) -> [[int]]: """ - >>> rotate_90([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) - [[4, 8, 12, 16], [3, 7, 11, 15], [2, 6, 10, 14], [1, 5, 9, 13]] + >>> make_matrix() + [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]] + >>> make_matrix(1) + [[1]] + >>> make_matrix(-2) + [[1, 2], [3, 4]] + >>> make_matrix(3) + [[1, 2, 3], [4, 5, 6], [7, 8, 9]] + >>> make_matrix() == make_matrix(4) + True """ - - transpose(matrix) - reverse_row(matrix) - return matrix - + row_size = abs(row_size) or 4 + return [[1 + x + y * row_size for x in range(row_size)] for y in range(row_size)] + -def rotate_180(matrix: [[]]): +def rotate_90(matrix: [[]]) -> [[]]: """ - >>> rotate_180([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) - [[16, 15, 14, 13], [12, 11, 10, 9], [8, 7, 6, 5], [4, 3, 2, 1]] + >>> rotate_90(make_matrix()) + [[4, 8, 12, 16], [3, 7, 11, 15], [2, 6, 10, 14], [1, 5, 9, 13]] + >>> rotate_90(make_matrix()) == transpose(reverse_column(make_matrix())) + True """ - - reverse_column(matrix) - reverse_row(matrix) - + + return reverse_row(transpose(matrix)) + # OR.. transpose(reverse_column(matrix)) + + +def rotate_180(matrix: [[]]) -> [[]]: """ - OR - - reverse_row(matrix) - reverse_column(matrix) + >>> rotate_180(make_matrix()) + [[16, 15, 14, 13], [12, 11, 10, 9], [8, 7, 6, 5], [4, 3, 2, 1]] + >>> rotate_180(make_matrix()) == reverse_column(reverse_row(make_matrix())) + True """ - - return matrix - -def rotate_270(matrix: [[]]): + return reverse_row(reverse_column(matrix)) + # OR.. reverse_column(reverse_row(matrix)) + + +def rotate_270(matrix: [[]]) -> [[]]: """ - >>> rotate_270([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) + >>> rotate_270(make_matrix()) [[13, 9, 5, 1], [14, 10, 6, 2], [15, 11, 7, 3], [16, 12, 8, 4]] + >>> rotate_270(make_matrix()) == transpose(reverse_row(make_matrix())) + True """ - - transpose(matrix) - reverse_column(matrix) - - """ - OR - - reverse_row(matrix) - transpose(matrix) - """ - - return matrix + return reverse_column(transpose(matrix)) + # OR.. transpose(reverse_row(matrix)) -def transpose(matrix: [[]]): + +def transpose(matrix: [[]]) -> [[]]: matrix[:] = [list(x) for x in zip(*matrix)] return matrix - - -def reverse_row(matrix: [[]]): + + +def reverse_row(matrix: [[]]) -> [[]]: matrix[:] = matrix[::-1] return matrix -def reverse_column(matrix: [[]]): +def reverse_column(matrix: [[]]) -> [[]]: matrix[:] = [x[::-1] for x in matrix] return matrix - - -def print_matrix(matrix: [[]]): + + +def print_matrix(matrix: [[]]) -> [[]]: for i in matrix: print(*i) -if __name__ == '__main__': - matrix = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]] +if __name__ == "__main__": + matrix = make_matrix() print("\norigin:\n") print_matrix(matrix) - rotate_90(matrix) print("\nrotate 90 counterclockwise:\n") - print_matrix(matrix) + print_matrix(rotate_90(matrix)) - matrix = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]] + matrix = make_matrix() print("\norigin:\n") print_matrix(matrix) - rotate_180(matrix) print("\nrotate 180:\n") - print_matrix(matrix) + print_matrix(rotate_180(matrix)) - matrix = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]] + matrix = make_matrix() print("\norigin:\n") print_matrix(matrix) - rotate_270(matrix) print("\nrotate 270 counterclockwise:\n") - print_matrix(matrix) + print_matrix(rotate_270(matrix)) From 7c3ef9885393681c83c68e9733fa2a5fd9651203 Mon Sep 17 00:00:00 2001 From: "Md. Mahbubur Rahman" Date: Wed, 24 Jul 2019 18:32:05 +0900 Subject: [PATCH 101/193] Implement ruling hash to appropriate complexity of Rabin Karp (#1066) * Added matrix exponentiation approach for finding fibonacci number. * Implemented the way of finding nth fibonacci. * Complexity is about O(log(n)*8) * Updated the matrix exponentiation approach of finding nth fibonacci. - Removed some extra spaces - Added the complexity of bruteforce algorithm - Removed unused function called zerro() - Added some docktest based on request * Updated the matrix exponentiation approach of finding nth fibonacci. - Removed some extra spaces - Added the complexity of bruteforce algorithm - Removed unused function called zerro() - Added some docktest based on request * Updated Rabin Karp algorithm. - Previous solution is based on the hash function of python. - Implemented ruling hash to get the appropriate complexity of rabin karp. * Updated Rabin Karp algorithm. - Previous solution is based on the hash function of python. - Implemented ruling hash to get the appropriate complexity of rabin karp. * Implemented ruling hash to appropriate complexity of Rabin Karp Added unit pattern testing --- strings/rabin_karp.py | 48 +++++++++++++++++++++++++++++++++++-------- 1 file changed, 39 insertions(+), 9 deletions(-) diff --git a/strings/rabin_karp.py b/strings/rabin_karp.py index 04a849266ead..7c36f7659e24 100644 --- a/strings/rabin_karp.py +++ b/strings/rabin_karp.py @@ -1,6 +1,11 @@ +# Numbers of alphabet which we call base +alphabet_size = 256 +# Modulus to hash a string +modulus = 1000003 + + def rabin_karp(pattern, text): """ - The Rabin-Karp Algorithm for finding a pattern within a piece of text with complexity O(nm), most efficient when it is used with multiple patterns as it is able to check if any of a set of patterns match a section of text in o(1) given the precomputed hashes. @@ -12,22 +17,42 @@ def rabin_karp(pattern, text): 2) Step through the text one character at a time passing a window with the same length as the pattern calculating the hash of the text within the window compare it with the hash of the pattern. Only testing equality if the hashes match - """ p_len = len(pattern) - p_hash = hash(pattern) + t_len = len(text) + if p_len > t_len: + return False + + p_hash = 0 + text_hash = 0 + modulus_power = 1 - for i in range(0, len(text) - (p_len - 1)): + # Calculating the hash of pattern and substring of text + for i in range(p_len): + p_hash = (ord(pattern[i]) + p_hash * alphabet_size) % modulus + text_hash = (ord(text[i]) + text_hash * alphabet_size) % modulus + if i == p_len - 1: + continue + modulus_power = (modulus_power * alphabet_size) % modulus - # written like this t - text_hash = hash(text[i:i + p_len]) - if text_hash == p_hash and \ - text[i:i + p_len] == pattern: + for i in range(0, t_len - p_len + 1): + if text_hash == p_hash and text[i : i + p_len] == pattern: return True + if i == t_len - p_len: + continue + # Calculating the ruling hash + text_hash = ( + (text_hash - ord(text[i]) * modulus_power) * alphabet_size + + ord(text[i + p_len]) + ) % modulus return False -if __name__ == '__main__': +def test_rabin_karp(): + """ + >>> test_rabin_karp() + Success. + """ # Test 1) pattern = "abc1abc12" text1 = "alskfjaldsabc1abc1abc12k23adsfabcabc" @@ -48,3 +73,8 @@ def rabin_karp(pattern, text): pattern = "abcdabcy" text = "abcxabcdabxabcdabcdabcy" assert rabin_karp(pattern, text) + print("Success.") + + +if __name__ == "__main__": + test_rabin_karp() From 46bcee0978fe507891a8e3b8892437fafed28c08 Mon Sep 17 00:00:00 2001 From: QuantumNovice <43876848+QuantumNovice@users.noreply.github.com> Date: Wed, 24 Jul 2019 17:34:22 +0500 Subject: [PATCH 102/193] Add badges to the top of README.md (#1064) * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md --- README.md | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 30eccd361673..d4f4acbadb6d 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,11 @@ # The Algorithms - Python - -[![Donate](https://img.shields.io/badge/Donate-PayPal-green.svg)](https://www.paypal.me/TheAlgorithms/100)   -[![Gitter chat](https://badges.gitter.im/gitterHQ/gitter.png)](https://gitter.im/TheAlgorithms)   -[![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg)](https://gitpod.io/#https://github.com/TheAlgorithms/Python) - +[![Donate](https://img.shields.io/badge/Donate-PayPal-green.svg?logo=paypal&style=flat-square)](https://www.paypal.me/TheAlgorithms/100)  +[![Build Status](https://img.shields.io/travis/TheAlgorithms/Python.svg?label=Travis%20CI&logo=travis&style=flat-square)](https://travis-ci.org/TheAlgorithms/Python)  +[![LGTM](https://img.shields.io/lgtm/alerts/github/TheAlgorithms/Python.svg?label=LGTM&logo=LGTM&style=flat-square)](https://lgtm.com/projects/g/TheAlgorithms/Python/alerts)  +[![Gitter chat](https://img.shields.io/badge/Chat-Gitter-ff69b4.svg?label=Chat&logo=gitter&style=flat-square)](https://gitter.im/TheAlgorithms)  +[![contributions welcome](https://img.shields.io/static/v1.svg?label=Contributions&message=Welcome&color=0059b3&style=flat-square)](https://github.com/TheAlgorithms/Python/blob/master/CONTRIBUTING.md)  +![](https://img.shields.io/github/repo-size/TheAlgorithms/Python.svg?label=Repo%20size&style=flat-square)  + ### All algorithms implemented in Python (for education) These implementations are for learning purposes. They may be less efficient than the implementations in the Python standard library. @@ -24,6 +26,8 @@ Chetan Kaushik Read our [Contribution Guidelines](CONTRIBUTING.md) before you contribute. +[![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg?style=flat-square)](https://gitpod.io/#https://github.com/TheAlgorithms/Python) + ## Community Channel We're on [Gitter](https://gitter.im/TheAlgorithms)! Please join us. From 3c8e9314b6b36b3721d9df50ffd7dc20bfb2fc7d Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Thu, 25 Jul 2019 09:49:00 +0200 Subject: [PATCH 103/193] Travis CI: Add a flake8 test for unused imports (#1038) --- .travis.yml | 2 +- graphs/bfs.py | 21 +- maths/volume.py | 10 +- other/primelib.py | 390 +++++++++++++++---------------- project_euler/problem_13/sol1.py | 10 +- 5 files changed, 212 insertions(+), 221 deletions(-) diff --git a/.travis.yml b/.travis.yml index bea512264c19..6d432c660ddd 100644 --- a/.travis.yml +++ b/.travis.yml @@ -6,7 +6,7 @@ before_install: pip install --upgrade pip setuptools install: pip install -r requirements.txt before_script: - black --check . || true - - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + - flake8 . --count --select=E9,F401,F63,F7,F82 --show-source --statistics script: - mypy --ignore-missing-imports . - pytest . --doctest-modules diff --git a/graphs/bfs.py b/graphs/bfs.py index 6bbdd9e25435..ebbde0c82ce6 100644 --- a/graphs/bfs.py +++ b/graphs/bfs.py @@ -16,10 +16,19 @@ """ -import collections +G = {'A': ['B', 'C'], + 'B': ['A', 'D', 'E'], + 'C': ['A', 'F'], + 'D': ['B'], + 'E': ['B', 'F'], + 'F': ['C', 'E']} def bfs(graph, start): + """ + >>> ''.join(sorted(bfs(G, 'A'))) + 'ABCDEF' + """ explored, queue = set(), [start] # collections.deque([start]) explored.add(start) while queue: @@ -31,11 +40,5 @@ def bfs(graph, start): return explored -G = {'A': ['B', 'C'], - 'B': ['A', 'D', 'E'], - 'C': ['A', 'F'], - 'D': ['B'], - 'E': ['B', 'F'], - 'F': ['C', 'E']} - -print(bfs(G, 'A')) +if __name__ == '__main__': + print(bfs(G, 'A')) diff --git a/maths/volume.py b/maths/volume.py index 171bc538f5a4..38de7516d9b2 100644 --- a/maths/volume.py +++ b/maths/volume.py @@ -6,8 +6,6 @@ from math import pi -PI = pi - def vol_cube(side_length): """Calculate the Volume of a Cube.""" @@ -39,9 +37,7 @@ def vol_right_circ_cone(radius, height): volume = (1/3) * pi * radius^2 * height """ - import math - - return (float(1) / 3) * PI * (radius ** 2) * height + return (float(1) / 3) * pi * (radius ** 2) * height def vol_prism(area_of_base, height): @@ -71,7 +67,7 @@ def vol_sphere(radius): V = (4/3) * pi * r^3 Wikipedia reference: https://en.wikipedia.org/wiki/Sphere """ - return (float(4) / 3) * PI * radius ** 3 + return (float(4) / 3) * pi * radius ** 3 def vol_circular_cylinder(radius, height): @@ -80,7 +76,7 @@ def vol_circular_cylinder(radius, height): Wikipedia reference: https://en.wikipedia.org/wiki/Cylinder volume = pi * radius^2 * height """ - return PI * radius ** 2 * height + return pi * radius ** 2 * height def main(): diff --git a/other/primelib.py b/other/primelib.py index c371bc1b9861..c000213a7a42 100644 --- a/other/primelib.py +++ b/other/primelib.py @@ -16,7 +16,7 @@ greatestPrimeFactor(number) smallestPrimeFactor(number) getPrime(n) -getPrimesBetween(pNumber1, pNumber2) +getPrimesBetween(pNumber1, pNumber2) ---- @@ -39,34 +39,36 @@ """ +from math import sqrt + + def isPrime(number): """ input: positive integer 'number' returns true if 'number' is prime otherwise false. """ - import math # for function sqrt - + # precondition assert isinstance(number,int) and (number >= 0) , \ "'number' must been an int and positive" - + status = True - - # 0 and 1 are none primes. + + # 0 and 1 are none primes. if number <= 1: status = False - - for divisor in range(2,int(round(math.sqrt(number)))+1): - + + for divisor in range(2,int(round(sqrt(number)))+1): + # if 'number' divisible by 'divisor' then sets 'status' - # of false and break up the loop. + # of false and break up the loop. if number % divisor == 0: status = False break - + # precondition - assert isinstance(status,bool), "'status' must been from type bool" - + assert isinstance(status,bool), "'status' must been from type bool" + return status # ------------------------------------------ @@ -75,37 +77,37 @@ def sieveEr(N): """ input: positive integer 'N' > 2 returns a list of prime numbers from 2 up to N. - + This function implements the algorithm called - sieve of erathostenes. - + sieve of erathostenes. + """ - + # precondition assert isinstance(N,int) and (N > 2), "'N' must been an int and > 2" - + # beginList: conatins all natural numbers from 2 upt to N beginList = [x for x in range(2,N+1)] - ans = [] # this list will be returns. - + ans = [] # this list will be returns. + # actual sieve of erathostenes for i in range(len(beginList)): - + for j in range(i+1,len(beginList)): - + if (beginList[i] != 0) and \ (beginList[j] % beginList[i] == 0): beginList[j] = 0 - - # filters actual prime numbers. + + # filters actual prime numbers. ans = [x for x in beginList if x != 0] - + # precondition - assert isinstance(ans,list), "'ans' must been from type list" - + assert isinstance(ans,list), "'ans' must been from type list" + return ans - + # -------------------------------- @@ -114,203 +116,201 @@ def getPrimeNumbers(N): input: positive integer 'N' > 2 returns a list of prime numbers from 2 up to N (inclusive) This function is more efficient as function 'sieveEr(...)' - """ - + """ + # precondition assert isinstance(N,int) and (N > 2), "'N' must been an int and > 2" - - ans = [] - - # iterates over all numbers between 2 up to N+1 + + ans = [] + + # iterates over all numbers between 2 up to N+1 # if a number is prime then appends to list 'ans' for number in range(2,N+1): - + if isPrime(number): - + ans.append(number) - + # precondition assert isinstance(ans,list), "'ans' must been from type list" - + return ans # ----------------------------------------- - + def primeFactorization(number): """ - input: positive integer 'number' + input: positive integer 'number' returns a list of the prime number factors of 'number' """ - import math # for function sqrt - # precondition assert isinstance(number,int) and number >= 0, \ "'number' must been an int and >= 0" - + ans = [] # this list will be returns of the function. # potential prime number factors. - factor = 2 + factor = 2 quotient = number - - + + if number == 0 or number == 1: - + ans.append(number) - - # if 'number' not prime then builds the prime factorization of 'number' + + # if 'number' not prime then builds the prime factorization of 'number' elif not isPrime(number): - + while (quotient != 1): - + if isPrime(factor) and (quotient % factor == 0): ans.append(factor) quotient /= factor else: factor += 1 - + else: ans.append(number) - + # precondition - assert isinstance(ans,list), "'ans' must been from type list" - + assert isinstance(ans,list), "'ans' must been from type list" + return ans - + # ----------------------------------------- - + def greatestPrimeFactor(number): """ input: positive integer 'number' >= 0 returns the greatest prime number factor of 'number' """ - + # precondition assert isinstance(number,int) and (number >= 0), \ "'number' bust been an int and >= 0" - - ans = 0 - + + ans = 0 + # prime factorization of 'number' primeFactors = primeFactorization(number) - ans = max(primeFactors) - + ans = max(primeFactors) + # precondition - assert isinstance(ans,int), "'ans' must been from type int" - + assert isinstance(ans,int), "'ans' must been from type int" + return ans - + # ---------------------------------------------- - - + + def smallestPrimeFactor(number): """ input: integer 'number' >= 0 returns the smallest prime number factor of 'number' """ - + # precondition assert isinstance(number,int) and (number >= 0), \ "'number' bust been an int and >= 0" - - ans = 0 - + + ans = 0 + # prime factorization of 'number' primeFactors = primeFactorization(number) - + ans = min(primeFactors) # precondition - assert isinstance(ans,int), "'ans' must been from type int" - + assert isinstance(ans,int), "'ans' must been from type int" + return ans - - + + # ---------------------- - + def isEven(number): """ input: integer 'number' returns true if 'number' is even, otherwise false. - """ + """ # precondition - assert isinstance(number, int), "'number' must been an int" + assert isinstance(number, int), "'number' must been an int" assert isinstance(number % 2 == 0, bool), "compare bust been from type bool" - + return number % 2 == 0 - + # ------------------------ - + def isOdd(number): """ input: integer 'number' returns true if 'number' is odd, otherwise false. - """ + """ # precondition - assert isinstance(number, int), "'number' must been an int" + assert isinstance(number, int), "'number' must been an int" assert isinstance(number % 2 != 0, bool), "compare bust been from type bool" - + return number % 2 != 0 - + # ------------------------ - - + + def goldbach(number): """ Goldbach's assumption input: a even positive integer 'number' > 2 returns a list of two prime numbers whose sum is equal to 'number' """ - + # precondition assert isinstance(number,int) and (number > 2) and isEven(number), \ "'number' must been an int, even and > 2" - + ans = [] # this list will returned - + # creates a list of prime numbers between 2 up to 'number' primeNumbers = getPrimeNumbers(number) - lenPN = len(primeNumbers) + lenPN = len(primeNumbers) # run variable for while-loops. i = 0 j = None - + # exit variable. for break up the loops loop = True - + while (i < lenPN and loop): - + j = i+1 - - + + while (j < lenPN and loop): - + if primeNumbers[i] + primeNumbers[j] == number: loop = False ans.append(primeNumbers[i]) ans.append(primeNumbers[j]) - + j += 1 i += 1 - + # precondition assert isinstance(ans,list) and (len(ans) == 2) and \ (ans[0] + ans[1] == number) and isPrime(ans[0]) and isPrime(ans[1]), \ "'ans' must contains two primes. And sum of elements must been eq 'number'" - + return ans - + # ---------------------------------------------- def gcd(number1,number2): @@ -319,173 +319,173 @@ def gcd(number1,number2): input: two positive integer 'number1' and 'number2' returns the greatest common divisor of 'number1' and 'number2' """ - + # precondition assert isinstance(number1,int) and isinstance(number2,int) \ and (number1 >= 0) and (number2 >= 0), \ "'number1' and 'number2' must been positive integer." - rest = 0 - + rest = 0 + while number2 != 0: - + rest = number1 % number2 number1 = number2 number2 = rest # precondition assert isinstance(number1,int) and (number1 >= 0), \ - "'number' must been from type int and positive" - + "'number' must been from type int and positive" + return number1 - + # ---------------------------------------------------- - + def kgV(number1, number2): """ Least common multiple input: two positive integer 'number1' and 'number2' returns the least common multiple of 'number1' and 'number2' """ - + # precondition assert isinstance(number1,int) and isinstance(number2,int) \ and (number1 >= 1) and (number2 >= 1), \ "'number1' and 'number2' must been positive integer." - + ans = 1 # actual answer that will be return. - + # for kgV (x,1) if number1 > 1 and number2 > 1: - + # builds the prime factorization of 'number1' and 'number2' primeFac1 = primeFactorization(number1) primeFac2 = primeFactorization(number2) - + elif number1 == 1 or number2 == 1: - + primeFac1 = [] primeFac2 = [] ans = max(number1,number2) - + count1 = 0 count2 = 0 - + done = [] # captured numbers int both 'primeFac1' and 'primeFac2' - + # iterates through primeFac1 for n in primeFac1: - + if n not in done: - + if n in primeFac2: - + count1 = primeFac1.count(n) count2 = primeFac2.count(n) - + for i in range(max(count1,count2)): ans *= n - + else: - + count1 = primeFac1.count(n) - + for i in range(count1): ans *= n - + done.append(n) - + # iterates through primeFac2 for n in primeFac2: - + if n not in done: - + count2 = primeFac2.count(n) - + for i in range(count2): ans *= n - + done.append(n) - + # precondition assert isinstance(ans,int) and (ans >= 0), \ - "'ans' must been from type int and positive" - + "'ans' must been from type int and positive" + return ans - + # ---------------------------------- - + def getPrime(n): """ Gets the n-th prime number. input: positive integer 'n' >= 0 returns the n-th prime number, beginning at index 0 """ - + # precondition assert isinstance(n,int) and (n >= 0), "'number' must been a positive int" - + index = 0 ans = 2 # this variable holds the answer - + while index < n: - + index += 1 - - ans += 1 # counts to the next number - + + ans += 1 # counts to the next number + # if ans not prime then - # runs to the next prime number. + # runs to the next prime number. while not isPrime(ans): ans += 1 - + # precondition assert isinstance(ans,int) and isPrime(ans), \ - "'ans' must been a prime number and from type int" - + "'ans' must been a prime number and from type int" + return ans - + # --------------------------------------------------- - + def getPrimesBetween(pNumber1, pNumber2): """ input: prime numbers 'pNumber1' and 'pNumber2' pNumber1 < pNumber2 returns a list of all prime numbers between 'pNumber1' (exclusiv) - and 'pNumber2' (exclusiv) + and 'pNumber2' (exclusiv) """ - + # precondition assert isPrime(pNumber1) and isPrime(pNumber2) and (pNumber1 < pNumber2), \ "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'" - + number = pNumber1 + 1 # jump to the next number - + ans = [] # this list will be returns. - + # if number is not prime then - # fetch the next prime number. + # fetch the next prime number. while not isPrime(number): number += 1 - + while number < pNumber2: - + ans.append(number) - + number += 1 - - # fetch the next prime number. + + # fetch the next prime number. while not isPrime(number): number += 1 - + # precondition assert isinstance(ans,list) and ans[0] != pNumber1 \ and ans[len(ans)-1] != pNumber2, \ "'ans' must been a list without the arguments" - + # 'ans' contains not 'pNumber1' and 'pNumber2' ! return ans - + # ---------------------------------------------------- def getDivisors(n): @@ -493,25 +493,23 @@ def getDivisors(n): input: positive integer 'n' >= 1 returns all divisors of n (inclusive 1 and 'n') """ - + # precondition assert isinstance(n,int) and (n >= 1), "'n' must been int and >= 1" - from math import sqrt - ans = [] # will be returned. - + for divisor in range(1,n+1): - + if n % divisor == 0: ans.append(divisor) - - + + #precondition assert ans[0] == 1 and ans[len(ans)-1] == n, \ "Error in function getDivisiors(...)" - - + + return ans @@ -523,18 +521,18 @@ def isPerfectNumber(number): input: positive integer 'number' > 1 returns true if 'number' is a perfect number otherwise false. """ - + # precondition assert isinstance(number,int) and (number > 1), \ "'number' must been an int and >= 1" - + divisors = getDivisors(number) - + # precondition assert isinstance(divisors,list) and(divisors[0] == 1) and \ (divisors[len(divisors)-1] == number), \ "Error in help-function getDivisiors(...)" - + # summed all divisors up to 'number' (exclusive), hence [:-1] return sum(divisors[:-1]) == number @@ -545,13 +543,13 @@ def simplifyFraction(numerator, denominator): input: two integer 'numerator' and 'denominator' assumes: 'denominator' != 0 returns: a tuple with simplify numerator and denominator. - """ - + """ + # precondition assert isinstance(numerator, int) and isinstance(denominator,int) \ and (denominator != 0), \ "The arguments must been from type int and 'denominator' != 0" - + # build the greatest common divisor of numerator and denominator. gcdOfFraction = gcd(abs(numerator), abs(denominator)) @@ -559,46 +557,46 @@ def simplifyFraction(numerator, denominator): assert isinstance(gcdOfFraction, int) and (numerator % gcdOfFraction == 0) \ and (denominator % gcdOfFraction == 0), \ "Error in function gcd(...,...)" - + return (numerator // gcdOfFraction, denominator // gcdOfFraction) - + # ----------------------------------------------------------------- - + def factorial(n): """ input: positive integer 'n' returns the factorial of 'n' (n!) """ - + # precondition assert isinstance(n,int) and (n >= 0), "'n' must been a int and >= 0" - + ans = 1 # this will be return. - + for factor in range(1,n+1): ans *= factor - + return ans - + # ------------------------------------------------------------------- - + def fib(n): """ input: positive integer 'n' returns the n-th fibonacci term , indexing by 0 - """ - + """ + # precondition assert isinstance(n, int) and (n >= 0), "'n' must been an int and >= 0" - + tmp = 0 fib1 = 1 ans = 1 # this will be return - + for i in range(n-1): - + tmp = ans ans += fib1 fib1 = tmp - + return ans diff --git a/project_euler/problem_13/sol1.py b/project_euler/problem_13/sol1.py index 983347675b3f..e36065ec8e11 100644 --- a/project_euler/problem_13/sol1.py +++ b/project_euler/problem_13/sol1.py @@ -3,18 +3,12 @@ Work out the first ten digits of the sum of the following one-hundred 50-digit numbers. """ -from __future__ import print_function -import os - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 def solution(array): """Returns the first ten digits of the sum of the array elements. - + + >>> import os >>> sum = 0 >>> array = [] >>> with open(os.path.dirname(__file__) + "/num.txt","r") as f: From c27bd5144fd48c1a30d10f0d230fabb4fd0b3925 Mon Sep 17 00:00:00 2001 From: QuantumNovice <43876848+QuantumNovice@users.noreply.github.com> Date: Thu, 25 Jul 2019 23:38:24 +0500 Subject: [PATCH 104/193] in_static_equilibrium checks if a 2D static system is in equilibrium (#1062) * Add files via upload * Add files via upload * Create .a * Add files via upload * Add files via upload * Rename static_solver.py to in_static_equilibrium.py * Delete .a * Update in_static_equilibrium.py * Add files via upload * Add files via upload * Update in_static_equilibrium.py * Add files via upload * Add files via upload * Add files via upload * Add files via upload * pyTests 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zr3k+FaM#om9{PH`*+JOu2sQpNf%L&_u7#l8hma;`A9??J$%=1Lpw(5(?bubBMn^Y0 zcWH=C6^zf_FT=Aq7t>pGyVgmpyfl4_PuHC3>CHgP`}=H}Q(I$=X+S)ca1BOPi=43; zL+^0!Q$&s5aEkGZ&&t$rR-$w1<&31K@#Bpl()wFFP5is;uV(E3C%u9HkKJ4TV*39A Dx^_5` literal 0 HcmV?d00001 diff --git a/arithmetic_analysis/in_static_equilibrium.py b/arithmetic_analysis/in_static_equilibrium.py new file mode 100644 index 000000000000..48eb6135eba7 --- /dev/null +++ b/arithmetic_analysis/in_static_equilibrium.py @@ -0,0 +1,89 @@ +""" +Checks if a system of forces is in static equilibrium. + +python/black : true +flake8 : passed +mypy : passed +""" + +from numpy import array, cos, sin, radians, cross # type: ignore +from typing import List + + +def polar_force( + magnitude: float, angle: float, radian_mode: bool = False +) -> List[float]: + """ + Resolves force along rectangular components. + (force, angle) => (force_x, force_y) + >>> polar_force(10, 45) + [7.0710678118654755, 7.071067811865475] + >>> polar_force(10, 3.14, radian_mode=True) + [-9.999987317275394, 0.01592652916486828] + """ + if radian_mode: + return [magnitude * cos(angle), magnitude * sin(angle)] + return [magnitude * cos(radians(angle)), magnitude * sin(radians(angle))] + + +def in_static_equilibrium( + forces: array, location: array, eps: float = 10 ** -1 +) -> bool: + """ + Check if a system is in equilibrium. + It takes two numpy.array objects. + forces ==> [ + [force1_x, force1_y], + [force2_x, force2_y], + ....] + location ==> [ + [x1, y1], + [x2, y2], + ....] + >>> force = array([[1, 1], [-1, 2]]) + >>> location = array([[1, 0], [10, 0]]) + >>> in_static_equilibrium(force, location) + False + """ + # summation of moments is zero + moments: array = cross(location, forces) + sum_moments: float = sum(moments) + return abs(sum_moments) < eps + + +if __name__ == "__main__": + # Test to check if it works + forces = array( + [ + polar_force(718.4, 180 - 30), + polar_force(879.54, 45), + polar_force(100, -90) + ]) + + location = array([[0, 0], [0, 0], [0, 0]]) + + assert in_static_equilibrium(forces, location) + + # Problem 1 in image_data/2D_problems.jpg + forces = array( + [ + polar_force(30 * 9.81, 15), + polar_force(215, 180 - 45), + polar_force(264, 90 - 30), + ] + ) + + location = array([[0, 0], [0, 0], [0, 0]]) + + assert in_static_equilibrium(forces, location) + + # Problem in image_data/2D_problems_1.jpg + forces = array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]]) + + location = array([[0, 0], [6, 0], [10, 0], [12, 0]]) + + assert in_static_equilibrium(forces, location) + + import doctest + + doctest.testmod() diff --git a/data_structures/binary_tree/red_black_tree.py b/data_structures/binary_tree/red_black_tree.py index 4ca1301dd8fe..526f5ec27987 100644 --- a/data_structures/binary_tree/red_black_tree.py +++ b/data_structures/binary_tree/red_black_tree.py @@ -1,665 +1,711 @@ -class RedBlackTree: - """ - A Red-Black tree, which is a self-balancing BST (binary search - tree). - - This tree has similar performance to AVL trees, but the balancing is - less strict, so it will perform faster for writing/deleting nodes - and slower for reading in the average case, though, because they're - both balanced binary search trees, both will get the same asymptotic - perfomance. - - To read more about them, https://en.wikipedia.org/wiki/Red–black_tree - - Unless otherwise specified, all asymptotic runtimes are specified in - terms of the size of the tree. - """ - def __init__(self, label=None, color=0, parent=None, left=None, right=None): - """Initialize a new Red-Black Tree node with the given values: - label: The value associated with this node - color: 0 if black, 1 if red - parent: The parent to this node - left: This node's left child - right: This node's right child - """ - self.label = label - self.parent = parent - self.left = left - self.right = right - self.color = color - - # Here are functions which are specific to red-black trees - - def rotate_left(self): - """Rotate the subtree rooted at this node to the left and - returns the new root to this subtree. - - Perfoming one rotation can be done in O(1). - """ - parent = self.parent - right = self.right - self.right = right.left - if self.right: - self.right.parent = self - self.parent = right - right.left = self - if parent is not None: - if parent.left is self: - parent.left = right - else: - parent.right = right - right.parent = parent - return right - - def rotate_right(self): - """Rotate the subtree rooted at this node to the right and - returns the new root to this subtree. - - Performing one rotation can be done in O(1). - """ - parent = self.parent - left = self.left - self.left = left.right - if self.left: - self.left.parent = self - self.parent = left - left.right = self - if parent is not None: - if parent.right is self: - parent.right = left - else: - parent.left = left - left.parent = parent - return left - - def insert(self, label): - """Inserts label into the subtree rooted at self, performs any - rotations necessary to maintain balance, and then returns the - new root to this subtree (likely self). - - This is guaranteed to run in O(log(n)) time. - """ - if self.label is None: - # Only possible with an empty tree - self.label = label - return self - if self.label == label: - return self - elif self.label > label: - if self.left: - self.left.insert(label) - else: - self.left = RedBlackTree(label, 1, self) - self.left._insert_repair() - else: - if self.right: - self.right.insert(label) - else: - self.right = RedBlackTree(label, 1, self) - self.right._insert_repair() - return self.parent or self - - def _insert_repair(self): - """Repair the coloring from inserting into a tree.""" - if self.parent is None: - # This node is the root, so it just needs to be black - self.color = 0 - elif color(self.parent) == 0: - # If the parent is black, then it just needs to be red - self.color = 1 - else: - uncle = self.parent.sibling - if color(uncle) == 0: - if self.is_left() and self.parent.is_right(): - self.parent.rotate_right() - self.right._insert_repair() - elif self.is_right() and self.parent.is_left(): - self.parent.rotate_left() - self.left._insert_repair() - elif self.is_left(): - self.grandparent.rotate_right() - self.parent.color = 0 - self.parent.right.color = 1 - else: - self.grandparent.rotate_left() - self.parent.color = 0 - self.parent.left.color = 1 - else: - self.parent.color = 0 - uncle.color = 0 - self.grandparent.color = 1 - self.grandparent._insert_repair() - - def remove(self, label): - """Remove label from this tree.""" - if self.label == label: - if self.left and self.right: - # It's easier to balance a node with at most one child, - # so we replace this node with the greatest one less than - # it and remove that. - value = self.left.get_max() - self.label = value - self.left.remove(value) - else: - # This node has at most one non-None child, so we don't - # need to replace - child = self.left or self.right - if self.color == 1: - # This node is red, and its child is black - # The only way this happens to a node with one child - # is if both children are None leaves. - # We can just remove this node and call it a day. - if self.is_left(): - self.parent.left = None - else: - self.parent.right = None - else: - # The node is black - if child is None: - # This node and its child are black - if self.parent is None: - # The tree is now empty - return RedBlackTree(None) - else: - self._remove_repair() - if self.is_left(): - self.parent.left = None - else: - self.parent.right = None - self.parent = None - else: - # This node is black and its child is red - # Move the child node here and make it black - self.label = child.label - self.left = child.left - self.right = child.right - if self.left: - self.left.parent = self - if self.right: - self.right.parent = self - elif self.label > label: - if self.left: - self.left.remove(label) - else: - if self.right: - self.right.remove(label) - return self.parent or self - - def _remove_repair(self): - """Repair the coloring of the tree that may have been messed up.""" - if color(self.sibling) == 1: - self.sibling.color = 0 - self.parent.color = 1 - if self.is_left(): - self.parent.rotate_left() - else: - self.parent.rotate_right() - if color(self.parent) == 0 and color(self.sibling) == 0 \ - and color(self.sibling.left) == 0 \ - and color(self.sibling.right) == 0: - self.sibling.color = 1 - self.parent._remove_repair() - return - if color(self.parent) == 1 and color(self.sibling) == 0 \ - and color(self.sibling.left) == 0 \ - and color(self.sibling.right) == 0: - self.sibling.color = 1 - self.parent.color = 0 - return - if (self.is_left() - and color(self.sibling) == 0 - and color(self.sibling.right) == 0 - and color(self.sibling.left) == 1): - self.sibling.rotate_right() - self.sibling.color = 0 - self.sibling.right.color = 1 - if (self.is_right() - and color(self.sibling) == 0 - and color(self.sibling.right) == 1 - and color(self.sibling.left) == 0): - self.sibling.rotate_left() - self.sibling.color = 0 - self.sibling.left.color = 1 - if (self.is_left() - and color(self.sibling) == 0 - and color(self.sibling.right) == 1): - self.parent.rotate_left() - self.grandparent.color = self.parent.color - self.parent.color = 0 - self.parent.sibling.color = 0 - if (self.is_right() - and color(self.sibling) == 0 - and color(self.sibling.left) == 1): - self.parent.rotate_right() - self.grandparent.color = self.parent.color - self.parent.color = 0 - self.parent.sibling.color = 0 - - def check_color_properties(self): - """Check the coloring of the tree, and return True iff the tree - is colored in a way which matches these five properties: - (wording stolen from wikipedia article) - 1. Each node is either red or black. - 2. The root node is black. - 3. All leaves are black. - 4. If a node is red, then both its children are black. - 5. Every path from any node to all of its descendent NIL nodes - has the same number of black nodes. - - This function runs in O(n) time, because properties 4 and 5 take - that long to check. - """ - # I assume property 1 to hold because there is nothing that can - # make the color be anything other than 0 or 1. - - # Property 2 - if self.color: - # The root was red - print('Property 2') - return False; - - # Property 3 does not need to be checked, because None is assumed - # to be black and is all the leaves. - - # Property 4 - if not self.check_coloring(): - print('Property 4') - return False - - # Property 5 - if self.black_height() is None: - print('Property 5') - return False - # All properties were met - return True - - def check_coloring(self): - """A helper function to recursively check Property 4 of a - Red-Black Tree. See check_color_properties for more info. - """ - if self.color == 1: - if color(self.left) == 1 or color(self.right) == 1: - return False - if self.left and not self.left.check_coloring(): - return False - if self.right and not self.right.check_coloring(): - return False - return True - - def black_height(self): - """Returns the number of black nodes from this node to the - leaves of the tree, or None if there isn't one such value (the - tree is color incorrectly). - """ - if self is None: - # If we're already at a leaf, there is no path - return 1 - left = RedBlackTree.black_height(self.left) - right = RedBlackTree.black_height(self.right) - if left is None or right is None: - # There are issues with coloring below children nodes - return None - if left != right: - # The two children have unequal depths - return None - # Return the black depth of children, plus one if this node is - # black - return left + (1-self.color) - - # Here are functions which are general to all binary search trees - - def __contains__(self, label): - """Search through the tree for label, returning True iff it is - found somewhere in the tree. - - Guaranteed to run in O(log(n)) time. - """ - return self.search(label) is not None - - def search(self, label): - """Search through the tree for label, returning its node if - it's found, and None otherwise. - - This method is guaranteed to run in O(log(n)) time. - """ - if self.label == label: - return self - elif label > self.label: - if self.right is None: - return None - else: - return self.right.search(label) - else: - if self.left is None: - return None - else: - return self.left.search(label) - - def floor(self, label): - """Returns the largest element in this tree which is at most label. - - This method is guaranteed to run in O(log(n)) time.""" - if self.label == label: - return self.label - elif self.label > label: - if self.left: - return self.left.floor(label) - else: - return None - else: - if self.right: - attempt = self.right.floor(label) - if attempt is not None: - return attempt - return self.label - - def ceil(self, label): - """Returns the smallest element in this tree which is at least label. - - This method is guaranteed to run in O(log(n)) time. - """ - if self.label == label: - return self.label - elif self.label < label: - if self.right: - return self.right.ceil(label) - else: - return None - else: - if self.left: - attempt = self.left.ceil(label) - if attempt is not None: - return attempt - return self.label - - def get_max(self): - """Returns the largest element in this tree. - - This method is guaranteed to run in O(log(n)) time. - """ - if self.right: - # Go as far right as possible - return self.right.get_max() - else: - return self.label - - def get_min(self): - """Returns the smallest element in this tree. - - This method is guaranteed to run in O(log(n)) time. - """ - if self.left: - # Go as far left as possible - return self.left.get_min() - else: - return self.label - - @property - def grandparent(self): - """Get the current node's grandparent, or None if it doesn't exist.""" - if self.parent is None: - return None - else: - return self.parent.parent - - @property - def sibling(self): - """Get the current node's sibling, or None if it doesn't exist.""" - if self.parent is None: - return None - elif self.parent.left is self: - return self.parent.right - else: - return self.parent.left - - def is_left(self): - """Returns true iff this node is the left child of its parent.""" - return self.parent and self.parent.left is self - - def is_right(self): - """Returns true iff this node is the right child of its parent.""" - return self.parent and self.parent.right is self - - def __bool__(self): - return True - - def __len__(self): - """ - Return the number of nodes in this tree. - """ - ln = 1 - if self.left: - ln += len(self.left) - if self.right: - ln += len(self.right) - return ln - - def preorder_traverse(self): - yield self.label - if self.left: - yield from self.left.preorder_traverse() - if self.right: - yield from self.right.preorder_traverse() - - def inorder_traverse(self): - if self.left: - yield from self.left.inorder_traverse() - yield self.label - if self.right: - yield from self.right.inorder_traverse() - - - def postorder_traverse(self): - if self.left: - yield from self.left.postorder_traverse() - if self.right: - yield from self.right.postorder_traverse() - yield self.label - - def __repr__(self): - from pprint import pformat - if self.left is None and self.right is None: - return "'%s %s'" % (self.label, (self.color and 'red') or 'blk') - return pformat({'%s %s' % (self.label, (self.color and 'red') or 'blk'): - (self.left, self.right)}, - indent=1) - - def __eq__(self, other): - """Test if two trees are equal.""" - if self.label == other.label: - return self.left == other.left and self.right == other.right - else: - return False - -def color(node): - """Returns the color of a node, allowing for None leaves.""" - if node is None: - return 0 - else: - return node.color - -""" -Code for testing the various functions of the red-black tree. -""" - -def test_rotations(): - """Test that the rotate_left and rotate_right functions work.""" - # Make a tree to test on - tree = RedBlackTree(0) - tree.left = RedBlackTree(-10, parent=tree) - tree.right = RedBlackTree(10, parent=tree) - tree.left.left = RedBlackTree(-20, parent=tree.left) - tree.left.right = RedBlackTree(-5, parent=tree.left) - tree.right.left = RedBlackTree(5, parent=tree.right) - tree.right.right = RedBlackTree(20, parent=tree.right) - # Make the right rotation - left_rot = RedBlackTree(10) - left_rot.left = RedBlackTree(0, parent=left_rot) - left_rot.left.left = RedBlackTree(-10, parent=left_rot.left) - left_rot.left.right = RedBlackTree(5, parent=left_rot.left) - left_rot.left.left.left = RedBlackTree(-20, parent=left_rot.left.left) - left_rot.left.left.right = RedBlackTree(-5, parent=left_rot.left.left) - left_rot.right = RedBlackTree(20, parent=left_rot) - tree = tree.rotate_left() - if tree != left_rot: - return False - tree = tree.rotate_right() - tree = tree.rotate_right() - # Make the left rotation - right_rot = RedBlackTree(-10) - right_rot.left = RedBlackTree(-20, parent=right_rot) - right_rot.right = RedBlackTree(0, parent=right_rot) - right_rot.right.left = RedBlackTree(-5, parent=right_rot.right) - right_rot.right.right = RedBlackTree(10, parent=right_rot.right) - right_rot.right.right.left = RedBlackTree(5, parent=right_rot.right.right) - right_rot.right.right.right = RedBlackTree(20, parent=right_rot.right.right) - if tree != right_rot: - return False - return True - -def test_insertion_speed(): - """Test that the tree balances inserts to O(log(n)) by doing a lot - of them. - """ - tree = RedBlackTree(-1) - for i in range(300000): - tree = tree.insert(i) - return True - -def test_insert(): - """Test the insert() method of the tree correctly balances, colors, - and inserts. - """ - tree = RedBlackTree(0) - tree.insert(8) - tree.insert(-8) - tree.insert(4) - tree.insert(12) - tree.insert(10) - tree.insert(11) - ans = RedBlackTree(0, 0) - ans.left = RedBlackTree(-8, 0, ans) - ans.right = RedBlackTree(8, 1, ans) - ans.right.left = RedBlackTree(4, 0, ans.right) - ans.right.right = RedBlackTree(11, 0, ans.right) - ans.right.right.left = RedBlackTree(10, 1, ans.right.right) - ans.right.right.right = RedBlackTree(12, 1, ans.right.right) - return tree == ans - -def test_insert_and_search(): - """Tests searching through the tree for values.""" - tree = RedBlackTree(0) - tree.insert(8) - tree.insert(-8) - tree.insert(4) - tree.insert(12) - tree.insert(10) - tree.insert(11) - if 5 in tree or -6 in tree or -10 in tree or 13 in tree: - # Found something not in there - return False - if not (11 in tree and 12 in tree and -8 in tree and 0 in tree): - # Didn't find something in there - return False - return True - -def test_insert_delete(): - """Test the insert() and delete() method of the tree, verifying the - insertion and removal of elements, and the balancing of the tree. - """ - tree = RedBlackTree(0) - tree = tree.insert(-12) - tree = tree.insert(8) - tree = tree.insert(-8) - tree = tree.insert(15) - tree = tree.insert(4) - tree = tree.insert(12) - tree = tree.insert(10) - tree = tree.insert(9) - tree = tree.insert(11) - tree = tree.remove(15) - tree = tree.remove(-12) - tree = tree.remove(9) - if not tree.check_color_properties(): - return False - if list(tree.inorder_traverse()) != [-8, 0, 4, 8, 10, 11, 12]: - return False - return True - -def test_floor_ceil(): - """Tests the floor and ceiling functions in the tree.""" - tree = RedBlackTree(0) - tree.insert(-16) - tree.insert(16) - tree.insert(8) - tree.insert(24) - tree.insert(20) - tree.insert(22) - tuples = [(-20, None, -16), (-10, -16, 0), (8, 8, 8), (50, 24, None)] - for val, floor, ceil in tuples: - if tree.floor(val) != floor or tree.ceil(val) != ceil: - return False - return True - -def test_min_max(): - """Tests the min and max functions in the tree.""" - tree = RedBlackTree(0) - tree.insert(-16) - tree.insert(16) - tree.insert(8) - tree.insert(24) - tree.insert(20) - tree.insert(22) - if tree.get_max() != 22 or tree.get_min() != -16: - return False - return True - -def test_tree_traversal(): - """Tests the three different tree traversal functions.""" - tree = RedBlackTree(0) - tree.insert(-16) - tree.insert(16) - tree.insert(8) - tree.insert(24) - tree.insert(20) - tree.insert(22) - if list(tree.inorder_traverse()) != [-16, 0, 8, 16, 20, 22, 24]: - return False - if list(tree.preorder_traverse()) != [0, -16, 16, 8, 22, 20, 24]: - return False - if list(tree.postorder_traverse()) != [-16, 8, 20, 24, 22, 16, 0]: - return False - return True - -def main(): - if test_rotations(): - print('Rotating right and left works!') - else: - print('Rotating right and left doesn\'t work. :(') - if test_insert(): - print('Inserting works!') - else: - print('Inserting doesn\'t work :(') - if test_insert_and_search(): - print('Searching works!') - else: - print('Searching doesn\'t work :(') - if test_insert_delete(): - print('Deleting works!') - else: - print('Deleting doesn\'t work :(') - if test_floor_ceil(): - print('Floor and ceil work!') - else: - print('Floor and ceil don\'t work :(') - if test_tree_traversal(): - print('Tree traversal works!') - else: - print('Tree traversal doesn\'t work :(') - print('Testing tree balancing...') - print('This should only be a few seconds.') - test_insertion_speed() - print('Done!') - -if __name__ == '__main__': - main() +""" +python/black : true +flake8 : passed +""" + + +class RedBlackTree: + """ + A Red-Black tree, which is a self-balancing BST (binary search + tree). + This tree has similar performance to AVL trees, but the balancing is + less strict, so it will perform faster for writing/deleting nodes + and slower for reading in the average case, though, because they're + both balanced binary search trees, both will get the same asymptotic + perfomance. + To read more about them, https://en.wikipedia.org/wiki/Red–black_tree + Unless otherwise specified, all asymptotic runtimes are specified in + terms of the size of the tree. + """ + + def __init__(self, label=None, color=0, parent=None, left=None, right=None): + """Initialize a new Red-Black Tree node with the given values: + label: The value associated with this node + color: 0 if black, 1 if red + parent: The parent to this node + left: This node's left child + right: This node's right child + """ + self.label = label + self.parent = parent + self.left = left + self.right = right + self.color = color + + # Here are functions which are specific to red-black trees + + def rotate_left(self): + """Rotate the subtree rooted at this node to the left and + returns the new root to this subtree. + Perfoming one rotation can be done in O(1). + """ + parent = self.parent + right = self.right + self.right = right.left + if self.right: + self.right.parent = self + self.parent = right + right.left = self + if parent is not None: + if parent.left == self: + parent.left = right + else: + parent.right = right + right.parent = parent + return right + + def rotate_right(self): + """Rotate the subtree rooted at this node to the right and + returns the new root to this subtree. + Performing one rotation can be done in O(1). + """ + parent = self.parent + left = self.left + self.left = left.right + if self.left: + self.left.parent = self + self.parent = left + left.right = self + if parent is not None: + if parent.right is self: + parent.right = left + else: + parent.left = left + left.parent = parent + return left + + def insert(self, label): + """Inserts label into the subtree rooted at self, performs any + rotations necessary to maintain balance, and then returns the + new root to this subtree (likely self). + This is guaranteed to run in O(log(n)) time. + """ + if self.label is None: + # Only possible with an empty tree + self.label = label + return self + if self.label == label: + return self + elif self.label > label: + if self.left: + self.left.insert(label) + else: + self.left = RedBlackTree(label, 1, self) + self.left._insert_repair() + else: + if self.right: + self.right.insert(label) + else: + self.right = RedBlackTree(label, 1, self) + self.right._insert_repair() + return self.parent or self + + def _insert_repair(self): + """Repair the coloring from inserting into a tree.""" + if self.parent is None: + # This node is the root, so it just needs to be black + self.color = 0 + elif color(self.parent) == 0: + # If the parent is black, then it just needs to be red + self.color = 1 + else: + uncle = self.parent.sibling + if color(uncle) == 0: + if self.is_left() and self.parent.is_right(): + self.parent.rotate_right() + self.right._insert_repair() + elif self.is_right() and self.parent.is_left(): + self.parent.rotate_left() + self.left._insert_repair() + elif self.is_left(): + self.grandparent.rotate_right() + self.parent.color = 0 + self.parent.right.color = 1 + else: + self.grandparent.rotate_left() + self.parent.color = 0 + self.parent.left.color = 1 + else: + self.parent.color = 0 + uncle.color = 0 + self.grandparent.color = 1 + self.grandparent._insert_repair() + + def remove(self, label): + """Remove label from this tree.""" + if self.label == label: + if self.left and self.right: + # It's easier to balance a node with at most one child, + # so we replace this node with the greatest one less than + # it and remove that. + value = self.left.get_max() + self.label = value + self.left.remove(value) + else: + # This node has at most one non-None child, so we don't + # need to replace + child = self.left or self.right + if self.color == 1: + # This node is red, and its child is black + # The only way this happens to a node with one child + # is if both children are None leaves. + # We can just remove this node and call it a day. + if self.is_left(): + self.parent.left = None + else: + self.parent.right = None + else: + # The node is black + if child is None: + # This node and its child are black + if self.parent is None: + # The tree is now empty + return RedBlackTree(None) + else: + self._remove_repair() + if self.is_left(): + self.parent.left = None + else: + self.parent.right = None + self.parent = None + else: + # This node is black and its child is red + # Move the child node here and make it black + self.label = child.label + self.left = child.left + self.right = child.right + if self.left: + self.left.parent = self + if self.right: + self.right.parent = self + elif self.label > label: + if self.left: + self.left.remove(label) + else: + if self.right: + self.right.remove(label) + return self.parent or self + + def _remove_repair(self): + """Repair the coloring of the tree that may have been messed up.""" + if color(self.sibling) == 1: + self.sibling.color = 0 + self.parent.color = 1 + if self.is_left(): + self.parent.rotate_left() + else: + self.parent.rotate_right() + if ( + color(self.parent) == 0 + and color(self.sibling) == 0 + and color(self.sibling.left) == 0 + and color(self.sibling.right) == 0 + ): + self.sibling.color = 1 + self.parent._remove_repair() + return + if ( + color(self.parent) == 1 + and color(self.sibling) == 0 + and color(self.sibling.left) == 0 + and color(self.sibling.right) == 0 + ): + self.sibling.color = 1 + self.parent.color = 0 + return + if ( + self.is_left() + and color(self.sibling) == 0 + and color(self.sibling.right) == 0 + and color(self.sibling.left) == 1 + ): + self.sibling.rotate_right() + self.sibling.color = 0 + self.sibling.right.color = 1 + if ( + self.is_right() + and color(self.sibling) == 0 + and color(self.sibling.right) == 1 + and color(self.sibling.left) == 0 + ): + self.sibling.rotate_left() + self.sibling.color = 0 + self.sibling.left.color = 1 + if ( + self.is_left() + and color(self.sibling) == 0 + and color(self.sibling.right) == 1 + ): + self.parent.rotate_left() + self.grandparent.color = self.parent.color + self.parent.color = 0 + self.parent.sibling.color = 0 + if ( + self.is_right() + and color(self.sibling) == 0 + and color(self.sibling.left) == 1 + ): + self.parent.rotate_right() + self.grandparent.color = self.parent.color + self.parent.color = 0 + self.parent.sibling.color = 0 + + def check_color_properties(self): + """Check the coloring of the tree, and return True iff the tree + is colored in a way which matches these five properties: + (wording stolen from wikipedia article) + 1. Each node is either red or black. + 2. The root node is black. + 3. All leaves are black. + 4. If a node is red, then both its children are black. + 5. Every path from any node to all of its descendent NIL nodes + has the same number of black nodes. + This function runs in O(n) time, because properties 4 and 5 take + that long to check. + """ + # I assume property 1 to hold because there is nothing that can + # make the color be anything other than 0 or 1. + + # Property 2 + if self.color: + # The root was red + print("Property 2") + return False + + # Property 3 does not need to be checked, because None is assumed + # to be black and is all the leaves. + + # Property 4 + if not self.check_coloring(): + print("Property 4") + return False + + # Property 5 + if self.black_height() is None: + print("Property 5") + return False + # All properties were met + return True + + def check_coloring(self): + """A helper function to recursively check Property 4 of a + Red-Black Tree. See check_color_properties for more info. + """ + if self.color == 1: + if color(self.left) == 1 or color(self.right) == 1: + return False + if self.left and not self.left.check_coloring(): + return False + if self.right and not self.right.check_coloring(): + return False + return True + + def black_height(self): + """Returns the number of black nodes from this node to the + leaves of the tree, or None if there isn't one such value (the + tree is color incorrectly). + """ + if self is None: + # If we're already at a leaf, there is no path + return 1 + left = RedBlackTree.black_height(self.left) + right = RedBlackTree.black_height(self.right) + if left is None or right is None: + # There are issues with coloring below children nodes + return None + if left != right: + # The two children have unequal depths + return None + # Return the black depth of children, plus one if this node is + # black + return left + (1 - self.color) + + # Here are functions which are general to all binary search trees + + def __contains__(self, label): + """Search through the tree for label, returning True iff it is + found somewhere in the tree. + Guaranteed to run in O(log(n)) time. + """ + return self.search(label) is not None + + def search(self, label): + """Search through the tree for label, returning its node if + it's found, and None otherwise. + This method is guaranteed to run in O(log(n)) time. + """ + if self.label == label: + return self + elif label > self.label: + if self.right is None: + return None + else: + return self.right.search(label) + else: + if self.left is None: + return None + else: + return self.left.search(label) + + def floor(self, label): + """Returns the largest element in this tree which is at most label. + This method is guaranteed to run in O(log(n)) time.""" + if self.label == label: + return self.label + elif self.label > label: + if self.left: + return self.left.floor(label) + else: + return None + else: + if self.right: + attempt = self.right.floor(label) + if attempt is not None: + return attempt + return self.label + + def ceil(self, label): + """Returns the smallest element in this tree which is at least label. + This method is guaranteed to run in O(log(n)) time. + """ + if self.label == label: + return self.label + elif self.label < label: + if self.right: + return self.right.ceil(label) + else: + return None + else: + if self.left: + attempt = self.left.ceil(label) + if attempt is not None: + return attempt + return self.label + + def get_max(self): + """Returns the largest element in this tree. + This method is guaranteed to run in O(log(n)) time. + """ + if self.right: + # Go as far right as possible + return self.right.get_max() + else: + return self.label + + def get_min(self): + """Returns the smallest element in this tree. + This method is guaranteed to run in O(log(n)) time. + """ + if self.left: + # Go as far left as possible + return self.left.get_min() + else: + return self.label + + @property + def grandparent(self): + """Get the current node's grandparent, or None if it doesn't exist.""" + if self.parent is None: + return None + else: + return self.parent.parent + + @property + def sibling(self): + """Get the current node's sibling, or None if it doesn't exist.""" + if self.parent is None: + return None + elif self.parent.left is self: + return self.parent.right + else: + return self.parent.left + + def is_left(self): + """Returns true iff this node is the left child of its parent.""" + return self.parent and self.parent.left is self + + def is_right(self): + """Returns true iff this node is the right child of its parent.""" + return self.parent and self.parent.right is self + + def __bool__(self): + return True + + def __len__(self): + """ + Return the number of nodes in this tree. + """ + ln = 1 + if self.left: + ln += len(self.left) + if self.right: + ln += len(self.right) + return ln + + def preorder_traverse(self): + yield self.label + if self.left: + yield from self.left.preorder_traverse() + if self.right: + yield from self.right.preorder_traverse() + + def inorder_traverse(self): + if self.left: + yield from self.left.inorder_traverse() + yield self.label + if self.right: + yield from self.right.inorder_traverse() + + def postorder_traverse(self): + if self.left: + yield from self.left.postorder_traverse() + if self.right: + yield from self.right.postorder_traverse() + yield self.label + + def __repr__(self): + from pprint import pformat + + if self.left is None and self.right is None: + return "'%s %s'" % (self.label, (self.color and "red") or "blk") + return pformat( + { + "%s %s" + % (self.label, (self.color and "red") or "blk"): (self.left, self.right) + }, + indent=1, + ) + + def __eq__(self, other): + """Test if two trees are equal.""" + if self.label == other.label: + return self.left == other.left and self.right == other.right + else: + return False + + +def color(node): + """Returns the color of a node, allowing for None leaves.""" + if node is None: + return 0 + else: + return node.color + + +""" +Code for testing the various +functions of the red-black tree. +""" + + +def test_rotations(): + """Test that the rotate_left and rotate_right functions work.""" + # Make a tree to test on + tree = RedBlackTree(0) + tree.left = RedBlackTree(-10, parent=tree) + tree.right = RedBlackTree(10, parent=tree) + tree.left.left = RedBlackTree(-20, parent=tree.left) + tree.left.right = RedBlackTree(-5, parent=tree.left) + tree.right.left = RedBlackTree(5, parent=tree.right) + tree.right.right = RedBlackTree(20, parent=tree.right) + # Make the right rotation + left_rot = RedBlackTree(10) + left_rot.left = RedBlackTree(0, parent=left_rot) + left_rot.left.left = RedBlackTree(-10, parent=left_rot.left) + left_rot.left.right = RedBlackTree(5, parent=left_rot.left) + left_rot.left.left.left = RedBlackTree(-20, parent=left_rot.left.left) + left_rot.left.left.right = RedBlackTree(-5, parent=left_rot.left.left) + left_rot.right = RedBlackTree(20, parent=left_rot) + tree = tree.rotate_left() + if tree != left_rot: + return False + tree = tree.rotate_right() + tree = tree.rotate_right() + # Make the left rotation + right_rot = RedBlackTree(-10) + right_rot.left = RedBlackTree(-20, parent=right_rot) + right_rot.right = RedBlackTree(0, parent=right_rot) + right_rot.right.left = RedBlackTree(-5, parent=right_rot.right) + right_rot.right.right = RedBlackTree(10, parent=right_rot.right) + right_rot.right.right.left = RedBlackTree(5, parent=right_rot.right.right) + right_rot.right.right.right = RedBlackTree(20, parent=right_rot.right.right) + if tree != right_rot: + return False + return True + + +def test_insertion_speed(): + """Test that the tree balances inserts to O(log(n)) by doing a lot + of them. + """ + tree = RedBlackTree(-1) + for i in range(300000): + tree = tree.insert(i) + return True + + +def test_insert(): + """Test the insert() method of the tree correctly balances, colors, + and inserts. + """ + tree = RedBlackTree(0) + tree.insert(8) + tree.insert(-8) + tree.insert(4) + tree.insert(12) + tree.insert(10) + tree.insert(11) + ans = RedBlackTree(0, 0) + ans.left = RedBlackTree(-8, 0, ans) + ans.right = RedBlackTree(8, 1, ans) + ans.right.left = RedBlackTree(4, 0, ans.right) + ans.right.right = RedBlackTree(11, 0, ans.right) + ans.right.right.left = RedBlackTree(10, 1, ans.right.right) + ans.right.right.right = RedBlackTree(12, 1, ans.right.right) + return tree == ans + + +def test_insert_and_search(): + """Tests searching through the tree for values.""" + tree = RedBlackTree(0) + tree.insert(8) + tree.insert(-8) + tree.insert(4) + tree.insert(12) + tree.insert(10) + tree.insert(11) + if 5 in tree or -6 in tree or -10 in tree or 13 in tree: + # Found something not in there + return False + if not (11 in tree and 12 in tree and -8 in tree and 0 in tree): + # Didn't find something in there + return False + return True + + +def test_insert_delete(): + """Test the insert() and delete() method of the tree, verifying the + insertion and removal of elements, and the balancing of the tree. + """ + tree = RedBlackTree(0) + tree = tree.insert(-12) + tree = tree.insert(8) + tree = tree.insert(-8) + tree = tree.insert(15) + tree = tree.insert(4) + tree = tree.insert(12) + tree = tree.insert(10) + tree = tree.insert(9) + tree = tree.insert(11) + tree = tree.remove(15) + tree = tree.remove(-12) + tree = tree.remove(9) + if not tree.check_color_properties(): + return False + if list(tree.inorder_traverse()) != [-8, 0, 4, 8, 10, 11, 12]: + return False + return True + + +def test_floor_ceil(): + """Tests the floor and ceiling functions in the tree.""" + tree = RedBlackTree(0) + tree.insert(-16) + tree.insert(16) + tree.insert(8) + tree.insert(24) + tree.insert(20) + tree.insert(22) + tuples = [(-20, None, -16), (-10, -16, 0), (8, 8, 8), (50, 24, None)] + for val, floor, ceil in tuples: + if tree.floor(val) != floor or tree.ceil(val) != ceil: + return False + return True + + +def test_min_max(): + """Tests the min and max functions in the tree.""" + tree = RedBlackTree(0) + tree.insert(-16) + tree.insert(16) + tree.insert(8) + tree.insert(24) + tree.insert(20) + tree.insert(22) + if tree.get_max() != 22 or tree.get_min() != -16: + return False + return True + + +def test_tree_traversal(): + """Tests the three different tree traversal functions.""" + tree = RedBlackTree(0) + tree = tree.insert(-16) + tree.insert(16) + tree.insert(8) + tree.insert(24) + tree.insert(20) + tree.insert(22) + if list(tree.inorder_traverse()) != [-16, 0, 8, 16, 20, 22, 24]: + return False + if list(tree.preorder_traverse()) != [0, -16, 16, 8, 22, 20, 24]: + return False + if list(tree.postorder_traverse()) != [-16, 8, 20, 24, 22, 16, 0]: + return False + return True + + +def test_tree_chaining(): + """Tests the three different tree chaning functions.""" + tree = RedBlackTree(0) + tree = tree.insert(-16).insert(16).insert(8).insert(24).insert(20).insert(22) + if list(tree.inorder_traverse()) != [-16, 0, 8, 16, 20, 22, 24]: + return False + if list(tree.preorder_traverse()) != [0, -16, 16, 8, 22, 20, 24]: + return False + if list(tree.postorder_traverse()) != [-16, 8, 20, 24, 22, 16, 0]: + return False + return True + + +def print_results(msg: str, passes: bool) -> None: + print(str(msg), "works!" if passes else "doesn't work :(") + + +def pytests(): + assert test_rotations() + assert test_insert() + assert test_insert_and_search() + assert test_insert_delete() + assert test_floor_ceil() + assert test_tree_traversal() + assert test_tree_chaining() + + +def main(): + """ + >>> pytests() + """ + print_results("Rotating right and left", test_rotations()) + + print_results("Inserting", test_insert()) + + print_results("Searching", test_insert_and_search()) + + print_results("Deleting", test_insert_delete()) + + print_results("Floor and ceil", test_floor_ceil()) + + print_results("Tree traversal", test_tree_traversal()) + + print_results("Tree traversal", test_tree_chaining()) + + + print("Testing tree balancing...") + print("This should only be a few seconds.") + test_insertion_speed() + print("Done!") + + +if __name__ == "__main__": + main() From 46bc6738d78de3a05901881c919e8d91a28b8ef4 Mon Sep 17 00:00:00 2001 From: obelisk0114 Date: Fri, 26 Jul 2019 03:25:38 -0700 Subject: [PATCH 105/193] Add doctest to maths/sieve_of_eratosthenes.py and remove other/finding_primes.py (#1078) Both of the two files implemented sieve of eratosthenes. However, there was a bug in other/finding_primes.py, and the time complexity was larger than the other. Therefore, remove other/finding_primes.py and add doctest tomaths/sieve_of_eratosthenes.py. --- maths/sieve_of_eratosthenes.py | 38 ++++++++++++++++++++++++++++++++-- other/finding_primes.py | 21 ------------------- 2 files changed, 36 insertions(+), 23 deletions(-) delete mode 100644 other/finding_primes.py diff --git a/maths/sieve_of_eratosthenes.py b/maths/sieve_of_eratosthenes.py index cedd04f92aa0..44c7f8a02682 100644 --- a/maths/sieve_of_eratosthenes.py +++ b/maths/sieve_of_eratosthenes.py @@ -1,19 +1,53 @@ -"""Sieve of Eratosthones.""" +# -*- coding: utf-8 -*- + +""" +Sieve of Eratosthones + +The sieve of Eratosthenes is an algorithm used to find prime numbers, less than or equal to a given value. +Illustration: https://upload.wikimedia.org/wikipedia/commons/b/b9/Sieve_of_Eratosthenes_animation.gif +Reference: https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes + +doctest provider: Bruno Simas Hadlich (https://github.com/brunohadlich) +Also thanks Dmitry (https://github.com/LizardWizzard) for finding the problem +""" + import math + def sieve(n): - """Sieve of Eratosthones.""" + """ + Returns a list with all prime numbers up to n. + + >>> sieve(50) + [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47] + >>> sieve(25) + [2, 3, 5, 7, 11, 13, 17, 19, 23] + >>> sieve(10) + [2, 3, 5, 7] + >>> sieve(9) + [2, 3, 5, 7] + >>> sieve(2) + [2] + >>> sieve(1) + [] + """ + l = [True] * (n + 1) prime = [] start = 2 end = int(math.sqrt(n)) + while start <= end: + # If start is a prime if l[start] is True: prime.append(start) + + # Set multiples of start be False for i in range(start * start, n + 1, start): if l[i] is True: l[i] = False + start += 1 for j in range(end + 1, n + 1): diff --git a/other/finding_primes.py b/other/finding_primes.py deleted file mode 100644 index 035a14f4a335..000000000000 --- a/other/finding_primes.py +++ /dev/null @@ -1,21 +0,0 @@ -''' --The sieve of Eratosthenes is an algorithm used to find prime numbers, less than or equal to a given value. --Illustration: https://upload.wikimedia.org/wikipedia/commons/b/b9/Sieve_of_Eratosthenes_animation.gif -''' -from __future__ import print_function - - -from math import sqrt -def SOE(n): - check = round(sqrt(n)) #Need not check for multiples past the square root of n - - sieve = [False if i <2 else True for i in range(n+1)] #Set every index to False except for index 0 and 1 - - for i in range(2, check): - if(sieve[i] == True): #If i is a prime - for j in range(i+i, n+1, i): #Step through the list in increments of i(the multiples of the prime) - sieve[j] = False #Sets every multiple of i to False - - for i in range(n+1): - if(sieve[i] == True): - print(i, end=" ") From 3b63857b657ef9e552368d66fe536a0454c6307f Mon Sep 17 00:00:00 2001 From: Jasper <46252815+jasper256@users.noreply.github.com> Date: Fri, 26 Jul 2019 12:28:32 -0400 Subject: [PATCH 106/193] added automated doctest to decimal_to_hexadecimal.py in conversions (#1071) * added automated doctest to decimal_to_hexadecimal.py in conversions * improved error handling and added more test cases in decimal_to_hexadecimal.py * implemented 0x notation and simplified AssertionError * fixed negative notation and added comparison test against Python hex function --- conversions/decimal_to_hexadecimal.py | 59 ++++++++++++++++++++------- 1 file changed, 45 insertions(+), 14 deletions(-) diff --git a/conversions/decimal_to_hexadecimal.py b/conversions/decimal_to_hexadecimal.py index f91fac063adc..e6435f1ef570 100644 --- a/conversions/decimal_to_hexadecimal.py +++ b/conversions/decimal_to_hexadecimal.py @@ -21,23 +21,54 @@ } def decimal_to_hexadecimal(decimal): - """ take decimal value, return hexadecimal representation as str """ + """ + take integer decimal value, return hexadecimal representation as str beginning with 0x + >>> decimal_to_hexadecimal(5) + '0x5' + >>> decimal_to_hexadecimal(15) + '0xf' + >>> decimal_to_hexadecimal(37) + '0x25' + >>> decimal_to_hexadecimal(255) + '0xff' + >>> decimal_to_hexadecimal(4096) + '0x1000' + >>> decimal_to_hexadecimal(999098) + '0xf3eba' + >>> # negatives work too + >>> decimal_to_hexadecimal(-256) + '-0x100' + >>> # floats are acceptable if equivalent to an int + >>> decimal_to_hexadecimal(17.0) + '0x11' + >>> # other floats will error + >>> decimal_to_hexadecimal(16.16) # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + AssertionError + >>> # strings will error as well + >>> decimal_to_hexadecimal('0xfffff') # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + AssertionError + >>> # results are the same when compared to Python's default hex function + >>> decimal_to_hexadecimal(-256) == hex(-256) + True + """ + assert type(decimal) in (int, float) and decimal == int(decimal) hexadecimal = '' + negative = False + if decimal < 0: + negative = True + decimal *= -1 while decimal > 0: - remainder = decimal % 16 - decimal -= remainder + decimal, remainder = divmod(decimal, 16) hexadecimal = values[remainder] + hexadecimal - decimal /= 16 + hexadecimal = '0x' + hexadecimal + if negative: + hexadecimal = '-' + hexadecimal return hexadecimal -def main(): - """ print test cases """ - print("5 in hexadecimal is", decimal_to_hexadecimal(5)) - print("15 in hexadecimal is", decimal_to_hexadecimal(15)) - print("37 in hexadecimal is", decimal_to_hexadecimal(37)) - print("255 in hexadecimal is", decimal_to_hexadecimal(255)) - print("4096 in hexadecimal is", decimal_to_hexadecimal(4096)) - print("999098 in hexadecimal is", decimal_to_hexadecimal(999098)) - if __name__ == '__main__': - main() \ No newline at end of file + import doctest + doctest.testmod() From a0817bdcf0c5ce17a7798fc0ede1821cd1bc983f Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Sun, 28 Jul 2019 17:27:23 +0200 Subject: [PATCH 107/193] Rewrite build_directory_md.py (#1076) * Rewrite build_directory_md.py * Regenerate DIRECTORY.md --- .travis.yml | 2 +- DIRECTORY.md | 23 +++++---- scripts/build_directory_md.py | 94 +++++++++++++---------------------- 3 files changed, 48 insertions(+), 71 deletions(-) mode change 100644 => 100755 scripts/build_directory_md.py diff --git a/.travis.yml b/.travis.yml index 6d432c660ddd..d09ef9de262d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -19,5 +19,5 @@ script: --ignore=machine_learning/random_forest_classification/random_forest_classification.py --ignore=machine_learning/random_forest_regression/random_forest_regression.py after_success: - - python scripts/build_directory_md.py + - scripts/build_directory_md.py > DIRECTORY.md - cat DIRECTORY.md diff --git a/DIRECTORY.md b/DIRECTORY.md index fc06a8cd1548..d97791bb5dd3 100644 --- a/DIRECTORY.md +++ b/DIRECTORY.md @@ -1,5 +1,6 @@ ## Arithmetic Analysis * [bisection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/bisection.py) + * [in static equilibrium](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/in_static_equilibrium.py) * [intersection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/intersection.py) * [lu decomposition](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/lu_decomposition.py) * [newton method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_method.py) @@ -42,7 +43,6 @@ * [burrows wheeler](https://github.com/TheAlgorithms/Python/blob/master/compression/burrows_wheeler.py) * [huffman](https://github.com/TheAlgorithms/Python/blob/master/compression/huffman.py) * [peak signal to noise ratio](https://github.com/TheAlgorithms/Python/blob/master/compression/peak_signal_to_noise_ratio.py) - * Image Data ## Conversions * [decimal to binary](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary.py) * [decimal to hexadecimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_hexadecimal.py) @@ -62,9 +62,9 @@ * [double hash](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/double_hash.py) * [hash table](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table.py) * [hash table with linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table_with_linked_list.py) - * [quadratic probing](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/quadratic_probing.py) * Number Theory * [prime numbers](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/number_theory/prime_numbers.py) + * [quadratic probing](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/quadratic_probing.py) * Heap * [heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap.py) * Linked List @@ -87,6 +87,7 @@ * Trie * [trie](https://github.com/TheAlgorithms/Python/blob/master/data_structures/trie/trie.py) ## Digital Image Processing + * [change contrast](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_contrast.py) * Edge Detection * [canny](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/edge_detection/canny.py) * Filters @@ -94,7 +95,6 @@ * [gaussian filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/gaussian_filter.py) * [median filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/median_filter.py) * [sobel filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/sobel_filter.py) - * Image Data ## Divide And Conquer * [closest pair of points](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/closest_pair_of_points.py) * [max subarray sum](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/max_subarray_sum.py) @@ -167,24 +167,22 @@ * [lib](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra_python/src/lib.py) * [tests](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra_python/src/tests.py) ## Machine Learning + * [NaiveBayes](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/NaiveBayes.ipynb) * [decision tree](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/decision_tree.py) * [gradient descent](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_descent.py) * [k means clust](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_means_clust.py) * [knn sklearn](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/knn_sklearn.py) * [linear regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_regression.py) * [logistic regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/logistic_regression.py) - * [NaiveBayes](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/NaiveBayes.ipynb) * [perceptron](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/perceptron.py) - * [reuters one vs rest classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/reuters_one_vs_rest_classifier.ipynb) - * [scoring functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/scoring_functions.py) * Random Forest Classification * [random forest classification](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classification/random_forest_classification.py) * [random forest classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classification/random_forest_classifier.ipynb) - * [Social Network Ads](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classification/Social_Network_Ads.csv) * Random Forest Regression - * [Position Salaries](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression/Position_Salaries.csv) * [random forest regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression/random_forest_regression.ipynb) * [random forest regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression/random_forest_regression.py) + * [reuters one vs rest classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/reuters_one_vs_rest_classifier.ipynb) + * [scoring functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/scoring_functions.py) ## Maths * [3n+1](https://github.com/TheAlgorithms/Python/blob/master/maths/3n+1.py) * [abs](https://github.com/TheAlgorithms/Python/blob/master/maths/abs.py) @@ -203,11 +201,15 @@ * [find lcm](https://github.com/TheAlgorithms/Python/blob/master/maths/find_lcm.py) * [find max](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max.py) * [find min](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min.py) + * [gaussian](https://github.com/TheAlgorithms/Python/blob/master/maths/gaussian.py) * [greater common divisor](https://github.com/TheAlgorithms/Python/blob/master/maths/greater_common_divisor.py) + * [is square free](https://github.com/TheAlgorithms/Python/blob/master/maths/is_square_free.py) * [lucas series](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_series.py) + * [mobius function](https://github.com/TheAlgorithms/Python/blob/master/maths/mobius_function.py) * [modular exponential](https://github.com/TheAlgorithms/Python/blob/master/maths/modular_exponential.py) * [newton raphson](https://github.com/TheAlgorithms/Python/blob/master/maths/newton_raphson.py) * [prime check](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_check.py) + * [prime factors](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_factors.py) * [segmented sieve](https://github.com/TheAlgorithms/Python/blob/master/maths/segmented_sieve.py) * [sieve of eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/sieve_of_eratosthenes.py) * [simpson rule](https://github.com/TheAlgorithms/Python/blob/master/maths/simpson_rule.py) @@ -219,6 +221,8 @@ * [rotate matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/rotate_matrix.py) * [searching in sorted matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/searching_in_sorted_matrix.py) * [spiral print](https://github.com/TheAlgorithms/Python/blob/master/matrix/spiral_print.py) + * Tests + * [test matrix operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/tests/test_matrix_operation.py) ## Networking Flow * [ford fulkerson](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/ford_fulkerson.py) * [minimum cut](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/minimum_cut.py) @@ -228,6 +232,7 @@ * [fully connected neural network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/fully_connected_neural_network.ipynb) * [perceptron](https://github.com/TheAlgorithms/Python/blob/master/neural_network/perceptron.py) ## Other + * [Food wastage analysis from 1961-2013 (FAO)](https://github.com/TheAlgorithms/Python/blob/master/other/Food%20wastage%20analysis%20from%201961-2013%20(FAO).ipynb) * [anagrams](https://github.com/TheAlgorithms/Python/blob/master/other/anagrams.py) * [binary exponentiation](https://github.com/TheAlgorithms/Python/blob/master/other/binary_exponentiation.py) * [binary exponentiation 2](https://github.com/TheAlgorithms/Python/blob/master/other/binary_exponentiation_2.py) @@ -235,7 +240,6 @@ * [euclidean gcd](https://github.com/TheAlgorithms/Python/blob/master/other/euclidean_gcd.py) * [finding primes](https://github.com/TheAlgorithms/Python/blob/master/other/finding_primes.py) * [fischer yates shuffle](https://github.com/TheAlgorithms/Python/blob/master/other/fischer_yates_shuffle.py) - * [Food wastage analysis from 1961-2013 (FAO)](https://github.com/TheAlgorithms/Python/blob/master/other/Food%20wastage%20analysis%20from%201961-2013%20(FAO).ipynb) * [frequency finder](https://github.com/TheAlgorithms/Python/blob/master/other/frequency_finder.py) * [game of life](https://github.com/TheAlgorithms/Python/blob/master/other/game_of_life.py) * [linear congruential generator](https://github.com/TheAlgorithms/Python/blob/master/other/linear_congruential_generator.py) @@ -247,7 +251,6 @@ * [tower of hanoi](https://github.com/TheAlgorithms/Python/blob/master/other/tower_of_hanoi.py) * [two sum](https://github.com/TheAlgorithms/Python/blob/master/other/two_sum.py) * [word patterns](https://github.com/TheAlgorithms/Python/blob/master/other/word_patterns.py) - * [words](https://github.com/TheAlgorithms/Python/blob/master/other/words) ## Project Euler * Problem 01 * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol1.py) diff --git a/scripts/build_directory_md.py b/scripts/build_directory_md.py old mode 100644 new mode 100755 index 47192701880d..2ebd445b3667 --- a/scripts/build_directory_md.py +++ b/scripts/build_directory_md.py @@ -1,71 +1,45 @@ -""" -This is a simple script that will scan through the current directory -and generate the corresponding DIRECTORY.md file, can also specify -files or folders to be ignored. -""" +#!/usr/bin/env python3 + import os +from typing import Iterator + +URL_BASE = "https://github.com/TheAlgorithms/Python/blob/master" + +def good_filepaths(top_dir: str = ".") -> Iterator[str]: + for dirpath, dirnames, filenames in os.walk(top_dir): + dirnames[:] = [d for d in dirnames if d != "scripts" and d[0] not in "._"] + for filename in filenames: + if filename == "__init__.py": + continue + if os.path.splitext(filename)[1] in (".py", ".ipynb"): + yield os.path.join(dirpath, filename).lstrip("./") -# Target URL (master) -URL = "https://github.com/TheAlgorithms/Python/blob/master/" +def md_prefix(i): + return f"{i * ' '}*" if i else "##" -def tree(d, ignores, ignores_ext): - return _markdown(d, ignores, ignores_ext, 0) - -def _markdown(parent, ignores, ignores_ext, depth): - out = "" - dirs, files = [], [] - for i in os.listdir(parent): - full = os.path.join(parent, i) - name, ext = os.path.splitext(i) - if i not in ignores and ext not in ignores_ext: - if os.path.isfile(full): - # generate list - pre = parent.replace("./", "").replace(" ", "%20") - # replace all spaces to safe URL - child = i.replace(" ", "%20") - files.append((pre, child, name)) - else: - dirs.append(i) - # Sort files - files.sort(key=lambda e: e[2].lower()) - for f in files: - pre, child, name = f - out += " " * depth + "* [" + name.replace("_", " ") + "](" + URL + pre + "/" + child + ")\n" - # Sort directories - dirs.sort() - for i in dirs: - full = os.path.join(parent, i) - i = i.replace("_", " ").title() - if depth == 0: - out += "## " + i + "\n" - else: - out += " " * depth + "* " + i + "\n" - out += _markdown(full, ignores, ignores_ext, depth+1) - return out +def print_path(old_path: str, new_path: str) -> str: + old_parts = old_path.split(os.sep) + for i, new_part in enumerate(new_path.split(os.sep)): + if i + 1 > len(old_parts) or old_parts[i] != new_part: + if new_part: + print(f"{md_prefix(i)} {new_part.replace('_', ' ').title()}") + return new_path -# Specific files or folders with the given names will be ignored -ignores = [".vs", - ".gitignore", - ".git", - "scripts", - "__init__.py", - "requirements.txt", - ".github" -] -# Files with given entensions will be ignored -ignores_ext = [ - ".md", - ".ipynb", - ".png", - ".jpg", - ".yml" -] +def print_directory_md(top_dir: str = ".") -> None: + old_path = "" + for filepath in sorted(good_filepaths()): + filepath, filename = os.path.split(filepath) + if filepath != old_path: + old_path = print_path(old_path, filepath) + indent = (filepath.count(os.sep) + 1) if filepath else 0 + url = "/".join((URL_BASE, filepath, filename)).replace(" ", "%20") + filename = os.path.splitext(filename.replace("_", " "))[0] + print(f"{md_prefix(indent)} [{filename}]({url})") if __name__ == "__main__": - with open("DIRECTORY.md", "w+") as f: - f.write(tree(".", ignores, ignores_ext)) + print_directory_md(".") From 7b2c9541691a8a1d6ac523287225bfc368146403 Mon Sep 17 00:00:00 2001 From: Abhijeeth S Date: Tue, 30 Jul 2019 12:17:54 +0530 Subject: [PATCH 108/193] LargestOfVeryLargeNumbers (#818) * LargestOfVeryLargeNumbers Finds the largest among two very large numbers of the form x^y. Numbers like 512^513 etc * Rename LargestOfVeryLargeNumbers to LargestOfVeryLargeNumbers.py * Input() statements have been indented. input() statements are indented under if __name__ == "__main__": * largest_of_very_large_numbers.py --- maths/largest_of_very_large_numbers.py | 35 ++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 maths/largest_of_very_large_numbers.py diff --git a/maths/largest_of_very_large_numbers.py b/maths/largest_of_very_large_numbers.py new file mode 100644 index 000000000000..d2dc0af18126 --- /dev/null +++ b/maths/largest_of_very_large_numbers.py @@ -0,0 +1,35 @@ +# Author: Abhijeeth S + +import math + + +def res(x, y): + if 0 not in (x, y): + # We use the relation x^y = y*log10(x), where 10 is the base. + return y * math.log10(x) + else: + if x == 0: # 0 raised to any number is 0 + return 0 + elif y == 0: + return 1 # any number raised to 0 is 1 + + +if __name__ == "__main__": # Main function + # Read two numbers from input and typecast them to int using map function. + # Here x is the base and y is the power. + prompt = "Enter the base and the power separated by a comma: " + x1, y1 = map(int, input(prompt).split(",")) + x2, y2 = map(int, input(prompt).split(",")) + + # We find the log of each number, using the function res(), which takes two + # arguments. + res1 = res(x1, y1) + res2 = res(x2, y2) + + # We check for the largest number + if res1 > res2: + print("Largest number is", x1, "^", y1) + elif res2 > res1: + print("Largest number is", x2, "^", y2) + else: + print("Both are equal") From a9ecdb33ca04d44c979d8d9c1c99df312f4dd50c Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Tue, 30 Jul 2019 12:02:13 +0200 Subject: [PATCH 109/193] Validate Python filenames (#1086) --- .travis.yml | 1 + .../{NaiveBayes.ipynb => naive_bayes.ipynb} | 0 ...wastage_analysis_from_1961-2013_fao.ipynb} | 0 scripts/validate_filenames.py | 28 +++++++++++++++++++ 4 files changed, 29 insertions(+) rename machine_learning/{NaiveBayes.ipynb => naive_bayes.ipynb} (100%) rename other/{Food wastage analysis from 1961-2013 (FAO).ipynb => food_wastage_analysis_from_1961-2013_fao.ipynb} (100%) create mode 100755 scripts/validate_filenames.py diff --git a/.travis.yml b/.travis.yml index d09ef9de262d..c46d0d1d653a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -8,6 +8,7 @@ before_script: - black --check . || true - flake8 . --count --select=E9,F401,F63,F7,F82 --show-source --statistics script: + - scripts/validate_filenames.py # no uppercase and no spaces - mypy --ignore-missing-imports . - pytest . --doctest-modules --ignore=data_structures/stacks/balanced_parentheses.py diff --git a/machine_learning/NaiveBayes.ipynb b/machine_learning/naive_bayes.ipynb similarity index 100% rename from machine_learning/NaiveBayes.ipynb rename to machine_learning/naive_bayes.ipynb diff --git a/other/Food wastage analysis from 1961-2013 (FAO).ipynb b/other/food_wastage_analysis_from_1961-2013_fao.ipynb similarity index 100% rename from other/Food wastage analysis from 1961-2013 (FAO).ipynb rename to other/food_wastage_analysis_from_1961-2013_fao.ipynb diff --git a/scripts/validate_filenames.py b/scripts/validate_filenames.py new file mode 100755 index 000000000000..9e1f1503321b --- /dev/null +++ b/scripts/validate_filenames.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python3 + +import os +from build_directory_md import good_filepaths + +filepaths = list(good_filepaths()) +assert filepaths, "good_filepaths() failed!" + + +upper_files = [file for file in filepaths if file != file.lower()] +if upper_files: + print(f"{len(upper_files)} files contain uppercase characters:") + print("\n".join(upper_files) + "\n") + +space_files = [file for file in filepaths if " " in file] +if space_files: + print(f"{len(space_files)} files contain space characters:") + print("\n".join(space_files) + "\n") + +nodir_files = [file for file in filepaths if os.sep not in file] +if nodir_files: + print(f"{len(nodir_files)} files are not in a directory:") + print("\n".join(nodir_files) + "\n") + +bad_files = len(upper_files + space_files + nodir_files) +if bad_files: + import sys + sys.exit(bad_files) From 861a8c36316a0bb10ee93f5560ce3313ef991399 Mon Sep 17 00:00:00 2001 From: obelisk0114 Date: Tue, 30 Jul 2019 09:00:24 -0700 Subject: [PATCH 110/193] Add Lucas_Lehmer_primality_test (#1050) * Add Lucas_Lehmer_primality_test * Add explanation for Lucas_Lehmer_primality_test * Update and rename Lucas_Lehmer_primality_test.py to lucas_lehmer_primality_test.py --- maths/lucas_lehmer_primality_test.py | 42 ++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 maths/lucas_lehmer_primality_test.py diff --git a/maths/lucas_lehmer_primality_test.py b/maths/lucas_lehmer_primality_test.py new file mode 100644 index 000000000000..44e41ba58d93 --- /dev/null +++ b/maths/lucas_lehmer_primality_test.py @@ -0,0 +1,42 @@ +# -*- coding: utf-8 -*- +""" + In mathematics, the Lucas–Lehmer test (LLT) is a primality test for Mersenne numbers. + https://en.wikipedia.org/wiki/Lucas%E2%80%93Lehmer_primality_test + + A Mersenne number is a number that is one less than a power of two. + That is M_p = 2^p - 1 + https://en.wikipedia.org/wiki/Mersenne_prime + + The Lucas–Lehmer test is the primality test used by the + Great Internet Mersenne Prime Search (GIMPS) to locate large primes. +""" + + +# Primality test 2^p - 1 +# Return true if 2^p - 1 is prime +def lucas_lehmer_test(p: int) -> bool: + """ + >>> lucas_lehmer_test(p=7) + True + + >>> lucas_lehmer_test(p=11) + False + + # M_11 = 2^11 - 1 = 2047 = 23 * 89 + """ + + if p < 2: + raise ValueError("p should not be less than 2!") + elif p == 2: + return True + + s = 4 + M = (1 << p) - 1 + for i in range(p - 2): + s = ((s * s) - 2) % M + return s == 0 + + +if __name__ == "__main__": + print(lucas_lehmer_test(7)) + print(lucas_lehmer_test(11)) From e58a5e68424df74a4c7b30df04162c775044405c Mon Sep 17 00:00:00 2001 From: FrogBattle <44649323+FrogBattle@users.noreply.github.com> Date: Tue, 30 Jul 2019 17:06:48 +0100 Subject: [PATCH 111/193] Update tim_sort.py (#972) * Update tim_sort.py Update tim_sort.py The previous algorithm was skipping numbers, according to issue #959, and my own tests. The version I am applying uses a while loop, which works correctly and is easier to compute, as there is no break statement. * Update tim_sort.py --- sorts/tim_sort.py | 47 ++++++++++++++++++++++++----------------------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/sorts/tim_sort.py b/sorts/tim_sort.py index b4032b91aec1..b95ff34cf384 100644 --- a/sorts/tim_sort.py +++ b/sorts/tim_sort.py @@ -1,10 +1,6 @@ -from __future__ import print_function def binary_search(lst, item, start, end): if start == end: - if lst[start] > item: - return start - else: - return start + 1 + return start if lst[start] > item else start + 1 if start > end: return start @@ -23,7 +19,7 @@ def insertion_sort(lst): for index in range(1, length): value = lst[index] pos = binary_search(lst, value, 0, index - 1) - lst = lst[:pos] + [value] + lst[pos:index] + lst[index+1:] + lst = lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :] return lst @@ -42,30 +38,34 @@ def merge(left, right): def tim_sort(lst): - runs, sorted_runs = [], [] + """ + >>> tim_sort("Python") + ['P', 'h', 'n', 'o', 't', 'y'] + >>> tim_sort((1.1, 1, 0, -1, -1.1)) + [-1.1, -1, 0, 1, 1.1] + >>> tim_sort(list(reversed(list(range(7))))) + [0, 1, 2, 3, 4, 5, 6] + >>> tim_sort([3, 2, 1]) == insertion_sort([3, 2, 1]) + True + >>> tim_sort([3, 2, 1]) == sorted([3, 2, 1]) + True + """ length = len(lst) + runs, sorted_runs = [], [] new_run = [lst[0]] sorted_array = [] - - for i in range(1, length): - if i == length - 1: - new_run.append(lst[i]) - runs.append(new_run) - break - + i = 1 + while i < length: if lst[i] < lst[i - 1]: - if not new_run: - runs.append([lst[i - 1]]) - new_run.append(lst[i]) - else: - runs.append(new_run) - new_run = [] + runs.append(new_run) + new_run = [lst[i]] else: new_run.append(lst[i]) + i += 1 + runs.append(new_run) for run in runs: sorted_runs.append(insertion_sort(run)) - for run in sorted_runs: sorted_array = merge(sorted_array, run) @@ -74,9 +74,10 @@ def tim_sort(lst): def main(): - lst = [5,9,10,3,-4,5,178,92,46,-18,0,7] + lst = [5, 9, 10, 3, -4, 5, 178, 92, 46, -18, 0, 7] sorted_lst = tim_sort(lst) print(sorted_lst) -if __name__ == '__main__': + +if __name__ == "__main__": main() From 4a5589f4fcb71fc6101132bad44981693ef65d2c Mon Sep 17 00:00:00 2001 From: vinayak Date: Wed, 31 Jul 2019 16:50:32 +0530 Subject: [PATCH 112/193] project_euler/problem_10 (#1089) * project_euler/problem_10 * update project_euler/problem_10 * update project_euler/problem_10 * Negative user tests added. --- project_euler/problem_10/sol3.py | 58 ++++++++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) create mode 100644 project_euler/problem_10/sol3.py diff --git a/project_euler/problem_10/sol3.py b/project_euler/problem_10/sol3.py new file mode 100644 index 000000000000..e5bc0731d8ab --- /dev/null +++ b/project_euler/problem_10/sol3.py @@ -0,0 +1,58 @@ +""" +https://projecteuler.net/problem=10 + +Problem Statement: +The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17. + +Find the sum of all the primes below two million using Sieve_of_Eratosthenes: + +The sieve of Eratosthenes is one of the most efficient ways to find all primes +smaller than n when n is smaller than 10 million. Only for positive numbers. +""" + + +def prime_sum(n: int) -> int: + """ Returns the sum of all the primes below n. + + >>> prime_sum(2_000_000) + 142913828922 + >>> prime_sum(1_000) + 76127 + >>> prime_sum(5_000) + 1548136 + >>> prime_sum(10_000) + 5736396 + >>> prime_sum(7) + 10 + >>> prime_sum(7.1) # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + TypeError: 'float' object cannot be interpreted as an integer + >>> prime_sum(-7) # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + IndexError: list assignment index out of range + >>> prime_sum("seven") # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + TypeError: can only concatenate str (not "int") to str + """ + list_ = [0 for i in range(n + 1)] + list_[0] = 1 + list_[1] = 1 + + for i in range(2, int(n ** 0.5) + 1): + if list_[i] == 0: + for j in range(i * i, n + 1, i): + list_[j] = 1 + s = 0 + for i in range(n): + if list_[i] == 0: + s += i + return s + + +if __name__ == "__main__": + # import doctest + # doctest.testmod() + print(prime_sum(int(input().strip()))) From 7b267e5e4f8ccb72dd58fcf0057642fd62a36bdf Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Wed, 31 Jul 2019 17:14:35 +0200 Subject: [PATCH 113/193] Fix data_structures to pass our Travis CI pytests (#1088) * Fix data_structures to pass pytests * Restore data_structures/stacks/__init__.py --- .travis.yml | 2 -- data_structures/stacks/balanced_parentheses.py | 3 ++- data_structures/stacks/infix_to_postfix_conversion.py | 2 +- 3 files changed, 3 insertions(+), 4 deletions(-) diff --git a/.travis.yml b/.travis.yml index c46d0d1d653a..eab55af63492 100644 --- a/.travis.yml +++ b/.travis.yml @@ -11,8 +11,6 @@ script: - scripts/validate_filenames.py # no uppercase and no spaces - mypy --ignore-missing-imports . - pytest . --doctest-modules - --ignore=data_structures/stacks/balanced_parentheses.py - --ignore=data_structures/stacks/infix_to_postfix_conversion.py --ignore=file_transfer_protocol/ftp_send_receive.py --ignore=file_transfer_protocol/ftp_client_server.py --ignore=machine_learning/linear_regression.py diff --git a/data_structures/stacks/balanced_parentheses.py b/data_structures/stacks/balanced_parentheses.py index 3229d19c8621..36a4e07a97a3 100644 --- a/data_structures/stacks/balanced_parentheses.py +++ b/data_structures/stacks/balanced_parentheses.py @@ -1,6 +1,7 @@ from __future__ import print_function from __future__ import absolute_import -from stack import Stack + +from .stack import Stack __author__ = 'Omkar Pathak' diff --git a/data_structures/stacks/infix_to_postfix_conversion.py b/data_structures/stacks/infix_to_postfix_conversion.py index e71dccf1f45c..9376b55b8b23 100644 --- a/data_structures/stacks/infix_to_postfix_conversion.py +++ b/data_structures/stacks/infix_to_postfix_conversion.py @@ -2,7 +2,7 @@ from __future__ import absolute_import import string -from stack import Stack +from .stack import Stack __author__ = 'Omkar Pathak' From 9c0cbe33076a570a3c02825b7c6d9866a760e777 Mon Sep 17 00:00:00 2001 From: Sanders Lin <45224617+SandersLin@users.noreply.github.com> Date: Thu, 1 Aug 2019 23:54:03 +0800 Subject: [PATCH 114/193] Create collatz_sequence.py (#639) * Create collatz_sequence.py * Update and rename collatz_sequence.py to maths/collatz_sequence.py * doctest --- maths/collatz_sequence.py | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 maths/collatz_sequence.py diff --git a/maths/collatz_sequence.py b/maths/collatz_sequence.py new file mode 100644 index 000000000000..9f88453d518b --- /dev/null +++ b/maths/collatz_sequence.py @@ -0,0 +1,28 @@ +def collatz_sequence(n): + """ + Collatz conjecture: start with any positive integer n.Next termis obtained from the previous term as follows: + if the previous term is even, the next term is one half the previous term. + If the previous term is odd, the next term is 3 times the previous term plus 1. + The conjecture states the sequence will always reach 1 regaardess of starting n. + Example: + >>> collatz_sequence(43) + [43, 130, 65, 196, 98, 49, 148, 74, 37, 112, 56, 28, 14, 7, 22, 11, 34, 17, 52, 26, 13, 40, 20, 10, 5, 16, 8, 4, 2, 1] + """ + sequence = [n] + while n != 1: + if n % 2 == 0:# even + n //= 2 + else: + n = 3*n +1 + sequence.append(n) + return sequence + + +def main(): + n = 43 + sequence = collatz_sequence(n) + print(sequence) + print("collatz sequence from %d took %d steps."%(n,len(sequence))) + +if __name__ == '__main__': + main() From e3131419048010d9d67441387da4e73755364cf2 Mon Sep 17 00:00:00 2001 From: Syed Waleed Hyder Date: Sat, 3 Aug 2019 20:00:10 +0200 Subject: [PATCH 115/193] bin(num). convert ZERO and negative decimal numbers to binary. (#1093) * bin(num) can convert ZERO and negative decimal numbers to binary. Consistent with built-in python bin(x) function. * bin(num) can convert ZERO and negative decimal numbers to binary. Consistent with built-in python bin(x) function. * Added doctests. bin(num) can convert ZERO and negative decimal numbers to binary. Consistent with built-in python bin(x) function. * Added doctests. bin(num) can convert ZERO and negative decimal numbers to binary. Consistent with built-in python bin(x) function. * Added doctests. bin(num) can convert ZERO and negative decimal numbers to binary. Consistent with built-in python bin(x) function. * doctests still failing. * Doctests added. --- conversions/decimal_to_binary.py | 59 +++++++++++++++++++++++++------- 1 file changed, 46 insertions(+), 13 deletions(-) diff --git a/conversions/decimal_to_binary.py b/conversions/decimal_to_binary.py index 43ceee61a388..934cf0dfb363 100644 --- a/conversions/decimal_to_binary.py +++ b/conversions/decimal_to_binary.py @@ -2,24 +2,57 @@ def decimal_to_binary(num): - """Convert a Decimal Number to a Binary Number.""" + + """ + Convert a Integer Decimal Number to a Binary Number as str. + >>> decimal_to_binary(0) + '0b0' + >>> decimal_to_binary(2) + '0b10' + >>> decimal_to_binary(7) + '0b111' + >>> decimal_to_binary(35) + '0b100011' + >>> # negatives work too + >>> decimal_to_binary(-2) + '-0b10' + >>> # other floats will error + >>> decimal_to_binary(16.16) # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + TypeError: 'float' object cannot be interpreted as an integer + >>> # strings will error as well + >>> decimal_to_binary('0xfffff') # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + TypeError: 'str' object cannot be interpreted as an integer + """ + + if type(num) == float: + raise TypeError("'float' object cannot be interpreted as an integer") + if type(num) == str: + raise TypeError("'str' object cannot be interpreted as an integer") + + if num == 0: + return "0b0" + + negative = False + + if num < 0: + negative = True + num = -num + binary = [] while num > 0: binary.insert(0, num % 2) num >>= 1 - return "".join(str(e) for e in binary) + if negative: + return "-0b" + "".join(str(e) for e in binary) -def main(): - """Print binary equivelents of decimal numbers.""" - print("\n2 in binary is:") - print(decimal_to_binary(2)) # = 10 - print("\n7 in binary is:") - print(decimal_to_binary(7)) # = 111 - print("\n35 in binary is:") - print(decimal_to_binary(35)) # = 100011 - print("\n") + return "0b" + "".join(str(e) for e in binary) -if __name__ == '__main__': - main() +if __name__ == "__main__": + import doctest + doctest.testmod() From bdbe6825684d61131e0caee3a7361bd581c2442b Mon Sep 17 00:00:00 2001 From: "Marvin M. Michum" Date: Sun, 4 Aug 2019 23:22:28 -0400 Subject: [PATCH 116/193] Zeller's Congruence Algorithm (#1095) * doctest updates * remove unused math import * cleanup (suggestions) * cleanup - Dict fix (TravisCI error) --- maths/zellers_congruence.py | 157 ++++++++++++++++++++++++++++++++++++ 1 file changed, 157 insertions(+) create mode 100644 maths/zellers_congruence.py diff --git a/maths/zellers_congruence.py b/maths/zellers_congruence.py new file mode 100644 index 000000000000..e04425eec903 --- /dev/null +++ b/maths/zellers_congruence.py @@ -0,0 +1,157 @@ +from __future__ import annotations +import datetime +import argparse + + +def zeller(date_input: str) -> str: + + """ + Zellers Congruence Algorithm + Find the day of the week for nearly any Gregorian or Julian calendar date + + >>> zeller('01-31-2010') + 'Your date 01-31-2010, is a Sunday!' + + Validate out of range month + >>> zeller('13-31-2010') + Traceback (most recent call last): + ... + ValueError: Month must be between 1 - 12 + >>> zeller('.2-31-2010') + Traceback (most recent call last): + ... + ValueError: invalid literal for int() with base 10: '.2' + + Validate out of range date: + >>> zeller('01-33-2010') + Traceback (most recent call last): + ... + ValueError: Date must be between 1 - 31 + >>> zeller('01-.4-2010') + Traceback (most recent call last): + ... + ValueError: invalid literal for int() with base 10: '.4' + + Validate second seperator: + >>> zeller('01-31*2010') + Traceback (most recent call last): + ... + ValueError: Date seperator must be '-' or '/' + + Validate first seperator: + >>> zeller('01^31-2010') + Traceback (most recent call last): + ... + ValueError: Date seperator must be '-' or '/' + + Validate out of range year: + >>> zeller('01-31-8999') + Traceback (most recent call last): + ... + ValueError: Year out of range. There has to be some sort of limit...right? + + Test null input: + >>> zeller() + Traceback (most recent call last): + ... + TypeError: zeller() missing 1 required positional argument: 'date_input' + + Test length fo date_input: + >>> zeller('') + Traceback (most recent call last): + ... + ValueError: Must be 10 characters long + >>> zeller('01-31-19082939') + Traceback (most recent call last): + ... + ValueError: Must be 10 characters long +""" + + # Days of the week for response + days = { + '0': 'Sunday', + '1': 'Monday', + '2': 'Tuesday', + '3': 'Wednesday', + '4': 'Thursday', + '5': 'Friday', + '6': 'Saturday' + } + + convert_datetime_days = { + 0:1, + 1:2, + 2:3, + 3:4, + 4:5, + 5:6, + 6:0 + } + + # Validate + if not 0 < len(date_input) < 11: + raise ValueError("Must be 10 characters long") + + # Get month + m: int = int(date_input[0] + date_input[1]) + # Validate + if not 0 < m < 13: + raise ValueError("Month must be between 1 - 12") + + sep_1:str = date_input[2] + # Validate + if sep_1 not in ["-","/"]: + raise ValueError("Date seperator must be '-' or '/'") + + # Get day + d: int = int(date_input[3] + date_input[4]) + # Validate + if not 0 < d < 32: + raise ValueError("Date must be between 1 - 31") + + # Get second seperator + sep_2: str = date_input[5] + # Validate + if sep_2 not in ["-","/"]: + raise ValueError("Date seperator must be '-' or '/'") + + # Get year + y: int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9]) + # Arbitrary year range + if not 45 < y < 8500: + raise ValueError("Year out of range. There has to be some sort of limit...right?") + + # Get datetime obj for validation + dt_ck = datetime.date(int(y), int(m), int(d)) + + # Start math + if m <= 2: + y = y - 1 + m = m + 12 + # maths var + c: int = int(str(y)[:2]) + k: int = int(str(y)[2:]) + t: int = int(2.6*m - 5.39) + u: int = int(c / 4) + v: int = int(k / 4) + x: int = int(d + k) + z: int = int(t + u + v + x) + w: int = int(z - (2 * c)) + f: int = round(w%7) + # End math + + # Validate math + if f != convert_datetime_days[dt_ck.weekday()]: + raise AssertionError("The date was evaluated incorrectly. Contact developer.") + + # Response + response: str = f"Your date {date_input}, is a {days[str(f)]}!" + return response + +if __name__ == '__main__': + import doctest + doctest.testmod() + parser = argparse.ArgumentParser(description='Find out what day of the week nearly any date is or was. Enter date as a string in the mm-dd-yyyy or mm/dd/yyyy format') + parser.add_argument('date_input', type=str, help='Date as a string (mm-dd-yyyy or mm/dd/yyyy)') + args = parser.parse_args() + zeller(args.date_input) From 87a789af515333c35aed1060c7a2b5c7d287123c Mon Sep 17 00:00:00 2001 From: "Marvin M. Michum" Date: Mon, 5 Aug 2019 01:05:36 -0400 Subject: [PATCH 117/193] Boolean algebra pytests (#1097) * Added Zeller's congruence algorithm * Update args help * add a few doctests * remove old file --- boolean_algebra/quine_mc_cluskey.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/boolean_algebra/quine_mc_cluskey.py b/boolean_algebra/quine_mc_cluskey.py index db4d153cbfd7..94319ca45482 100644 --- a/boolean_algebra/quine_mc_cluskey.py +++ b/boolean_algebra/quine_mc_cluskey.py @@ -1,3 +1,18 @@ +""" + doctests + + >>> decimal_to_binary(3,[1.5]) + ['0.00.01.5'] + + >>> check(['0.00.01.5']) + ['0.00.01.5'] + + >>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5']) + [[1]] + + >>> selection([[1]],['0.00.01.5']) + ['0.00.01.5'] +""" def compare_string(string1, string2): l1 = list(string1); l2 = list(string2) count = 0 @@ -113,4 +128,6 @@ def main(): print(essential_prime_implicants) if __name__ == '__main__': + import doctest + doctest.testmod() main() From 4437439363c8aa3347dfcd817afe7c69b3d7f59e Mon Sep 17 00:00:00 2001 From: Hector S Date: Mon, 5 Aug 2019 01:07:52 -0400 Subject: [PATCH 118/193] Added Unicode test to strings/rabin_karp.py (#1096) * Added print function into matrix_multiplication_addition.py and removed blank space in data_structures/binary tree directory * Removed .vs/ folder per #893 * Rename matrix_multiplication_addition.py to matrix_operation.py * Unicode test on strings/rabin_karp.py per #1067 --- strings/rabin_karp.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/strings/rabin_karp.py b/strings/rabin_karp.py index 7c36f7659e24..1fb145ec97fa 100644 --- a/strings/rabin_karp.py +++ b/strings/rabin_karp.py @@ -73,6 +73,13 @@ def test_rabin_karp(): pattern = "abcdabcy" text = "abcxabcdabxabcdabcdabcy" assert rabin_karp(pattern, text) + + # Test 5) + pattern = "Lü" + text = "Lüsai" + assert rabin_karp(pattern, text) + pattern = "Lue" + assert not rabin_karp(pattern, text) print("Success.") From 47bc34ac268d1b0102c72d9cf5f2b7cb52db6a5e Mon Sep 17 00:00:00 2001 From: QuantumNovice <43876848+QuantumNovice@users.noreply.github.com> Date: Tue, 6 Aug 2019 05:06:15 +0500 Subject: [PATCH 119/193] Added pytests to sha1.py (#1098) --- 16L' | 0 Q' | 0 hashes/sha1.py | 10 ++++++++-- 3 files changed, 8 insertions(+), 2 deletions(-) create mode 100644 16L' create mode 100644 Q' diff --git a/16L' b/16L' new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/Q' b/Q' new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/hashes/sha1.py b/hashes/sha1.py index 4c78ad3a89e5..511ea6363733 100644 --- a/hashes/sha1.py +++ b/hashes/sha1.py @@ -2,7 +2,7 @@ Demonstrates implementation of SHA1 Hash function in a Python class and gives utilities to find hash of string or hash of text from a file. Usage: python sha1.py --string "Hello World!!" - pyhton sha1.py --file "hello_world.txt" + python sha1.py --file "hello_world.txt" When run without any arguments, it prints the hash of the string "Hello World!! Welcome to Cryptography" Also contains a Test class to verify that the generated Hash is same as that returned by the hashlib library @@ -32,6 +32,8 @@ class SHA1Hash: """ Class to contain the entire pipeline for SHA1 Hashing Algorithm + >>> SHA1Hash(bytes('Allan', 'utf-8')).final_hash() + '872af2d8ac3d8695387e7c804bf0e02c18df9e6e' """ def __init__(self, data): """ @@ -47,6 +49,8 @@ def __init__(self, data): def rotate(n, b): """ Static method to be used inside other methods. Left rotates n by b. + >>> SHA1Hash('').rotate(12,2) + 48 """ return ((n << b) | (n >> (32 - b))) & 0xffffffff @@ -68,7 +72,7 @@ def split_blocks(self): def expand_block(self, block): """ Takes a bytestring-block of length 64, unpacks it to a list of integers and returns a - list of 80 integers pafter some bit operations + list of 80 integers after some bit operations """ w = list(struct.unpack('>16L', block)) + [0] * 64 for i in range(16, 80): @@ -146,3 +150,5 @@ def main(): if __name__ == '__main__': main() + import doctest + doctest.testmod() \ No newline at end of file From 22d2453773522b677052d074ca0b8f2315309a01 Mon Sep 17 00:00:00 2001 From: AugustofCravo <49079453+AugustofCravo@users.noreply.github.com> Date: Mon, 5 Aug 2019 21:22:34 -0300 Subject: [PATCH 120/193] Create Quadratic Equations(Complexes Numbers) (#941) * Create Quadratic Equations(Complexes Numbers) Created function that solves quadratic equations treating the cases with complexes numbers. Giving an answer with the imaginary unit "i". * Update Quadratic Equations(Complexes Numbers) Since there was no response from the owner of this PR, I made this little change which I hope will solve the issue! --- maths/Quadratic Equations(Complexes Numbers) | 40 ++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 maths/Quadratic Equations(Complexes Numbers) diff --git a/maths/Quadratic Equations(Complexes Numbers) b/maths/Quadratic Equations(Complexes Numbers) new file mode 100644 index 000000000000..8e8e78fec68f --- /dev/null +++ b/maths/Quadratic Equations(Complexes Numbers) @@ -0,0 +1,40 @@ +from __future__ import print_function +import math + +def QuadraticEquation(a,b,c): + """ + Prints the solutions for a quadratic equation, given the numerical coefficients a, b and c, + for a*x*x + b*x + c. + Ex.: a = 1, b = 3, c = -4 + Solution1 = 1 and Solution2 = -4 + """ + Delta = b*b - 4*a*c + if a != 0: + if Delta >= 0: + Solution1 = (-b + math.sqrt(Delta))/(2*a) + Solution2 = (-b - math.sqrt(Delta))/(2*a) + print ("The equation solutions are: ", Solution1," and ", Solution2) + else: + """ + Treats cases of Complexes Solutions(i = imaginary unit) + Ex.: a = 5, b = 2, c = 1 + Solution1 = (- 2 + 4.0 *i)/2 and Solution2 = (- 2 + 4.0 *i)/ 10 + """ + if b > 0: + print("The equation solutions are: (-",b,"+",math.sqrt(-Delta),"*i)/2 and (-",b,"+",math.sqrt(-Delta),"*i)/", 2*a) + if b < 0: + print("The equation solutions are: (",b,"+",math.sqrt(-Delta),"*i)/2 and (",b,"+",math.sqrt(-Delta),"*i/",2*a) + if b == 0: + print("The equation solutions are: (",math.sqrt(-Delta),"*i)/2 and ",math.sqrt(-Delta),"*i)/", 2*a) + else: + print("Error. Please, coeficient 'a' must not be zero for quadratic equations.") +def main(): + a = 5 + b = 6 + c = 1 + + QuadraticEquation(a,b,c) # The equation solutions are: -0.2 and -1.0 + + +if __name__ == '__main__': + main() From 58126406fd345c4f0dda6b7d8d5beaa1b9360810 Mon Sep 17 00:00:00 2001 From: rsun0013 <50036197+rsun0013@users.noreply.github.com> Date: Tue, 6 Aug 2019 19:17:17 +1000 Subject: [PATCH 121/193] pytests for closest_pair_of_points.py (#1099) added some tests to the file --- divide_and_conquer/closest_pair_of_points.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/divide_and_conquer/closest_pair_of_points.py b/divide_and_conquer/closest_pair_of_points.py index ee06d27063df..b6f63396410c 100644 --- a/divide_and_conquer/closest_pair_of_points.py +++ b/divide_and_conquer/closest_pair_of_points.py @@ -19,6 +19,19 @@ Time complexity: O(n * log n) """ +""" + doctests + >>> euclidean_distance_sqr([1,2],[2,4]) + 5 + >>> dis_between_closest_pair([[1,2],[2,4],[5,7],[8,9],[11,0]],5) + 5 + >>> dis_between_closest_in_strip([[1,2],[2,4],[5,7],[8,9],[11,0]],5) + 85 + >>> points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)] + >>> print("Distance:", closest_pair_of_points(points, len(points))) + "Distance: 1.4142135623730951" +""" + def euclidean_distance_sqr(point1, point2): return (point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2 From 6654e1ec7de8c4ca6ee604799645b4b210856190 Mon Sep 17 00:00:00 2001 From: Harshil Date: Tue, 6 Aug 2019 11:41:23 +0200 Subject: [PATCH 122/193] remove from __future__, propre filename (#1102) --- ...Complexes Numbers) => quadratic_equations_complex_numbers.py} | 1 - 1 file changed, 1 deletion(-) rename maths/{Quadratic Equations(Complexes Numbers) => quadratic_equations_complex_numbers.py} (97%) diff --git a/maths/Quadratic Equations(Complexes Numbers) b/maths/quadratic_equations_complex_numbers.py similarity index 97% rename from maths/Quadratic Equations(Complexes Numbers) rename to maths/quadratic_equations_complex_numbers.py index 8e8e78fec68f..f05b938fefe9 100644 --- a/maths/Quadratic Equations(Complexes Numbers) +++ b/maths/quadratic_equations_complex_numbers.py @@ -1,4 +1,3 @@ -from __future__ import print_function import math def QuadraticEquation(a,b,c): From 89acf5d01733754b1403df2313a0a6ef17b4b051 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Tue, 6 Aug 2019 12:14:23 +0200 Subject: [PATCH 123/193] print() is a function just like every other function (#1101) * print() is a function just like every other function --- arithmetic_analysis/newton_raphson_method.py | 16 ++-- ciphers/caesar_cipher.py | 6 +- ciphers/morse_code_implementation.py | 4 +- ciphers/trafid_cipher.py | 14 +-- ciphers/xor_cipher.py | 18 ++-- data_structures/binary_tree/fenwick_tree.py | 12 +-- .../binary_tree/lazy_segment_tree.py | 18 ++-- data_structures/binary_tree/segment_tree.py | 20 ++-- data_structures/queue/double_ended_queue.py | 42 ++++----- data_structures/stacks/stock_span_problem.py | 92 +++++++++---------- machine_learning/logistic_regression.py | 10 +- maths/quadratic_equations_complex_numbers.py | 8 +- other/fischer_yates_shuffle.py | 6 +- 13 files changed, 133 insertions(+), 133 deletions(-) diff --git a/arithmetic_analysis/newton_raphson_method.py b/arithmetic_analysis/newton_raphson_method.py index 569f96476afc..bb6fdd2193ec 100644 --- a/arithmetic_analysis/newton_raphson_method.py +++ b/arithmetic_analysis/newton_raphson_method.py @@ -8,25 +8,25 @@ def NewtonRaphson(func, a): ''' Finds root from the point 'a' onwards by Newton-Raphson method ''' while True: c = Decimal(a) - ( Decimal(eval(func)) / Decimal(eval(str(diff(func)))) ) - + a = c # This number dictates the accuracy of the answer if abs(eval(func)) < 10**-15: return c - + # Let's Execute if __name__ == '__main__': # Find root of trigonometric function # Find value of pi - print ('sin(x) = 0', NewtonRaphson('sin(x)', 2)) - + print('sin(x) = 0', NewtonRaphson('sin(x)', 2)) + # Find root of polynomial - print ('x**2 - 5*x +2 = 0', NewtonRaphson('x**2 - 5*x +2', 0.4)) - + print('x**2 - 5*x +2 = 0', NewtonRaphson('x**2 - 5*x +2', 0.4)) + # Find Square Root of 5 - print ('x**2 - 5 = 0', NewtonRaphson('x**2 - 5', 0.1)) + print('x**2 - 5 = 0', NewtonRaphson('x**2 - 5', 0.1)) # Exponential Roots - print ('exp(x) - 1 = 0', NewtonRaphson('exp(x) - 1', 0)) + print('exp(x) - 1 = 0', NewtonRaphson('exp(x) - 1', 0)) diff --git a/ciphers/caesar_cipher.py b/ciphers/caesar_cipher.py index 872b5d8195c1..95d65d404266 100644 --- a/ciphers/caesar_cipher.py +++ b/ciphers/caesar_cipher.py @@ -41,12 +41,12 @@ def main(): print("4.Quit") choice = input("What would you like to do?: ") if choice not in ['1', '2', '3', '4']: - print ("Invalid choice, please enter a valid choice") + print("Invalid choice, please enter a valid choice") elif choice == '1': strng = input("Please enter the string to be encrypted: ") key = int(input("Please enter off-set between 1-94: ")) if key in range(1, 95): - print (encrypt(strng.lower(), key)) + print(encrypt(strng.lower(), key)) elif choice == '2': strng = input("Please enter the string to be decrypted: ") key = int(input("Please enter off-set between 1-94: ")) @@ -57,7 +57,7 @@ def main(): brute_force(strng) main() elif choice == '4': - print ("Goodbye.") + print("Goodbye.") break diff --git a/ciphers/morse_code_implementation.py b/ciphers/morse_code_implementation.py index 7b2d0a94b24b..5d0e7b2779b1 100644 --- a/ciphers/morse_code_implementation.py +++ b/ciphers/morse_code_implementation.py @@ -71,11 +71,11 @@ def decrypt(message): def main(): message = "Morse code here" result = encrypt(message.upper()) - print (result) + print(result) message = result result = decrypt(message) - print (result) + print(result) if __name__ == '__main__': diff --git a/ciphers/trafid_cipher.py b/ciphers/trafid_cipher.py index 0453272f26a0..53f4d288bfe2 100644 --- a/ciphers/trafid_cipher.py +++ b/ciphers/trafid_cipher.py @@ -3,7 +3,7 @@ def __encryptPart(messagePart, character2Number): one, two, three = "", "", "" tmp = [] - + for character in messagePart: tmp.append(character2Number[character]) @@ -11,7 +11,7 @@ def __encryptPart(messagePart, character2Number): one += each[0] two += each[1] three += each[2] - + return one+two+three def __decryptPart(messagePart, character2Number): @@ -25,7 +25,7 @@ def __decryptPart(messagePart, character2Number): tmp += digit if len(tmp) == len(messagePart): result.append(tmp) - tmp = "" + tmp = "" return result[0], result[1], result[2] @@ -48,7 +48,7 @@ def __prepare(message, alphabet): for letter, number in zip(alphabet, numbers): character2Number[letter] = number number2Character[number] = letter - + return message, alphabet, character2Number, number2Character def encryptMessage(message, alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period=5): @@ -57,7 +57,7 @@ def encryptMessage(message, alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period=5): for i in range(0, len(message)+1, period): encrypted_numeric += __encryptPart(message[i:i+period], character2Number) - + for i in range(0, len(encrypted_numeric), 3): encrypted += number2Character[encrypted_numeric[i:i+3]] @@ -70,7 +70,7 @@ def decryptMessage(message, alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period=5): for i in range(0, len(message)+1, period): a,b,c = __decryptPart(message[i:i+period], character2Number) - + for j in range(0, len(a)): decrypted_numeric.append(a[j]+b[j]+c[j]) @@ -83,4 +83,4 @@ def decryptMessage(message, alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.", period=5): msg = "DEFEND THE EAST WALL OF THE CASTLE." encrypted = encryptMessage(msg,"EPSDUCVWYM.ZLKXNBTFGORIJHAQ") decrypted = decryptMessage(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ") - print ("Encrypted: {}\nDecrypted: {}".format(encrypted, decrypted)) \ No newline at end of file + print("Encrypted: {}\nDecrypted: {}".format(encrypted, decrypted)) diff --git a/ciphers/xor_cipher.py b/ciphers/xor_cipher.py index 727fac3b0703..8bb94212c15a 100644 --- a/ciphers/xor_cipher.py +++ b/ciphers/xor_cipher.py @@ -122,7 +122,7 @@ def decrypt_string(self,content,key = 0): # This will be returned ans = "" - + for ch in content: ans += chr(ord(ch) ^ key) @@ -188,22 +188,22 @@ def decrypt_file(self,file, key): # key = 67 # # test enrcypt -# print crypt.encrypt("hallo welt",key) +# print(crypt.encrypt("hallo welt",key)) # # test decrypt -# print crypt.decrypt(crypt.encrypt("hallo welt",key), key) +# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key)) # # test encrypt_string -# print crypt.encrypt_string("hallo welt",key) +# print(crypt.encrypt_string("hallo welt",key)) # # test decrypt_string -# print crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key) +# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key)) # if (crypt.encrypt_file("test.txt",key)): -# print "encrypt successful" +# print("encrypt successful") # else: -# print "encrypt unsuccessful" +# print("encrypt unsuccessful") # if (crypt.decrypt_file("encrypt.out",key)): -# print "decrypt successful" +# print("decrypt successful") # else: -# print "decrypt unsuccessful" \ No newline at end of file +# print("decrypt unsuccessful") diff --git a/data_structures/binary_tree/fenwick_tree.py b/data_structures/binary_tree/fenwick_tree.py index f429161c8c36..ef984082d9e8 100644 --- a/data_structures/binary_tree/fenwick_tree.py +++ b/data_structures/binary_tree/fenwick_tree.py @@ -16,14 +16,14 @@ def query(self, i): # query cumulative data from index 0 to i in O(lg N) ret += self.ft[i] i -= i & (-i) return ret - + if __name__ == '__main__': f = FenwickTree(100) f.update(1,20) f.update(4,4) - print (f.query(1)) - print (f.query(3)) - print (f.query(4)) + print(f.query(1)) + print(f.query(3)) + print(f.query(4)) f.update(2,-5) - print (f.query(1)) - print (f.query(3)) + print(f.query(1)) + print(f.query(3)) diff --git a/data_structures/binary_tree/lazy_segment_tree.py b/data_structures/binary_tree/lazy_segment_tree.py index 9b14b24e81fa..215399976dd3 100644 --- a/data_structures/binary_tree/lazy_segment_tree.py +++ b/data_structures/binary_tree/lazy_segment_tree.py @@ -2,13 +2,13 @@ import math class SegmentTree: - + def __init__(self, N): self.N = N self.st = [0 for i in range(0,4*N)] # approximate the overall size of segment tree with array N self.lazy = [0 for i in range(0,4*N)] # create array to store lazy update self.flag = [0 for i in range(0,4*N)] # flag for lazy update - + def left(self, idx): return idx*2 @@ -34,7 +34,7 @@ def update(self, idx, l, r, a, b, val): # update(1, 1, N, a, b, v) for update va self.lazy[self.right(idx)] = self.lazy[idx] self.flag[self.left(idx)] = True self.flag[self.right(idx)] = True - + if r < a or l > b: return True if l >= a and r <= b : @@ -74,18 +74,18 @@ def showData(self): showList = [] for i in range(1,N+1): showList += [self.query(1, 1, self.N, i, i)] - print (showList) - + print(showList) + if __name__ == '__main__': A = [1,2,-4,7,3,-5,6,11,-20,9,14,15,5,2,-8] N = 15 segt = SegmentTree(N) segt.build(1,1,N,A) - print (segt.query(1,1,N,4,6)) - print (segt.query(1,1,N,7,11)) - print (segt.query(1,1,N,7,12)) + print(segt.query(1,1,N,4,6)) + print(segt.query(1,1,N,7,11)) + print(segt.query(1,1,N,7,12)) segt.update(1,1,N,1,3,111) - print (segt.query(1,1,N,1,15)) + print(segt.query(1,1,N,1,15)) segt.update(1,1,N,7,8,235) segt.showData() diff --git a/data_structures/binary_tree/segment_tree.py b/data_structures/binary_tree/segment_tree.py index 001bf999f391..7e61198ca59c 100644 --- a/data_structures/binary_tree/segment_tree.py +++ b/data_structures/binary_tree/segment_tree.py @@ -2,12 +2,12 @@ import math class SegmentTree: - + def __init__(self, A): self.N = len(A) self.st = [0] * (4 * self.N) # approximate the overall size of segment tree with array N self.build(1, 0, self.N - 1) - + def left(self, idx): return idx * 2 @@ -22,10 +22,10 @@ def build(self, idx, l, r): self.build(self.left(idx), l, mid) self.build(self.right(idx), mid + 1, r) self.st[idx] = max(self.st[self.left(idx)] , self.st[self.right(idx)]) - + def update(self, a, b, val): return self.update_recursive(1, 0, self.N - 1, a - 1, b - 1, val) - + def update_recursive(self, idx, l, r, a, b, val): # update(1, 1, N, a, b, v) for update val v to [a,b] if r < a or l > b: return True @@ -55,17 +55,17 @@ def showData(self): showList = [] for i in range(1,N+1): showList += [self.query(i, i)] - print (showList) - + print(showList) + if __name__ == '__main__': A = [1,2,-4,7,3,-5,6,11,-20,9,14,15,5,2,-8] N = 15 segt = SegmentTree(A) - print (segt.query(4, 6)) - print (segt.query(7, 11)) - print (segt.query(7, 12)) + print(segt.query(4, 6)) + print(segt.query(7, 11)) + print(segt.query(7, 12)) segt.update(1,3,111) - print (segt.query(1, 15)) + print(segt.query(1, 15)) segt.update(7,8,235) segt.showData() diff --git a/data_structures/queue/double_ended_queue.py b/data_structures/queue/double_ended_queue.py index fdee64eb6ae0..838bf2f4bc36 100644 --- a/data_structures/queue/double_ended_queue.py +++ b/data_structures/queue/double_ended_queue.py @@ -1,40 +1,40 @@ from __future__ import print_function -# Python code to demonstrate working of +# Python code to demonstrate working of # extend(), extendleft(), rotate(), reverse() - + # importing "collections" for deque operations import collections - + # initializing deque de = collections.deque([1, 2, 3,]) - -# using extend() to add numbers to right end + +# using extend() to add numbers to right end # adds 4,5,6 to right end de.extend([4,5,6]) - + # printing modified deque -print ("The deque after extending deque at end is : ") -print (de) - -# using extendleft() to add numbers to left end +print("The deque after extending deque at end is : ") +print(de) + +# using extendleft() to add numbers to left end # adds 7,8,9 to right end de.extendleft([7,8,9]) - + # printing modified deque -print ("The deque after extending deque at beginning is : ") -print (de) - +print("The deque after extending deque at beginning is : ") +print(de) + # using rotate() to rotate the deque # rotates by 3 to left de.rotate(-3) - + # printing modified deque -print ("The deque after rotating deque is : ") -print (de) - +print("The deque after rotating deque is : ") +print(de) + # using reverse() to reverse the deque de.reverse() - + # printing modified deque -print ("The deque after reversing deque is : ") -print (de) +print("The deque after reversing deque is : ") +print(de) diff --git a/data_structures/stacks/stock_span_problem.py b/data_structures/stacks/stock_span_problem.py index 9628864edd10..e9afebc193b6 100644 --- a/data_structures/stacks/stock_span_problem.py +++ b/data_structures/stacks/stock_span_problem.py @@ -1,52 +1,52 @@ ''' -The stock span problem is a financial problem where we have a series of n daily +The stock span problem is a financial problem where we have a series of n daily price quotes for a stock and we need to calculate span of stock's price for all n days. -The span Si of the stock's price on a given day i is defined as the maximum -number of consecutive days just before the given day, for which the price of the stock +The span Si of the stock's price on a given day i is defined as the maximum +number of consecutive days just before the given day, for which the price of the stock on the current day is less than or equal to its price on the given day. ''' from __future__ import print_function -def calculateSpan(price, S): - - n = len(price) - # Create a stack and push index of fist element to it - st = [] - st.append(0) - - # Span value of first element is always 1 - S[0] = 1 - - # Calculate span values for rest of the elements - for i in range(1, n): - - # Pop elements from stack whlie stack is not - # empty and top of stack is smaller than price[i] - while( len(st) > 0 and price[st[0]] <= price[i]): - st.pop() - - # If stack becomes empty, then price[i] is greater - # than all elements on left of it, i.e. price[0], - # price[1], ..price[i-1]. Else the price[i] is - # greater than elements after top of stack - S[i] = i+1 if len(st) <= 0 else (i - st[0]) - - # Push this element to stack - st.append(i) - - -# A utility function to print elements of array -def printArray(arr, n): - for i in range(0,n): - print (arr[i],end =" ") - - -# Driver program to test above function -price = [10, 4, 5, 90, 120, 80] -S = [0 for i in range(len(price)+1)] - -# Fill the span values in array S[] -calculateSpan(price, S) - -# Print the calculated span values -printArray(S, len(price)) +def calculateSpan(price, S): + + n = len(price) + # Create a stack and push index of fist element to it + st = [] + st.append(0) + + # Span value of first element is always 1 + S[0] = 1 + + # Calculate span values for rest of the elements + for i in range(1, n): + + # Pop elements from stack whlie stack is not + # empty and top of stack is smaller than price[i] + while( len(st) > 0 and price[st[0]] <= price[i]): + st.pop() + + # If stack becomes empty, then price[i] is greater + # than all elements on left of it, i.e. price[0], + # price[1], ..price[i-1]. Else the price[i] is + # greater than elements after top of stack + S[i] = i+1 if len(st) <= 0 else (i - st[0]) + + # Push this element to stack + st.append(i) + + +# A utility function to print elements of array +def printArray(arr, n): + for i in range(0,n): + print(arr[i],end =" ") + + +# Driver program to test above function +price = [10, 4, 5, 90, 120, 80] +S = [0 for i in range(len(price)+1)] + +# Fill the span values in array S[] +calculateSpan(price, S) + +# Print the calculated span values +printArray(S, len(price)) diff --git a/machine_learning/logistic_regression.py b/machine_learning/logistic_regression.py index 9a60831862da..853de7896af1 100644 --- a/machine_learning/logistic_regression.py +++ b/machine_learning/logistic_regression.py @@ -9,7 +9,7 @@ # importing all the required libraries -''' Implementing logistic regression for classification problem +''' Implementing logistic regression for classification problem Helpful resources : 1.Coursera ML course 2.https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac''' import numpy as np @@ -63,10 +63,10 @@ def logistic_reg( if step % 10000 == 0: print(log_likelihood(X,y,weights)) # Print log-likelihood every so often return weights - + if iterations == max_iterations: - print ('Maximum iterations exceeded!') - print ('Minimal cost function J=', J) + print('Maximum iterations exceeded!') + print('Minimal cost function J=', J) converged = True return theta @@ -79,7 +79,7 @@ def logistic_reg( alpha = 0.1 theta = logistic_reg(alpha,X,y,max_iterations=70000,num_steps=30000) - print (theta) + print(theta) def predict_prob(X): diff --git a/maths/quadratic_equations_complex_numbers.py b/maths/quadratic_equations_complex_numbers.py index f05b938fefe9..c3842fee5f96 100644 --- a/maths/quadratic_equations_complex_numbers.py +++ b/maths/quadratic_equations_complex_numbers.py @@ -12,7 +12,7 @@ def QuadraticEquation(a,b,c): if Delta >= 0: Solution1 = (-b + math.sqrt(Delta))/(2*a) Solution2 = (-b - math.sqrt(Delta))/(2*a) - print ("The equation solutions are: ", Solution1," and ", Solution2) + print("The equation solutions are: ", Solution1," and ", Solution2) else: """ Treats cases of Complexes Solutions(i = imaginary unit) @@ -25,7 +25,7 @@ def QuadraticEquation(a,b,c): print("The equation solutions are: (",b,"+",math.sqrt(-Delta),"*i)/2 and (",b,"+",math.sqrt(-Delta),"*i/",2*a) if b == 0: print("The equation solutions are: (",math.sqrt(-Delta),"*i)/2 and ",math.sqrt(-Delta),"*i)/", 2*a) - else: + else: print("Error. Please, coeficient 'a' must not be zero for quadratic equations.") def main(): a = 5 @@ -33,7 +33,7 @@ def main(): c = 1 QuadraticEquation(a,b,c) # The equation solutions are: -0.2 and -1.0 - - + + if __name__ == '__main__': main() diff --git a/other/fischer_yates_shuffle.py b/other/fischer_yates_shuffle.py index d87792f45558..bc2b136344c7 100644 --- a/other/fischer_yates_shuffle.py +++ b/other/fischer_yates_shuffle.py @@ -17,6 +17,6 @@ def FYshuffle(LIST): if __name__ == '__main__': integers = [0,1,2,3,4,5,6,7] strings = ['python', 'says', 'hello', '!'] - print ('Fisher-Yates Shuffle:') - print ('List',integers, strings) - print ('FY Shuffle',FYshuffle(integers), FYshuffle(strings)) + print('Fisher-Yates Shuffle:') + print('List',integers, strings) + print('FY Shuffle',FYshuffle(integers), FYshuffle(strings)) From d21b4cfb4839833b2302da72a646f5a4ecd1bf3b Mon Sep 17 00:00:00 2001 From: QuantumNovice <43876848+QuantumNovice@users.noreply.github.com> Date: Tue, 6 Aug 2019 16:16:30 +0500 Subject: [PATCH 124/193] Added pytests to hashes/md5.py (#1100) * Added pytests to sha1.py * tweaking md5 * Added Pytests to hashes/md5.py --- hashes/md5.py | 287 +++++++++++++++++++++++++++----------------------- 1 file changed, 154 insertions(+), 133 deletions(-) diff --git a/hashes/md5.py b/hashes/md5.py index d3f15510874e..7891f2077986 100644 --- a/hashes/md5.py +++ b/hashes/md5.py @@ -1,155 +1,176 @@ from __future__ import print_function import math + def rearrange(bitString32): - """[summary] - Regroups the given binary string. - - Arguments: - bitString32 {[string]} -- [32 bit binary] - - Raises: - ValueError -- [if the given string not are 32 bit binary string] - - Returns: - [string] -- [32 bit binary string] - """ - - if len(bitString32) != 32: - raise ValueError("Need length 32") - newString = "" - for i in [3,2,1,0]: - newString += bitString32[8*i:8*i+8] - return newString + """[summary] + Regroups the given binary string. + + Arguments: + bitString32 {[string]} -- [32 bit binary] + + Raises: + ValueError -- [if the given string not are 32 bit binary string] + + Returns: + [string] -- [32 bit binary string] + >>> rearrange('1234567890abcdfghijklmnopqrstuvw') + 'pqrstuvwhijklmno90abcdfg12345678' + """ + + if len(bitString32) != 32: + raise ValueError("Need length 32") + newString = "" + for i in [3, 2,1,0]: + newString += bitString32[8*i:8*i+8] + return newString + def reformatHex(i): - """[summary] - Converts the given integer into 8-digit hex number. + """[summary] + Converts the given integer into 8-digit hex number. - Arguments: - i {[int]} -- [integer] - """ + Arguments: + i {[int]} -- [integer] + >>> reformatHex(666) + '9a020000' + """ + + hexrep = format(i, '08x') + thing = "" + for i in [3, 2,1,0]: + thing += hexrep[2*i:2*i+2] + return thing - hexrep = format(i,'08x') - thing = "" - for i in [3,2,1,0]: - thing += hexrep[2*i:2*i+2] - return thing def pad(bitString): - """[summary] - Fills up the binary string to a 512 bit binary string - - Arguments: - bitString {[string]} -- [binary string] - - Returns: - [string] -- [binary string] - """ - - startLength = len(bitString) - bitString += '1' - while len(bitString) % 512 != 448: - bitString += '0' - lastPart = format(startLength,'064b') - bitString += rearrange(lastPart[32:]) + rearrange(lastPart[:32]) - return bitString + """[summary] + Fills up the binary string to a 512 bit binary string + + Arguments: + bitString {[string]} -- [binary string] + + Returns: + [string] -- [binary string] + """ + startLength = len(bitString) + bitString += '1' + while len(bitString) % 512 != 448: + bitString += '0' + lastPart = format(startLength, '064b') + bitString += rearrange(lastPart[32:]) + rearrange(lastPart[:32]) + return bitString + def getBlock(bitString): - """[summary] - Iterator: - Returns by each call a list of length 16 with the 32 bit - integer blocks. - - Arguments: - bitString {[string]} -- [binary string >= 512] - """ - - currPos = 0 - while currPos < len(bitString): - currPart = bitString[currPos:currPos+512] - mySplits = [] - for i in range(16): - mySplits.append(int(rearrange(currPart[32*i:32*i+32]),2)) - yield mySplits - currPos += 512 + """[summary] + Iterator: + Returns by each call a list of length 16 with the 32 bit + integer blocks. -def not32(i): - i_str = format(i,'032b') - new_str = '' - for c in i_str: - new_str += '1' if c=='0' else '0' - return int(new_str,2) + Arguments: + bitString {[string]} -- [binary string >= 512] + """ -def sum32(a,b): - return (a + b) % 2**32 + currPos = 0 + while currPos < len(bitString): + currPart = bitString[currPos:currPos+512] + mySplits = [] + for i in range(16): + mySplits.append(int(rearrange(currPart[32*i:32*i+32]), 2)) + yield mySplits + currPos += 512 + + +def not32(i): + ''' + >>> not32(34) + 4294967261 + ''' + i_str = format(i, '032b') + new_str = '' + for c in i_str: + new_str += '1' if c == '0' else '0' + return int(new_str, 2) + +def sum32(a, b): + ''' + + ''' + return (a + b) % 2**32 + +def leftrot32(i, s): + return (i << s) ^ (i >> (32-s)) -def leftrot32(i,s): - return (i << s) ^ (i >> (32-s)) def md5me(testString): - """[summary] - Returns a 32-bit hash code of the string 'testString' - - Arguments: - testString {[string]} -- [message] - """ - - bs ='' - for i in testString: - bs += format(ord(i),'08b') - bs = pad(bs) - - tvals = [int(2**32 * abs(math.sin(i+1))) for i in range(64)] - - a0 = 0x67452301 - b0 = 0xefcdab89 - c0 = 0x98badcfe - d0 = 0x10325476 - - s = [7, 12, 17, 22, 7, 12, 17, 22, 7, 12, 17, 22, 7, 12, 17, 22, \ - 5, 9, 14, 20, 5, 9, 14, 20, 5, 9, 14, 20, 5, 9, 14, 20, \ - 4, 11, 16, 23, 4, 11, 16, 23, 4, 11, 16, 23, 4, 11, 16, 23, \ - 6, 10, 15, 21, 6, 10, 15, 21, 6, 10, 15, 21, 6, 10, 15, 21 ] - - for m in getBlock(bs): - A = a0 - B = b0 - C = c0 - D = d0 - for i in range(64): - if i <= 15: - #f = (B & C) | (not32(B) & D) - f = D ^ (B & (C ^ D)) - g = i - elif i<= 31: - #f = (D & B) | (not32(D) & C) - f = C ^ (D & (B ^ C)) - g = (5*i+1) % 16 - elif i <= 47: - f = B ^ C ^ D - g = (3*i+5) % 16 - else: - f = C ^ (B | not32(D)) - g = (7*i) % 16 - dtemp = D - D = C - C = B - B = sum32(B,leftrot32((A + f + tvals[i] + m[g]) % 2**32, s[i])) - A = dtemp - a0 = sum32(a0, A) - b0 = sum32(b0, B) - c0 = sum32(c0, C) - d0 = sum32(d0, D) - - digest = reformatHex(a0) + reformatHex(b0) + reformatHex(c0) + reformatHex(d0) - return digest + """[summary] + Returns a 32-bit hash code of the string 'testString' + + Arguments: + testString {[string]} -- [message] + """ + + bs = '' + for i in testString: + bs += format(ord(i), '08b') + bs = pad(bs) + + tvals = [int(2**32 * abs(math.sin(i+1))) for i in range(64)] + + a0 = 0x67452301 + b0 = 0xefcdab89 + c0 = 0x98badcfe + d0 = 0x10325476 + + s = [7, 12, 17, 22, 7, 12, 17, 22, 7, 12, 17, 22, 7, 12, 17, 22, + 5, 9, 14, 20, 5, 9, 14, 20, 5, 9, 14, 20, 5, 9, 14, 20, \ + 4, 11, 16, 23, 4, 11, 16, 23, 4, 11, 16, 23, 4, 11, 16, 23, \ + 6, 10, 15, 21, 6, 10, 15, 21, 6, 10, 15, 21, 6, 10, 15, 21 ] + + for m in getBlock(bs): + A = a0 + B = b0 + C = c0 + D = d0 + for i in range(64): + if i <= 15: + #f = (B & C) | (not32(B) & D) + f = D ^ (B & (C ^ D)) + g = i + elif i <= 31: + #f = (D & B) | (not32(D) & C) + f = C ^ (D & (B ^ C)) + g = (5*i+1) % 16 + elif i <= 47: + f = B ^ C ^ D + g = (3*i+5) % 16 + else: + f = C ^ (B | not32(D)) + g = (7*i) % 16 + dtemp = D + D = C + C = B + B = sum32(B, leftrot32((A + f + tvals[i] + m[g]) % 2**32, s[i])) + A = dtemp + a0 = sum32(a0, A) + b0 = sum32(b0, B) + c0 = sum32(c0, C) + d0 = sum32(d0, D) + + digest = reformatHex(a0) + reformatHex(b0) + \ + reformatHex(c0) + reformatHex(d0) + return digest + def test(): - assert md5me("") == "d41d8cd98f00b204e9800998ecf8427e" - assert md5me("The quick brown fox jumps over the lazy dog") == "9e107d9d372bb6826bd81d3542a419d6" - print("Success.") + assert md5me("") == "d41d8cd98f00b204e9800998ecf8427e" + assert md5me( + "The quick brown fox jumps over the lazy dog") == "9e107d9d372bb6826bd81d3542a419d6" + print("Success.") if __name__ == "__main__": - test() + test() + import doctest + doctest.testmod() From 762482dc40bdd067e2ab01d94fd2c35857e7de9b Mon Sep 17 00:00:00 2001 From: Harshil Date: Tue, 6 Aug 2019 21:31:03 +0200 Subject: [PATCH 125/193] Update closest_pair_of_points.py (#1109) --- divide_and_conquer/closest_pair_of_points.py | 107 ++++++++++--------- 1 file changed, 57 insertions(+), 50 deletions(-) diff --git a/divide_and_conquer/closest_pair_of_points.py b/divide_and_conquer/closest_pair_of_points.py index b6f63396410c..11dac7e0ab2a 100644 --- a/divide_and_conquer/closest_pair_of_points.py +++ b/divide_and_conquer/closest_pair_of_points.py @@ -1,55 +1,54 @@ """ -The algorithm finds distance between closest pair of points +The algorithm finds distance between closest pair of points in the given n points. -Approach used -> Divide and conquer -The points are sorted based on Xco-ords and +Approach used -> Divide and conquer +The points are sorted based on Xco-ords and then based on Yco-ords separately. -And by applying divide and conquer approach, +And by applying divide and conquer approach, minimum distance is obtained recursively. >> Closest points can lie on different sides of partition. -This case handled by forming a strip of points +This case handled by forming a strip of points whose Xco-ords distance is less than closest_pair_dis -from mid-point's Xco-ords. Points sorted based on Yco-ords +from mid-point's Xco-ords. Points sorted based on Yco-ords are used in this step to reduce sorting time. Closest pair distance is found in the strip of points. (closest_in_strip) min(closest_pair_dis, closest_in_strip) would be the final answer. - -Time complexity: O(n * log n) -""" -""" - doctests - >>> euclidean_distance_sqr([1,2],[2,4]) - 5 - >>> dis_between_closest_pair([[1,2],[2,4],[5,7],[8,9],[11,0]],5) - 5 - >>> dis_between_closest_in_strip([[1,2],[2,4],[5,7],[8,9],[11,0]],5) - 85 - >>> points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)] - >>> print("Distance:", closest_pair_of_points(points, len(points))) - "Distance: 1.4142135623730951" +Time complexity: O(n * log n) """ def euclidean_distance_sqr(point1, point2): + """ + >>> euclidean_distance_sqr([1,2],[2,4]) + 5 + """ return (point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2 def column_based_sort(array, column = 0): + """ + >>> column_based_sort([(5, 1), (4, 2), (3, 0)], 1) + [(3, 0), (5, 1), (4, 2)] + """ return sorted(array, key = lambda x: x[column]) - + def dis_between_closest_pair(points, points_counts, min_dis = float("inf")): - """ brute force approach to find distance between closest pair points + """ + brute force approach to find distance between closest pair points + + Parameters : + points, points_count, min_dis (list(tuple(int, int)), int, int) - Parameters : - points, points_count, min_dis (list(tuple(int, int)), int, int) - - Returns : + Returns : min_dis (float): distance between closest pair of points + >>> dis_between_closest_pair([[1,2],[2,4],[5,7],[8,9],[11,0]],5) + 5 + """ for i in range(points_counts - 1): @@ -61,14 +60,17 @@ def dis_between_closest_pair(points, points_counts, min_dis = float("inf")): def dis_between_closest_in_strip(points, points_counts, min_dis = float("inf")): - """ closest pair of points in strip + """ + closest pair of points in strip + + Parameters : + points, points_count, min_dis (list(tuple(int, int)), int, int) - Parameters : - points, points_count, min_dis (list(tuple(int, int)), int, int) - - Returns : + Returns : min_dis (float): distance btw closest pair of points in the strip (< min_dis) + >>> dis_between_closest_in_strip([[1,2],[2,4],[5,7],[8,9],[11,0]],5) + 85 """ for i in range(min(6, points_counts - 1), points_counts): @@ -82,29 +84,32 @@ def dis_between_closest_in_strip(points, points_counts, min_dis = float("inf")): def closest_pair_of_points_sqr(points_sorted_on_x, points_sorted_on_y, points_counts): """ divide and conquer approach - Parameters : - points, points_count (list(tuple(int, int)), int) - - Returns : - (float): distance btw closest pair of points + Parameters : + points, points_count (list(tuple(int, int)), int) + + Returns : + (float): distance btw closest pair of points + >>> closest_pair_of_points_sqr([(1, 2), (3, 4)], [(5, 6), (7, 8)], 2) + 8 """ # base case if points_counts <= 3: return dis_between_closest_pair(points_sorted_on_x, points_counts) - + # recursion mid = points_counts//2 - closest_in_left = closest_pair_of_points_sqr(points_sorted_on_x, - points_sorted_on_y[:mid], + closest_in_left = closest_pair_of_points_sqr(points_sorted_on_x, + points_sorted_on_y[:mid], mid) - closest_in_right = closest_pair_of_points_sqr(points_sorted_on_y, - points_sorted_on_y[mid:], + closest_in_right = closest_pair_of_points_sqr(points_sorted_on_y, + points_sorted_on_y[mid:], points_counts - mid) closest_pair_dis = min(closest_in_left, closest_in_right) - - """ cross_strip contains the points, whose Xcoords are at a + + """ + cross_strip contains the points, whose Xcoords are at a distance(< closest_pair_dis) from mid's Xcoord """ @@ -113,21 +118,23 @@ def closest_pair_of_points_sqr(points_sorted_on_x, points_sorted_on_y, points_co if abs(point[0] - points_sorted_on_x[mid][0]) < closest_pair_dis: cross_strip.append(point) - closest_in_strip = dis_between_closest_in_strip(cross_strip, + closest_in_strip = dis_between_closest_in_strip(cross_strip, len(cross_strip), closest_pair_dis) return min(closest_pair_dis, closest_in_strip) - + def closest_pair_of_points(points, points_counts): + """ + >>> closest_pair_of_points([(2, 3), (12, 30)], len([(2, 3), (12, 30)])) + 28.792360097775937 + """ points_sorted_on_x = column_based_sort(points, column = 0) points_sorted_on_y = column_based_sort(points, column = 1) - return (closest_pair_of_points_sqr(points_sorted_on_x, - points_sorted_on_y, + return (closest_pair_of_points_sqr(points_sorted_on_x, + points_sorted_on_y, points_counts)) ** 0.5 if __name__ == "__main__": - points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)] + points = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)] print("Distance:", closest_pair_of_points(points, len(points))) - - From 7b5a18453b0abe64350930d675cdfe9bef19c57a Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Tue, 6 Aug 2019 21:31:45 +0200 Subject: [PATCH 126/193] print() is a function just like every other function (#1104) From 7cf3db184320a454e545882408b8c2f561ef0cdb Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Tue, 6 Aug 2019 21:32:27 +0200 Subject: [PATCH 127/193] Add test for QuadraticEquation() (#1107) --- maths/quadratic_equations_complex_numbers.py | 64 ++++++++++---------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/maths/quadratic_equations_complex_numbers.py b/maths/quadratic_equations_complex_numbers.py index c3842fee5f96..8f97508609bf 100644 --- a/maths/quadratic_equations_complex_numbers.py +++ b/maths/quadratic_equations_complex_numbers.py @@ -1,39 +1,39 @@ -import math +from math import sqrt +from typing import Tuple -def QuadraticEquation(a,b,c): + +def QuadraticEquation(a: int, b: int, c: int) -> Tuple[str, str]: + """ + Given the numerical coefficients a, b and c, + prints the solutions for a quadratic equation, for a*x*x + b*x + c. + + >>> QuadraticEquation(a=1, b=3, c=-4) + ('1.0', '-4.0') + >>> QuadraticEquation(5, 6, 1) + ('-0.2', '-1.0') """ - Prints the solutions for a quadratic equation, given the numerical coefficients a, b and c, - for a*x*x + b*x + c. - Ex.: a = 1, b = 3, c = -4 - Solution1 = 1 and Solution2 = -4 + if a == 0: + raise ValueError("Coefficient 'a' must not be zero for quadratic equations.") + delta = b * b - 4 * a * c + if delta >= 0: + return str((-b + sqrt(delta)) / (2 * a)), str((-b - sqrt(delta)) / (2 * a)) """ - Delta = b*b - 4*a*c - if a != 0: - if Delta >= 0: - Solution1 = (-b + math.sqrt(Delta))/(2*a) - Solution2 = (-b - math.sqrt(Delta))/(2*a) - print("The equation solutions are: ", Solution1," and ", Solution2) - else: - """ - Treats cases of Complexes Solutions(i = imaginary unit) - Ex.: a = 5, b = 2, c = 1 - Solution1 = (- 2 + 4.0 *i)/2 and Solution2 = (- 2 + 4.0 *i)/ 10 - """ - if b > 0: - print("The equation solutions are: (-",b,"+",math.sqrt(-Delta),"*i)/2 and (-",b,"+",math.sqrt(-Delta),"*i)/", 2*a) - if b < 0: - print("The equation solutions are: (",b,"+",math.sqrt(-Delta),"*i)/2 and (",b,"+",math.sqrt(-Delta),"*i/",2*a) - if b == 0: - print("The equation solutions are: (",math.sqrt(-Delta),"*i)/2 and ",math.sqrt(-Delta),"*i)/", 2*a) - else: - print("Error. Please, coeficient 'a' must not be zero for quadratic equations.") -def main(): - a = 5 - b = 6 - c = 1 + Treats cases of Complexes Solutions(i = imaginary unit) + Ex.: a = 5, b = 2, c = 1 + Solution1 = (- 2 + 4.0 *i)/2 and Solution2 = (- 2 + 4.0 *i)/ 10 + """ + snd = sqrt(-delta) + if b == 0: + return f"({snd} * i) / 2", f"({snd} * i) / {2 * a}" + b = -abs(b) + return f"({b}+{snd} * i) / 2", f"({b}+{snd} * i) / {2 * a}" + - QuadraticEquation(a,b,c) # The equation solutions are: -0.2 and -1.0 +def main(): + solutions = QuadraticEquation(a=5, b=6, c=1) + print("The equation solutions are: {} and {}".format(*solutions)) + # The equation solutions are: -0.2 and -1.0 -if __name__ == '__main__': +if __name__ == "__main__": main() From 561a41464f6dca6c15656ac2257370410b1e9efa Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Tue, 6 Aug 2019 21:53:12 +0200 Subject: [PATCH 128/193] Travis CI: Run each failing pytest in allow_failures mode (#1087) * Travis CI: Run failing pytest in allow_failures mode * Sync with master * Sync with master --- .travis.yml | 28 +++++++++++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index eab55af63492..9abbb0365bc6 100644 --- a/.travis.yml +++ b/.travis.yml @@ -4,11 +4,37 @@ python: 3.7 cache: pip before_install: pip install --upgrade pip setuptools install: pip install -r requirements.txt +matrix: + include: + - name: "Main tests" + # The following files currently fail pytests. See issues: #1016, #1044, #1080 + # Here they are run allow_failures mode and when each passes pytest, it can be + # removed BOTH lists below. Complex now but simple once all files pass pytest. + # - env: FILE=pytest file_transfer_protocol/ftp_client_server.py + # before_script: true + # script: pytest ${FILE} --doctest-modules + - env: FILE=pytest file_transfer_protocol/ftp_send_receive.py + before_script: true + script: pytest ${FILE} --doctest-modules + - env: FILE=pytest machine_learning/linear_regression.py + before_script: true + script: pytest ${FILE} --doctest-modules + - env: FILE=pytest machine_learning/perceptron.py + before_script: true + script: pytest ${FILE} --doctest-modules + - env: FILE=pytest machine_learning/random_forest_classification/random_forest_classification.py + before_script: true + script: pytest ${FILE} --doctest-modules + - env: FILE=pytest machine_learning/random_forest_regression/random_forest_regression.py + before_script: true + script: pytest ${FILE} --doctest-modules + allow_failures: + - before_script: true before_script: - black --check . || true - flake8 . --count --select=E9,F401,F63,F7,F82 --show-source --statistics script: - - scripts/validate_filenames.py # no uppercase and no spaces + - scripts/validate_filenames.py # no uppercase, no spaces, in a directory - mypy --ignore-missing-imports . - pytest . --doctest-modules --ignore=file_transfer_protocol/ftp_send_receive.py From 9456e81437bbab7b1781750fed07121c6cd6e301 Mon Sep 17 00:00:00 2001 From: AlexDvorak Date: Wed, 7 Aug 2019 09:44:48 -0400 Subject: [PATCH 129/193] Seperate client and server of FTP (#1106) * added sample file to transfer * split client and server into separate files * client and server now work in python2 * server works on python3 * client works on python3 * allow configurable ONE_CONNECTION_ONLY for testing server * allow testing of ftp server + client * use f-strings * removed single letter vars * fixed bad quote marks * clearer file handler names * 'with open() as' syntax * unicode and emojis in the test data * s -> sock * consistent comment spacing * remove closing formalities * swap in and out_file * f-string * if __name__ == '__main__': --- .travis.yml | 1 - file_transfer_protocol/client.py | 23 +++++++++ file_transfer_protocol/ftp_client_server.py | 57 --------------------- file_transfer_protocol/mytext.txt | 6 +++ file_transfer_protocol/server.py | 34 ++++++++++++ 5 files changed, 63 insertions(+), 58 deletions(-) create mode 100644 file_transfer_protocol/client.py delete mode 100644 file_transfer_protocol/ftp_client_server.py create mode 100644 file_transfer_protocol/mytext.txt create mode 100644 file_transfer_protocol/server.py diff --git a/.travis.yml b/.travis.yml index 9abbb0365bc6..2536e72fadff 100644 --- a/.travis.yml +++ b/.travis.yml @@ -38,7 +38,6 @@ script: - mypy --ignore-missing-imports . - pytest . --doctest-modules --ignore=file_transfer_protocol/ftp_send_receive.py - --ignore=file_transfer_protocol/ftp_client_server.py --ignore=machine_learning/linear_regression.py --ignore=machine_learning/perceptron.py --ignore=machine_learning/random_forest_classification/random_forest_classification.py diff --git a/file_transfer_protocol/client.py b/file_transfer_protocol/client.py new file mode 100644 index 000000000000..f404546d7765 --- /dev/null +++ b/file_transfer_protocol/client.py @@ -0,0 +1,23 @@ +if __name__ == '__main__': + import socket # Import socket module + + sock = socket.socket() # Create a socket object + host = socket.gethostname() # Get local machine name + port = 12312 + + sock.connect((host, port)) + sock.send(b'Hello server!') + + with open('Received_file', 'wb') as out_file: + print('File opened') + print('Receiving data...') + while True: + data = sock.recv(1024) + print(f"data={data}") + if not data: + break + out_file.write(data) # Write data to a file + + print('Successfully got the file') + sock.close() + print('Connection closed') diff --git a/file_transfer_protocol/ftp_client_server.py b/file_transfer_protocol/ftp_client_server.py deleted file mode 100644 index 414c336dee9f..000000000000 --- a/file_transfer_protocol/ftp_client_server.py +++ /dev/null @@ -1,57 +0,0 @@ -# server - -import socket # Import socket module - -port = 60000 # Reserve a port for your service. -s = socket.socket() # Create a socket object -host = socket.gethostname() # Get local machine name -s.bind((host, port)) # Bind to the port -s.listen(5) # Now wait for client connection. - -print('Server listening....') - -while True: - conn, addr = s.accept() # Establish connection with client. - print('Got connection from', addr) - data = conn.recv(1024) - print('Server received', repr(data)) - - filename = 'mytext.txt' - with open(filename, 'rb') as f: - in_data = f.read(1024) - while in_data: - conn.send(in_data) - print('Sent ', repr(in_data)) - in_data = f.read(1024) - - print('Done sending') - conn.send('Thank you for connecting') - conn.close() - - -# client side server - -import socket # Import socket module - -s = socket.socket() # Create a socket object -host = socket.gethostname() # Get local machine name -port = 60000 # Reserve a port for your service. - -s.connect((host, port)) -s.send("Hello server!") - -with open('received_file', 'wb') as f: - print('file opened') - while True: - print('receiving data...') - data = s.recv(1024) - print('data=%s', (data)) - if not data: - break - # write data to a file - f.write(data) - -f.close() -print('Successfully get the file') -s.close() -print('connection closed') diff --git a/file_transfer_protocol/mytext.txt b/file_transfer_protocol/mytext.txt new file mode 100644 index 000000000000..54cfa7f766c7 --- /dev/null +++ b/file_transfer_protocol/mytext.txt @@ -0,0 +1,6 @@ +Hello +This is sample data +«küßî» +“ЌύБЇ” +😀😉 +😋 diff --git a/file_transfer_protocol/server.py b/file_transfer_protocol/server.py new file mode 100644 index 000000000000..92fab206c1a1 --- /dev/null +++ b/file_transfer_protocol/server.py @@ -0,0 +1,34 @@ +if __name__ == '__main__': + import socket # Import socket module + + ONE_CONNECTION_ONLY = True # Set this to False if you wish to continuously accept connections + + filename='mytext.txt' + port = 12312 # Reserve a port for your service. + sock = socket.socket() # Create a socket object + host = socket.gethostname() # Get local machine name + sock.bind((host, port)) # Bind to the port + sock.listen(5) # Now wait for client connection. + + print('Server listening....') + + while True: + conn, addr = sock.accept() # Establish connection with client. + print(f"Got connection from {addr}") + data = conn.recv(1024) + print(f"Server received {data}") + + with open(filename,'rb') as in_file: + data = in_file.read(1024) + while (data): + conn.send(data) + print(f"Sent {data!r}") + data = in_file.read(1024) + + print('Done sending') + conn.close() + if ONE_CONNECTION_ONLY: # This is to make sure that the program doesn't hang while testing + break + + sock.shutdown(1) + sock.close() From c92d06bf1f82f21ecb74650b63fd72f07b0a1a70 Mon Sep 17 00:00:00 2001 From: John Law Date: Thu, 8 Aug 2019 01:35:36 +0800 Subject: [PATCH 130/193] Delete redundant files (#1115) * Delete 16L' * Delete Q' --- 16L' | 0 Q' | 0 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 16L' delete mode 100644 Q' diff --git a/16L' b/16L' deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/Q' b/Q' deleted file mode 100644 index e69de29bb2d1..000000000000 From 32c0418f635824a14665c58c71bc7c220c509a78 Mon Sep 17 00:00:00 2001 From: Amrit Khera <31596604+AmritK10@users.noreply.github.com> Date: Thu, 8 Aug 2019 01:09:44 +0530 Subject: [PATCH 131/193] Infinite loop was fixed. (#1105) * Infinite loop was fixed. Removed issue of unused variables. * Update logistic_regression.py * Update logistic_regression.py * correct spacing according to PEP8 --- machine_learning/logistic_regression.py | 27 +++++++------------------ 1 file changed, 7 insertions(+), 20 deletions(-) diff --git a/machine_learning/logistic_regression.py b/machine_learning/logistic_regression.py index 853de7896af1..b2749f1be260 100644 --- a/machine_learning/logistic_regression.py +++ b/machine_learning/logistic_regression.py @@ -40,34 +40,20 @@ def logistic_reg( alpha, X, y, - num_steps, max_iterations=70000, ): - converged = False - iterations = 0 theta = np.zeros(X.shape[1]) - while not converged: + for iterations in range(max_iterations): z = np.dot(X, theta) h = sigmoid_function(z) gradient = np.dot(X.T, h - y) / y.size - theta = theta - alpha * gradient + theta = theta - alpha * gradient # updating the weights z = np.dot(X, theta) h = sigmoid_function(z) J = cost_function(h, y) - iterations += 1 # update iterations - weights = np.zeros(X.shape[1]) - for step in range(num_steps): - scores = np.dot(X, weights) - predictions = sigmoid_function(scores) - if step % 10000 == 0: - print(log_likelihood(X,y,weights)) # Print log-likelihood every so often - return weights - - if iterations == max_iterations: - print('Maximum iterations exceeded!') - print('Minimal cost function J=', J) - converged = True + if iterations % 100 == 0: + print(f'loss: {J} \t') # printing the loss after every 100 iterations return theta # In[68]: @@ -78,8 +64,8 @@ def logistic_reg( y = (iris.target != 0) * 1 alpha = 0.1 - theta = logistic_reg(alpha,X,y,max_iterations=70000,num_steps=30000) - print(theta) + theta = logistic_reg(alpha,X,y,max_iterations=70000) + print("theta: ",theta) # printing the theta i.e our weights vector def predict_prob(X): @@ -105,3 +91,4 @@ def predict_prob(X): ) plt.legend() + plt.show() From 3ba67c7d2d72b166d7fd0a8a549de46951002f4f Mon Sep 17 00:00:00 2001 From: AlexDvorak Date: Wed, 7 Aug 2019 16:02:31 -0400 Subject: [PATCH 132/193] rename non-ftp files (#1116) --- file_transfer_protocol/{client.py => recieve_file.py} | 0 file_transfer_protocol/{server.py => send_file.py} | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename file_transfer_protocol/{client.py => recieve_file.py} (100%) rename file_transfer_protocol/{server.py => send_file.py} (100%) diff --git a/file_transfer_protocol/client.py b/file_transfer_protocol/recieve_file.py similarity index 100% rename from file_transfer_protocol/client.py rename to file_transfer_protocol/recieve_file.py diff --git a/file_transfer_protocol/server.py b/file_transfer_protocol/send_file.py similarity index 100% rename from file_transfer_protocol/server.py rename to file_transfer_protocol/send_file.py From c686cc5863c2ac2530ed1f34bd9ce758bb17945a Mon Sep 17 00:00:00 2001 From: "Marvin M. Michum" Date: Thu, 8 Aug 2019 11:59:15 -0400 Subject: [PATCH 133/193] fix outdated fork error (#1117) --- .../filters/median_filter.py | 2 +- .../test_digital_image_processing.py | 62 +++++++++++++++++++ 2 files changed, 63 insertions(+), 1 deletion(-) create mode 100644 digital_image_processing/test_digital_image_processing.py diff --git a/digital_image_processing/filters/median_filter.py b/digital_image_processing/filters/median_filter.py index ed20b1ab7f78..4b21b96b080b 100644 --- a/digital_image_processing/filters/median_filter.py +++ b/digital_image_processing/filters/median_filter.py @@ -15,7 +15,7 @@ def median_filter(gray_img, mask=3): # set image borders bd = int(mask / 2) # copy image size - median_img = zeros_like(gray) + median_img = zeros_like(gray_img) for i in range(bd, gray_img.shape[0] - bd): for j in range(bd, gray_img.shape[1] - bd): # get mask according with mask diff --git a/digital_image_processing/test_digital_image_processing.py b/digital_image_processing/test_digital_image_processing.py new file mode 100644 index 000000000000..0ff9e3333ca8 --- /dev/null +++ b/digital_image_processing/test_digital_image_processing.py @@ -0,0 +1,62 @@ +""" +PyTest's for Digital Image Processing +""" + +import digital_image_processing.edge_detection.canny as canny +import digital_image_processing.filters.gaussian_filter as gg +import digital_image_processing.filters.median_filter as med +import digital_image_processing.filters.sobel_filter as sob +import digital_image_processing.filters.convolve as conv +import digital_image_processing.change_contrast as cc +from cv2 import imread, cvtColor, COLOR_BGR2GRAY +from numpy import array, uint8 +from PIL import Image + +img = imread(r"digital_image_processing/image_data/lena.jpg") +gray = cvtColor(img, COLOR_BGR2GRAY) + +# Test: change_contrast() +def test_change_contrast(): + with Image.open("digital_image_processing/image_data/lena.jpg") as img: + # Work around assertion for response + assert str(cc.change_contrast(img, 110)).startswith( + " Date: Fri, 9 Aug 2019 21:37:16 +0200 Subject: [PATCH 134/193] Rename file_transfer and linear_algebra (#1118) * Rename file_transfer and linear_algebra * Rename file_transfer and linear_algebra --- .travis.yml | 4 ++-- {file_transfer_protocol => file_transfer}/ftp_send_receive.py | 0 {file_transfer_protocol => file_transfer}/mytext.txt | 0 {file_transfer_protocol => file_transfer}/recieve_file.py | 0 {file_transfer_protocol => file_transfer}/send_file.py | 0 {linear_algebra_python => linear_algebra}/README.md | 0 {linear_algebra_python => linear_algebra}/src/lib.py | 0 {linear_algebra_python => linear_algebra}/src/tests.py | 0 8 files changed, 2 insertions(+), 2 deletions(-) rename {file_transfer_protocol => file_transfer}/ftp_send_receive.py (100%) rename {file_transfer_protocol => file_transfer}/mytext.txt (100%) rename {file_transfer_protocol => file_transfer}/recieve_file.py (100%) rename {file_transfer_protocol => file_transfer}/send_file.py (100%) rename {linear_algebra_python => linear_algebra}/README.md (100%) rename {linear_algebra_python => linear_algebra}/src/lib.py (100%) rename {linear_algebra_python => linear_algebra}/src/tests.py (100%) diff --git a/.travis.yml b/.travis.yml index 2536e72fadff..532f73f5e895 100644 --- a/.travis.yml +++ b/.travis.yml @@ -13,7 +13,7 @@ matrix: # - env: FILE=pytest file_transfer_protocol/ftp_client_server.py # before_script: true # script: pytest ${FILE} --doctest-modules - - env: FILE=pytest file_transfer_protocol/ftp_send_receive.py + - env: FILE=pytest file_transfer/ftp_send_receive.py before_script: true script: pytest ${FILE} --doctest-modules - env: FILE=pytest machine_learning/linear_regression.py @@ -37,7 +37,7 @@ script: - scripts/validate_filenames.py # no uppercase, no spaces, in a directory - mypy --ignore-missing-imports . - pytest . --doctest-modules - --ignore=file_transfer_protocol/ftp_send_receive.py + --ignore=file_transfer/ftp_send_receive.py --ignore=machine_learning/linear_regression.py --ignore=machine_learning/perceptron.py --ignore=machine_learning/random_forest_classification/random_forest_classification.py diff --git a/file_transfer_protocol/ftp_send_receive.py b/file_transfer/ftp_send_receive.py similarity index 100% rename from file_transfer_protocol/ftp_send_receive.py rename to file_transfer/ftp_send_receive.py diff --git a/file_transfer_protocol/mytext.txt b/file_transfer/mytext.txt similarity index 100% rename from file_transfer_protocol/mytext.txt rename to file_transfer/mytext.txt diff --git a/file_transfer_protocol/recieve_file.py b/file_transfer/recieve_file.py similarity index 100% rename from file_transfer_protocol/recieve_file.py rename to file_transfer/recieve_file.py diff --git a/file_transfer_protocol/send_file.py b/file_transfer/send_file.py similarity index 100% rename from file_transfer_protocol/send_file.py rename to file_transfer/send_file.py diff --git a/linear_algebra_python/README.md b/linear_algebra/README.md similarity index 100% rename from linear_algebra_python/README.md rename to linear_algebra/README.md diff --git a/linear_algebra_python/src/lib.py b/linear_algebra/src/lib.py similarity index 100% rename from linear_algebra_python/src/lib.py rename to linear_algebra/src/lib.py diff --git a/linear_algebra_python/src/tests.py b/linear_algebra/src/tests.py similarity index 100% rename from linear_algebra_python/src/tests.py rename to linear_algebra/src/tests.py From 36684db2780d695add9bd0a4523d73496cb35664 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Sat, 10 Aug 2019 22:48:00 +0200 Subject: [PATCH 135/193] Travis CI: Add pytest --doctest-modules machine_learning (#1016) * Travis CI: Add pytest --doctest-modules neural_network Fixes #987 ``` neural_network/perceptron.py:123: in sample.insert(i, float(input('value: '))) ../lib/python3.7/site-packages/_pytest/capture.py:693: in read raise IOError("reading from stdin while output is captured") E OSError: reading from stdin while output is captured -------------------------------------------------------------------------------- Captured stdout -------------------------------------------------------------------------------- ('\nEpoch:\n', 399) ------------------------ value: ``` * Adding fix from #1056 -- thanks @QuantumNovice * if __name__ == '__main__': * pytest --ignore=virtualenv # do not test our dependencies --- machine_learning/perceptron.py | 124 ------------------ .../random_forest_classification.py | 8 +- .../random_forest_regression.py | 8 +- neural_network/perceptron.py | 8 +- requirements.txt | 1 + 5 files changed, 15 insertions(+), 134 deletions(-) delete mode 100644 machine_learning/perceptron.py diff --git a/machine_learning/perceptron.py b/machine_learning/perceptron.py deleted file mode 100644 index fe1032aff4af..000000000000 --- a/machine_learning/perceptron.py +++ /dev/null @@ -1,124 +0,0 @@ -''' - - Perceptron - w = w + N * (d(k) - y) * x(k) - - Using perceptron network for oil analysis, - with Measuring of 3 parameters that represent chemical characteristics we can classify the oil, in p1 or p2 - p1 = -1 - p2 = 1 - -''' -from __future__ import print_function - -import random - - -class Perceptron: - def __init__(self, sample, exit, learn_rate=0.01, epoch_number=1000, bias=-1): - self.sample = sample - self.exit = exit - self.learn_rate = learn_rate - self.epoch_number = epoch_number - self.bias = bias - self.number_sample = len(sample) - self.col_sample = len(sample[0]) - self.weight = [] - - def trannig(self): - for sample in self.sample: - sample.insert(0, self.bias) - - for i in range(self.col_sample): - self.weight.append(random.random()) - - self.weight.insert(0, self.bias) - - epoch_count = 0 - - while True: - erro = False - for i in range(self.number_sample): - u = 0 - for j in range(self.col_sample + 1): - u = u + self.weight[j] * self.sample[i][j] - y = self.sign(u) - if y != self.exit[i]: - - for j in range(self.col_sample + 1): - - self.weight[j] = self.weight[j] + self.learn_rate * (self.exit[i] - y) * self.sample[i][j] - erro = True - #print('Epoch: \n',epoch_count) - epoch_count = epoch_count + 1 - # if you want controle the epoch or just by erro - if erro == False: - print(('\nEpoch:\n',epoch_count)) - print('------------------------\n') - #if epoch_count > self.epoch_number or not erro: - break - - def sort(self, sample): - sample.insert(0, self.bias) - u = 0 - for i in range(self.col_sample + 1): - u = u + self.weight[i] * sample[i] - - y = self.sign(u) - - if y == -1: - print(('Sample: ', sample)) - print('classification: P1') - else: - print(('Sample: ', sample)) - print('classification: P2') - - def sign(self, u): - return 1 if u >= 0 else -1 - - -samples = [ - [-0.6508, 0.1097, 4.0009], - [-1.4492, 0.8896, 4.4005], - [2.0850, 0.6876, 12.0710], - [0.2626, 1.1476, 7.7985], - [0.6418, 1.0234, 7.0427], - [0.2569, 0.6730, 8.3265], - [1.1155, 0.6043, 7.4446], - [0.0914, 0.3399, 7.0677], - [0.0121, 0.5256, 4.6316], - [-0.0429, 0.4660, 5.4323], - [0.4340, 0.6870, 8.2287], - [0.2735, 1.0287, 7.1934], - [0.4839, 0.4851, 7.4850], - [0.4089, -0.1267, 5.5019], - [1.4391, 0.1614, 8.5843], - [-0.9115, -0.1973, 2.1962], - [0.3654, 1.0475, 7.4858], - [0.2144, 0.7515, 7.1699], - [0.2013, 1.0014, 6.5489], - [0.6483, 0.2183, 5.8991], - [-0.1147, 0.2242, 7.2435], - [-0.7970, 0.8795, 3.8762], - [-1.0625, 0.6366, 2.4707], - [0.5307, 0.1285, 5.6883], - [-1.2200, 0.7777, 1.7252], - [0.3957, 0.1076, 5.6623], - [-0.1013, 0.5989, 7.1812], - [2.4482, 0.9455, 11.2095], - [2.0149, 0.6192, 10.9263], - [0.2012, 0.2611, 5.4631] - -] - -exit = [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1] - -network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1) - -network.trannig() - -while True: - sample = [] - for i in range(3): - sample.insert(i, float(input('value: '))) - network.sort(sample) diff --git a/machine_learning/random_forest_classification/random_forest_classification.py b/machine_learning/random_forest_classification/random_forest_classification.py index d5dde4b13822..81016387ecc7 100644 --- a/machine_learning/random_forest_classification/random_forest_classification.py +++ b/machine_learning/random_forest_classification/random_forest_classification.py @@ -1,17 +1,19 @@ # Random Forest Classification # Importing the libraries +import os import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset -dataset = pd.read_csv('Social_Network_Ads.csv') +script_dir = os.path.dirname(os.path.realpath(__file__)) +dataset = pd.read_csv(os.path.join(script_dir, 'Social_Network_Ads.csv')) X = dataset.iloc[:, [2, 3]].values y = dataset.iloc[:, 4].values # Splitting the dataset into the Training set and Test set -from sklearn.cross_validation import train_test_split +from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0) # Feature Scaling @@ -66,4 +68,4 @@ plt.xlabel('Age') plt.ylabel('Estimated Salary') plt.legend() -plt.show() \ No newline at end of file +plt.show() diff --git a/machine_learning/random_forest_regression/random_forest_regression.py b/machine_learning/random_forest_regression/random_forest_regression.py index fce58b1fe283..85ce0676b598 100644 --- a/machine_learning/random_forest_regression/random_forest_regression.py +++ b/machine_learning/random_forest_regression/random_forest_regression.py @@ -1,12 +1,14 @@ # Random Forest Regression # Importing the libraries +import os import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset -dataset = pd.read_csv('Position_Salaries.csv') +script_dir = os.path.dirname(os.path.realpath(__file__)) +dataset = pd.read_csv(os.path.join(script_dir, 'Position_Salaries.csv')) X = dataset.iloc[:, 1:2].values y = dataset.iloc[:, 2].values @@ -28,7 +30,7 @@ regressor.fit(X, y) # Predicting a new result -y_pred = regressor.predict(6.5) +y_pred = regressor.predict([[6.5]]) # Visualising the Random Forest Regression results (higher resolution) X_grid = np.arange(min(X), max(X), 0.01) @@ -38,4 +40,4 @@ plt.title('Truth or Bluff (Random Forest Regression)') plt.xlabel('Position level') plt.ylabel('Salary') -plt.show() \ No newline at end of file +plt.show() diff --git a/neural_network/perceptron.py b/neural_network/perceptron.py index 787ea8f73bf1..871eca20273b 100644 --- a/neural_network/perceptron.py +++ b/neural_network/perceptron.py @@ -113,13 +113,13 @@ def sign(self, u): exit = [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1] -if __name__ == '__main__': - network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1) +network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1) - network.training() +network.training() +if __name__ == '__main__': while True: sample = [] for i in range(3): - sample.insert(i, float(input('value: ').strip())) + sample.insert(i, float(input('value: '))) network.sort(sample) diff --git a/requirements.txt b/requirements.txt index a3e62cf968f7..f5790ad53c30 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,6 +7,7 @@ opencv-python pandas pillow pytest +requests sklearn sympy tensorflow From 55cea57ffa45ad4ef062363c8ec214e81f8c2448 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Sun, 11 Aug 2019 13:00:58 +0200 Subject: [PATCH 136/193] Fix tests for file_transfer and perceptron.py (#1121) --- .travis.yml | 31 -------------------------- file_transfer/ftp_send_receive.py | 36 ------------------------------- 2 files changed, 67 deletions(-) delete mode 100644 file_transfer/ftp_send_receive.py diff --git a/.travis.yml b/.travis.yml index 532f73f5e895..f7a9264803f8 100644 --- a/.travis.yml +++ b/.travis.yml @@ -4,32 +4,6 @@ python: 3.7 cache: pip before_install: pip install --upgrade pip setuptools install: pip install -r requirements.txt -matrix: - include: - - name: "Main tests" - # The following files currently fail pytests. See issues: #1016, #1044, #1080 - # Here they are run allow_failures mode and when each passes pytest, it can be - # removed BOTH lists below. Complex now but simple once all files pass pytest. - # - env: FILE=pytest file_transfer_protocol/ftp_client_server.py - # before_script: true - # script: pytest ${FILE} --doctest-modules - - env: FILE=pytest file_transfer/ftp_send_receive.py - before_script: true - script: pytest ${FILE} --doctest-modules - - env: FILE=pytest machine_learning/linear_regression.py - before_script: true - script: pytest ${FILE} --doctest-modules - - env: FILE=pytest machine_learning/perceptron.py - before_script: true - script: pytest ${FILE} --doctest-modules - - env: FILE=pytest machine_learning/random_forest_classification/random_forest_classification.py - before_script: true - script: pytest ${FILE} --doctest-modules - - env: FILE=pytest machine_learning/random_forest_regression/random_forest_regression.py - before_script: true - script: pytest ${FILE} --doctest-modules - allow_failures: - - before_script: true before_script: - black --check . || true - flake8 . --count --select=E9,F401,F63,F7,F82 --show-source --statistics @@ -37,11 +11,6 @@ script: - scripts/validate_filenames.py # no uppercase, no spaces, in a directory - mypy --ignore-missing-imports . - pytest . --doctest-modules - --ignore=file_transfer/ftp_send_receive.py - --ignore=machine_learning/linear_regression.py - --ignore=machine_learning/perceptron.py - --ignore=machine_learning/random_forest_classification/random_forest_classification.py - --ignore=machine_learning/random_forest_regression/random_forest_regression.py after_success: - scripts/build_directory_md.py > DIRECTORY.md - cat DIRECTORY.md diff --git a/file_transfer/ftp_send_receive.py b/file_transfer/ftp_send_receive.py deleted file mode 100644 index 6a9819ef3f21..000000000000 --- a/file_transfer/ftp_send_receive.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -File transfer protocol used to send and receive files using FTP server. -Use credentials to provide access to the FTP client - -Note: Do not use root username & password for security reasons -Create a seperate user and provide access to a home directory of the user -Use login id and password of the user created -cwd here stands for current working directory -""" - -from ftplib import FTP -ftp = FTP('xxx.xxx.x.x') # Enter the ip address or the domain name here -ftp.login(user='username', passwd='password') -ftp.cwd('/Enter the directory here/') - -""" -The file which will be received via the FTP server -Enter the location of the file where the file is received -""" - -def ReceiveFile(): - FileName = 'example.txt' """ Enter the location of the file """ - with open(FileName, 'wb') as LocalFile: - ftp.retrbinary('RETR ' + FileName, LocalFile.write, 1024) - ftp.quit() - -""" -The file which will be sent via the FTP server -The file send will be send to the current working directory -""" - -def SendFile(): - FileName = 'example.txt' """ Enter the name of the file """ - with open(FileName, 'rb') as LocalFile: - ftp.storbinary('STOR ' + FileName, LocalFile) - ftp.quit() From 158b319d22e6ffa5399ce42bcfe1a7c968ebaa66 Mon Sep 17 00:00:00 2001 From: Niclas Dern <52120196+nic-dern@users.noreply.github.com> Date: Mon, 12 Aug 2019 09:13:57 +0200 Subject: [PATCH 137/193] New linear algebra algorithm (#1122) * Added new algorithm which takes points as an input and outputs a polynom connecting them * Rename Python-Polynom-for-points.py to python-polynom-for-points.py * Update python-polynom-for-points.py * Update python-polynom-for-points.py * Update python-polynom-for-points.py * Update python-polynom-for-points.py * Update python-polynom-for-points.py * Update python-polynom-for-points.py * Update python-polynom-for-points.py * Add doctests and run thru psf/black --- .../src/python-polynom-for-points.py | 130 ++++++++++++++++++ 1 file changed, 130 insertions(+) create mode 100644 linear_algebra/src/python-polynom-for-points.py diff --git a/linear_algebra/src/python-polynom-for-points.py b/linear_algebra/src/python-polynom-for-points.py new file mode 100644 index 000000000000..c884416b6dad --- /dev/null +++ b/linear_algebra/src/python-polynom-for-points.py @@ -0,0 +1,130 @@ +def points_to_polynomial(coordinates): + """ + coordinates is a two dimensional matrix: [[x, y], [x, y], ...] + number of points you want to use + + >>> print(points_to_polynomial([])) + The program cannot work out a fitting polynomial. + >>> print(points_to_polynomial([[]])) + The program cannot work out a fitting polynomial. + >>> print(points_to_polynomial([[1, 0], [2, 0], [3, 0]])) + f(x)=x^2*0.0+x^1*-0.0+x^0*0.0 + >>> print(points_to_polynomial([[1, 1], [2, 1], [3, 1]])) + f(x)=x^2*0.0+x^1*-0.0+x^0*1.0 + >>> print(points_to_polynomial([[1, 3], [2, 3], [3, 3]])) + f(x)=x^2*0.0+x^1*-0.0+x^0*3.0 + >>> print(points_to_polynomial([[1, 1], [2, 2], [3, 3]])) + f(x)=x^2*0.0+x^1*1.0+x^0*0.0 + >>> print(points_to_polynomial([[1, 1], [2, 4], [3, 9]])) + f(x)=x^2*1.0+x^1*-0.0+x^0*0.0 + >>> print(points_to_polynomial([[1, 3], [2, 6], [3, 11]])) + f(x)=x^2*1.0+x^1*-0.0+x^0*2.0 + >>> print(points_to_polynomial([[1, -3], [2, -6], [3, -11]])) + f(x)=x^2*-1.0+x^1*-0.0+x^0*-2.0 + >>> print(points_to_polynomial([[1, 5], [2, 2], [3, 9]])) + f(x)=x^2*5.0+x^1*-18.0+x^0*18.0 + """ + try: + check = 1 + more_check = 0 + d = coordinates[0][0] + for j in range(len(coordinates)): + if j == 0: + continue + if d == coordinates[j][0]: + more_check += 1 + solved = "x=" + str(coordinates[j][0]) + if more_check == len(coordinates) - 1: + check = 2 + break + elif more_check > 0 and more_check != len(coordinates) - 1: + check = 3 + else: + check = 1 + + if len(coordinates) == 1 and coordinates[0][0] == 0: + check = 2 + solved = "x=0" + except Exception: + check = 3 + + x = len(coordinates) + + if check == 1: + count_of_line = 0 + matrix = [] + # put the x and x to the power values in a matrix + while count_of_line < x: + count_in_line = 0 + a = coordinates[count_of_line][0] + count_line = [] + while count_in_line < x: + count_line.append(a ** (x - (count_in_line + 1))) + count_in_line += 1 + matrix.append(count_line) + count_of_line += 1 + + count_of_line = 0 + # put the y values into a vector + vector = [] + while count_of_line < x: + count_in_line = 0 + vector.append(coordinates[count_of_line][1]) + count_of_line += 1 + + count = 0 + + while count < x: + zahlen = 0 + while zahlen < x: + if count == zahlen: + zahlen += 1 + if zahlen == x: + break + bruch = (matrix[zahlen][count]) / (matrix[count][count]) + for counting_columns, item in enumerate(matrix[count]): + # manipulating all the values in the matrix + matrix[zahlen][counting_columns] -= item * bruch + # manipulating the values in the vector + vector[zahlen] -= vector[count] * bruch + zahlen += 1 + count += 1 + + count = 0 + # make solutions + solution = [] + while count < x: + solution.append(vector[count] / matrix[count][count]) + count += 1 + + count = 0 + solved = "f(x)=" + + while count < x: + remove_e = str(solution[count]).split("E") + if len(remove_e) > 1: + solution[count] = remove_e[0] + "*10^" + remove_e[1] + solved += "x^" + str(x - (count + 1)) + "*" + str(solution[count]) + if count + 1 != x: + solved += "+" + count += 1 + + return solved + + elif check == 2: + return solved + else: + return "The program cannot work out a fitting polynomial." + + +if __name__ == "__main__": + print(points_to_polynomial([])) + print(points_to_polynomial([[]])) + print(points_to_polynomial([[1, 0], [2, 0], [3, 0]])) + print(points_to_polynomial([[1, 1], [2, 1], [3, 1]])) + print(points_to_polynomial([[1, 3], [2, 3], [3, 3]])) + print(points_to_polynomial([[1, 1], [2, 2], [3, 3]])) + print(points_to_polynomial([[1, 1], [2, 4], [3, 9]])) + print(points_to_polynomial([[1, 3], [2, 6], [3, 11]])) + print(points_to_polynomial([[1, -3], [2, -6], [3, -11]])) + print(points_to_polynomial([[1, 5], [2, 2], [3, 9]])) From 4fea48072ae88c19f5133a2303e6707eddf96700 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Mon, 12 Aug 2019 17:59:59 +0200 Subject: [PATCH 138/193] Add type hints to binary_tree_traversals.py (#1123) --- traversals/binary_tree_traversals.py | 103 +++++++++++++-------------- 1 file changed, 50 insertions(+), 53 deletions(-) diff --git a/traversals/binary_tree_traversals.py b/traversals/binary_tree_traversals.py index 393664579146..7fd9f7111844 100644 --- a/traversals/binary_tree_traversals.py +++ b/traversals/binary_tree_traversals.py @@ -1,14 +1,8 @@ """ This is pure python implementation of tree traversal algorithms """ -from __future__ import print_function - import queue - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 +from typing import List class TreeNode: @@ -20,35 +14,31 @@ def __init__(self, data): def build_tree(): print("\n********Press N to stop entering at any point of time********\n") - print("Enter the value of the root node: ", end="") - check = raw_input().strip().lower() - if check == 'n': + check = input("Enter the value of the root node: ").strip().lower() or "n" + if check == "n": return None - data = int(check) - q = queue.Queue() - tree_node = TreeNode(data) + q: queue.Queue = queue.Queue() + tree_node = TreeNode(int(check)) q.put(tree_node) while not q.empty(): node_found = q.get() - print("Enter the left node of %s: " % node_found.data, end="") - check = raw_input().strip().lower() - if check == 'n': + msg = "Enter the left node of %s: " % node_found.data + check = input(msg).strip().lower() or "n" + if check == "n": return tree_node - left_data = int(check) - left_node = TreeNode(left_data) + left_node = TreeNode(int(check)) node_found.left = left_node q.put(left_node) - print("Enter the right node of %s: " % node_found.data, end="") - check = raw_input().strip().lower() - if check == 'n': + msg = "Enter the right node of %s: " % node_found.data + check = input(msg).strip().lower() or "n" + if check == "n": return tree_node - right_data = int(check) - right_node = TreeNode(right_data) + right_node = TreeNode(int(check)) node_found.right = right_node q.put(right_node) -def pre_order(node): +def pre_order(node: TreeNode) -> None: if not isinstance(node, TreeNode) or not node: return print(node.data, end=" ") @@ -56,7 +46,7 @@ def pre_order(node): pre_order(node.right) -def in_order(node): +def in_order(node: TreeNode) -> None: if not isinstance(node, TreeNode) or not node: return in_order(node.left) @@ -64,7 +54,7 @@ def in_order(node): in_order(node.right) -def post_order(node): +def post_order(node: TreeNode) -> None: if not isinstance(node, TreeNode) or not node: return post_order(node.left) @@ -72,10 +62,10 @@ def post_order(node): print(node.data, end=" ") -def level_order(node): +def level_order(node: TreeNode) -> None: if not isinstance(node, TreeNode) or not node: return - q = queue.Queue() + q: queue.Queue = queue.Queue() q.put(node) while not q.empty(): node_dequeued = q.get() @@ -86,10 +76,10 @@ def level_order(node): q.put(node_dequeued.right) -def level_order_actual(node): +def level_order_actual(node: TreeNode) -> None: if not isinstance(node, TreeNode) or not node: return - q = queue.Queue() + q: queue.Queue = queue.Queue() q.put(node) while not q.empty(): list = [] @@ -106,10 +96,10 @@ def level_order_actual(node): # iteration version -def pre_order_iter(node): +def pre_order_iter(node: TreeNode) -> None: if not isinstance(node, TreeNode) or not node: return - stack = [] + stack: List[TreeNode] = [] n = node while n or stack: while n: # start from root node, find its left child @@ -122,10 +112,10 @@ def pre_order_iter(node): n = n.right -def in_order_iter(node): +def in_order_iter(node: TreeNode) -> None: if not isinstance(node, TreeNode) or not node: return - stack = [] + stack: List[TreeNode] = [] n = node while n or stack: while n: @@ -136,7 +126,7 @@ def in_order_iter(node): n = n.right -def post_order_iter(node): +def post_order_iter(node: TreeNode) -> None: if not isinstance(node, TreeNode) or not node: return stack1, stack2 = [], [] @@ -153,38 +143,45 @@ def post_order_iter(node): print(stack2.pop().data, end=" ") -if __name__ == '__main__': - print("\n********* Binary Tree Traversals ************\n") +def prompt(s: str = "", width=50, char="*") -> str: + if not s: + return "\n" + width * char + left, extra = divmod(width - len(s) - 2, 2) + return f"{left * char} {s} {(left + extra) * char}" + + +if __name__ == "__main__": + print(prompt("Binary Tree Traversals")) node = build_tree() - print("\n********* Pre Order Traversal ************") + print(prompt("Pre Order Traversal")) pre_order(node) - print("\n******************************************\n") + print(prompt() + "\n") - print("\n********* In Order Traversal ************") + print(prompt("In Order Traversal")) in_order(node) - print("\n******************************************\n") + print(prompt() + "\n") - print("\n********* Post Order Traversal ************") + print(prompt("Post Order Traversal")) post_order(node) - print("\n******************************************\n") + print(prompt() + "\n") - print("\n********* Level Order Traversal ************") + print(prompt("Level Order Traversal")) level_order(node) - print("\n******************************************\n") + print(prompt() + "\n") - print("\n********* Actual Level Order Traversal ************") + print(prompt("Actual Level Order Traversal")) level_order_actual(node) - print("\n******************************************\n") + print("*" * 50 + "\n") - print("\n********* Pre Order Traversal - Iteration Version ************") + print(prompt("Pre Order Traversal - Iteration Version")) pre_order_iter(node) - print("\n******************************************\n") + print(prompt() + "\n") - print("\n********* In Order Traversal - Iteration Version ************") + print(prompt("In Order Traversal - Iteration Version")) in_order_iter(node) - print("\n******************************************\n") + print(prompt() + "\n") - print("\n********* Post Order Traversal - Iteration Version ************") + print(prompt("Post Order Traversal - Iteration Version")) post_order_iter(node) - print("\n******************************************\n") + print(prompt()) From c74fd0c9bf984613d9087a793f832d25d19e855f Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Tue, 13 Aug 2019 11:50:13 +0200 Subject: [PATCH 139/193] Add maths/test_prime_check.py (#1125) * Add maths/test_prime_check.py * Add comments on why this file is required --- maths/test_prime_check.py | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 maths/test_prime_check.py diff --git a/maths/test_prime_check.py b/maths/test_prime_check.py new file mode 100644 index 000000000000..b6389684af9e --- /dev/null +++ b/maths/test_prime_check.py @@ -0,0 +1,8 @@ +""" +Minimalist file that allows pytest to find and run the Test unittest. For details, see: +http://doc.pytest.org/en/latest/goodpractices.html#conventions-for-python-test-discovery +""" + +from .prime_check import Test + +Test() From dc2b575274ad4920a0e3f2303a80796daafce84f Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Tue, 13 Aug 2019 11:59:49 +0200 Subject: [PATCH 140/193] Add doctests to networking_flow/minimum_cut.py (#1126) --- networking_flow/minimum_cut.py | 57 +++++++++++++++++++--------------- 1 file changed, 32 insertions(+), 25 deletions(-) diff --git a/networking_flow/minimum_cut.py b/networking_flow/minimum_cut.py index 8ad6e03b00c6..7773df72f8f0 100644 --- a/networking_flow/minimum_cut.py +++ b/networking_flow/minimum_cut.py @@ -1,12 +1,21 @@ # Minimum cut on Ford_Fulkerson algorithm. - + +test_graph = [ + [0, 16, 13, 0, 0, 0], + [0, 0, 10, 12, 0, 0], + [0, 4, 0, 0, 14, 0], + [0, 0, 9, 0, 0, 20], + [0, 0, 0, 7, 0, 4], + [0, 0, 0, 0, 0, 0], +] + + def BFS(graph, s, t, parent): # Return True if there is node that has not iterated. - visited = [False]*len(graph) - queue=[] - queue.append(s) + visited = [False] * len(graph) + queue = [s] visited[s] = True - + while queue: u = queue.pop(0) for ind in range(len(graph[u])): @@ -16,26 +25,30 @@ def BFS(graph, s, t, parent): parent[ind] = u return True if visited[t] else False - + + def mincut(graph, source, sink): - # This array is filled by BFS and to store path - parent = [-1]*(len(graph)) - max_flow = 0 + """This array is filled by BFS and to store path + >>> mincut(test_graph, source=0, sink=5) + [(1, 3), (4, 3), (4, 5)] + """ + parent = [-1] * (len(graph)) + max_flow = 0 res = [] - temp = [i[:] for i in graph] # Record orignial cut, copy. - while BFS(graph, source, sink, parent) : + temp = [i[:] for i in graph] # Record orignial cut, copy. + while BFS(graph, source, sink, parent): path_flow = float("Inf") s = sink - while(s != source): + while s != source: # Find the minimum value in select path - path_flow = min (path_flow, graph[parent[s]][s]) + path_flow = min(path_flow, graph[parent[s]][s]) s = parent[s] - max_flow += path_flow + max_flow += path_flow v = sink - - while(v != source): + + while v != source: u = parent[v] graph[u][v] -= path_flow graph[v][u] += path_flow @@ -44,16 +57,10 @@ def mincut(graph, source, sink): for i in range(len(graph)): for j in range(len(graph[0])): if graph[i][j] == 0 and temp[i][j] > 0: - res.append((i,j)) + res.append((i, j)) return res -graph = [[0, 16, 13, 0, 0, 0], - [0, 0, 10 ,12, 0, 0], - [0, 4, 0, 0, 14, 0], - [0, 0, 9, 0, 0, 20], - [0, 0, 0, 7, 0, 4], - [0, 0, 0, 0, 0, 0]] -source, sink = 0, 5 -print(mincut(graph, source, sink)) \ No newline at end of file +if __name__ == "__main__": + print(mincut(test_graph, source=0, sink=5)) From f3c0b132bcfcf2773734e9418153af01f37475a2 Mon Sep 17 00:00:00 2001 From: adith bharadwaj Date: Tue, 13 Aug 2019 19:21:06 +0530 Subject: [PATCH 141/193] Added sudoku solving program in backtracking algorithms (#1128) * Added sudoku solver in backtracking * Added sudoku solver program * Added sudoku solver * Added sudoku solver * Format with black, add doctests, cleanup main --- backtracking/sudoku.py | 151 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 151 insertions(+) create mode 100644 backtracking/sudoku.py diff --git a/backtracking/sudoku.py b/backtracking/sudoku.py new file mode 100644 index 000000000000..b33351fd4911 --- /dev/null +++ b/backtracking/sudoku.py @@ -0,0 +1,151 @@ +""" + + Given a partially filled 9×9 2D array, the objective is to fill a 9×9 + square grid with digits numbered 1 to 9, so that every row, column, and + and each of the nine 3×3 sub-grids contains all of the digits. + + This can be solved using Backtracking and is similar to n-queens. + We check to see if a cell is safe or not and recursively call the + function on the next column to see if it returns True. if yes, we + have solved the puzzle. else, we backtrack and place another number + in that cell and repeat this process. + +""" + +# assigning initial values to the grid +initial_grid = [ + [3, 0, 6, 5, 0, 8, 4, 0, 0], + [5, 2, 0, 0, 0, 0, 0, 0, 0], + [0, 8, 7, 0, 0, 0, 0, 3, 1], + [0, 0, 3, 0, 1, 0, 0, 8, 0], + [9, 0, 0, 8, 6, 3, 0, 0, 5], + [0, 5, 0, 0, 9, 0, 6, 0, 0], + [1, 3, 0, 0, 0, 0, 2, 5, 0], + [0, 0, 0, 0, 0, 0, 0, 7, 4], + [0, 0, 5, 2, 0, 6, 3, 0, 0], +] +# a grid with no solution +no_solution = [ + [5, 0, 6, 5, 0, 8, 4, 0, 3], + [5, 2, 0, 0, 0, 0, 0, 0, 2], + [1, 8, 7, 0, 0, 0, 0, 3, 1], + [0, 0, 3, 0, 1, 0, 0, 8, 0], + [9, 0, 0, 8, 6, 3, 0, 0, 5], + [0, 5, 0, 0, 9, 0, 6, 0, 0], + [1, 3, 0, 0, 0, 0, 2, 5, 0], + [0, 0, 0, 0, 0, 0, 0, 7, 4], + [0, 0, 5, 2, 0, 6, 3, 0, 0], +] + + +def is_safe(grid, row, column, n): + """ + This function checks the grid to see if each row, + column, and the 3x3 subgrids contain the digit 'n'. + It returns False if it is not 'safe' (a duplicate digit + is found) else returns True if it is 'safe' + + """ + + for i in range(9): + if grid[row][i] == n or grid[i][column] == n: + return False + + for i in range(3): + for j in range(3): + if grid[(row - row % 3) + i][(column - column % 3) + j] == n: + return False + + return True + + +def is_completed(grid): + """ + This function checks if the puzzle is completed or not. + it is completed when all the cells are assigned with a number(not zero) + and There is no repeating number in any column, row or 3x3 subgrid. + + """ + + for row in grid: + for cell in row: + if cell == 0: + return False + + return True + + +def find_empty_location(grid): + """ + This function finds an empty location so that we can assign a number + for that particular row and column. + + """ + + for i in range(9): + for j in range(9): + if grid[i][j] == 0: + return i, j + + +def sudoku(grid): + """ + Takes a partially filled-in grid and attempts to assign values to + all unassigned locations in such a way to meet the requirements + for Sudoku solution (non-duplication across rows, columns, and boxes) + + >>> sudoku(initial_grid) # doctest: +NORMALIZE_WHITESPACE + [[3, 1, 6, 5, 7, 8, 4, 9, 2], + [5, 2, 9, 1, 3, 4, 7, 6, 8], + [4, 8, 7, 6, 2, 9, 5, 3, 1], + [2, 6, 3, 4, 1, 5, 9, 8, 7], + [9, 7, 4, 8, 6, 3, 1, 2, 5], + [8, 5, 1, 7, 9, 2, 6, 4, 3], + [1, 3, 8, 9, 4, 7, 2, 5, 6], + [6, 9, 2, 3, 5, 1, 8, 7, 4], + [7, 4, 5, 2, 8, 6, 3, 1, 9]] + >>> sudoku(no_solution) + False + """ + + if is_completed(grid): + return grid + + row, column = find_empty_location(grid) + + for digit in range(1, 10): + if is_safe(grid, row, column, digit): + grid[row][column] = digit + + if sudoku(grid): + return grid + + grid[row][column] = 0 + + return False + + +def print_solution(grid): + """ + A function to print the solution in the form + of a 9x9 grid + + """ + + for row in grid: + for cell in row: + print(cell, end=" ") + print() + + +if __name__ == "__main__": + + # make a copy of grid so that you can compare with the unmodified grid + for grid in (initial_grid, no_solution): + grid = list(map(list, grid)) + solution = sudoku(grid) + if solution: + print("grid after solving:") + print_solution(solution) + else: + print("Cannot find a solution.") From 8eab2f17f4e3378995b8ccdc8f547784e046fc31 Mon Sep 17 00:00:00 2001 From: Alok Shukla Date: Tue, 13 Aug 2019 22:46:11 +0530 Subject: [PATCH 142/193] Solution for Problem Euler 56 (#1131) * Solution for Euler 56 * Adding Type and Doctest as per guideline * removing unused import * correcting the way type check works --- project_euler/problem_56/__init__.py | 0 project_euler/problem_56/sol1.py | 26 ++++++++++++++++++++++++++ 2 files changed, 26 insertions(+) create mode 100644 project_euler/problem_56/__init__.py create mode 100644 project_euler/problem_56/sol1.py diff --git a/project_euler/problem_56/__init__.py b/project_euler/problem_56/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_56/sol1.py b/project_euler/problem_56/sol1.py new file mode 100644 index 000000000000..194a7a37af43 --- /dev/null +++ b/project_euler/problem_56/sol1.py @@ -0,0 +1,26 @@ + + +def maximum_digital_sum(a: int, b: int) -> int: + """ + Considering natural numbers of the form, a**b, where a, b < 100, + what is the maximum digital sum? + :param a: + :param b: + :return: + >>> maximum_digital_sum(10,10) + 45 + + >>> maximum_digital_sum(100,100) + 972 + + >>> maximum_digital_sum(100,200) + 1872 + """ + + # RETURN the MAXIMUM from the list of SUMs of the list of INT converted from STR of BASE raised to the POWER + return max([sum([int(x) for x in str(base**power)]) for base in range(a) for power in range(b)]) + +#Tests +if __name__ == "__main__": + import doctest + doctest.testmod() From 27205d454877e76fc753feb3dfd528d5d29b93f2 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Wed, 14 Aug 2019 23:24:58 +0200 Subject: [PATCH 143/193] Update DIRECTORY.md (#1129) --- DIRECTORY.md | 430 +++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 420 insertions(+), 10 deletions(-) diff --git a/DIRECTORY.md b/DIRECTORY.md index d97791bb5dd3..80bf64ef4c30 100644 --- a/DIRECTORY.md +++ b/DIRECTORY.md @@ -1,395 +1,805 @@ ## Arithmetic Analysis + * [bisection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/bisection.py) + * [in static equilibrium](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/in_static_equilibrium.py) + * [intersection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/intersection.py) + * [lu decomposition](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/lu_decomposition.py) + * [newton method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_method.py) + * [newton raphson method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_raphson_method.py) + ## Backtracking + * [all combinations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_combinations.py) + * [all permutations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_permutations.py) + * [all subsequences](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_subsequences.py) + * [minimax](https://github.com/TheAlgorithms/Python/blob/master/backtracking/minimax.py) + * [n queens](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens.py) + + * [sudoku](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sudoku.py) + * [sum of subsets](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sum_of_subsets.py) + ## Boolean Algebra + * [quine mc cluskey](https://github.com/TheAlgorithms/Python/blob/master/boolean_algebra/quine_mc_cluskey.py) + ## Ciphers + * [affine cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/affine_cipher.py) + * [atbash](https://github.com/TheAlgorithms/Python/blob/master/ciphers/atbash.py) + * [base16](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base16.py) + * [base32](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base32.py) + * [base64 cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base64_cipher.py) + * [base85](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base85.py) + * [brute force caesar cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/brute_force_caesar_cipher.py) + * [caesar cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/caesar_cipher.py) + * [cryptomath module](https://github.com/TheAlgorithms/Python/blob/master/ciphers/cryptomath_module.py) + * [elgamal key generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/elgamal_key_generator.py) + * [hill cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/hill_cipher.py) + * [morse code implementation](https://github.com/TheAlgorithms/Python/blob/master/ciphers/morse_code_implementation.py) + * [onepad cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/onepad_cipher.py) + * [playfair cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/playfair_cipher.py) + * [rabin miller](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rabin_miller.py) + * [rot13](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rot13.py) + * [rsa cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_cipher.py) + * [rsa key generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_key_generator.py) + * [simple substitution cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/simple_substitution_cipher.py) + * [trafid cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/trafid_cipher.py) + * [transposition cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher.py) + * [transposition cipher encrypt decrypt file](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher_encrypt_decrypt_file.py) + * [vigenere cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/vigenere_cipher.py) + * [xor cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/xor_cipher.py) + ## Compression + * [burrows wheeler](https://github.com/TheAlgorithms/Python/blob/master/compression/burrows_wheeler.py) + * [huffman](https://github.com/TheAlgorithms/Python/blob/master/compression/huffman.py) + * [peak signal to noise ratio](https://github.com/TheAlgorithms/Python/blob/master/compression/peak_signal_to_noise_ratio.py) + ## Conversions + * [decimal to binary](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary.py) + * [decimal to hexadecimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_hexadecimal.py) + * [decimal to octal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_octal.py) + ## Data Structures + * Binary Tree + * [avl tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/avl_tree.py) + * [basic binary tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/basic_binary_tree.py) + * [binary search tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_search_tree.py) + * [fenwick tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/fenwick_tree.py) + * [lazy segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lazy_segment_tree.py) + * [lca](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lca.py) + * [red black tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/red_black_tree.py) + * [segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/segment_tree.py) + * [treap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/treap.py) + * Hashing + * [double hash](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/double_hash.py) + * [hash table](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table.py) + * [hash table with linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table_with_linked_list.py) + * Number Theory + * [prime numbers](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/number_theory/prime_numbers.py) + * [quadratic probing](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/quadratic_probing.py) + * Heap + * [heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap.py) + * Linked List + * [doubly linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/doubly_linked_list.py) + * [is palindrome](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/is_palindrome.py) + * [singly linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/singly_linked_list.py) + * [swap nodes](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/swap_nodes.py) + * Queue + * [double ended queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/double_ended_queue.py) + * [queue on list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_list.py) + * [queue on pseudo stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_pseudo_stack.py) + * Stacks + * [balanced parentheses](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/balanced_parentheses.py) + * [infix to postfix conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_postfix_conversion.py) + * [infix to prefix conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_prefix_conversion.py) + * [next greater element](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/next_greater_element.py) + * [postfix evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/postfix_evaluation.py) + * [stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack.py) + * [stock span problem](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stock_span_problem.py) + * Trie + * [trie](https://github.com/TheAlgorithms/Python/blob/master/data_structures/trie/trie.py) + ## Digital Image Processing + * [change contrast](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_contrast.py) + * Edge Detection + * [canny](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/edge_detection/canny.py) + * Filters + * [convolve](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/convolve.py) + * [gaussian filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/gaussian_filter.py) + * [median filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/median_filter.py) + * [sobel filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/sobel_filter.py) + + * [test digital image processing](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/test_digital_image_processing.py) + ## Divide And Conquer + * [closest pair of points](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/closest_pair_of_points.py) + * [max subarray sum](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/max_subarray_sum.py) + ## Dynamic Programming + * [abbreviation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/abbreviation.py) + * [bitmask](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/bitmask.py) + * [climbing stairs](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/climbing_stairs.py) + * [coin change](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/coin_change.py) + * [edit distance](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/edit_distance.py) + * [factorial](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/factorial.py) + * [fast fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fast_fibonacci.py) + * [fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fibonacci.py) + * [floyd warshall](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/floyd_warshall.py) + * [fractional knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fractional_knapsack.py) + * [integer partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/integer_partition.py) + * [k means clustering tensorflow](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/k_means_clustering_tensorflow.py) + * [knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/knapsack.py) + * [longest common subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_common_subsequence.py) + * [longest increasing subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence.py) + * [longest increasing subsequence o(nlogn)](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence_o(nlogn).py) + * [longest sub array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_sub_array.py) + * [matrix chain order](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/matrix_chain_order.py) + * [max sub array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sub_array.py) + * [minimum partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_partition.py) + * [rod cutting](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/rod_cutting.py) + * [subset generation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/subset_generation.py) + * [sum of subset](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/sum_of_subset.py) -## File Transfer Protocol - * [ftp client server](https://github.com/TheAlgorithms/Python/blob/master/file_transfer_protocol/ftp_client_server.py) - * [ftp send receive](https://github.com/TheAlgorithms/Python/blob/master/file_transfer_protocol/ftp_send_receive.py) + +## File Transfer + + * [recieve file](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/recieve_file.py) + + * [send file](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/send_file.py) + ## Graphs + * [a star](https://github.com/TheAlgorithms/Python/blob/master/graphs/a_star.py) + * [articulation points](https://github.com/TheAlgorithms/Python/blob/master/graphs/articulation_points.py) + * [basic graphs](https://github.com/TheAlgorithms/Python/blob/master/graphs/basic_graphs.py) + * [bellman ford](https://github.com/TheAlgorithms/Python/blob/master/graphs/bellman_ford.py) + * [bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs.py) + * [bfs shortest path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_shortest_path.py) + * [breadth first search](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search.py) + * [check bipartite graph bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_bfs.py) + * [check bipartite graph dfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_dfs.py) + * [depth first search](https://github.com/TheAlgorithms/Python/blob/master/graphs/depth_first_search.py) + * [dfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/dfs.py) + * [dijkstra](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra.py) + * [dijkstra 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_2.py) + * [dijkstra algorithm](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_algorithm.py) + * [directed and undirected (weighted) graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/directed_and_undirected_(weighted)_graph.py) + * [edmonds karp multiple source and sink](https://github.com/TheAlgorithms/Python/blob/master/graphs/edmonds_karp_multiple_source_and_sink.py) + * [eulerian path and circuit for undirected graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/eulerian_path_and_circuit_for_undirected_graph.py) + * [even tree](https://github.com/TheAlgorithms/Python/blob/master/graphs/even_tree.py) + * [finding bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py) + * [graph list](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py) + * [graph matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py) + * [graphs floyd warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/graphs_floyd_warshall.py) + * [kahns algorithm long](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_long.py) + * [kahns algorithm topo](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_topo.py) + * [minimum spanning tree kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal.py) + * [minimum spanning tree prims](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims.py) + * [multi hueristic astar](https://github.com/TheAlgorithms/Python/blob/master/graphs/multi_hueristic_astar.py) + * [page rank](https://github.com/TheAlgorithms/Python/blob/master/graphs/page_rank.py) + * [prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/prim.py) + * [scc kosaraju](https://github.com/TheAlgorithms/Python/blob/master/graphs/scc_kosaraju.py) + * [tarjans scc](https://github.com/TheAlgorithms/Python/blob/master/graphs/tarjans_scc.py) + ## Hashes + * [chaos machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/chaos_machine.py) + * [enigma machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/enigma_machine.py) + * [md5](https://github.com/TheAlgorithms/Python/blob/master/hashes/md5.py) + * [sha1](https://github.com/TheAlgorithms/Python/blob/master/hashes/sha1.py) -## Linear Algebra Python + +## Linear Algebra + * Src - * [lib](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra_python/src/lib.py) - * [tests](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra_python/src/tests.py) + + * [lib](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/lib.py) + + * [python-polynom-for-points](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/python-polynom-for-points.py) + + * [tests](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/tests.py) + ## Machine Learning - * [NaiveBayes](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/NaiveBayes.ipynb) + * [decision tree](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/decision_tree.py) + * [gradient descent](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_descent.py) + * [k means clust](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_means_clust.py) + * [knn sklearn](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/knn_sklearn.py) + * [linear regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_regression.py) + * [logistic regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/logistic_regression.py) - * [perceptron](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/perceptron.py) + + * [naive bayes](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/naive_bayes.ipynb) + * Random Forest Classification + * [random forest classification](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classification/random_forest_classification.py) + * [random forest classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classification/random_forest_classifier.ipynb) + * Random Forest Regression + * [random forest regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression/random_forest_regression.ipynb) + * [random forest regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression/random_forest_regression.py) + * [reuters one vs rest classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/reuters_one_vs_rest_classifier.ipynb) + * [scoring functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/scoring_functions.py) + ## Maths + * [3n+1](https://github.com/TheAlgorithms/Python/blob/master/maths/3n+1.py) + * [abs](https://github.com/TheAlgorithms/Python/blob/master/maths/abs.py) + * [abs max](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_max.py) + * [abs min](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_min.py) + * [average mean](https://github.com/TheAlgorithms/Python/blob/master/maths/average_mean.py) + * [average median](https://github.com/TheAlgorithms/Python/blob/master/maths/average_median.py) + * [basic maths](https://github.com/TheAlgorithms/Python/blob/master/maths/basic_maths.py) + * [binary exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation.py) + + * [collatz sequence](https://github.com/TheAlgorithms/Python/blob/master/maths/collatz_sequence.py) + * [extended euclidean algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/extended_euclidean_algorithm.py) + * [factorial python](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_python.py) + * [factorial recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_recursive.py) + * [fermat little theorem](https://github.com/TheAlgorithms/Python/blob/master/maths/fermat_little_theorem.py) + * [fibonacci](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci.py) + * [fibonacci sequence recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci_sequence_recursion.py) + * [find lcm](https://github.com/TheAlgorithms/Python/blob/master/maths/find_lcm.py) + * [find max](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max.py) + * [find min](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min.py) + * [gaussian](https://github.com/TheAlgorithms/Python/blob/master/maths/gaussian.py) + * [greater common divisor](https://github.com/TheAlgorithms/Python/blob/master/maths/greater_common_divisor.py) + * [is square free](https://github.com/TheAlgorithms/Python/blob/master/maths/is_square_free.py) + + * [largest of very large numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/largest_of_very_large_numbers.py) + + * [lucas lehmer primality test](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_lehmer_primality_test.py) + * [lucas series](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_series.py) + * [mobius function](https://github.com/TheAlgorithms/Python/blob/master/maths/mobius_function.py) + * [modular exponential](https://github.com/TheAlgorithms/Python/blob/master/maths/modular_exponential.py) + * [newton raphson](https://github.com/TheAlgorithms/Python/blob/master/maths/newton_raphson.py) + * [prime check](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_check.py) + * [prime factors](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_factors.py) + + * [quadratic equations complex numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/quadratic_equations_complex_numbers.py) + * [segmented sieve](https://github.com/TheAlgorithms/Python/blob/master/maths/segmented_sieve.py) + * [sieve of eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/sieve_of_eratosthenes.py) + * [simpson rule](https://github.com/TheAlgorithms/Python/blob/master/maths/simpson_rule.py) + + * [test prime check](https://github.com/TheAlgorithms/Python/blob/master/maths/test_prime_check.py) + * [trapezoidal rule](https://github.com/TheAlgorithms/Python/blob/master/maths/trapezoidal_rule.py) + * [volume](https://github.com/TheAlgorithms/Python/blob/master/maths/volume.py) + + * [zellers congruence](https://github.com/TheAlgorithms/Python/blob/master/maths/zellers_congruence.py) + ## Matrix + * [matrix operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_operation.py) + * [nth fibonacci using matrix exponentiation](https://github.com/TheAlgorithms/Python/blob/master/matrix/nth_fibonacci_using_matrix_exponentiation.py) + * [rotate matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/rotate_matrix.py) + * [searching in sorted matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/searching_in_sorted_matrix.py) + * [spiral print](https://github.com/TheAlgorithms/Python/blob/master/matrix/spiral_print.py) + * Tests + * [test matrix operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/tests/test_matrix_operation.py) + ## Networking Flow + * [ford fulkerson](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/ford_fulkerson.py) + * [minimum cut](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/minimum_cut.py) + ## Neural Network + * [back propagation neural network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/back_propagation_neural_network.py) + * [convolution neural network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/convolution_neural_network.py) + * [fully connected neural network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/fully_connected_neural_network.ipynb) + * [perceptron](https://github.com/TheAlgorithms/Python/blob/master/neural_network/perceptron.py) + ## Other - * [Food wastage analysis from 1961-2013 (FAO)](https://github.com/TheAlgorithms/Python/blob/master/other/Food%20wastage%20analysis%20from%201961-2013%20(FAO).ipynb) + * [anagrams](https://github.com/TheAlgorithms/Python/blob/master/other/anagrams.py) + * [binary exponentiation](https://github.com/TheAlgorithms/Python/blob/master/other/binary_exponentiation.py) + * [binary exponentiation 2](https://github.com/TheAlgorithms/Python/blob/master/other/binary_exponentiation_2.py) + * [detecting english programmatically](https://github.com/TheAlgorithms/Python/blob/master/other/detecting_english_programmatically.py) + * [euclidean gcd](https://github.com/TheAlgorithms/Python/blob/master/other/euclidean_gcd.py) - * [finding primes](https://github.com/TheAlgorithms/Python/blob/master/other/finding_primes.py) + * [fischer yates shuffle](https://github.com/TheAlgorithms/Python/blob/master/other/fischer_yates_shuffle.py) + + * [food wastage analysis from 1961-2013 fao](https://github.com/TheAlgorithms/Python/blob/master/other/food_wastage_analysis_from_1961-2013_fao.ipynb) + * [frequency finder](https://github.com/TheAlgorithms/Python/blob/master/other/frequency_finder.py) + * [game of life](https://github.com/TheAlgorithms/Python/blob/master/other/game_of_life.py) + * [linear congruential generator](https://github.com/TheAlgorithms/Python/blob/master/other/linear_congruential_generator.py) + * [nested brackets](https://github.com/TheAlgorithms/Python/blob/master/other/nested_brackets.py) + * [palindrome](https://github.com/TheAlgorithms/Python/blob/master/other/palindrome.py) + * [password generator](https://github.com/TheAlgorithms/Python/blob/master/other/password_generator.py) + * [primelib](https://github.com/TheAlgorithms/Python/blob/master/other/primelib.py) + * [sierpinski triangle](https://github.com/TheAlgorithms/Python/blob/master/other/sierpinski_triangle.py) + * [tower of hanoi](https://github.com/TheAlgorithms/Python/blob/master/other/tower_of_hanoi.py) + * [two sum](https://github.com/TheAlgorithms/Python/blob/master/other/two_sum.py) + * [word patterns](https://github.com/TheAlgorithms/Python/blob/master/other/word_patterns.py) + ## Project Euler + * Problem 01 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol3.py) + * [sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol4.py) + * [sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol5.py) + * [sol6](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol6.py) + * Problem 02 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol3.py) + * [sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol4.py) + * Problem 03 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_03/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_03/sol2.py) + * Problem 04 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_04/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_04/sol2.py) + * Problem 05 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_05/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_05/sol2.py) + * Problem 06 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_06/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_06/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_06/sol3.py) + * Problem 07 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_07/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_07/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_07/sol3.py) + * Problem 08 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_08/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_08/sol2.py) + * Problem 09 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_09/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_09/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_09/sol3.py) + * Problem 10 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_10/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_10/sol2.py) + + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_10/sol3.py) + * Problem 11 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_11/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_11/sol2.py) + * Problem 12 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_12/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_12/sol2.py) + * Problem 13 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_13/sol1.py) + * Problem 14 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_14/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_14/sol2.py) + * Problem 15 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_15/sol1.py) + * Problem 16 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_16/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_16/sol2.py) + * Problem 17 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_17/sol1.py) + * Problem 19 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_19/sol1.py) + * Problem 20 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_20/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_20/sol2.py) + * Problem 21 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_21/sol1.py) + * Problem 22 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/sol2.py) + * Problem 234 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_234/sol1.py) + * Problem 24 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_24/sol1.py) + * Problem 25 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_25/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_25/sol2.py) + * Problem 28 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_28/sol1.py) + * Problem 29 + * [solution](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_29/solution.py) + * Problem 31 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_31/sol1.py) + * Problem 36 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_36/sol1.py) + * Problem 40 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_40/sol1.py) + * Problem 48 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_48/sol1.py) + * Problem 52 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_52/sol1.py) + * Problem 53 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_53/sol1.py) + * Problem 76 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_76/sol1.py) + ## Searches + * [binary search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py) + * [interpolation search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py) + * [jump search](https://github.com/TheAlgorithms/Python/blob/master/searches/jump_search.py) + * [linear search](https://github.com/TheAlgorithms/Python/blob/master/searches/linear_search.py) + * [quick select](https://github.com/TheAlgorithms/Python/blob/master/searches/quick_select.py) + * [sentinel linear search](https://github.com/TheAlgorithms/Python/blob/master/searches/sentinel_linear_search.py) + * [tabu search](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_search.py) + * [ternary search](https://github.com/TheAlgorithms/Python/blob/master/searches/ternary_search.py) + ## Sorts + * [bitonic sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bitonic_sort.py) + * [bogo sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bogo_sort.py) + * [bubble sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bubble_sort.py) + * [bucket sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bucket_sort.py) + * [cocktail shaker sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cocktail_shaker_sort.py) + * [comb sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/comb_sort.py) + * [counting sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/counting_sort.py) + * [cycle sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cycle_sort.py) + * [external sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/external_sort.py) + * [gnome sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/gnome_sort.py) + * [heap sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/heap_sort.py) + * [insertion sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/insertion_sort.py) + * [merge sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort.py) + * [merge sort fastest](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort_fastest.py) + * [odd even transposition parallel](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_parallel.py) + * [odd even transposition single threaded](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_single_threaded.py) + * [pancake sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pancake_sort.py) + * [pigeon sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pigeon_sort.py) + * [quick sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort.py) + * [quick sort 3 partition](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort_3_partition.py) + * [radix sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/radix_sort.py) + * [random normal distribution quicksort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_normal_distribution_quicksort.py) + * [random pivot quick sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_pivot_quick_sort.py) + * [selection sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/selection_sort.py) + * [shell sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/shell_sort.py) + * [tim sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tim_sort.py) + * [topological sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/topological_sort.py) + * [tree sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tree_sort.py) + * [wiggle sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/wiggle_sort.py) + ## Strings + * [boyer moore search](https://github.com/TheAlgorithms/Python/blob/master/strings/boyer_moore_search.py) + * [knuth morris pratt](https://github.com/TheAlgorithms/Python/blob/master/strings/knuth_morris_pratt.py) + * [levenshtein distance](https://github.com/TheAlgorithms/Python/blob/master/strings/levenshtein_distance.py) + * [manacher](https://github.com/TheAlgorithms/Python/blob/master/strings/manacher.py) + * [min cost string conversion](https://github.com/TheAlgorithms/Python/blob/master/strings/min_cost_string_conversion.py) + * [naive string search](https://github.com/TheAlgorithms/Python/blob/master/strings/naive_string_search.py) + * [rabin karp](https://github.com/TheAlgorithms/Python/blob/master/strings/rabin_karp.py) + ## Traversals + * [binary tree traversals](https://github.com/TheAlgorithms/Python/blob/master/traversals/binary_tree_traversals.py) From 3e69733e44d2789647b434a1a6d112062b143e00 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Thu, 15 Aug 2019 13:19:38 +0200 Subject: [PATCH 144/193] Remove 'python' from the filename (#1130) --- .../src/{python-polynom-for-points.py => polynom-for-points.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename linear_algebra/src/{python-polynom-for-points.py => polynom-for-points.py} (100%) diff --git a/linear_algebra/src/python-polynom-for-points.py b/linear_algebra/src/polynom-for-points.py similarity index 100% rename from linear_algebra/src/python-polynom-for-points.py rename to linear_algebra/src/polynom-for-points.py From 5bdcd4836c1a47fb14f7bf89339b3c99bf67fe51 Mon Sep 17 00:00:00 2001 From: Maxwell Aladago Date: Thu, 15 Aug 2019 14:07:43 -0400 Subject: [PATCH 145/193] =?UTF-8?q?EHN:=20A=20divide-and-conquer,=20and=20?= =?UTF-8?q?brute-force=20algorithms=20for=20array=20inversions=20co?= =?UTF-8?q?=E2=80=A6=20(#1133)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * divide and conquer and brute force algorithms for array-inversions counting * divide and conquer and brute force algorithms for array-inversions counting * divide and conquer and brute force algorithms for array-inversions counting --- divide_and_conquer/inversions.py | 173 +++++++++++++++++++++++++++++++ 1 file changed, 173 insertions(+) create mode 100644 divide_and_conquer/inversions.py diff --git a/divide_and_conquer/inversions.py b/divide_and_conquer/inversions.py new file mode 100644 index 000000000000..527741cad3b7 --- /dev/null +++ b/divide_and_conquer/inversions.py @@ -0,0 +1,173 @@ +from __future__ import print_function, absolute_import, division + +""" +Given an array-like data structure A[1..n], how many pairs +(i, j) for all 1 <= i < j <= n such that A[i] > A[j]? These pairs are +called inversions. Counting the number of such inversions in an array-like +object is the important. Among other things, counting inversions can help +us determine how close a given array is to being sorted + +In this implementation, I provide two algorithms, a divide-and-conquer +algorithm which runs in nlogn and the brute-force n^2 algorithm. + +""" + + +def count_inversions_bf(arr): + """ + Counts the number of inversions using a a naive brute-force algorithm + + Parameters + ---------- + arr: arr: array-like, the list containing the items for which the number + of inversions is desired. The elements of `arr` must be comparable. + + Returns + ------- + num_inversions: The total number of inversions in `arr` + + Examples + --------- + + >>> count_inversions_bf([1, 4, 2, 4, 1]) + 4 + >>> count_inversions_bf([1, 1, 2, 4, 4]) + 0 + >>> count_inversions_bf([]) + 0 + """ + + num_inversions = 0 + n = len(arr) + + for i in range(n-1): + for j in range(i + 1, n): + if arr[i] > arr[j]: + num_inversions += 1 + + return num_inversions + + +def count_inversions_recursive(arr): + """ + Counts the number of inversions using a divide-and-conquer algorithm + + Parameters + ----------- + arr: array-like, the list containing the items for which the number + of inversions is desired. The elements of `arr` must be comparable. + + Returns + ------- + C: a sorted copy of `arr`. + num_inversions: int, the total number of inversions in 'arr' + + Examples + -------- + + >>> count_inversions_recursive([1, 4, 2, 4, 1]) + ([1, 1, 2, 4, 4], 4) + >>> count_inversions_recursive([1, 1, 2, 4, 4]) + ([1, 1, 2, 4, 4], 0) + >>> count_inversions_recursive([]) + ([], 0) + """ + if len(arr) <= 1: + return arr, 0 + else: + mid = len(arr)//2 + P = arr[0:mid] + Q = arr[mid:] + + A, inversion_p = count_inversions_recursive(P) + B, inversions_q = count_inversions_recursive(Q) + C, cross_inversions = _count_cross_inversions(A, B) + + num_inversions = inversion_p + inversions_q + cross_inversions + return C, num_inversions + + +def _count_cross_inversions(P, Q): + """ + Counts the inversions across two sorted arrays. + And combine the two arrays into one sorted array + + For all 1<= i<=len(P) and for all 1 <= j <= len(Q), + if P[i] > Q[j], then (i, j) is a cross inversion + + Parameters + ---------- + P: array-like, sorted in non-decreasing order + Q: array-like, sorted in non-decreasing order + + Returns + ------ + R: array-like, a sorted array of the elements of `P` and `Q` + num_inversion: int, the number of inversions across `P` and `Q` + + Examples + -------- + + >>> _count_cross_inversions([1, 2, 3], [0, 2, 5]) + ([0, 1, 2, 2, 3, 5], 4) + >>> _count_cross_inversions([1, 2, 3], [3, 4, 5]) + ([1, 2, 3, 3, 4, 5], 0) + """ + + R = [] + i = j = num_inversion = 0 + while i < len(P) and j < len(Q): + if P[i] > Q[j]: + # if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P) + # These are all inversions. The claim emerges from the + # property that P is sorted. + num_inversion += (len(P) - i) + R.append(Q[j]) + j += 1 + else: + R.append(P[i]) + i += 1 + + if i < len(P): + R.extend(P[i:]) + else: + R.extend(Q[j:]) + + return R, num_inversion + + +def main(): + arr_1 = [10, 2, 1, 5, 5, 2, 11] + + # this arr has 8 inversions: + # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2) + + num_inversions_bf = count_inversions_bf(arr_1) + _, num_inversions_recursive = count_inversions_recursive(arr_1) + + assert num_inversions_bf == num_inversions_recursive == 8 + + print("number of inversions = ", num_inversions_bf) + + # testing an array with zero inversion (a sorted arr_1) + + arr_1.sort() + num_inversions_bf = count_inversions_bf(arr_1) + _, num_inversions_recursive = count_inversions_recursive(arr_1) + + assert num_inversions_bf == num_inversions_recursive == 0 + print("number of inversions = ", num_inversions_bf) + + # an empty list should also have zero inversions + arr_1 = [] + num_inversions_bf = count_inversions_bf(arr_1) + _, num_inversions_recursive = count_inversions_recursive(arr_1) + + assert num_inversions_bf == num_inversions_recursive == 0 + print("number of inversions = ", num_inversions_bf) + + +if __name__ == "__main__": + main() + + From a18a8fe2b9e8cfa659d59e14b9a2077f37d86719 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Sat, 17 Aug 2019 00:46:33 +0200 Subject: [PATCH 146/193] Update .gitignore to remove __pycache__/ (#1127) --- .gitignore | 34 +++++++++++++++++++++++++--------- 1 file changed, 25 insertions(+), 9 deletions(-) diff --git a/.gitignore b/.gitignore index 0c3f33058614..b840d4ed0490 100644 --- a/.gitignore +++ b/.gitignore @@ -7,9 +7,7 @@ __pycache__/ *.so # Distribution / packaging -.vscode/ .Python -env/ build/ develop-eggs/ dist/ @@ -21,9 +19,11 @@ lib64/ parts/ sdist/ var/ +wheels/ *.egg-info/ .installed.cfg *.egg +MANIFEST # PyInstaller # Usually these files are written by a python script from a template @@ -43,8 +43,9 @@ htmlcov/ .cache nosetests.xml coverage.xml -*,cover +*.cover .hypothesis/ +.pytest_cache/ # Translations *.mo @@ -53,6 +54,7 @@ coverage.xml # Django stuff: *.log local_settings.py +db.sqlite3 # Flask stuff: instance/ @@ -67,7 +69,7 @@ docs/_build/ # PyBuilder target/ -# IPython Notebook +# Jupyter Notebook .ipynb_checkpoints # pyenv @@ -76,18 +78,32 @@ target/ # celery beat schedule file celerybeat-schedule -# dotenv -.env +# SageMath parsed files +*.sage.py -# virtualenv +# Environments +.env +.venv +env/ venv/ ENV/ +env.bak/ +venv.bak/ # Spyder project settings .spyderproject +.spyproject # Rope project settings .ropeproject -.idea + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + .DS_Store -.try \ No newline at end of file +.idea +.try +.vscode/ From 05c9a05f3663df7a93c0e50f9035c8b2c5f2754b Mon Sep 17 00:00:00 2001 From: Maxwell Aladago Date: Sat, 17 Aug 2019 11:36:31 -0400 Subject: [PATCH 147/193] ENH: two algorithms for the convex hull problem of a set of 2d points on a plain (#1135) * divide and conquer and brute force algorithms for array-inversions counting * divide and conquer and brute force algorithms for array-inversions counting * divide and conquer and brute force algorithms for array-inversions counting * a naive and divide-and-conquer algorithms for the convex-hull problem * two convex-hull algorithms, a divide-and-conquer and a naive algorithm * two convex-hull algorithms, a divide-and-conquer and a naive algorithm * two convex-hull algorithms, a divide-and-conquer and a naive algorithm --- divide_and_conquer/convex_hull.py | 431 ++++++++++++++++++++++++++++++ 1 file changed, 431 insertions(+) create mode 100644 divide_and_conquer/convex_hull.py diff --git a/divide_and_conquer/convex_hull.py b/divide_and_conquer/convex_hull.py new file mode 100644 index 000000000000..f15d74ddea68 --- /dev/null +++ b/divide_and_conquer/convex_hull.py @@ -0,0 +1,431 @@ +from __future__ import print_function, absolute_import, division + +from numbers import Number +""" +The convex hull problem is problem of finding all the vertices of convex polygon, P of +a set of points in a plane such that all the points are either on the vertices of P or +inside P. TH convex hull problem has several applications in geometrical problems, +computer graphics and game development. + +Two algorithms have been implemented for the convex hull problem here. +1. A brute-force algorithm which runs in O(n^3) +2. A divide-and-conquer algorithm which runs in O(n^3) + +There are other several other algorithms for the convex hull problem +which have not been implemented here, yet. + +""" + + +class Point: + """ + Defines a 2-d point for use by all convex-hull algorithms. + + Parameters + ---------- + x: an int or a float, the x-coordinate of the 2-d point + y: an int or a float, the y-coordinate of the 2-d point + + Examples + -------- + >>> Point(1, 2) + (1, 2) + >>> Point("1", "2") + (1.0, 2.0) + >>> Point(1, 2) > Point(0, 1) + True + >>> Point(1, 1) == Point(1, 1) + True + >>> Point(-0.5, 1) == Point(0.5, 1) + False + >>> Point("pi", "e") + Traceback (most recent call last): + ... + ValueError: x and y must be both numeric types but got , instead + """ + + def __init__(self, x, y): + if not (isinstance(x, Number) and isinstance(y, Number)): + try: + x, y = float(x), float(y) + except ValueError as e: + e.args = ("x and y must be both numeric types " + "but got {}, {} instead".format(type(x), type(y)), ) + raise + + self.x = x + self.y = y + + def __eq__(self, other): + return self.x == other.x and self.y == other.y + + def __ne__(self, other): + return not self == other + + def __gt__(self, other): + if self.x > other.x: + return True + elif self.x == other.x: + return self.y > other.y + return False + + def __lt__(self, other): + return not self > other + + def __ge__(self, other): + if self.x > other.x: + return True + elif self.x == other.x: + return self.y >= other.y + return False + + def __le__(self, other): + if self.x < other.x: + return True + elif self.x == other.x: + return self.y <= other.y + return False + + def __repr__(self): + return "({}, {})".format(self.x, self.y) + + def __hash__(self): + return hash(self.x) + + +def _construct_points(list_of_tuples): + """ + constructs a list of points from an array-like object of numbers + + Arguments + --------- + + list_of_tuples: array-like object of type numbers. Acceptable types so far + are lists, tuples and sets. + + Returns + -------- + points: a list where each item is of type Point. This contains only objects + which can be converted into a Point. + + Examples + ------- + >>> _construct_points([[1, 1], [2, -1], [0.3, 4]]) + [(1, 1), (2, -1), (0.3, 4)] + >>> _construct_points(([1, 1], [2, -1], [0.3, 4])) + [(1, 1), (2, -1), (0.3, 4)] + >>> _construct_points([(1, 1), (2, -1), (0.3, 4)]) + [(1, 1), (2, -1), (0.3, 4)] + >>> _construct_points([[1, 1], (2, -1), [0.3, 4]]) + [(1, 1), (2, -1), (0.3, 4)] + >>> _construct_points([1, 2]) + Ignoring deformed point 1. All points must have at least 2 coordinates. + Ignoring deformed point 2. All points must have at least 2 coordinates. + [] + >>> _construct_points([]) + [] + >>> _construct_points(None) + [] + """ + + points = [] + if list_of_tuples: + for p in list_of_tuples: + try: + points.append(Point(p[0], p[1])) + except (IndexError, TypeError): + print("Ignoring deformed point {}. All points" + " must have at least 2 coordinates.".format(p)) + return points + + +def _validate_input(points): + """ + validates an input instance before a convex-hull algorithms uses it + + Parameters + --------- + points: array-like, the 2d points to validate before using with + a convex-hull algorithm. The elements of points must be either lists, tuples or + Points. + + Returns + ------- + points: array_like, an iterable of all well-defined Points constructed passed in. + + + Exception + --------- + ValueError: if points is empty or None, or if a wrong data structure like a scalar is passed + + TypeError: if an iterable but non-indexable object (eg. dictionary) is passed. + The exception to this a set which we'll convert to a list before using + + + Examples + ------- + >>> _validate_input([[1, 2]]) + [(1, 2)] + >>> _validate_input([(1, 2)]) + [(1, 2)] + >>> _validate_input([Point(2, 1), Point(-1, 2)]) + [(2, 1), (-1, 2)] + >>> _validate_input([]) + Traceback (most recent call last): + ... + ValueError: Expecting a list of points but got [] + >>> _validate_input(1) + Traceback (most recent call last): + ... + ValueError: Expecting an iterable object but got an non-iterable type 1 + """ + + if not points: + raise ValueError("Expecting a list of points but got {}".format(points)) + + if isinstance(points, set): + points = list(points) + + try: + if hasattr(points, "__iter__") and not isinstance(points[0], Point): + if isinstance(points[0], (list, tuple)): + points = _construct_points(points) + else: + raise ValueError("Expecting an iterable of type Point, list or tuple. " + "Found objects of type {} instead" + .format(["point", "list", "tuple"], type(points[0]))) + elif not hasattr(points, "__iter__"): + raise ValueError("Expecting an iterable object " + "but got an non-iterable type {}".format(points)) + except TypeError as e: + print("Expecting an iterable of type Point, list or tuple.") + raise + + return points + + +def _det(a, b, c): + """ + Computes the sign perpendicular distance of a 2d point c from a line segment + ab. The sign indicates the direction of c relative to ab. + A Positive value means c is above ab (to the left), while a negative value + means c is below ab (to the right). 0 means all three points are on a straight line. + + As a side note, 0.5 * abs|det| is the area of triangle abc + + Parameters + ---------- + a: point, the point on the left end of line segment ab + b: point, the point on the right end of line segment ab + c: point, the point for which the direction and location is desired. + + Returns + -------- + det: float, abs(det) is the distance of c from ab. The sign + indicates which side of line segment ab c is. det is computed as + (a_xb_y + c_xa_y + b_xc_y) - (a_yb_x + c_ya_x + b_yc_x) + + Examples + ---------- + >>> _det(Point(1, 1), Point(1, 2), Point(1, 5)) + 0 + >>> _det(Point(0, 0), Point(10, 0), Point(0, 10)) + 100 + >>> _det(Point(0, 0), Point(10, 0), Point(0, -10)) + -100 + """ + + det = (a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x) + return det + + +def convex_hull_bf(points): + """ + Constructs the convex hull of a set of 2D points using a brute force algorithm. + The algorithm basically considers all combinations of points (i, j) and uses the + definition of convexity to determine whether (i, j) is part of the convex hull or not. + (i, j) is part of the convex hull if and only iff there are no points on both sides + of the line segment connecting the ij, and there is no point k such that k is on either end + of the ij. + + Runtime: O(n^3) - definitely horrible + + Parameters + --------- + points: array-like of object of Points, lists or tuples. + The set of 2d points for which the convex-hull is needed + + Returns + ------ + convex_set: list, the convex-hull of points sorted in non-decreasing order. + + See Also + -------- + convex_hull_recursive, + + Examples + --------- + >>> convex_hull_bf([[0, 0], [1, 0], [10, 1]]) + [(0, 0), (1, 0), (10, 1)] + >>> convex_hull_bf([[0, 0], [1, 0], [10, 0]]) + [(0, 0), (10, 0)] + >>> convex_hull_bf([[-1, 1],[-1, -1], [0, 0], [0.5, 0.5], [1, -1], [1, 1], [-0.75, 1]]) + [(-1, -1), (-1, 1), (1, -1), (1, 1)] + >>> convex_hull_bf([(0, 3), (2, 2), (1, 1), (2, 1), (3, 0), (0, 0), (3, 3), (2, -1), (2, -4), (1, -3)]) + [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)] + """ + + points = sorted(_validate_input(points)) + n = len(points) + convex_set = set() + + for i in range(n-1): + for j in range(i + 1, n): + points_left_of_ij = points_right_of_ij = False + ij_part_of_convex_hull = True + for k in range(n): + if k != i and k != j: + det_k = _det(points[i], points[j], points[k]) + + if det_k > 0: + points_left_of_ij = True + elif det_k < 0: + points_right_of_ij = True + else: + # point[i], point[j], point[k] all lie on a straight line + # if point[k] is to the left of point[i] or it's to the + # right of point[j], then point[i], point[j] cannot be + # part of the convex hull of A + if points[k] < points[i] or points[k] > points[j]: + ij_part_of_convex_hull = False + break + + if points_left_of_ij and points_right_of_ij: + ij_part_of_convex_hull = False + break + + if ij_part_of_convex_hull: + convex_set.update([points[i], points[j]]) + + return sorted(convex_set) + + +def convex_hull_recursive(points): + """ + Constructs the convex hull of a set of 2D points using a divide-and-conquer strategy + The algorithm exploits the geometric properties of the problem by repeatedly partitioning + the set of points into smaller hulls, and finding the convex hull of these smaller hulls. + The union of the convex hull from smaller hulls is the solution to the convex hull of the larger problem. + + Parameter + --------- + points: array-like of object of Points, lists or tuples. + The set of 2d points for which the convex-hull is needed + + Runtime: O(n log n) + + Returns + ------- + convex_set: list, the convex-hull of points sorted in non-decreasing order. + + Examples + --------- + >>> convex_hull_recursive([[0, 0], [1, 0], [10, 1]]) + [(0, 0), (1, 0), (10, 1)] + >>> convex_hull_recursive([[0, 0], [1, 0], [10, 0]]) + [(0, 0), (10, 0)] + >>> convex_hull_recursive([[-1, 1],[-1, -1], [0, 0], [0.5, 0.5], [1, -1], [1, 1], [-0.75, 1]]) + [(-1, -1), (-1, 1), (1, -1), (1, 1)] + >>> convex_hull_recursive([(0, 3), (2, 2), (1, 1), (2, 1), (3, 0), (0, 0), (3, 3), (2, -1), (2, -4), (1, -3)]) + [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)] + + """ + points = sorted(_validate_input(points)) + n = len(points) + + # divide all the points into an upper hull and a lower hull + # the left most point and the right most point are definitely + # members of the convex hull by definition. + # use these two anchors to divide all the points into two hulls, + # an upper hull and a lower hull. + + # all points to the left (above) the line joining the extreme points belong to the upper hull + # all points to the right (below) the line joining the extreme points below to the lower hull + # ignore all points on the line joining the extreme points since they cannot be part of the + # convex hull + + left_most_point = points[0] + right_most_point = points[n-1] + + convex_set = {left_most_point, right_most_point} + upperhull = [] + lowerhull = [] + + for i in range(1, n-1): + det = _det(left_most_point, right_most_point, points[i]) + + if det > 0: + upperhull.append(points[i]) + elif det < 0: + lowerhull.append(points[i]) + + _construct_hull(upperhull, left_most_point, right_most_point, convex_set) + _construct_hull(lowerhull, right_most_point, left_most_point, convex_set) + + return sorted(convex_set) + + +def _construct_hull(points, left, right, convex_set): + """ + + Parameters + --------- + points: list or None, the hull of points from which to choose the next convex-hull point + left: Point, the point to the left of line segment joining left and right + right: The point to the right of the line segment joining left and right + convex_set: set, the current convex-hull. The state of convex-set gets updated by this function + + Note + ---- + For the line segment 'ab', 'a' is on the left and 'b' on the right. + but the reverse is true for the line segment 'ba'. + + Returns + ------- + Nothing, only updates the state of convex-set + """ + if points: + extreme_point = None + extreme_point_distance = float('-inf') + candidate_points = [] + + for p in points: + det = _det(left, right, p) + + if det > 0: + candidate_points.append(p) + + if det > extreme_point_distance: + extreme_point_distance = det + extreme_point = p + + if extreme_point: + _construct_hull(candidate_points, left, extreme_point, convex_set) + convex_set.add(extreme_point) + _construct_hull(candidate_points, extreme_point, right, convex_set) + + +def main(): + points = [(0, 3), (2, 2), (1, 1), (2, 1), (3, 0), + (0, 0), (3, 3), (2, -1), (2, -4), (1, -3)] + # the convex set of points is + # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)] + results_recursive = convex_hull_recursive(points) + results_bf = convex_hull_bf(points) + assert results_bf == results_recursive + + print(results_bf) + + +if __name__ == '__main__': + main() From 5d46a4dd7beeeeb27f393b972effe785feab7306 Mon Sep 17 00:00:00 2001 From: Maxwell Aladago Date: Mon, 19 Aug 2019 01:39:39 -0400 Subject: [PATCH 148/193] ENH: Added a functionality to make it possible to reconstruct an optimal subset for the dynamic programming problem (#1139) * function for the knapsack problem which returns one of the optimal subsets * function for the knapsack problem which returns one of the optimal subsets * function for the knapsack problem which returns one of the optimal subsets * function for the knapsack problem which returns one of the optimal subsets * function for the knapsack problem which returns one of the optimal subsets * some pep8 cleanup too --- dynamic_programming/knapsack.py | 131 ++++++++++++++++++++++++++++---- 1 file changed, 116 insertions(+), 15 deletions(-) diff --git a/dynamic_programming/knapsack.py b/dynamic_programming/knapsack.py index 27d1cfed799b..488059d6244d 100644 --- a/dynamic_programming/knapsack.py +++ b/dynamic_programming/knapsack.py @@ -1,7 +1,13 @@ """ -Given weights and values of n items, put these items in a knapsack of capacity W to get the maximum total value in the knapsack. +Given weights and values of n items, put these items in a knapsack of + capacity W to get the maximum total value in the knapsack. + +Note that only the integer weights 0-1 knapsack problem is solvable + using dynamic programming. """ -def MF_knapsack(i,wt,val,j): + + +def MF_knapsack(i, wt, val, j): ''' This code involves the concept of memory functions. Here we solve the subproblems which are needed unlike the below example @@ -9,34 +15,129 @@ def MF_knapsack(i,wt,val,j): ''' global F # a global dp table for knapsack if F[i][j] < 0: - if j < wt[i - 1]: - val = MF_knapsack(i - 1,wt,val,j) + if j < wt[i-1]: + val = MF_knapsack(i-1, wt, val, j) else: - val = max(MF_knapsack(i - 1,wt,val,j),MF_knapsack(i - 1,wt,val,j - wt[i - 1]) + val[i - 1]) + val = max(MF_knapsack(i-1, wt, val, j), + MF_knapsack(i-1, wt, val, j - wt[i-1]) + val[i-1]) F[i][j] = val return F[i][j] + def knapsack(W, wt, val, n): dp = [[0 for i in range(W+1)]for j in range(n+1)] for i in range(1,n+1): - for w in range(1,W+1): - if(wt[i-1]<=w): - dp[i][w] = max(val[i-1]+dp[i-1][w-wt[i-1]],dp[i-1][w]) + for w in range(1, W+1): + if wt[i-1] <= w: + dp[i][w] = max(val[i-1] + dp[i-1][w-wt[i-1]], dp[i-1][w]) else: dp[i][w] = dp[i-1][w] - return dp[n][w] + return dp[n][W], dp + + +def knapsack_with_example_solution(W: int, wt: list, val:list): + """ + Solves the integer weights knapsack problem returns one of + the several possible optimal subsets. + + Parameters + --------- + + W: int, the total maximum weight for the given knapsack problem. + wt: list, the vector of weights for all items where wt[i] is the weight + of the ith item. + val: list, the vector of values for all items where val[i] is the value + of te ith item + + Returns + ------- + optimal_val: float, the optimal value for the given knapsack problem + example_optional_set: set, the indices of one of the optimal subsets + which gave rise to the optimal value. + + Examples + ------- + >>> knapsack_with_example_solution(10, [1, 3, 5, 2], [10, 20, 100, 22]) + (142, {2, 3, 4}) + >>> knapsack_with_example_solution(6, [4, 3, 2, 3], [3, 2, 4, 4]) + (8, {3, 4}) + >>> knapsack_with_example_solution(6, [4, 3, 2, 3], [3, 2, 4]) + Traceback (most recent call last): + ... + ValueError: The number of weights must be the same as the number of values. + But got 4 weights and 3 values + """ + if not (isinstance(wt, (list, tuple)) and isinstance(val, (list, tuple))): + raise ValueError("Both the weights and values vectors must be either lists or tuples") + + num_items = len(wt) + if num_items != len(val): + raise ValueError("The number of weights must be the " + "same as the number of values.\nBut " + "got {} weights and {} values".format(num_items, len(val))) + for i in range(num_items): + if not isinstance(wt[i], int): + raise TypeError("All weights must be integers but " + "got weight of type {} at index {}".format(type(wt[i]), i)) + + optimal_val, dp_table = knapsack(W, wt, val, num_items) + example_optional_set = set() + _construct_solution(dp_table, wt, num_items, W, example_optional_set) + + return optimal_val, example_optional_set + + +def _construct_solution(dp:list, wt:list, i:int, j:int, optimal_set:set): + """ + Recursively reconstructs one of the optimal subsets given + a filled DP table and the vector of weights + + Parameters + --------- + + dp: list of list, the table of a solved integer weight dynamic programming problem + + wt: list or tuple, the vector of weights of the items + i: int, the index of the item under consideration + j: int, the current possible maximum weight + optimal_set: set, the optimal subset so far. This gets modified by the function. + + Returns + ------- + None + + """ + # for the current item i at a maximum weight j to be part of an optimal subset, + # the optimal value at (i, j) must be greater than the optimal value at (i-1, j). + # where i - 1 means considering only the previous items at the given maximum weight + if i > 0 and j > 0: + if dp[i - 1][j] == dp[i][j]: + _construct_solution(dp, wt, i - 1, j, optimal_set) + else: + optimal_set.add(i) + _construct_solution(dp, wt, i - 1, j - wt[i-1], optimal_set) + if __name__ == '__main__': ''' Adding test case for knapsack ''' - val = [3,2,4,4] - wt = [4,3,2,3] + val = [3, 2, 4, 4] + wt = [4, 3, 2, 3] n = 4 w = 6 - F = [[0]*(w + 1)] + [[0] + [-1 for i in range(w + 1)] for j in range(n + 1)] - print(knapsack(w,wt,val,n)) - print(MF_knapsack(n,wt,val,w)) # switched the n and w - + F = [[0] * (w + 1)] + [[0] + [-1 for i in range(w + 1)] for j in range(n + 1)] + optimal_solution, _ = knapsack(w,wt,val, n) + print(optimal_solution) + print(MF_knapsack(n,wt,val,w)) # switched the n and w + + # testing the dynamic programming problem with example + # the optimal subset for the above example are items 3 and 4 + optimal_solution, optimal_subset = knapsack_with_example_solution(w, wt, val) + assert optimal_solution == 8 + assert optimal_subset == {3, 4} + print("optimal_value = ", optimal_solution) + print("An optimal subset corresponding to the optimal value", optimal_subset) + From 32aa7ff0819dba3d2166b8a66317999a7790e510 Mon Sep 17 00:00:00 2001 From: Maxwell Aladago Date: Mon, 19 Aug 2019 03:40:36 -0400 Subject: [PATCH 149/193] ENH: refactored longest common subsequence, also fixed a bug with the sequence returned (#1142) * function for the knapsack problem which returns one of the optimal subsets * function for the knapsack problem which returns one of the optimal subsets * function for the knapsack problem which returns one of the optimal subsets * function for the knapsack problem which returns one of the optimal subsets * function for the knapsack problem which returns one of the optimal subsets * some pep8 cleanup too * ENH: refactored longest common subsequence, also fixed a bug with the sequence returned * renamed function --- .../longest_common_subsequence.py | 90 ++++++++++++++----- 1 file changed, 68 insertions(+), 22 deletions(-) diff --git a/dynamic_programming/longest_common_subsequence.py b/dynamic_programming/longest_common_subsequence.py index 0a4771cb2efd..7836fe303688 100644 --- a/dynamic_programming/longest_common_subsequence.py +++ b/dynamic_programming/longest_common_subsequence.py @@ -1,37 +1,83 @@ """ LCS Problem Statement: Given two sequences, find the length of longest subsequence present in both of them. -A subsequence is a sequence that appears in the same relative order, but not necessarily continious. +A subsequence is a sequence that appears in the same relative order, but not necessarily continuous. Example:"abc", "abg" are subsequences of "abcdefgh". """ from __future__ import print_function -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 -def lcs_dp(x, y): +def longest_common_subsequence(x: str, y: str): + """ + Finds the longest common subsequence between two strings. Also returns the + The subsequence found + + Parameters + ---------- + + x: str, one of the strings + y: str, the other string + + Returns + ------- + L[m][n]: int, the length of the longest subsequence. Also equal to len(seq) + Seq: str, the subsequence found + + >>> longest_common_subsequence("programming", "gaming") + (6, 'gaming') + >>> longest_common_subsequence("physics", "smartphone") + (2, 'ph') + >>> longest_common_subsequence("computer", "food") + (1, 'o') + """ # find the length of strings + + assert x is not None + assert y is not None + m = len(x) n = len(y) # declaring the array for storing the dp values - L = [[None] * (n + 1) for i in xrange(m + 1)] - seq = [] - - for i in range(m + 1): - for j in range(n + 1): - if i == 0 or j == 0: - L[i][j] = 0 - elif x[i - 1] == y[ j - 1]: - L[i][j] = L[i - 1][j - 1] + 1 - seq.append(x[i -1]) + L = [[0] * (n + 1) for _ in range(m + 1)] + + for i in range(1, m + 1): + for j in range(1, n + 1): + if x[i-1] == y[j-1]: + match = 1 else: - L[i][j] = max(L[i - 1][j], L[i][j - 1]) - # L[m][n] contains the length of LCS of X[0..n-1] & Y[0..m-1] + match = 0 + + L[i][j] = max(L[i-1][j], L[i][j-1], L[i-1][j-1] + match) + + seq = "" + i, j = m, n + while i > 0 and i > 0: + if x[i - 1] == y[j - 1]: + match = 1 + else: + match = 0 + + if L[i][j] == L[i - 1][j - 1] + match: + if match == 1: + seq = x[i - 1] + seq + i -= 1 + j -= 1 + elif L[i][j] == L[i - 1][j]: + i -= 1 + else: + j -= 1 + return L[m][n], seq -if __name__=='__main__': - x = 'AGGTAB' - y = 'GXTXAYB' - print(lcs_dp(x, y)) + +if __name__ == '__main__': + a = 'AGGTAB' + b = 'GXTXAYB' + expected_ln = 4 + expected_subseq = "GTAB" + + ln, subseq = longest_common_subsequence(a, b) + assert expected_ln == ln + assert expected_subseq == subseq + print("len =", ln, ", sub-sequence =", subseq) + From 47a9ea2b0b4eaef3e748d4d61763a77abc3e48cb Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Mon, 19 Aug 2019 15:37:49 +0200 Subject: [PATCH 150/193] Simplify code by dropping support for legacy Python (#1143) * Simplify code by dropping support for legacy Python * sort() --> sorted() --- CONTRIBUTING.md | 20 +++--- ciphers/affine_cipher.py | 1 - ciphers/atbash.py | 20 ++---- ciphers/brute_force_caesar_cipher.py | 1 - ciphers/onepad_cipher.py | 4 +- ciphers/rabin_miller.py | 1 - ciphers/rot13.py | 1 - ciphers/rsa_cipher.py | 3 +- ciphers/rsa_key_generator.py | 1 - ciphers/simple_substitution_cipher.py | 5 +- ciphers/transposition_cipher.py | 1 - ...ansposition_cipher_encrypt_decrypt_file.py | 7 +- ciphers/vigenere_cipher.py | 1 - .../binary_tree/binary_search_tree.py | 15 ++-- data_structures/binary_tree/fenwick_tree.py | 1 - .../binary_tree/lazy_segment_tree.py | 1 - data_structures/binary_tree/segment_tree.py | 1 - data_structures/heap/heap.py | 13 +--- .../linked_list/doubly_linked_list.py | 27 ++++--- .../linked_list/singly_linked_list.py | 13 ++-- data_structures/queue/double_ended_queue.py | 1 - .../stacks/balanced_parentheses.py | 3 - .../stacks/infix_to_postfix_conversion.py | 2 - .../stacks/next_greater_element.py | 9 ++- data_structures/stacks/stack.py | 1 - data_structures/stacks/stock_span_problem.py | 1 - divide_and_conquer/convex_hull.py | 12 ++-- divide_and_conquer/inversions.py | 14 ++-- dynamic_programming/bitmask.py | 35 +++++---- dynamic_programming/coin_change.py | 3 - dynamic_programming/edit_distance.py | 23 ++---- dynamic_programming/fast_fibonacci.py | 1 - dynamic_programming/fibonacci.py | 14 ++-- dynamic_programming/integer_partition.py | 22 ++---- .../longest_common_subsequence.py | 2 - .../longest_increasing_subsequence.py | 4 +- ...longest_increasing_subsequence_o(nlogn).py | 21 +++--- dynamic_programming/longest_sub_array.py | 1 - dynamic_programming/matrix_chain_order.py | 2 - dynamic_programming/max_sub_array.py | 23 +++--- graphs/a_star.py | 16 ++--- graphs/basic_graphs.py | 53 ++++++-------- graphs/bellman_ford.py | 2 - graphs/breadth_first_search.py | 2 - graphs/depth_first_search.py | 1 - graphs/dijkstra_2.py | 2 - graphs/dijkstra_algorithm.py | 1 - graphs/even_tree.py | 1 - graphs/graph_list.py | 1 - graphs/graph_matrix.py | 3 - graphs/graphs_floyd_warshall.py | 8 +-- graphs/minimum_spanning_tree_kruskal.py | 2 - graphs/multi_hueristic_astar.py | 8 +-- graphs/scc_kosaraju.py | 3 - hashes/chaos_machine.py | 8 +-- hashes/enigma_machine.py | 2 - hashes/md5.py | 5 +- machine_learning/decision_tree.py | 10 ++- machine_learning/gradient_descent.py | 1 - machine_learning/k_means_clust.py | 71 +++++++++---------- machine_learning/linear_regression.py | 2 - maths/simpson_rule.py | 3 - maths/trapezoidal_rule.py | 4 +- maths/zellers_congruence.py | 5 +- ...h_fibonacci_using_matrix_exponentiation.py | 3 - neural_network/convolution_neural_network.py | 2 - neural_network/perceptron.py | 2 - other/anagrams.py | 1 - other/euclidean_gcd.py | 1 - other/linear_congruential_generator.py | 7 +- other/nested_brackets.py | 3 - other/password_generator.py | 1 - other/tower_of_hanoi.py | 3 +- other/two_sum.py | 4 +- other/word_patterns.py | 1 - project_euler/problem_01/sol1.py | 12 +--- project_euler/problem_01/sol2.py | 10 +-- project_euler/problem_01/sol3.py | 12 +--- project_euler/problem_01/sol4.py | 12 +--- project_euler/problem_01/sol5.py | 10 +-- project_euler/problem_01/sol6.py | 12 +--- project_euler/problem_02/sol1.py | 12 +--- project_euler/problem_02/sol2.py | 12 +--- project_euler/problem_02/sol3.py | 12 +--- project_euler/problem_02/sol4.py | 10 +-- project_euler/problem_03/sol1.py | 8 +-- project_euler/problem_03/sol2.py | 8 +-- project_euler/problem_04/sol1.py | 12 +--- project_euler/problem_04/sol2.py | 12 +--- project_euler/problem_05/sol1.py | 10 +-- project_euler/problem_05/sol2.py | 11 +-- project_euler/problem_06/sol1.py | 12 +--- project_euler/problem_06/sol2.py | 12 +--- project_euler/problem_06/sol3.py | 10 +-- project_euler/problem_07/sol1.py | 10 +-- project_euler/problem_07/sol2.py | 12 +--- project_euler/problem_07/sol3.py | 10 +-- project_euler/problem_09/sol1.py | 3 +- project_euler/problem_09/sol2.py | 12 +--- project_euler/problem_09/sol3.py | 3 - project_euler/problem_10/sol1.py | 19 ++--- project_euler/problem_10/sol2.py | 10 +-- project_euler/problem_11/sol1.py | 14 ++-- project_euler/problem_11/sol2.py | 26 +++---- project_euler/problem_12/sol1.py | 10 +-- project_euler/problem_12/sol2.py | 5 +- project_euler/problem_14/sol1.py | 12 +--- project_euler/problem_14/sol2.py | 10 +-- project_euler/problem_21/sol1.py | 8 +-- project_euler/problem_22/sol1.py | 6 -- project_euler/problem_25/sol1.py | 8 +-- project_euler/problem_28/sol1.py | 7 +- project_euler/problem_29/solution.py | 5 +- project_euler/problem_31/sol1.py | 8 --- project_euler/problem_36/sol1.py | 8 +-- project_euler/problem_40/sol1.py | 5 +- project_euler/problem_48/sol1.py | 7 +- project_euler/problem_53/sol1.py | 10 +-- project_euler/problem_76/sol1.py | 14 ++-- searches/binary_search.py | 14 ++-- searches/interpolation_search.py | 24 +++---- searches/jump_search.py | 1 - searches/linear_search.py | 9 +-- searches/sentinel_linear_search.py | 9 +-- searches/ternary_search.py | 29 +++----- sorts/bogo_sort.py | 8 +-- sorts/bubble_sort.py | 19 +++-- sorts/cocktail_shaker_sort.py | 15 ++-- sorts/comb_sort.py | 7 +- sorts/counting_sort.py | 13 ++-- sorts/cycle_sort.py | 10 +-- sorts/gnome_sort.py | 9 +-- sorts/heap_sort.py | 10 +-- sorts/insertion_sort.py | 10 +-- sorts/merge_sort.py | 10 +-- sorts/merge_sort_fastest.py | 10 +-- sorts/pigeon_sort.py | 10 +-- sorts/quick_sort.py | 10 +-- sorts/quick_sort_3_partition.py | 9 +-- sorts/random_normal_distribution_quicksort.py | 22 +++--- sorts/selection_sort.py | 10 +-- sorts/shell_sort.py | 10 +-- sorts/topological_sort.py | 1 - strings/levenshtein_distance.py | 9 +-- strings/min_cost_string_conversion.py | 31 ++++---- 145 files changed, 367 insertions(+), 976 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 3202b817f1c5..8c0f54ad528d 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -56,7 +56,7 @@ We want your work to be readable by others; therefore, we encourage you to note ```python """ - This function sums a and b + This function sums a and b """ def sum(a, b): return a + b @@ -82,13 +82,13 @@ We want your work to be readable by others; therefore, we encourage you to note The following "testing" approaches are **not** encouraged: ```python - input('Enter your input:') + input('Enter your input:') # Or even worse... - input = eval(raw_input("Enter your input: ")) + input = eval(input("Enter your input: ")) ``` - + However, if your code uses __input()__ then we encourage you to gracefully deal with leading and trailing whitespace in user input by adding __.strip()__ to the end as in: - + ```python starting_value = int(input("Please enter a starting value: ").strip()) ``` @@ -99,13 +99,13 @@ We want your work to be readable by others; therefore, we encourage you to note def sumab(a, b): return a + b # Write tests this way: - print(sumab(1,2)) # 1+2 = 3 - print(sumab(6,4)) # 6+4 = 10 + print(sumab(1, 2)) # 1+2 = 3 + print(sumab(6, 4)) # 6+4 = 10 # Or this way: - print("1 + 2 = ", sumab(1,2)) # 1+2 = 3 - print("6 + 4 = ", sumab(6,4)) # 6+4 = 10 + print("1 + 2 = ", sumab(1, 2)) # 1+2 = 3 + print("6 + 4 = ", sumab(6, 4)) # 6+4 = 10 ``` - + Better yet, if you know how to write [__doctests__](https://docs.python.org/3/library/doctest.html), please consider adding them. - Avoid importing external libraries for basic algorithms. Only use those libraries for complicated algorithms. diff --git a/ciphers/affine_cipher.py b/ciphers/affine_cipher.py index af5f4e0ff4c6..a5d94f087dbf 100644 --- a/ciphers/affine_cipher.py +++ b/ciphers/affine_cipher.py @@ -1,4 +1,3 @@ -from __future__ import print_function import sys, random, cryptomath_module as cryptoMath SYMBOLS = r""" !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~""" diff --git a/ciphers/atbash.py b/ciphers/atbash.py index 5653f0213745..9ed47e0874f8 100644 --- a/ciphers/atbash.py +++ b/ciphers/atbash.py @@ -1,23 +1,15 @@ -try: # Python 2 - raw_input - unichr -except NameError: # Python 3 - raw_input = input - unichr = chr - - -def Atbash(): +def atbash(): output="" - for i in raw_input("Enter the sentence to be encrypted ").strip(): + for i in input("Enter the sentence to be encrypted ").strip(): extract = ord(i) if 65 <= extract <= 90: - output += unichr(155-extract) + output += chr(155-extract) elif 97 <= extract <= 122: - output += unichr(219-extract) + output += chr(219-extract) else: - output+=i + output += i print(output) if __name__ == '__main__': - Atbash() + atbash() diff --git a/ciphers/brute_force_caesar_cipher.py b/ciphers/brute_force_caesar_cipher.py index 3b0716442fc5..3e6e975c8297 100644 --- a/ciphers/brute_force_caesar_cipher.py +++ b/ciphers/brute_force_caesar_cipher.py @@ -1,4 +1,3 @@ -from __future__ import print_function def decrypt(message): """ >>> decrypt('TMDETUX PMDVU') diff --git a/ciphers/onepad_cipher.py b/ciphers/onepad_cipher.py index 6afbd45249ec..1dac270bda1f 100644 --- a/ciphers/onepad_cipher.py +++ b/ciphers/onepad_cipher.py @@ -1,5 +1,3 @@ -from __future__ import print_function - import random @@ -15,7 +13,7 @@ def encrypt(self, text): cipher.append(c) key.append(k) return cipher, key - + def decrypt(self, cipher, key): '''Function to decrypt text using psedo-random numbers.''' plain = [] diff --git a/ciphers/rabin_miller.py b/ciphers/rabin_miller.py index f71fb03c0051..21378cff6885 100644 --- a/ciphers/rabin_miller.py +++ b/ciphers/rabin_miller.py @@ -1,4 +1,3 @@ -from __future__ import print_function # Primality Testing with the Rabin-Miller Algorithm import random diff --git a/ciphers/rot13.py b/ciphers/rot13.py index 2abf981e9d7d..208de4890e67 100644 --- a/ciphers/rot13.py +++ b/ciphers/rot13.py @@ -1,4 +1,3 @@ -from __future__ import print_function def dencrypt(s, n): out = '' for c in s: diff --git a/ciphers/rsa_cipher.py b/ciphers/rsa_cipher.py index d81f1ffc1a1e..02e5d95d1e95 100644 --- a/ciphers/rsa_cipher.py +++ b/ciphers/rsa_cipher.py @@ -1,4 +1,3 @@ -from __future__ import print_function import sys, rsa_key_generator as rkg, os DEFAULT_BLOCK_SIZE = 128 @@ -16,7 +15,7 @@ def main(): if mode == 'encrypt': if not os.path.exists('rsa_pubkey.txt'): rkg.makeKeyFiles('rsa', 1024) - + message = input('\nEnter message: ') pubKeyFilename = 'rsa_pubkey.txt' print('Encrypting and writing to %s...' % (filename)) diff --git a/ciphers/rsa_key_generator.py b/ciphers/rsa_key_generator.py index 541e90d6e884..7cd7163b68d5 100644 --- a/ciphers/rsa_key_generator.py +++ b/ciphers/rsa_key_generator.py @@ -1,4 +1,3 @@ -from __future__ import print_function import random, sys, os import rabin_miller as rabinMiller, cryptomath_module as cryptoMath diff --git a/ciphers/simple_substitution_cipher.py b/ciphers/simple_substitution_cipher.py index 1bdd7dc04a57..5da07f8526b9 100644 --- a/ciphers/simple_substitution_cipher.py +++ b/ciphers/simple_substitution_cipher.py @@ -1,4 +1,3 @@ -from __future__ import print_function import sys, random LETTERS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' @@ -18,7 +17,7 @@ def main(): translated = decryptMessage(key, message) print('\n%sion: \n%s' % (mode.title(), translated)) - + def checkValidKey(key): keyList = list(key) lettersList = list(LETTERS) @@ -49,7 +48,7 @@ def translateMessage(key, message, mode): if mode == 'decrypt': charsA, charsB = charsB, charsA - + for symbol in message: if symbol.upper() in charsA: symIndex = charsA.find(symbol.upper()) diff --git a/ciphers/transposition_cipher.py b/ciphers/transposition_cipher.py index dbb358315d22..1c2ed0aa0452 100644 --- a/ciphers/transposition_cipher.py +++ b/ciphers/transposition_cipher.py @@ -1,4 +1,3 @@ -from __future__ import print_function import math def main(): diff --git a/ciphers/transposition_cipher_encrypt_decrypt_file.py b/ciphers/transposition_cipher_encrypt_decrypt_file.py index a186cf81cde7..8ebfc1ea7e0c 100644 --- a/ciphers/transposition_cipher_encrypt_decrypt_file.py +++ b/ciphers/transposition_cipher_encrypt_decrypt_file.py @@ -1,4 +1,3 @@ -from __future__ import print_function import time, os, sys import transposition_cipher as transCipher @@ -16,7 +15,7 @@ def main(): response = input('> ') if not response.lower().startswith('y'): sys.exit() - + startTime = time.time() if mode.lower().startswith('e'): with open(inputFile) as f: @@ -29,9 +28,9 @@ def main(): with open(outputFile, 'w') as outputObj: outputObj.write(translated) - + totalTime = round(time.time() - startTime, 2) print(('Done (', totalTime, 'seconds )')) - + if __name__ == '__main__': main() diff --git a/ciphers/vigenere_cipher.py b/ciphers/vigenere_cipher.py index 5d5be0792835..95eeb431109f 100644 --- a/ciphers/vigenere_cipher.py +++ b/ciphers/vigenere_cipher.py @@ -1,4 +1,3 @@ -from __future__ import print_function LETTERS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' def main(): diff --git a/data_structures/binary_tree/binary_search_tree.py b/data_structures/binary_tree/binary_search_tree.py index cef5b55f245d..634b6cbcc231 100644 --- a/data_structures/binary_tree/binary_search_tree.py +++ b/data_structures/binary_tree/binary_search_tree.py @@ -1,7 +1,6 @@ ''' A binary search Tree ''' -from __future__ import print_function class Node: def __init__(self, label, parent): @@ -66,8 +65,8 @@ def insert(self, label): else: parent_node.setRight(new_node) #Set parent to the new node - new_node.setParent(parent_node) - + new_node.setParent(parent_node) + def delete(self, label): if (not self.empty()): #Look for the node with that label @@ -92,7 +91,7 @@ def delete(self, label): self.delete(tmpNode.getLabel()) #Assigns the value to the node to delete and keesp tree structure node.setLabel(tmpNode.getLabel()) - + def getNode(self, label): curr_node = None #If the tree is not empty @@ -177,7 +176,7 @@ def traversalTree(self, traversalFunction = None, root = None): #Returns a list of nodes in the order that the users wants to return traversalFunction(self.root) - #Returns an string of all the nodes labels in the list + #Returns an string of all the nodes labels in the list #In Order Traversal def __str__(self): list = self.__InOrderTraversal(self.root) @@ -203,7 +202,7 @@ def testBinarySearchTree(): / \ \ 1 6 14 / \ / - 4 7 13 + 4 7 13 ''' r''' @@ -236,11 +235,11 @@ def testBinarySearchTree(): print("The label -1 exists") else: print("The label -1 doesn't exist") - + if(not t.empty()): print(("Max Value: ", t.getMax().getLabel())) print(("Min Value: ", t.getMin().getLabel())) - + t.delete(13) t.delete(10) t.delete(8) diff --git a/data_structures/binary_tree/fenwick_tree.py b/data_structures/binary_tree/fenwick_tree.py index ef984082d9e8..30a87fbd7fcf 100644 --- a/data_structures/binary_tree/fenwick_tree.py +++ b/data_structures/binary_tree/fenwick_tree.py @@ -1,4 +1,3 @@ -from __future__ import print_function class FenwickTree: def __init__(self, SIZE): # create fenwick tree with size SIZE diff --git a/data_structures/binary_tree/lazy_segment_tree.py b/data_structures/binary_tree/lazy_segment_tree.py index 215399976dd3..bbe37a6eb97f 100644 --- a/data_structures/binary_tree/lazy_segment_tree.py +++ b/data_structures/binary_tree/lazy_segment_tree.py @@ -1,4 +1,3 @@ -from __future__ import print_function import math class SegmentTree: diff --git a/data_structures/binary_tree/segment_tree.py b/data_structures/binary_tree/segment_tree.py index 7e61198ca59c..da3d15f26b6a 100644 --- a/data_structures/binary_tree/segment_tree.py +++ b/data_structures/binary_tree/segment_tree.py @@ -1,4 +1,3 @@ -from __future__ import print_function import math class SegmentTree: diff --git a/data_structures/heap/heap.py b/data_structures/heap/heap.py index 39778f725c3a..2373d71bb897 100644 --- a/data_structures/heap/heap.py +++ b/data_structures/heap/heap.py @@ -1,15 +1,8 @@ #!/usr/bin/python -from __future__ import print_function, division - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - -#This heap class start from here. +# This heap class start from here. class Heap: - def __init__(self): #Default constructor of heap class. + def __init__(self): # Default constructor of heap class. self.h = [] self.currsize = 0 @@ -79,7 +72,7 @@ def display(self): #This function is used to print the heap. print(self.h) def main(): - l = list(map(int, raw_input().split())) + l = list(map(int, input().split())) h = Heap() h.buildHeap(l) h.heapSort() diff --git a/data_structures/linked_list/doubly_linked_list.py b/data_structures/linked_list/doubly_linked_list.py index 75b1f889dfc2..23d91383fa0e 100644 --- a/data_structures/linked_list/doubly_linked_list.py +++ b/data_structures/linked_list/doubly_linked_list.py @@ -4,14 +4,13 @@ - Each link references the next link and the previous one. - A Doubly Linked List (DLL) contains an extra pointer, typically called previous pointer, together with next pointer and data which are there in singly linked list. - Advantages over SLL - IT can be traversed in both forward and backward direction.,Delete operation is more efficent''' -from __future__ import print_function class LinkedList: #making main class named linked list def __init__(self): self.head = None self.tail = None - + def insertHead(self, x): newLink = Link(x) #Create a new link with a value attached to it if(self.isEmpty() == True): #Set the first element added to be the tail @@ -20,52 +19,52 @@ def insertHead(self, x): self.head.previous = newLink # newLink <-- currenthead(head) newLink.next = self.head # newLink <--> currenthead(head) self.head = newLink # newLink(head) <--> oldhead - + def deleteHead(self): temp = self.head - self.head = self.head.next # oldHead <--> 2ndElement(head) + self.head = self.head.next # oldHead <--> 2ndElement(head) self.head.previous = None # oldHead --> 2ndElement(head) nothing pointing at it so the old head will be removed if(self.head is None): self.tail = None #if empty linked list return temp - + def insertTail(self, x): newLink = Link(x) newLink.next = None # currentTail(tail) newLink --> self.tail.next = newLink # currentTail(tail) --> newLink --> newLink.previous = self.tail #currentTail(tail) <--> newLink --> self.tail = newLink # oldTail <--> newLink(tail) --> - + def deleteTail(self): temp = self.tail self.tail = self.tail.previous # 2ndLast(tail) <--> oldTail --> None self.tail.next = None # 2ndlast(tail) --> None return temp - + def delete(self, x): current = self.head - + while(current.value != x): # Find the position to delete current = current.next - + if(current == self.head): self.deleteHead() - + elif(current == self.tail): self.deleteTail() - + else: #Before: 1 <--> 2(current) <--> 3 current.previous.next = current.next # 1 --> 3 current.next.previous = current.previous # 1 <--> 3 - + def isEmpty(self): #Will return True if the list is empty return(self.head is None) - + def display(self): #Prints contents of the list current = self.head while(current != None): current.displayLink() - current = current.next + current = current.next print() class Link: diff --git a/data_structures/linked_list/singly_linked_list.py b/data_structures/linked_list/singly_linked_list.py index 5ae97523b9a1..5943b88d5964 100644 --- a/data_structures/linked_list/singly_linked_list.py +++ b/data_structures/linked_list/singly_linked_list.py @@ -1,6 +1,3 @@ -from __future__ import print_function - - class Node: # create a Node def __init__(self, data): self.data = data # given data @@ -10,7 +7,7 @@ def __init__(self, data): class Linked_List: def __init__(self): self.Head = None # Initialize Head to None - + def insert_tail(self, data): if(self.Head is None): self.insert_head(data) #If this is first node, call insert_head else: @@ -37,7 +34,7 @@ def delete_head(self): # delete from head self.Head = self.Head.next temp.next = None return temp - + def delete_tail(self): # delete from tail tamp = self.Head if self.Head != None: @@ -46,7 +43,7 @@ def delete_tail(self): # delete from tail else: while tamp.next.next is not None: # find the 2nd last element tamp = tamp.next - tamp.next, tamp = None, tamp.next #(2nd last element).next = None and tamp = last element + tamp.next, tamp = None, tamp.next #(2nd last element).next = None and tamp = last element return tamp def isEmpty(self): @@ -79,7 +76,7 @@ def main(): print("\nPrint List : ") A.printList() print("\nInserting 1st at Tail") - a3=input() + a3=input() A.insert_tail(a3) print("Inserting 2nd at Tail") a4=input() @@ -96,6 +93,6 @@ def main(): A.reverse() print("\nPrint List : ") A.printList() - + if __name__ == '__main__': main() diff --git a/data_structures/queue/double_ended_queue.py b/data_structures/queue/double_ended_queue.py index 838bf2f4bc36..a2fc8f66ec22 100644 --- a/data_structures/queue/double_ended_queue.py +++ b/data_structures/queue/double_ended_queue.py @@ -1,4 +1,3 @@ -from __future__ import print_function # Python code to demonstrate working of # extend(), extendleft(), rotate(), reverse() diff --git a/data_structures/stacks/balanced_parentheses.py b/data_structures/stacks/balanced_parentheses.py index 36a4e07a97a3..3f43ccbf5760 100644 --- a/data_structures/stacks/balanced_parentheses.py +++ b/data_structures/stacks/balanced_parentheses.py @@ -1,6 +1,3 @@ -from __future__ import print_function -from __future__ import absolute_import - from .stack import Stack __author__ = 'Omkar Pathak' diff --git a/data_structures/stacks/infix_to_postfix_conversion.py b/data_structures/stacks/infix_to_postfix_conversion.py index 9376b55b8b23..84a5d1480a24 100644 --- a/data_structures/stacks/infix_to_postfix_conversion.py +++ b/data_structures/stacks/infix_to_postfix_conversion.py @@ -1,5 +1,3 @@ -from __future__ import print_function -from __future__ import absolute_import import string from .stack import Stack diff --git a/data_structures/stacks/next_greater_element.py b/data_structures/stacks/next_greater_element.py index bca83339592c..2e67f1764a5a 100644 --- a/data_structures/stacks/next_greater_element.py +++ b/data_structures/stacks/next_greater_element.py @@ -1,17 +1,16 @@ -from __future__ import print_function # Function to print element and NGE pair for all elements of list def printNGE(arr): - + for i in range(0, len(arr), 1): - + next = -1 for j in range(i+1, len(arr), 1): if arr[i] < arr[j]: next = arr[j] break - + print(str(arr[i]) + " -- " + str(next)) - + # Driver program to test above function arr = [11,13,21,3] printNGE(arr) diff --git a/data_structures/stacks/stack.py b/data_structures/stacks/stack.py index 7f979d927d08..387367db2fcc 100644 --- a/data_structures/stacks/stack.py +++ b/data_structures/stacks/stack.py @@ -1,4 +1,3 @@ -from __future__ import print_function __author__ = 'Omkar Pathak' diff --git a/data_structures/stacks/stock_span_problem.py b/data_structures/stacks/stock_span_problem.py index e9afebc193b6..47d916fde9ed 100644 --- a/data_structures/stacks/stock_span_problem.py +++ b/data_structures/stacks/stock_span_problem.py @@ -6,7 +6,6 @@ number of consecutive days just before the given day, for which the price of the stock on the current day is less than or equal to its price on the given day. ''' -from __future__ import print_function def calculateSpan(price, S): n = len(price) diff --git a/divide_and_conquer/convex_hull.py b/divide_and_conquer/convex_hull.py index f15d74ddea68..42219794aed1 100644 --- a/divide_and_conquer/convex_hull.py +++ b/divide_and_conquer/convex_hull.py @@ -1,18 +1,16 @@ -from __future__ import print_function, absolute_import, division - from numbers import Number """ -The convex hull problem is problem of finding all the vertices of convex polygon, P of +The convex hull problem is problem of finding all the vertices of convex polygon, P of a set of points in a plane such that all the points are either on the vertices of P or -inside P. TH convex hull problem has several applications in geometrical problems, -computer graphics and game development. +inside P. TH convex hull problem has several applications in geometrical problems, +computer graphics and game development. -Two algorithms have been implemented for the convex hull problem here. +Two algorithms have been implemented for the convex hull problem here. 1. A brute-force algorithm which runs in O(n^3) 2. A divide-and-conquer algorithm which runs in O(n^3) There are other several other algorithms for the convex hull problem -which have not been implemented here, yet. +which have not been implemented here, yet. """ diff --git a/divide_and_conquer/inversions.py b/divide_and_conquer/inversions.py index 527741cad3b7..e4d50b7d4729 100644 --- a/divide_and_conquer/inversions.py +++ b/divide_and_conquer/inversions.py @@ -1,15 +1,13 @@ -from __future__ import print_function, absolute_import, division - """ Given an array-like data structure A[1..n], how many pairs -(i, j) for all 1 <= i < j <= n such that A[i] > A[j]? These pairs are -called inversions. Counting the number of such inversions in an array-like -object is the important. Among other things, counting inversions can help +(i, j) for all 1 <= i < j <= n such that A[i] > A[j]? These pairs are +called inversions. Counting the number of such inversions in an array-like +object is the important. Among other things, counting inversions can help us determine how close a given array is to being sorted - + In this implementation, I provide two algorithms, a divide-and-conquer -algorithm which runs in nlogn and the brute-force n^2 algorithm. - +algorithm which runs in nlogn and the brute-force n^2 algorithm. + """ diff --git a/dynamic_programming/bitmask.py b/dynamic_programming/bitmask.py index 213b22fe9051..6685e1c68ee6 100644 --- a/dynamic_programming/bitmask.py +++ b/dynamic_programming/bitmask.py @@ -9,27 +9,26 @@ """ -from __future__ import print_function from collections import defaultdict class AssignmentUsingBitmask: def __init__(self,task_performed,total): - + self.total_tasks = total #total no of tasks (N) - + # DP table will have a dimension of (2^M)*N # initially all values are set to -1 self.dp = [[-1 for i in range(total+1)] for j in range(2**len(task_performed))] - + self.task = defaultdict(list) #stores the list of persons for each task - + #finalmask is used to check if all persons are included by setting all bits to 1 self.finalmask = (1< int: dp = [[0 for _ in range(n+1) ] for _ in range(m+1)] for i in range(m+1): for j in range(n+1): - + if i == 0: #first string is empty dp[i][j] = j - elif j == 0: #second string is empty - dp[i][j] = i + elif j == 0: #second string is empty + dp[i][j] = i elif word1[i-1] == word2[j-1]: #last character of both substing is equal dp[i][j] = dp[i-1][j-1] - else: + else: insert = dp[i][j-1] delete = dp[i-1][j] replace = dp[i-1][j-1] @@ -82,21 +81,13 @@ def min_distance_bottom_up(word1: str, word2: str) -> int: return dp[m][n] if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - solver = EditDistance() print("****************** Testing Edit Distance DP Algorithm ******************") print() - print("Enter the first string: ", end="") - S1 = raw_input().strip() - - print("Enter the second string: ", end="") - S2 = raw_input().strip() + S1 = input("Enter the first string: ").strip() + S2 = input("Enter the second string: ").strip() print() print("The minimum Edit Distance is: %d" % (solver.solve(S1, S2))) @@ -106,4 +97,4 @@ def min_distance_bottom_up(word1: str, word2: str) -> int: - + diff --git a/dynamic_programming/fast_fibonacci.py b/dynamic_programming/fast_fibonacci.py index cbc118467b3c..47248078bd81 100644 --- a/dynamic_programming/fast_fibonacci.py +++ b/dynamic_programming/fast_fibonacci.py @@ -5,7 +5,6 @@ This program calculates the nth Fibonacci number in O(log(n)). It's possible to calculate F(1000000) in less than a second. """ -from __future__ import print_function import sys diff --git a/dynamic_programming/fibonacci.py b/dynamic_programming/fibonacci.py index b453ce255853..90fe6386044a 100644 --- a/dynamic_programming/fibonacci.py +++ b/dynamic_programming/fibonacci.py @@ -1,7 +1,6 @@ """ This is a pure Python implementation of Dynamic Programming solution to the fibonacci sequence problem. """ -from __future__ import print_function class Fibonacci: @@ -29,21 +28,16 @@ def get(self, sequence_no=None): if __name__ == '__main__': print("\n********* Fibonacci Series Using Dynamic Programming ************\n") - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - print("\n Enter the upper limit for the fibonacci sequence: ", end="") try: - N = eval(raw_input().strip()) + N = int(input().strip()) fib = Fibonacci(N) print( - "\n********* Enter different values to get the corresponding fibonacci sequence, enter any negative number to exit. ************\n") + "\n********* Enter different values to get the corresponding fibonacci " + "sequence, enter any negative number to exit. ************\n") while True: - print("Enter value: ", end=" ") try: - i = eval(raw_input().strip()) + i = int(input("Enter value: ").strip()) if i < 0: print("\n********* Good Bye!! ************\n") break diff --git a/dynamic_programming/integer_partition.py b/dynamic_programming/integer_partition.py index 7b27afebaa6c..f17561fc135b 100644 --- a/dynamic_programming/integer_partition.py +++ b/dynamic_programming/integer_partition.py @@ -1,27 +1,15 @@ -from __future__ import print_function - -try: - xrange #Python 2 -except NameError: - xrange = range #Python 3 - -try: - raw_input #Python 2 -except NameError: - raw_input = input #Python 3 - ''' The number of partitions of a number n into at least k parts equals the number of partitions into exactly k parts plus the number of partitions into at least k-1 parts. Subtracting 1 from each part of a partition of n into k parts gives a partition of n-k into k parts. These two facts together are used for this algorithm. ''' def partition(m): - memo = [[0 for _ in xrange(m)] for _ in xrange(m+1)] - for i in xrange(m+1): + memo = [[0 for _ in range(m)] for _ in range(m+1)] + for i in range(m+1): memo[i][0] = 1 - for n in xrange(m+1): - for k in xrange(1, m): + for n in range(m+1): + for k in range(1, m): memo[n][k] += memo[n][k-1] if n-k > 0: memo[n][k] += memo[n-k-1][k] @@ -33,7 +21,7 @@ def partition(m): if len(sys.argv) == 1: try: - n = int(raw_input('Enter a number: ')) + n = int(input('Enter a number: ').strip()) print(partition(n)) except ValueError: print('Please enter a number.') diff --git a/dynamic_programming/longest_common_subsequence.py b/dynamic_programming/longest_common_subsequence.py index 7836fe303688..7447a0cc7810 100644 --- a/dynamic_programming/longest_common_subsequence.py +++ b/dynamic_programming/longest_common_subsequence.py @@ -3,7 +3,6 @@ A subsequence is a sequence that appears in the same relative order, but not necessarily continuous. Example:"abc", "abg" are subsequences of "abcdefgh". """ -from __future__ import print_function def longest_common_subsequence(x: str, y: str): @@ -80,4 +79,3 @@ def longest_common_subsequence(x: str, y: str): assert expected_ln == ln assert expected_subseq == subseq print("len =", ln, ", sub-sequence =", subseq) - diff --git a/dynamic_programming/longest_increasing_subsequence.py b/dynamic_programming/longest_increasing_subsequence.py index b6d165909e70..151a5e0b7c80 100644 --- a/dynamic_programming/longest_increasing_subsequence.py +++ b/dynamic_programming/longest_increasing_subsequence.py @@ -7,10 +7,8 @@ Given an ARRAY, to find the longest and increasing sub ARRAY in that given ARRAY and return it. Example: [10, 22, 9, 33, 21, 50, 41, 60, 80] as input will return [10, 22, 33, 41, 60, 80] as output ''' -from __future__ import print_function - def longestSub(ARRAY): #This function is recursive - + ARRAY_LENGTH = len(ARRAY) if(ARRAY_LENGTH <= 1): #If the array contains only one element, we return it (it's the stop condition of recursion) return ARRAY diff --git a/dynamic_programming/longest_increasing_subsequence_o(nlogn).py b/dynamic_programming/longest_increasing_subsequence_o(nlogn).py index 86bec089adc7..9b27ed6be303 100644 --- a/dynamic_programming/longest_increasing_subsequence_o(nlogn).py +++ b/dynamic_programming/longest_increasing_subsequence_o(nlogn).py @@ -1,9 +1,8 @@ -from __future__ import print_function ############################# # Author: Aravind Kashyap # File: lis.py # comments: This programme outputs the Longest Strictly Increasing Subsequence in O(NLogN) -# Where N is the Number of elements in the list +# Where N is the Number of elements in the list ############################# def CeilIndex(v,l,r,key): while r-l > 1: @@ -12,30 +11,30 @@ def CeilIndex(v,l,r,key): r = m else: l = m - + return r - + def LongestIncreasingSubsequenceLength(v): if(len(v) == 0): - return 0 - + return 0 + tail = [0]*len(v) length = 1 - + tail[0] = v[0] - + for i in range(1,len(v)): if v[i] < tail[0]: tail[0] = v[i] elif v[i] > tail[length-1]: tail[length] = v[i] - length += 1 + length += 1 else: tail[CeilIndex(tail,-1,length-1,v[i])] = v[i] - + return length - + if __name__ == "__main__": v = [2, 5, 3, 7, 11, 8, 10, 13, 6] diff --git a/dynamic_programming/longest_sub_array.py b/dynamic_programming/longest_sub_array.py index de2c88a8b525..856b31f03982 100644 --- a/dynamic_programming/longest_sub_array.py +++ b/dynamic_programming/longest_sub_array.py @@ -6,7 +6,6 @@ The problem is : Given an array, to find the longest and continuous sub array and get the max sum of the sub array in the given array. ''' -from __future__ import print_function class SubArray: diff --git a/dynamic_programming/matrix_chain_order.py b/dynamic_programming/matrix_chain_order.py index b8234a65acbe..cb4aec345437 100644 --- a/dynamic_programming/matrix_chain_order.py +++ b/dynamic_programming/matrix_chain_order.py @@ -1,5 +1,3 @@ -from __future__ import print_function - import sys ''' Dynamic Programming diff --git a/dynamic_programming/max_sub_array.py b/dynamic_programming/max_sub_array.py index 56983b7d22c2..d6084ecfd6d9 100644 --- a/dynamic_programming/max_sub_array.py +++ b/dynamic_programming/max_sub_array.py @@ -1,7 +1,6 @@ """ author : Mayank Kumar Jha (mk9440) """ -from __future__ import print_function from typing import List import time import matplotlib.pyplot as plt @@ -10,7 +9,7 @@ def find_max_sub_array(A,low,high): if low==high: return low,high,A[low] else : - mid=(low+high)//2 + mid=(low+high)//2 left_low,left_high,left_sum=find_max_sub_array(A,low,mid) right_low,right_high,right_sum=find_max_sub_array(A,mid+1,high) cross_left,cross_right,cross_sum=find_max_cross_sum(A,low,mid,high) @@ -30,7 +29,7 @@ def find_max_cross_sum(A,low,mid,high): if summ > left_sum: left_sum=summ max_left=i - summ=0 + summ=0 for i in range(mid+1,high+1): summ+=A[i] if summ > right_sum: @@ -40,7 +39,7 @@ def find_max_cross_sum(A,low,mid,high): def max_sub_array(nums: List[int]) -> int: """ - Finds the contiguous subarray (can be empty array) + Finds the contiguous subarray (can be empty array) which has the largest sum and return its sum. >>> max_sub_array([-2,1,-3,4,-1,2,1,-5,4]) @@ -50,14 +49,14 @@ def max_sub_array(nums: List[int]) -> int: >>> max_sub_array([-1,-2,-3]) 0 """ - best = 0 - current = 0 - for i in nums: - current += i + best = 0 + current = 0 + for i in nums: + current += i if current < 0: current = 0 best = max(best, current) - return best + return best if __name__=='__main__': inputs=[10,100,1000,10000,50000,100000,200000,300000,400000,500000] @@ -68,8 +67,8 @@ def max_sub_array(nums: List[int]) -> int: (find_max_sub_array(li,0,len(li)-1)) end=time.time() tim.append(end-strt) - print("No of Inputs Time Taken") - for i in range(len(inputs)): + print("No of Inputs Time Taken") + for i in range(len(inputs)): print(inputs[i],'\t\t',tim[i]) plt.plot(inputs,tim) plt.xlabel("Number of Inputs");plt.ylabel("Time taken in seconds ") @@ -77,4 +76,4 @@ def max_sub_array(nums: List[int]) -> int: - + diff --git a/graphs/a_star.py b/graphs/a_star.py index 584222e6f62b..09a7a0e579d8 100644 --- a/graphs/a_star.py +++ b/graphs/a_star.py @@ -1,5 +1,3 @@ -from __future__ import print_function - grid = [[0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0],#0 are free path whereas 1's are obstacles [0, 1, 0, 0, 0, 0], @@ -14,13 +12,13 @@ [5, 4, 3, 2, 1, 0]]''' init = [0, 0] -goal = [len(grid)-1, len(grid[0])-1] #all coordinates are given in format [y,x] +goal = [len(grid)-1, len(grid[0])-1] #all coordinates are given in format [y,x] cost = 1 #the cost map which pushes the path closer to the goal heuristic = [[0 for row in range(len(grid[0]))] for col in range(len(grid))] -for i in range(len(grid)): - for j in range(len(grid[0])): +for i in range(len(grid)): + for j in range(len(grid[0])): heuristic[i][j] = abs(i - goal[0]) + abs(j - goal[1]) if grid[i][j] == 1: heuristic[i][j] = 99 #added extra penalty in the heuristic map @@ -62,7 +60,7 @@ def search(grid,init,goal,cost,heuristic): g = next[1] f = next[0] - + if x == goal[0] and y == goal[1]: found = True else: @@ -93,10 +91,10 @@ def search(grid,init,goal,cost,heuristic): print("ACTION MAP") for i in range(len(action)): print(action[i]) - + return path - + a = search(grid,init,goal,cost,heuristic) for i in range(len(a)): - print(a[i]) + print(a[i]) diff --git a/graphs/basic_graphs.py b/graphs/basic_graphs.py index ee63ca995de6..64c51e139cca 100644 --- a/graphs/basic_graphs.py +++ b/graphs/basic_graphs.py @@ -1,23 +1,10 @@ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 - - if __name__ == "__main__": # Accept No. of Nodes and edges - n, m = map(int, raw_input().split(" ")) + n, m = map(int, input().split(" ")) # Initialising Dictionary of edges g = {} - for i in xrange(n): + for i in range(n): g[i + 1] = [] """ @@ -25,8 +12,8 @@ Accepting edges of Unweighted Directed Graphs ---------------------------------------------------------------------------- """ - for _ in xrange(m): - x, y = map(int, raw_input().strip().split(" ")) + for _ in range(m): + x, y = map(int, input().strip().split(" ")) g[x].append(y) """ @@ -34,8 +21,8 @@ Accepting edges of Unweighted Undirected Graphs ---------------------------------------------------------------------------- """ - for _ in xrange(m): - x, y = map(int, raw_input().strip().split(" ")) + for _ in range(m): + x, y = map(int, input().strip().split(" ")) g[x].append(y) g[y].append(x) @@ -44,8 +31,8 @@ Accepting edges of Weighted Undirected Graphs ---------------------------------------------------------------------------- """ - for _ in xrange(m): - x, y, r = map(int, raw_input().strip().split(" ")) + for _ in range(m): + x, y, r = map(int, input().strip().split(" ")) g[x].append([y, r]) g[y].append([x, r]) @@ -170,10 +157,10 @@ def topo(G, ind=None, Q=[1]): def adjm(): - n = raw_input().strip() + n = input().strip() a = [] - for i in xrange(n): - a.append(map(int, raw_input().strip().split())) + for i in range(n): + a.append(map(int, input().strip().split())) return a, n @@ -193,10 +180,10 @@ def adjm(): def floy(A_and_n): (A, n) = A_and_n dist = list(A) - path = [[0] * n for i in xrange(n)] - for k in xrange(n): - for i in xrange(n): - for j in xrange(n): + path = [[0] * n for i in range(n)] + for k in range(n): + for i in range(n): + for j in range(n): if dist[i][j] > dist[i][k] + dist[k][j]: dist[i][j] = dist[i][k] + dist[k][j] path[i][k] = k @@ -245,10 +232,10 @@ def prim(G, s): def edglist(): - n, m = map(int, raw_input().split(" ")) + n, m = map(int, input().split(" ")) l = [] - for i in xrange(m): - l.append(map(int, raw_input().split(' '))) + for i in range(m): + l.append(map(int, input().split(' '))) return l, n @@ -272,10 +259,10 @@ def krusk(E_and_n): break print(s) x = E.pop() - for i in xrange(len(s)): + for i in range(len(s)): if x[0] in s[i]: break - for j in xrange(len(s)): + for j in range(len(s)): if x[1] in s[j]: if i == j: break diff --git a/graphs/bellman_ford.py b/graphs/bellman_ford.py index f49157230054..bebe8f354b26 100644 --- a/graphs/bellman_ford.py +++ b/graphs/bellman_ford.py @@ -1,5 +1,3 @@ -from __future__ import print_function - def printDist(dist, V): print("\nVertex Distance") for i in range(V): diff --git a/graphs/breadth_first_search.py b/graphs/breadth_first_search.py index 3992e2d4d892..205f49a6172b 100644 --- a/graphs/breadth_first_search.py +++ b/graphs/breadth_first_search.py @@ -3,8 +3,6 @@ """ Author: OMKAR PATHAK """ -from __future__ import print_function - class Graph(): def __init__(self): diff --git a/graphs/depth_first_search.py b/graphs/depth_first_search.py index 98faf61354f9..2b03683c0047 100644 --- a/graphs/depth_first_search.py +++ b/graphs/depth_first_search.py @@ -2,7 +2,6 @@ # encoding=utf8 """ Author: OMKAR PATHAK """ -from __future__ import print_function class Graph(): diff --git a/graphs/dijkstra_2.py b/graphs/dijkstra_2.py index 8f39aec41906..f6118830c9c0 100644 --- a/graphs/dijkstra_2.py +++ b/graphs/dijkstra_2.py @@ -1,5 +1,3 @@ -from __future__ import print_function - def printDist(dist, V): print("\nVertex Distance") for i in range(V): diff --git a/graphs/dijkstra_algorithm.py b/graphs/dijkstra_algorithm.py index 985c7f6c1301..c43ff37f5336 100644 --- a/graphs/dijkstra_algorithm.py +++ b/graphs/dijkstra_algorithm.py @@ -2,7 +2,6 @@ # Author: Shubham Malik # References: https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm -from __future__ import print_function import math import sys # For storing the vertex set to retreive node with the lowest distance diff --git a/graphs/even_tree.py b/graphs/even_tree.py index 9383ea9a13c1..45d55eecff8a 100644 --- a/graphs/even_tree.py +++ b/graphs/even_tree.py @@ -12,7 +12,6 @@ Note: The tree input will be such that it can always be decomposed into components containing an even number of nodes. """ -from __future__ import print_function # pylint: disable=invalid-name from collections import defaultdict diff --git a/graphs/graph_list.py b/graphs/graph_list.py index 0c981c39d320..2ca363b1d746 100644 --- a/graphs/graph_list.py +++ b/graphs/graph_list.py @@ -1,7 +1,6 @@ #!/usr/bin/python # encoding=utf8 -from __future__ import print_function # Author: OMKAR PATHAK # We can use Python's dictionary for constructing the graph. diff --git a/graphs/graph_matrix.py b/graphs/graph_matrix.py index de25301d6dd1..1998fec8d6fe 100644 --- a/graphs/graph_matrix.py +++ b/graphs/graph_matrix.py @@ -1,6 +1,3 @@ -from __future__ import print_function - - class Graph: def __init__(self, vertex): diff --git a/graphs/graphs_floyd_warshall.py b/graphs/graphs_floyd_warshall.py index a1d12aac02b4..5f159683733f 100644 --- a/graphs/graphs_floyd_warshall.py +++ b/graphs/graphs_floyd_warshall.py @@ -4,8 +4,6 @@ have negative edge weights. """ -from __future__ import print_function - def _print_dist(dist, v): print("\nThe shortest path matrix using Floyd Warshall algorithm\n") @@ -34,9 +32,9 @@ def floyd_warshall(graph, v): 4. The above is repeated for each vertex k in the graph. 5. Whenever distance[i][j] is given a new minimum value, next vertex[i][j] is updated to the next vertex[i][k]. """ - + dist=[[float('inf') for _ in range(v)] for _ in range(v)] - + for i in range(v): for j in range(v): dist[i][j] = graph[i][j] @@ -53,7 +51,7 @@ def floyd_warshall(graph, v): _print_dist(dist, v) return dist, v - + if __name__== '__main__': v = int(input("Enter number of vertices: ")) diff --git a/graphs/minimum_spanning_tree_kruskal.py b/graphs/minimum_spanning_tree_kruskal.py index 975151c90ede..a2211582ec40 100644 --- a/graphs/minimum_spanning_tree_kruskal.py +++ b/graphs/minimum_spanning_tree_kruskal.py @@ -1,5 +1,3 @@ -from __future__ import print_function - if __name__ == "__main__": num_nodes, num_edges = list(map(int, input().strip().split())) diff --git a/graphs/multi_hueristic_astar.py b/graphs/multi_hueristic_astar.py index 1c01fe9aa6d3..3021c4162b8e 100644 --- a/graphs/multi_hueristic_astar.py +++ b/graphs/multi_hueristic_astar.py @@ -1,12 +1,6 @@ -from __future__ import print_function import heapq import numpy as np -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 - class PriorityQueue: def __init__(self): @@ -96,7 +90,7 @@ def do_something(back_pointer, goal, start): grid[(n-1)][0] = "-" - for i in xrange(n): + for i in range(n): for j in range(n): if (i, j) == (0, n-1): print(grid[i][j], end=' ') diff --git a/graphs/scc_kosaraju.py b/graphs/scc_kosaraju.py index 0d0375203b6d..99564a7cfa35 100644 --- a/graphs/scc_kosaraju.py +++ b/graphs/scc_kosaraju.py @@ -1,6 +1,3 @@ -from __future__ import print_function - - def dfs(u): global g, r, scc, component, visit, stack if visit[u]: return diff --git a/hashes/chaos_machine.py b/hashes/chaos_machine.py index f0a305bfeade..3a7c3950bb29 100644 --- a/hashes/chaos_machine.py +++ b/hashes/chaos_machine.py @@ -1,10 +1,4 @@ """example of simple chaos machine""" -from __future__ import print_function - -try: - input = raw_input # Python 2 -except NameError: - pass # Python 3 # Chaos Machine (K, t, m) K = [0.33, 0.44, 0.55, 0.44, 0.33]; t = 3; m = 5 @@ -96,7 +90,7 @@ def reset(): for chunk in message: push(chunk) -# for controlling +# for controlling inp = "" # Pulling Data (Output) diff --git a/hashes/enigma_machine.py b/hashes/enigma_machine.py index bd410c5cb21d..06215785765f 100644 --- a/hashes/enigma_machine.py +++ b/hashes/enigma_machine.py @@ -1,5 +1,3 @@ -from __future__ import print_function - alphabets = [chr(i) for i in range(32, 126)] gear_one = [i for i in range(len(alphabets))] gear_two = [i for i in range(len(alphabets))] diff --git a/hashes/md5.py b/hashes/md5.py index 7891f2077986..1ad43013363f 100644 --- a/hashes/md5.py +++ b/hashes/md5.py @@ -1,4 +1,3 @@ -from __future__ import print_function import math @@ -66,7 +65,7 @@ def getBlock(bitString): """[summary] Iterator: Returns by each call a list of length 16 with the 32 bit - integer blocks. + integer blocks. Arguments: bitString {[string]} -- [binary string >= 512] @@ -95,7 +94,7 @@ def not32(i): def sum32(a, b): ''' - + ''' return (a + b) % 2**32 diff --git a/machine_learning/decision_tree.py b/machine_learning/decision_tree.py index 71849904ccf2..acdf646875ac 100644 --- a/machine_learning/decision_tree.py +++ b/machine_learning/decision_tree.py @@ -1,10 +1,8 @@ """ Implementation of a basic regression decision tree. Input data set: The input data set must be 1-dimensional with continuous labels. -Output: The decision tree maps a real number input to a real number output. +Output: The decision tree maps a real number input to a real number output. """ -from __future__ import print_function - import numpy as np class Decision_Tree: @@ -19,7 +17,7 @@ def __init__(self, depth = 5, min_leaf_size = 5): def mean_squared_error(self, labels, prediction): """ mean_squared_error: - @param labels: a one dimensional numpy array + @param labels: a one dimensional numpy array @param prediction: a floating point value return value: mean_squared_error calculates the error if prediction is used to estimate the labels """ @@ -32,7 +30,7 @@ def train(self, X, y): """ train: @param X: a one dimensional numpy array - @param y: a one dimensional numpy array. + @param y: a one dimensional numpy array. The contents of y are the labels for the corresponding X values train does not have a return value @@ -135,6 +133,6 @@ def main(): print("Predictions: " + str(predictions)) print("Average error: " + str(avg_error)) - + if __name__ == '__main__': main() \ No newline at end of file diff --git a/machine_learning/gradient_descent.py b/machine_learning/gradient_descent.py index 6387d4939205..9a17113b7ddb 100644 --- a/machine_learning/gradient_descent.py +++ b/machine_learning/gradient_descent.py @@ -1,7 +1,6 @@ """ Implementation of gradient descent algorithm for minimizing cost of a linear hypothesis function. """ -from __future__ import print_function, division import numpy # List of input, output pairs diff --git a/machine_learning/k_means_clust.py b/machine_learning/k_means_clust.py index 368739a45fe9..d0ce0f2599e0 100644 --- a/machine_learning/k_means_clust.py +++ b/machine_learning/k_means_clust.py @@ -17,36 +17,35 @@ Usage: 1. define 'k' value, 'X' features array and 'hetrogeneity' empty list - + 2. create initial_centroids, initial_centroids = get_initial_centroids( - X, - k, + X, + k, seed=0 # seed value for initial centroid generation, None for randomness(default=None) ) 3. find centroids and clusters using kmeans function. - + centroids, cluster_assignment = kmeans( - X, - k, - initial_centroids, + X, + k, + initial_centroids, maxiter=400, - record_heterogeneity=heterogeneity, + record_heterogeneity=heterogeneity, verbose=True # whether to print logs in console or not.(default=False) ) - - + + 4. Plot the loss function, hetrogeneity values for every iteration saved in hetrogeneity list. plot_heterogeneity( - heterogeneity, + heterogeneity, k ) - + 5. Have fun.. - + ''' -from __future__ import print_function from sklearn.metrics import pairwise_distances import numpy as np @@ -57,30 +56,30 @@ def get_initial_centroids(data, k, seed=None): if seed is not None: # useful for obtaining consistent results np.random.seed(seed) n = data.shape[0] # number of data points - + # Pick K indices from range [0, N). rand_indices = np.random.randint(0, n, k) - + # Keep centroids as dense format, as many entries will be nonzero due to averaging. # As long as at least one document in a cluster contains a word, # it will carry a nonzero weight in the TF-IDF vector of the centroid. centroids = data[rand_indices,:] - + return centroids def centroid_pairwise_dist(X,centroids): return pairwise_distances(X,centroids,metric='euclidean') def assign_clusters(data, centroids): - + # Compute distances between each data point and the set of centroids: # Fill in the blank (RHS only) distances_from_centroids = centroid_pairwise_dist(data,centroids) - + # Compute cluster assignments for each data point: # Fill in the blank (RHS only) cluster_assignment = np.argmin(distances_from_centroids,axis=1) - + return cluster_assignment def revise_centroids(data, k, cluster_assignment): @@ -92,23 +91,23 @@ def revise_centroids(data, k, cluster_assignment): centroid = member_data_points.mean(axis=0) new_centroids.append(centroid) new_centroids = np.array(new_centroids) - + return new_centroids def compute_heterogeneity(data, k, centroids, cluster_assignment): - + heterogeneity = 0.0 for i in range(k): - + # Select all data points that belong to cluster i. Fill in the blank (RHS only) member_data_points = data[cluster_assignment==i, :] - + if member_data_points.shape[0] > 0: # check if i-th cluster is non-empty # Compute distances from centroid to data points (RHS only) distances = pairwise_distances(member_data_points, [centroids[i]], metric='euclidean') squared_distances = distances**2 heterogeneity += np.sum(squared_distances) - + return heterogeneity from matplotlib import pyplot as plt @@ -129,36 +128,36 @@ def kmeans(data, k, initial_centroids, maxiter=500, record_heterogeneity=None, v verbose: if True, print how many data points changed their cluster labels in each iteration''' centroids = initial_centroids[:] prev_cluster_assignment = None - - for itr in range(maxiter): + + for itr in range(maxiter): if verbose: print(itr, end='') - + # 1. Make cluster assignments using nearest centroids cluster_assignment = assign_clusters(data,centroids) - + # 2. Compute a new centroid for each of the k clusters, averaging all data points assigned to that cluster. centroids = revise_centroids(data,k, cluster_assignment) - + # Check for convergence: if none of the assignments changed, stop if prev_cluster_assignment is not None and \ (prev_cluster_assignment==cluster_assignment).all(): break - - # Print number of new assignments + + # Print number of new assignments if prev_cluster_assignment is not None: num_changed = np.sum(prev_cluster_assignment!=cluster_assignment) if verbose: - print(' {0:5d} elements changed their cluster assignment.'.format(num_changed)) - + print(' {0:5d} elements changed their cluster assignment.'.format(num_changed)) + # Record heterogeneity convergence metric if record_heterogeneity is not None: # YOUR CODE HERE score = compute_heterogeneity(data,k,centroids,cluster_assignment) record_heterogeneity.append(score) - + prev_cluster_assignment = cluster_assignment[:] - + return centroids, cluster_assignment # Mock test below diff --git a/machine_learning/linear_regression.py b/machine_learning/linear_regression.py index 03f16629e451..9d9738fced8d 100644 --- a/machine_learning/linear_regression.py +++ b/machine_learning/linear_regression.py @@ -7,8 +7,6 @@ fits our dataset. In this particular code, i had used a CSGO dataset (ADR vs Rating). We try to best fit a line through dataset and estimate the parameters. """ -from __future__ import print_function - import requests import numpy as np diff --git a/maths/simpson_rule.py b/maths/simpson_rule.py index 2b237d2e1a4e..5cf9c14b07ee 100644 --- a/maths/simpson_rule.py +++ b/maths/simpson_rule.py @@ -8,9 +8,6 @@ "Simpson Rule" """ -from __future__ import print_function - - def method_2(boundary, steps): # "Simpson Rule" # int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn) diff --git a/maths/trapezoidal_rule.py b/maths/trapezoidal_rule.py index 789f263c6991..f5e5fbbc2662 100644 --- a/maths/trapezoidal_rule.py +++ b/maths/trapezoidal_rule.py @@ -7,8 +7,6 @@ "extended trapezoidal rule" """ -from __future__ import print_function - def method_1(boundary, steps): # "extended trapezoidal rule" # int(f) = dx/2 * (f1 + 2f2 + ... + fn) @@ -19,7 +17,7 @@ def method_1(boundary, steps): y = 0.0 y += (h/2.0)*f(a) for i in x_i: - #print(i) + #print(i) y += h*f(i) y += (h/2.0)*f(b) return y diff --git a/maths/zellers_congruence.py b/maths/zellers_congruence.py index e04425eec903..67c5550802ea 100644 --- a/maths/zellers_congruence.py +++ b/maths/zellers_congruence.py @@ -1,4 +1,3 @@ -from __future__ import annotations import datetime import argparse @@ -7,7 +6,7 @@ def zeller(date_input: str) -> str: """ Zellers Congruence Algorithm - Find the day of the week for nearly any Gregorian or Julian calendar date + Find the day of the week for nearly any Gregorian or Julian calendar date >>> zeller('01-31-2010') 'Your date 01-31-2010, is a Sunday!' @@ -108,7 +107,7 @@ def zeller(date_input: str) -> str: # Validate if not 0 < d < 32: raise ValueError("Date must be between 1 - 31") - + # Get second seperator sep_2: str = date_input[5] # Validate diff --git a/matrix/nth_fibonacci_using_matrix_exponentiation.py b/matrix/nth_fibonacci_using_matrix_exponentiation.py index 7491abcae031..57cdfacd47dd 100644 --- a/matrix/nth_fibonacci_using_matrix_exponentiation.py +++ b/matrix/nth_fibonacci_using_matrix_exponentiation.py @@ -13,9 +13,6 @@ So we just need the n times multiplication of the matrix [1,1],[1,0]]. We can decrease the n times multiplication by following the divide and conquer approach. """ -from __future__ import print_function - - def multiply(matrix_a, matrix_b): matrix_c = [] n = len(matrix_a) diff --git a/neural_network/convolution_neural_network.py b/neural_network/convolution_neural_network.py index 0e72f0c0dca2..e4dd0a11db9d 100644 --- a/neural_network/convolution_neural_network.py +++ b/neural_network/convolution_neural_network.py @@ -15,8 +15,6 @@ Date: 2017.9.20 - - - - - -- - - - - - - - - - - - - - - - - - - - - - - ''' -from __future__ import print_function - import pickle import numpy as np import matplotlib.pyplot as plt diff --git a/neural_network/perceptron.py b/neural_network/perceptron.py index 871eca20273b..fdc710597241 100644 --- a/neural_network/perceptron.py +++ b/neural_network/perceptron.py @@ -9,8 +9,6 @@ p2 = 1 ''' -from __future__ import print_function - import random diff --git a/other/anagrams.py b/other/anagrams.py index 29b34fbdc5d3..1e6e38dee139 100644 --- a/other/anagrams.py +++ b/other/anagrams.py @@ -1,4 +1,3 @@ -from __future__ import print_function import collections, pprint, time, os start_time = time.time() diff --git a/other/euclidean_gcd.py b/other/euclidean_gcd.py index 30853e172076..13378379f286 100644 --- a/other/euclidean_gcd.py +++ b/other/euclidean_gcd.py @@ -1,4 +1,3 @@ -from __future__ import print_function # https://en.wikipedia.org/wiki/Euclidean_algorithm def euclidean_gcd(a, b): diff --git a/other/linear_congruential_generator.py b/other/linear_congruential_generator.py index 34abdf34eaf3..7c592a6400b5 100644 --- a/other/linear_congruential_generator.py +++ b/other/linear_congruential_generator.py @@ -1,4 +1,3 @@ -from __future__ import print_function __author__ = "Tobias Carryer" from time import time @@ -7,11 +6,11 @@ class LinearCongruentialGenerator(object): """ A pseudorandom number generator. """ - + def __init__( self, multiplier, increment, modulo, seed=int(time()) ): """ These parameters are saved and used when nextNumber() is called. - + modulo is the largest number that can be generated (exclusive). The most efficent values are powers of 2. 2^32 is a common value. """ @@ -19,7 +18,7 @@ def __init__( self, multiplier, increment, modulo, seed=int(time()) ): self.increment = increment self.modulo = modulo self.seed = seed - + def next_number( self ): """ The smallest number that can be generated is zero. diff --git a/other/nested_brackets.py b/other/nested_brackets.py index 76677d56439a..14147eaa6456 100644 --- a/other/nested_brackets.py +++ b/other/nested_brackets.py @@ -13,9 +13,6 @@ returns true if S is nested and false otherwise. ''' -from __future__ import print_function - - def is_balanced(S): stack = [] diff --git a/other/password_generator.py b/other/password_generator.py index fd0701041240..16b7e16b22a1 100644 --- a/other/password_generator.py +++ b/other/password_generator.py @@ -1,5 +1,4 @@ """Password generator allows you to generate a random password of length N.""" -from __future__ import print_function from random import choice from string import ascii_letters, digits, punctuation diff --git a/other/tower_of_hanoi.py b/other/tower_of_hanoi.py index 9cc5b9e40543..cd6fbf4d88ac 100644 --- a/other/tower_of_hanoi.py +++ b/other/tower_of_hanoi.py @@ -1,5 +1,4 @@ -from __future__ import print_function -def moveTower(height, fromPole, toPole, withPole): +def moveTower(height, fromPole, toPole, withPole): ''' >>> moveTower(3, 'A', 'B', 'C') moving disk from A to B diff --git a/other/two_sum.py b/other/two_sum.py index d4484aa85505..b784da82767a 100644 --- a/other/two_sum.py +++ b/other/two_sum.py @@ -9,8 +9,6 @@ Because nums[0] + nums[1] = 2 + 7 = 9, return [0, 1]. """ -from __future__ import print_function - def twoSum(nums, target): """ :type nums: List[int] @@ -20,7 +18,7 @@ def twoSum(nums, target): chk_map = {} for index, val in enumerate(nums): compl = target - val - if compl in chk_map: + if compl in chk_map: indices = [chk_map[compl], index] print(indices) return [indices] diff --git a/other/word_patterns.py b/other/word_patterns.py index c33d520087f7..1364d1277255 100644 --- a/other/word_patterns.py +++ b/other/word_patterns.py @@ -1,4 +1,3 @@ -from __future__ import print_function import pprint, time def getWordPattern(word): diff --git a/project_euler/problem_01/sol1.py b/project_euler/problem_01/sol1.py index 1433129af303..76b13b852c87 100644 --- a/project_euler/problem_01/sol1.py +++ b/project_euler/problem_01/sol1.py @@ -4,17 +4,9 @@ we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """Returns the sum of all the multiples of 3 or 5 below n. - + >>> solution(3) 0 >>> solution(4) @@ -31,4 +23,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_01/sol2.py b/project_euler/problem_01/sol2.py index e58fb03a8fb0..8041c7ffa589 100644 --- a/project_euler/problem_01/sol2.py +++ b/project_euler/problem_01/sol2.py @@ -4,17 +4,11 @@ we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 def solution(n): """Returns the sum of all the multiples of 3 or 5 below n. - + >>> solution(3) 0 >>> solution(4) @@ -36,4 +30,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_01/sol3.py b/project_euler/problem_01/sol3.py index 013ce5e54fdf..532203ddd95d 100644 --- a/project_euler/problem_01/sol3.py +++ b/project_euler/problem_01/sol3.py @@ -4,20 +4,12 @@ we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """ This solution is based on the pattern that the successive numbers in the series follow: 0+3,+2,+1,+3,+1,+2,+3. Returns the sum of all the multiples of 3 or 5 below n. - + >>> solution(3) 0 >>> solution(4) @@ -63,4 +55,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_01/sol4.py b/project_euler/problem_01/sol4.py index 90403c3bd6a3..3e6712618870 100644 --- a/project_euler/problem_01/sol4.py +++ b/project_euler/problem_01/sol4.py @@ -4,17 +4,9 @@ we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """Returns the sum of all the multiples of 3 or 5 below n. - + >>> solution(3) 0 >>> solution(4) @@ -50,4 +42,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_01/sol5.py b/project_euler/problem_01/sol5.py index 302fe44f8bfa..bd96d965f92d 100644 --- a/project_euler/problem_01/sol5.py +++ b/project_euler/problem_01/sol5.py @@ -4,19 +4,13 @@ we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 """A straightforward pythonic solution using list comprehension""" def solution(n): """Returns the sum of all the multiples of 3 or 5 below n. - + >>> solution(3) 0 >>> solution(4) @@ -31,4 +25,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_01/sol6.py b/project_euler/problem_01/sol6.py index cf6e751d4c05..b9c3db4f8550 100644 --- a/project_euler/problem_01/sol6.py +++ b/project_euler/problem_01/sol6.py @@ -4,17 +4,9 @@ we get 3,5,6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below N. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """Returns the sum of all the multiples of 3 or 5 below n. - + >>> solution(3) 0 >>> solution(4) @@ -37,4 +29,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_02/sol1.py b/project_euler/problem_02/sol1.py index f61d04e3dfce..d2ad67e2f424 100644 --- a/project_euler/problem_02/sol1.py +++ b/project_euler/problem_02/sol1.py @@ -9,18 +9,10 @@ n, find the sum of the even-valued terms. e.g. for n=10, we have {2,8}, sum is 10. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """Returns the sum of all fibonacci sequence even elements that are lower or equals to n. - + >>> solution(10) 10 >>> solution(15) @@ -44,4 +36,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_02/sol2.py b/project_euler/problem_02/sol2.py index 3e103a6a4373..71f51b695e84 100644 --- a/project_euler/problem_02/sol2.py +++ b/project_euler/problem_02/sol2.py @@ -9,18 +9,10 @@ n, find the sum of the even-valued terms. e.g. for n=10, we have {2,8}, sum is 10. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """Returns the sum of all fibonacci sequence even elements that are lower or equals to n. - + >>> solution(10) [2, 8] >>> solution(15) @@ -42,4 +34,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_02/sol3.py b/project_euler/problem_02/sol3.py index abd9d6c753b8..c698b8e38ab2 100644 --- a/project_euler/problem_02/sol3.py +++ b/project_euler/problem_02/sol3.py @@ -9,18 +9,10 @@ n, find the sum of the even-valued terms. e.g. for n=10, we have {2,8}, sum is 10. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """Returns the sum of all fibonacci sequence even elements that are lower or equals to n. - + >>> solution(10) 10 >>> solution(15) @@ -44,4 +36,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_02/sol4.py b/project_euler/problem_02/sol4.py index 5e8c04899f3d..92ea0a51e026 100644 --- a/project_euler/problem_02/sol4.py +++ b/project_euler/problem_02/sol4.py @@ -9,20 +9,14 @@ n, find the sum of the even-valued terms. e.g. for n=10, we have {2,8}, sum is 10. """ -from __future__ import print_function import math from decimal import Decimal, getcontext -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - def solution(n): """Returns the sum of all fibonacci sequence even elements that are lower or equals to n. - + >>> solution(10) 10 >>> solution(15) @@ -68,4 +62,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_03/sol1.py b/project_euler/problem_03/sol1.py index ab19d8b30457..9f8ecc5e6565 100644 --- a/project_euler/problem_03/sol1.py +++ b/project_euler/problem_03/sol1.py @@ -5,14 +5,8 @@ e.g. for 10, largest prime factor = 5. For 17, largest prime factor = 17. """ -from __future__ import print_function, division import math -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - def isprime(no): if no == 2: @@ -81,4 +75,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_03/sol2.py b/project_euler/problem_03/sol2.py index f93a0b75f4e0..b6fad079fa31 100644 --- a/project_euler/problem_03/sol2.py +++ b/project_euler/problem_03/sol2.py @@ -5,12 +5,6 @@ e.g. for 10, largest prime factor = 5. For 17, largest prime factor = 17. """ -from __future__ import print_function, division - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 def solution(n): @@ -60,4 +54,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_04/sol1.py b/project_euler/problem_04/sol1.py index 7a255f7308e6..51417b146bbf 100644 --- a/project_euler/problem_04/sol1.py +++ b/project_euler/problem_04/sol1.py @@ -6,18 +6,10 @@ Find the largest palindrome made from the product of two 3-digit numbers which is less than N. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """Returns the largest palindrome made from the product of two 3-digit numbers which is less than n. - + >>> solution(20000) 19591 >>> solution(30000) @@ -47,4 +39,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_04/sol2.py b/project_euler/problem_04/sol2.py index 45c6b256daf8..8740ee44a4b4 100644 --- a/project_euler/problem_04/sol2.py +++ b/project_euler/problem_04/sol2.py @@ -6,18 +6,10 @@ Find the largest palindrome made from the product of two 3-digit numbers which is less than N. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """Returns the largest palindrome made from the product of two 3-digit numbers which is less than n. - + >>> solution(20000) 19591 >>> solution(30000) @@ -35,4 +27,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_05/sol1.py b/project_euler/problem_05/sol1.py index e2deb91fb6aa..83c387e4ae6e 100644 --- a/project_euler/problem_05/sol1.py +++ b/project_euler/problem_05/sol1.py @@ -6,14 +6,6 @@ What is the smallest positive number that is evenly divisible(divisible with no remainder) by all of the numbers from 1 to N? """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """Returns the smallest positive number that is evenly divisible(divisible with no remainder) by all of the numbers from 1 to n. @@ -66,4 +58,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_05/sol2.py b/project_euler/problem_05/sol2.py index 293dd96f2294..5aa84d21c8e8 100644 --- a/project_euler/problem_05/sol2.py +++ b/project_euler/problem_05/sol2.py @@ -6,13 +6,6 @@ What is the smallest positive number that is evenly divisible(divisible with no remainder) by all of the numbers from 1 to N? """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - """ Euclidean GCD Algorithm """ @@ -30,7 +23,7 @@ def lcm(x, y): def solution(n): """Returns the smallest positive number that is evenly divisible(divisible with no remainder) by all of the numbers from 1 to n. - + >>> solution(10) 2520 >>> solution(15) @@ -47,4 +40,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_06/sol1.py b/project_euler/problem_06/sol1.py index 728701e167c3..0a964272e7e8 100644 --- a/project_euler/problem_06/sol1.py +++ b/project_euler/problem_06/sol1.py @@ -14,18 +14,10 @@ Find the difference between the sum of the squares of the first N natural numbers and the square of the sum. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """Returns the difference between the sum of the squares of the first n natural numbers and the square of the sum. - + >>> solution(10) 2640 >>> solution(15) @@ -45,4 +37,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_06/sol2.py b/project_euler/problem_06/sol2.py index 2c64812d56f8..45d08d244647 100644 --- a/project_euler/problem_06/sol2.py +++ b/project_euler/problem_06/sol2.py @@ -14,18 +14,10 @@ Find the difference between the sum of the squares of the first N natural numbers and the square of the sum. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """Returns the difference between the sum of the squares of the first n natural numbers and the square of the sum. - + >>> solution(10) 2640 >>> solution(15) @@ -42,4 +34,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_06/sol3.py b/project_euler/problem_06/sol3.py index 7d94b1e2254f..f9c5dacb3777 100644 --- a/project_euler/problem_06/sol3.py +++ b/project_euler/problem_06/sol3.py @@ -14,19 +14,13 @@ Find the difference between the sum of the squares of the first N natural numbers and the square of the sum. """ -from __future__ import print_function import math -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - def solution(n): """Returns the difference between the sum of the squares of the first n natural numbers and the square of the sum. - + >>> solution(10) 2640 >>> solution(15) @@ -42,4 +36,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_07/sol1.py b/project_euler/problem_07/sol1.py index 403ded568dda..d8d67e157860 100644 --- a/project_euler/problem_07/sol1.py +++ b/project_euler/problem_07/sol1.py @@ -6,14 +6,8 @@ We can see that the 6th prime is 13. What is the Nth prime number? """ -from __future__ import print_function from math import sqrt -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - def isprime(n): if n == 2: @@ -30,7 +24,7 @@ def isprime(n): def solution(n): """Returns the n-th prime number. - + >>> solution(6) 13 >>> solution(1) @@ -58,4 +52,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_07/sol2.py b/project_euler/problem_07/sol2.py index 67336f7c1c96..7d078af32176 100644 --- a/project_euler/problem_07/sol2.py +++ b/project_euler/problem_07/sol2.py @@ -6,14 +6,6 @@ We can see that the 6th prime is 13. What is the Nth prime number? """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def isprime(number): for i in range(2, int(number ** 0.5) + 1): if number % i == 0: @@ -23,7 +15,7 @@ def isprime(number): def solution(n): """Returns the n-th prime number. - + >>> solution(6) 13 >>> solution(1) @@ -73,4 +65,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_07/sol3.py b/project_euler/problem_07/sol3.py index bc94762604b3..3c28ecf7fb34 100644 --- a/project_euler/problem_07/sol3.py +++ b/project_euler/problem_07/sol3.py @@ -6,15 +6,9 @@ We can see that the 6th prime is 13. What is the Nth prime number? """ -from __future__ import print_function import math import itertools -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - def primeCheck(number): if number % 2 == 0 and number > 2: @@ -32,7 +26,7 @@ def prime_generator(): def solution(n): """Returns the n-th prime number. - + >>> solution(6) 13 >>> solution(1) @@ -50,4 +44,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_09/sol1.py b/project_euler/problem_09/sol1.py index d9ebe8760861..3bb5c968115d 100644 --- a/project_euler/problem_09/sol1.py +++ b/project_euler/problem_09/sol1.py @@ -7,7 +7,6 @@ There exists exactly one Pythagorean triplet for which a + b + c = 1000. Find the product abc. """ -from __future__ import print_function def solution(): @@ -17,7 +16,7 @@ def solution(): 1. a < b < c 2. a**2 + b**2 = c**2 3. a + b + c = 1000 - + # The code below has been commented due to slow execution affecting Travis. # >>> solution() # 31875000 diff --git a/project_euler/problem_09/sol2.py b/project_euler/problem_09/sol2.py index 674daae9ec8e..502f334417c8 100644 --- a/project_euler/problem_09/sol2.py +++ b/project_euler/problem_09/sol2.py @@ -7,14 +7,6 @@ There exists exactly one Pythagorean triplet for which a + b + c = 1000. Find the product abc. """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """ Return the product of a,b,c which are Pythagorean Triplet that satisfies @@ -22,7 +14,7 @@ def solution(n): 1. a < b < c 2. a**2 + b**2 = c**2 3. a + b + c = 1000 - + >>> solution(1000) 31875000 """ @@ -41,4 +33,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_09/sol3.py b/project_euler/problem_09/sol3.py index 006029c8a30d..bbe7dcf743e7 100644 --- a/project_euler/problem_09/sol3.py +++ b/project_euler/problem_09/sol3.py @@ -10,9 +10,6 @@ There exists exactly one Pythagorean triplet for which a + b + c = 1000. Find the product abc. """ -from __future__ import print_function - - def solution(): """ Returns the product of a,b,c which are Pythagorean Triplet that satisfies diff --git a/project_euler/problem_10/sol1.py b/project_euler/problem_10/sol1.py index 49384d7c78f0..c81085951ecf 100644 --- a/project_euler/problem_10/sol1.py +++ b/project_euler/problem_10/sol1.py @@ -4,22 +4,11 @@ Find the sum of all the primes below two million. """ -from __future__ import print_function from math import sqrt -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 - def is_prime(n): - for i in xrange(2, int(sqrt(n)) + 1): + for i in range(2, int(sqrt(n)) + 1): if n % i == 0: return False @@ -32,7 +21,7 @@ def sum_of_primes(n): else: return 0 - for i in xrange(3, n, 2): + for i in range(3, n, 2): if is_prime(i): sumOfPrimes += i @@ -41,7 +30,7 @@ def sum_of_primes(n): def solution(n): """Returns the sum of all the primes below n. - + # The code below has been commented due to slow execution affecting Travis. # >>> solution(2000000) # 142913828922 @@ -58,4 +47,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_10/sol2.py b/project_euler/problem_10/sol2.py index 451a4ae5e8f3..b2e2b6e1adf3 100644 --- a/project_euler/problem_10/sol2.py +++ b/project_euler/problem_10/sol2.py @@ -4,15 +4,9 @@ Find the sum of all the primes below two million. """ -from __future__ import print_function import math from itertools import takewhile -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - def primeCheck(number): if number % 2 == 0 and number > 2: @@ -30,7 +24,7 @@ def prime_generator(): def solution(n): """Returns the sum of all the primes below n. - + # The code below has been commented due to slow execution affecting Travis. # >>> solution(2000000) # 142913828922 @@ -47,4 +41,4 @@ def solution(n): if __name__ == "__main__": - print(solution(int(raw_input().strip()))) + print(solution(int(input().strip()))) diff --git a/project_euler/problem_11/sol1.py b/project_euler/problem_11/sol1.py index 3bdddc89d917..1473439ae00d 100644 --- a/project_euler/problem_11/sol1.py +++ b/project_euler/problem_11/sol1.py @@ -24,14 +24,8 @@ 01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48 """ -from __future__ import print_function import os -try: - xrange # Python 2 -except NameError: - xrange = range # Python 2 - def largest_product(grid): nColumns = len(grid[0]) @@ -43,8 +37,8 @@ def largest_product(grid): # Check vertically, horizontally, diagonally at the same time (only works # for nxn grid) - for i in xrange(nColumns): - for j in xrange(nRows - 3): + for i in range(nColumns): + for j in range(nRows - 3): vertProduct = ( grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i] ) @@ -81,7 +75,7 @@ def largest_product(grid): def solution(): """Returns the sum of all the multiples of 3 or 5 below n. - + >>> solution() 70600674 """ @@ -90,7 +84,7 @@ def solution(): for line in file: grid.append(line.strip("\n").split(" ")) - grid = [[int(i) for i in grid[j]] for j in xrange(len(grid))] + grid = [[int(i) for i in grid[j]] for j in range(len(grid))] return largest_product(grid) diff --git a/project_euler/problem_11/sol2.py b/project_euler/problem_11/sol2.py index 0a5785b42b2c..be6c11a378ad 100644 --- a/project_euler/problem_11/sol2.py +++ b/project_euler/problem_11/sol2.py @@ -24,45 +24,39 @@ 01 70 54 71 83 51 54 69 16 92 33 48 61 43 52 01 89 19 67 48 """ -from __future__ import print_function import os -try: - xrange # Python 2 -except NameError: - xrange = range # Python 2 - def solution(): """Returns the sum of all the multiples of 3 or 5 below n. - + >>> solution() 70600674 """ with open(os.path.dirname(__file__) + "/grid.txt") as f: l = [] - for i in xrange(20): + for i in range(20): l.append([int(x) for x in f.readline().split()]) maximum = 0 # right - for i in xrange(20): - for j in xrange(17): + for i in range(20): + for j in range(17): temp = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3] if temp > maximum: maximum = temp # down - for i in xrange(17): - for j in xrange(20): + for i in range(17): + for j in range(20): temp = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j] if temp > maximum: maximum = temp # diagonal 1 - for i in xrange(17): - for j in xrange(17): + for i in range(17): + for j in range(17): temp = ( l[i][j] * l[i + 1][j + 1] @@ -73,8 +67,8 @@ def solution(): maximum = temp # diagonal 2 - for i in xrange(17): - for j in xrange(3, 20): + for i in range(17): + for j in range(3, 20): temp = ( l[i][j] * l[i + 1][j - 1] diff --git a/project_euler/problem_12/sol1.py b/project_euler/problem_12/sol1.py index 54476110b503..7e080c4e45a1 100644 --- a/project_euler/problem_12/sol1.py +++ b/project_euler/problem_12/sol1.py @@ -21,18 +21,12 @@ What is the value of the first triangle number to have over five hundred divisors? """ -from __future__ import print_function from math import sqrt -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 - def count_divisors(n): nDivisors = 0 - for i in xrange(1, int(sqrt(n)) + 1): + for i in range(1, int(sqrt(n)) + 1): if n % i == 0: nDivisors += 2 # check if n is perfect square @@ -44,7 +38,7 @@ def count_divisors(n): def solution(): """Returns the value of the first triangle number to have over five hundred divisors. - + # The code below has been commented due to slow execution affecting Travis. # >>> solution() # 76576500 diff --git a/project_euler/problem_12/sol2.py b/project_euler/problem_12/sol2.py index 0d1502830bee..97a4910723ac 100644 --- a/project_euler/problem_12/sol2.py +++ b/project_euler/problem_12/sol2.py @@ -21,9 +21,6 @@ What is the value of the first triangle number to have over five hundred divisors? """ -from __future__ import print_function - - def triangle_number_generator(): for n in range(1, 1000000): yield n * (n + 1) // 2 @@ -38,7 +35,7 @@ def count_divisors(n): def solution(): """Returns the value of the first triangle number to have over five hundred divisors. - + # The code below has been commented due to slow execution affecting Travis. # >>> solution() # 76576500 diff --git a/project_euler/problem_14/sol1.py b/project_euler/problem_14/sol1.py index 8d3efbc59eb5..156322b7d507 100644 --- a/project_euler/problem_14/sol1.py +++ b/project_euler/problem_14/sol1.py @@ -16,20 +16,12 @@ Which starting number, under one million, produces the longest chain? """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def solution(n): """Returns the number under n that generates the longest sequence using the formula: n → n/2 (n is even) n → 3n + 1 (n is odd) - + # The code below has been commented due to slow execution affecting Travis. # >>> solution(1000000) # {'counter': 525, 'largest_number': 837799} @@ -62,7 +54,7 @@ def solution(n): if __name__ == "__main__": - result = solution(int(raw_input().strip())) + result = solution(int(input().strip())) print( ( "Largest Number:", diff --git a/project_euler/problem_14/sol2.py b/project_euler/problem_14/sol2.py index 0ec80e221f09..25ebd41571c2 100644 --- a/project_euler/problem_14/sol2.py +++ b/project_euler/problem_14/sol2.py @@ -24,14 +24,6 @@ Which starting number, under one million, produces the longest chain? """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def collatz_sequence(n): """Returns the Collatz sequence for n.""" sequence = [n] @@ -63,7 +55,7 @@ def solution(n): if __name__ == "__main__": - result = solution(int(raw_input().strip())) + result = solution(int(input().strip())) print( "Longest Collatz sequence under one million is %d with length %d" % (result["largest_number"], result["counter"]) diff --git a/project_euler/problem_21/sol1.py b/project_euler/problem_21/sol1.py index 9cf2a64cf2a9..a890e6a98611 100644 --- a/project_euler/problem_21/sol1.py +++ b/project_euler/problem_21/sol1.py @@ -16,15 +16,9 @@ Evaluate the sum of all the amicable numbers under 10000. """ -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 - - def sum_of_divisors(n): total = 0 - for i in xrange(1, int(sqrt(n) + 1)): + for i in range(1, int(sqrt(n) + 1)): if n % i == 0 and i != sqrt(n): total += i + n // i elif i == sqrt(n): diff --git a/project_euler/problem_22/sol1.py b/project_euler/problem_22/sol1.py index aa779f222eaa..f6275e2138bb 100644 --- a/project_euler/problem_22/sol1.py +++ b/project_euler/problem_22/sol1.py @@ -18,12 +18,6 @@ import os -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 - - def solution(): """Returns the total of all the name scores in the file. diff --git a/project_euler/problem_25/sol1.py b/project_euler/problem_25/sol1.py index be3b4d9b2d7d..4371c533ce16 100644 --- a/project_euler/problem_25/sol1.py +++ b/project_euler/problem_25/sol1.py @@ -25,12 +25,6 @@ digits? """ -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 - - def fibonacci(n): if n == 1 or type(n) is not int: return 0 @@ -38,7 +32,7 @@ def fibonacci(n): return 1 else: sequence = [0, 1] - for i in xrange(2, n + 1): + for i in range(2, n + 1): sequence.append(sequence[i - 1] + sequence[i - 2]) return sequence[n] diff --git a/project_euler/problem_28/sol1.py b/project_euler/problem_28/sol1.py index 63386ce3058c..11b48fea9adf 100644 --- a/project_euler/problem_28/sol1.py +++ b/project_euler/problem_28/sol1.py @@ -16,11 +16,6 @@ from math import ceil -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 - def diagonal_sum(n): """Returns the sum of the numbers on the diagonals in a n by n spiral @@ -39,7 +34,7 @@ def diagonal_sum(n): """ total = 1 - for i in xrange(1, int(ceil(n / 2.0))): + for i in range(1, int(ceil(n / 2.0))): odd = 2 * i + 1 even = 2 * i total = total + 4 * odd ** 2 - 6 * even diff --git a/project_euler/problem_29/solution.py b/project_euler/problem_29/solution.py index e67dafe4639d..24d3e20d94fe 100644 --- a/project_euler/problem_29/solution.py +++ b/project_euler/problem_29/solution.py @@ -14,13 +14,10 @@ How many distinct terms are in the sequence generated by ab for 2 <= a <= 100 and 2 <= b <= 100? """ -from __future__ import print_function - - def solution(n): """Returns the number of distinct terms in the sequence generated by a^b for 2 <= a <= 100 and 2 <= b <= 100. - + >>> solution(100) 9183 >>> solution(50) diff --git a/project_euler/problem_31/sol1.py b/project_euler/problem_31/sol1.py index e2a209e5df5a..f7439d346130 100644 --- a/project_euler/problem_31/sol1.py +++ b/project_euler/problem_31/sol1.py @@ -11,14 +11,6 @@ 1×£1 + 1×50p + 2×20p + 1×5p + 1×2p + 3×1p How many different ways can £2 be made using any number of coins? """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - - def one_pence(): return 1 diff --git a/project_euler/problem_36/sol1.py b/project_euler/problem_36/sol1.py index 38b60420992b..7ed74af8fd63 100644 --- a/project_euler/problem_36/sol1.py +++ b/project_euler/problem_36/sol1.py @@ -9,12 +9,6 @@ (Please note that the palindromic number, in either base, may not include leading zeros.) """ -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 - - def is_palindrome(n): n = str(n) @@ -47,7 +41,7 @@ def solution(n): """ total = 0 - for i in xrange(1, n): + for i in range(1, n): if is_palindrome(i) and is_palindrome(bin(i).split("b")[1]): total += i return total diff --git a/project_euler/problem_40/sol1.py b/project_euler/problem_40/sol1.py index accd7125354c..d15376b739db 100644 --- a/project_euler/problem_40/sol1.py +++ b/project_euler/problem_40/sol1.py @@ -14,11 +14,8 @@ d1 × d10 × d100 × d1000 × d10000 × d100000 × d1000000 """ -from __future__ import print_function - - def solution(): - """Returns + """Returns >>> solution() 210 diff --git a/project_euler/problem_48/sol1.py b/project_euler/problem_48/sol1.py index 95af951c0e8a..06ad1408dcef 100644 --- a/project_euler/problem_48/sol1.py +++ b/project_euler/problem_48/sol1.py @@ -7,11 +7,6 @@ Find the last ten digits of the series, 11 + 22 + 33 + ... + 10001000. """ -try: - xrange -except NameError: - xrange = range - def solution(): """Returns the last 10 digits of the series, 11 + 22 + 33 + ... + 10001000. @@ -20,7 +15,7 @@ def solution(): '9110846700' """ total = 0 - for i in xrange(1, 1001): + for i in range(1, 1001): total += i ** i return str(total)[-10:] diff --git a/project_euler/problem_53/sol1.py b/project_euler/problem_53/sol1.py index c72e0b993a34..f17508b005d1 100644 --- a/project_euler/problem_53/sol1.py +++ b/project_euler/problem_53/sol1.py @@ -17,14 +17,8 @@ How many, not necessarily distinct, values of nCr, for 1 ≤ n ≤ 100, are greater than one-million? """ -from __future__ import print_function from math import factorial -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 - def combinations(n, r): return factorial(n) / (factorial(r) * factorial(n - r)) @@ -39,8 +33,8 @@ def solution(): """ total = 0 - for i in xrange(1, 101): - for j in xrange(1, i + 1): + for i in range(1, 101): + for j in range(1, i + 1): if combinations(i, j) > 1e6: total += 1 return total diff --git a/project_euler/problem_76/sol1.py b/project_euler/problem_76/sol1.py index c9e3c452fbc4..ed0ee6b507e9 100644 --- a/project_euler/problem_76/sol1.py +++ b/project_euler/problem_76/sol1.py @@ -14,12 +14,6 @@ How many different ways can one hundred be written as a sum of at least two positive integers? """ -from __future__ import print_function - -try: - xrange # Python 2 -except NameError: - xrange = range # Python 3 def partition(m): @@ -43,12 +37,12 @@ def partition(m): >>> partition(1) 0 """ - memo = [[0 for _ in xrange(m)] for _ in xrange(m + 1)] - for i in xrange(m + 1): + memo = [[0 for _ in range(m)] for _ in range(m + 1)] + for i in range(m + 1): memo[i][0] = 1 - for n in xrange(m + 1): - for k in xrange(1, m): + for n in range(m + 1): + for k in range(1, m): memo[n][k] += memo[n][k - 1] if n > k: memo[n][k] += memo[n - k - 1][k] diff --git a/searches/binary_search.py b/searches/binary_search.py index e658dac2a3ef..77abf90239ab 100644 --- a/searches/binary_search.py +++ b/searches/binary_search.py @@ -9,14 +9,8 @@ For manual testing run: python binary_search.py """ -from __future__ import print_function import bisect -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - def binary_search(sorted_collection, item): """Pure implementation of binary search algorithm in Python @@ -112,7 +106,7 @@ def binary_search_by_recursion(sorted_collection, item, left, right): """ if (right < left): return None - + midpoint = left + (right - left) // 2 if sorted_collection[midpoint] == item: @@ -121,7 +115,7 @@ def binary_search_by_recursion(sorted_collection, item, left, right): return binary_search_by_recursion(sorted_collection, item, left, midpoint-1) else: return binary_search_by_recursion(sorted_collection, item, midpoint+1, right) - + def __assert_sorted(collection): """Check if collection is ascending sorted, if not - raises :py:class:`ValueError` @@ -145,14 +139,14 @@ def __assert_sorted(collection): if __name__ == '__main__': import sys - user_input = raw_input('Enter numbers separated by comma:\n').strip() + user_input = input('Enter numbers separated by comma:\n').strip() collection = [int(item) for item in user_input.split(',')] try: __assert_sorted(collection) except ValueError: sys.exit('Sequence must be ascending sorted to apply binary search') - target_input = raw_input('Enter a single number to be found in the list:\n') + target_input = input('Enter a single number to be found in the list:\n') target = int(target_input) result = binary_search(collection, target) if result is not None: diff --git a/searches/interpolation_search.py b/searches/interpolation_search.py index 329596d340a5..27ee979bb649 100644 --- a/searches/interpolation_search.py +++ b/searches/interpolation_search.py @@ -1,12 +1,6 @@ """ This is pure python implementation of interpolation search algorithm """ -from __future__ import print_function - -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 def interpolation_search(sorted_collection, item): @@ -29,7 +23,7 @@ def interpolation_search(sorted_collection, item): return None point = left + ((item - sorted_collection[left]) * (right - left)) // (sorted_collection[right] - sorted_collection[left]) - + #out of range check if point<0 or point>=len(sorted_collection): return None @@ -42,9 +36,9 @@ def interpolation_search(sorted_collection, item): right = left left = point elif point>right: - left = right + left = right right = point - else: + else: if item < current_item: right = point - 1 else: @@ -70,7 +64,7 @@ def interpolation_search_by_recursion(sorted_collection, item, left, right): return None point = left + ((item - sorted_collection[left]) * (right - left)) // (sorted_collection[right] - sorted_collection[left]) - + #out of range check if point<0 or point>=len(sorted_collection): return None @@ -86,7 +80,7 @@ def interpolation_search_by_recursion(sorted_collection, item, left, right): return interpolation_search_by_recursion(sorted_collection, item, left, point-1) else: return interpolation_search_by_recursion(sorted_collection, item, point+1, right) - + def __assert_sorted(collection): """Check if collection is ascending sorted, if not - raises :py:class:`ValueError` :param collection: collection @@ -107,16 +101,16 @@ def __assert_sorted(collection): if __name__ == '__main__': import sys - + """ - user_input = raw_input('Enter numbers separated by comma:\n').strip() + user_input = input('Enter numbers separated by comma:\n').strip() collection = [int(item) for item in user_input.split(',')] try: __assert_sorted(collection) except ValueError: sys.exit('Sequence must be ascending sorted to apply interpolation search') - target_input = raw_input('Enter a single number to be found in the list:\n') + target_input = input('Enter a single number to be found in the list:\n') target = int(target_input) """ @@ -128,7 +122,7 @@ def __assert_sorted(collection): except ValueError: sys.exit('Sequence must be ascending sorted to apply interpolation search') target = 67 - + result = interpolation_search(collection, target) if result is not None: print('{} found at positions: {}'.format(target, result)) diff --git a/searches/jump_search.py b/searches/jump_search.py index 10cb933f2f35..78d9f79dc6a8 100644 --- a/searches/jump_search.py +++ b/searches/jump_search.py @@ -1,4 +1,3 @@ -from __future__ import print_function import math def jump_search(arr, x): n = len(arr) diff --git a/searches/linear_search.py b/searches/linear_search.py index 058322f21d09..fb784924132e 100644 --- a/searches/linear_search.py +++ b/searches/linear_search.py @@ -9,12 +9,7 @@ For manual testing run: python linear_search.py """ -from __future__ import print_function -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 def linear_search(sequence, target): """Pure implementation of linear search algorithm in Python @@ -43,10 +38,10 @@ def linear_search(sequence, target): if __name__ == '__main__': - user_input = raw_input('Enter numbers separated by comma:\n').strip() + user_input = input('Enter numbers separated by comma:\n').strip() sequence = [int(item) for item in user_input.split(',')] - target_input = raw_input('Enter a single number to be found in the list:\n') + target_input = input('Enter a single number to be found in the list:\n') target = int(target_input) result = linear_search(sequence, target) if result is not None: diff --git a/searches/sentinel_linear_search.py b/searches/sentinel_linear_search.py index 336cc5ab3b74..eb9d32e5f503 100644 --- a/searches/sentinel_linear_search.py +++ b/searches/sentinel_linear_search.py @@ -45,15 +45,10 @@ def sentinel_linear_search(sequence, target): if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by comma:\n').strip() + user_input = input('Enter numbers separated by comma:\n').strip() sequence = [int(item) for item in user_input.split(',')] - target_input = raw_input('Enter a single number to be found in the list:\n') + target_input = input('Enter a single number to be found in the list:\n') target = int(target_input) result = sentinel_linear_search(sequence, target) if result is not None: diff --git a/searches/ternary_search.py b/searches/ternary_search.py index c610f9b3c6da..41033f33cec6 100644 --- a/searches/ternary_search.py +++ b/searches/ternary_search.py @@ -1,20 +1,13 @@ ''' This is a type of divide and conquer algorithm which divides the search space into -3 parts and finds the target value based on the property of the array or list +3 parts and finds the target value based on the property of the array or list (usually monotonic property). Time Complexity : O(log3 N) Space Complexity : O(1) ''' -from __future__ import print_function - import sys -try: - raw_input # Python 2 -except NameError: - raw_input = input # Python 3 - # This is the precision for this function which can be altered. # It is recommended for users to keep this number greater than or equal to 10. precision = 10 @@ -31,23 +24,23 @@ def ite_ternary_search(A, target): right = len(A) - 1; while(True): if(left>> bubble_sort([-2, -5, -45]) [-45, -5, -2] - - >>> bubble_sort([-23,0,6,-4,34]) + + >>> bubble_sort([-23, 0, 6, -4, 34]) [-23, -4, 0, 6, 34] + + >>> bubble_sort([-23, 0, 6, -4, 34]) == sorted([-23, 0, 6, -4, 34]) + True """ length = len(collection) for i in range(length-1): @@ -28,15 +28,12 @@ def bubble_sort(collection): if collection[j] > collection[j+1]: swapped = True collection[j], collection[j+1] = collection[j+1], collection[j] - if not swapped: break # Stop iteration if the collection is sorted. + if not swapped: + break # Stop iteration if the collection is sorted. return collection if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - user_input = raw_input('Enter numbers separated by a comma:').strip() + user_input = input('Enter numbers separated by a comma:').strip() unsorted = [int(item) for item in user_input.split(',')] print(*bubble_sort(unsorted), sep=',') diff --git a/sorts/cocktail_shaker_sort.py b/sorts/cocktail_shaker_sort.py index 8ad3383bbe9f..d486e6a11dfa 100644 --- a/sorts/cocktail_shaker_sort.py +++ b/sorts/cocktail_shaker_sort.py @@ -1,12 +1,10 @@ -from __future__ import print_function - def cocktail_shaker_sort(unsorted): """ Pure implementation of the cocktail shaker sort algorithm in Python. """ for i in range(len(unsorted)-1, 0, -1): swapped = False - + for j in range(i, 0, -1): if unsorted[j] < unsorted[j-1]: unsorted[j], unsorted[j-1] = unsorted[j-1], unsorted[j] @@ -16,17 +14,12 @@ def cocktail_shaker_sort(unsorted): if unsorted[j] > unsorted[j+1]: unsorted[j], unsorted[j+1] = unsorted[j+1], unsorted[j] swapped = True - + if not swapped: return unsorted - + if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() + user_input = input('Enter numbers separated by a comma:\n').strip() unsorted = [int(item) for item in user_input.split(',')] cocktail_shaker_sort(unsorted) print(unsorted) diff --git a/sorts/comb_sort.py b/sorts/comb_sort.py index 22b6f66f04cc..6ce6c1c094f9 100644 --- a/sorts/comb_sort.py +++ b/sorts/comb_sort.py @@ -48,11 +48,6 @@ def comb_sort(data): if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() + user_input = input('Enter numbers separated by a comma:\n').strip() unsorted = [int(item) for item in user_input.split(',')] print(comb_sort(unsorted)) diff --git a/sorts/counting_sort.py b/sorts/counting_sort.py index ad98f1a0da4c..a3de1811849e 100644 --- a/sorts/counting_sort.py +++ b/sorts/counting_sort.py @@ -8,8 +8,6 @@ python counting_sort.py """ -from __future__ import print_function - def counting_sort(collection): """Pure implementation of counting sort algorithm in Python @@ -58,6 +56,10 @@ def counting_sort(collection): return ordered def counting_sort_string(string): + """ + >>> counting_sort_string("thisisthestring") + 'eghhiiinrsssttt' + """ return ''.join([chr(i) for i in counting_sort([ord(c) for c in string])]) @@ -65,11 +67,6 @@ def counting_sort_string(string): # Test string sort assert "eghhiiinrsssttt" == counting_sort_string("thisisthestring") - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() + user_input = input('Enter numbers separated by a comma:\n').strip() unsorted = [int(item) for item in user_input.split(',')] print(counting_sort(unsorted)) diff --git a/sorts/cycle_sort.py b/sorts/cycle_sort.py index 492022164427..06a377cbd906 100644 --- a/sorts/cycle_sort.py +++ b/sorts/cycle_sort.py @@ -1,7 +1,4 @@ # Code contributed by Honey Sharma -from __future__ import print_function - - def cycle_sort(array): ans = 0 @@ -45,12 +42,7 @@ def cycle_sort(array): # Main Code starts here if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n') + user_input = input('Enter numbers separated by a comma:\n') unsorted = [int(item) for item in user_input.split(',')] n = len(unsorted) cycle_sort(unsorted) diff --git a/sorts/gnome_sort.py b/sorts/gnome_sort.py index 075749e37663..fed70eb6bc1b 100644 --- a/sorts/gnome_sort.py +++ b/sorts/gnome_sort.py @@ -1,7 +1,5 @@ """Gnome Sort Algorithm.""" -from __future__ import print_function - def gnome_sort(unsorted): """Pure implementation of the gnome sort algorithm in Python.""" @@ -21,12 +19,7 @@ def gnome_sort(unsorted): if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() + user_input = input('Enter numbers separated by a comma:\n').strip() unsorted = [int(item) for item in user_input.split(',')] gnome_sort(unsorted) print(unsorted) diff --git a/sorts/heap_sort.py b/sorts/heap_sort.py index 3c72abca8059..ca4a061afbb7 100644 --- a/sorts/heap_sort.py +++ b/sorts/heap_sort.py @@ -10,9 +10,6 @@ python heap_sort.py ''' -from __future__ import print_function - - def heapify(unsorted, index, heap_size): largest = index left_index = 2 * index + 1 @@ -54,11 +51,6 @@ def heap_sort(unsorted): return unsorted if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() + user_input = input('Enter numbers separated by a comma:\n').strip() unsorted = [int(item) for item in user_input.split(',')] print(heap_sort(unsorted)) diff --git a/sorts/insertion_sort.py b/sorts/insertion_sort.py index e088705947d4..e10497b0e282 100644 --- a/sorts/insertion_sort.py +++ b/sorts/insertion_sort.py @@ -9,9 +9,6 @@ For manual testing run: python insertion_sort.py """ -from __future__ import print_function - - def insertion_sort(collection): """Pure implementation of the insertion sort algorithm in Python @@ -40,11 +37,6 @@ def insertion_sort(collection): if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() + user_input = input('Enter numbers separated by a comma:\n').strip() unsorted = [int(item) for item in user_input.split(',')] print(insertion_sort(unsorted)) diff --git a/sorts/merge_sort.py b/sorts/merge_sort.py index 714861e72642..e64e90785a32 100644 --- a/sorts/merge_sort.py +++ b/sorts/merge_sort.py @@ -9,9 +9,6 @@ For manual testing run: python merge_sort.py """ -from __future__ import print_function - - def merge_sort(collection): """Pure implementation of the merge sort algorithm in Python @@ -46,11 +43,6 @@ def merge(left, right): if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() + user_input = input('Enter numbers separated by a comma:\n').strip() unsorted = [int(item) for item in user_input.split(',')] print(*merge_sort(unsorted), sep=',') diff --git a/sorts/merge_sort_fastest.py b/sorts/merge_sort_fastest.py index bd356c935ca0..3c9ed3e9e8ee 100644 --- a/sorts/merge_sort_fastest.py +++ b/sorts/merge_sort_fastest.py @@ -4,9 +4,6 @@ Best Case Scenario : O(n) Worst Case Scenario : O(n^2) because native python functions:min, max and remove are already O(n) ''' -from __future__ import print_function - - def merge_sort(collection): """Pure implementation of the fastest merge sort algorithm in Python @@ -36,11 +33,6 @@ def merge_sort(collection): if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() + user_input = input('Enter numbers separated by a comma:\n').strip() unsorted = [int(item) for item in user_input.split(',')] print(*merge_sort(unsorted), sep=',') diff --git a/sorts/pigeon_sort.py b/sorts/pigeon_sort.py index 65eb8896ea9c..5e5afa137685 100644 --- a/sorts/pigeon_sort.py +++ b/sorts/pigeon_sort.py @@ -1,9 +1,6 @@ ''' This is an implementation of Pigeon Hole Sort. ''' - -from __future__ import print_function - def pigeon_sort(array): # Manually finds the minimum and maximum of the array. min = array[0] @@ -38,12 +35,7 @@ def pigeon_sort(array): return array if __name__ == '__main__': - try: - raw_input # Python2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by comma:\n') + user_input = input('Enter numbers separated by comma:\n') unsorted = [int(x) for x in user_input.split(',')] sorted = pigeon_sort(unsorted) diff --git a/sorts/quick_sort.py b/sorts/quick_sort.py index 7e8c868ebb06..60f8803cb79c 100644 --- a/sorts/quick_sort.py +++ b/sorts/quick_sort.py @@ -9,9 +9,6 @@ For manual testing run: python quick_sort.py """ -from __future__ import print_function - - def quick_sort(collection): """Pure implementation of quick sort algorithm in Python @@ -47,11 +44,6 @@ def quick_sort(collection): if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() + user_input = input('Enter numbers separated by a comma:\n').strip() unsorted = [ int(item) for item in user_input.split(',') ] print( quick_sort(unsorted) ) diff --git a/sorts/quick_sort_3_partition.py b/sorts/quick_sort_3_partition.py index def646cdbc50..9056b204740a 100644 --- a/sorts/quick_sort_3_partition.py +++ b/sorts/quick_sort_3_partition.py @@ -1,5 +1,3 @@ -from __future__ import print_function - def quick_sort_3partition(sorting, left, right): if right <= left: return @@ -20,12 +18,7 @@ def quick_sort_3partition(sorting, left, right): quick_sort_3partition(sorting, b + 1, right) if __name__ == '__main__': - try: - raw_input # Python 2 - except NameError: - raw_input = input # Python 3 - - user_input = raw_input('Enter numbers separated by a comma:\n').strip() + user_input = input('Enter numbers separated by a comma:\n').strip() unsorted = [ int(item) for item in user_input.split(',') ] quick_sort_3partition(unsorted,0,len(unsorted)-1) print(unsorted) diff --git a/sorts/random_normal_distribution_quicksort.py b/sorts/random_normal_distribution_quicksort.py index dfa37da61e26..39c54c46e263 100644 --- a/sorts/random_normal_distribution_quicksort.py +++ b/sorts/random_normal_distribution_quicksort.py @@ -1,25 +1,23 @@ -from __future__ import print_function from random import randint from tempfile import TemporaryFile import numpy as np - -def _inPlaceQuickSort(A,start,end): +def _inPlaceQuickSort(A,start,end): count = 0 if start Date: Tue, 20 Aug 2019 01:02:43 -0400 Subject: [PATCH 151/193] Fixing lgtm issue in basic graphs (#1141) * Added print function into matrix_multiplication_addition.py and removed blank space in data_structures/binary tree directory * Removed .vs/ folder per #893 * Rename matrix_multiplication_addition.py to matrix_operation.py * Fixing lgtm issue in basic_graphs per ##1024 * Fixed lgtm issue per @cclauss recommendation in #1024 --- graphs/basic_graphs.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/graphs/basic_graphs.py b/graphs/basic_graphs.py index 64c51e139cca..308abc0839fa 100644 --- a/graphs/basic_graphs.py +++ b/graphs/basic_graphs.py @@ -128,7 +128,9 @@ def dijk(G, s): from collections import deque -def topo(G, ind=None, Q=[1]): +def topo(G, ind=None, Q=None): + if Q is None: + Q = [1] if ind is None: ind = [0] * (len(G) + 1) # SInce oth Index is ignored for u in G: From 47cb394b5c9a42682803f09d41b3cbe9d1b09304 Mon Sep 17 00:00:00 2001 From: pathak-deep15 <44609019+pathak-deep15@users.noreply.github.com> Date: Thu, 22 Aug 2019 22:25:41 +0530 Subject: [PATCH 152/193] added doctests for compare_string and is_for_table (#1138) * added doctests for compare_string and is_for_table >>>compare_string('0010','0110') '0_10' >>> is_for_table('__1','011',2) True The above doctests were added * Update quine_mc_cluskey.py * Update quine_mc_cluskey.py * Update quine_mc_cluskey.py --- boolean_algebra/quine_mc_cluskey.py | 48 ++++++++++++++++++++--------- 1 file changed, 33 insertions(+), 15 deletions(-) diff --git a/boolean_algebra/quine_mc_cluskey.py b/boolean_algebra/quine_mc_cluskey.py index 94319ca45482..b7ca8da437a3 100644 --- a/boolean_algebra/quine_mc_cluskey.py +++ b/boolean_algebra/quine_mc_cluskey.py @@ -1,19 +1,11 @@ -""" - doctests - - >>> decimal_to_binary(3,[1.5]) - ['0.00.01.5'] - - >>> check(['0.00.01.5']) - ['0.00.01.5'] - - >>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5']) - [[1]] - - >>> selection([[1]],['0.00.01.5']) - ['0.00.01.5'] -""" def compare_string(string1, string2): + """ + >>> compare_string('0010','0110') + '0_10' + + >>> compare_string('0110','1101') + -1 + """ l1 = list(string1); l2 = list(string2) count = 0 for i in range(len(l1)): @@ -26,6 +18,10 @@ def compare_string(string1, string2): return("".join(l1)) def check(binary): + """ + >>> check(['0.00.01.5']) + ['0.00.01.5'] + """ pi = [] while 1: check1 = ['$']*len(binary) @@ -45,6 +41,10 @@ def check(binary): binary = list(set(temp)) def decimal_to_binary(no_of_variable, minterms): + """ + >>> decimal_to_binary(3,[1.5]) + ['0.00.01.5'] + """ temp = [] s = '' for m in minterms: @@ -56,6 +56,13 @@ def decimal_to_binary(no_of_variable, minterms): return temp def is_for_table(string1, string2, count): + """ + >>> is_for_table('__1','011',2) + True + + >>> is_for_table('01_','001',1) + False + """ l1 = list(string1);l2=list(string2) count_n = 0 for i in range(len(l1)): @@ -67,6 +74,13 @@ def is_for_table(string1, string2, count): return False def selection(chart, prime_implicants): + """ + >>> selection([[1]],['0.00.01.5']) + ['0.00.01.5'] + + >>> selection([[1]],['0.00.01.5']) + ['0.00.01.5'] + """ temp = [] select = [0]*len(chart) for i in range(len(chart[0])): @@ -104,6 +118,10 @@ def selection(chart, prime_implicants): chart[j][i] = 0 def prime_implicant_chart(prime_implicants, binary): + """ + >>> prime_implicant_chart(['0.00.01.5'],['0.00.01.5']) + [[1]] + """ chart = [[0 for x in range(len(binary))] for x in range(len(prime_implicants))] for i in range(len(prime_implicants)): count = prime_implicants[i].count('_') From e694e596a3fc42ae5f28f0e267c44f74948e25ee Mon Sep 17 00:00:00 2001 From: Nishant Aklecha <31594715+Naklecha@users.noreply.github.com> Date: Sun, 25 Aug 2019 17:44:17 +0530 Subject: [PATCH 153/193] Added a few doctests for traversals (#1149) --- traversals/binary_tree_traversals.py | 116 +++++++++++++++++++++++++++ 1 file changed, 116 insertions(+) diff --git a/traversals/binary_tree_traversals.py b/traversals/binary_tree_traversals.py index 7fd9f7111844..389311a7cfde 100644 --- a/traversals/binary_tree_traversals.py +++ b/traversals/binary_tree_traversals.py @@ -39,6 +39,20 @@ def build_tree(): def pre_order(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> pre_order(root) + 1 2 4 5 3 6 7 + """ if not isinstance(node, TreeNode) or not node: return print(node.data, end=" ") @@ -47,6 +61,20 @@ def pre_order(node: TreeNode) -> None: def in_order(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> in_order(root) + 4 2 5 1 6 3 7 + """ if not isinstance(node, TreeNode) or not node: return in_order(node.left) @@ -55,6 +83,20 @@ def in_order(node: TreeNode) -> None: def post_order(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> post_order(root) + 4 5 2 6 7 3 1 + """ if not isinstance(node, TreeNode) or not node: return post_order(node.left) @@ -63,6 +105,20 @@ def post_order(node: TreeNode) -> None: def level_order(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> level_order(root) + 1 2 3 4 5 6 7 + """ if not isinstance(node, TreeNode) or not node: return q: queue.Queue = queue.Queue() @@ -77,6 +133,22 @@ def level_order(node: TreeNode) -> None: def level_order_actual(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> level_order_actual(root) + 1 + 2 3 + 4 5 6 7 + """ if not isinstance(node, TreeNode) or not node: return q: queue.Queue = queue.Queue() @@ -97,6 +169,20 @@ def level_order_actual(node: TreeNode) -> None: # iteration version def pre_order_iter(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> pre_order_iter(root) + 1 2 4 5 3 6 7 + """ if not isinstance(node, TreeNode) or not node: return stack: List[TreeNode] = [] @@ -113,6 +199,20 @@ def pre_order_iter(node: TreeNode) -> None: def in_order_iter(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> in_order_iter(root) + 4 2 5 1 6 3 7 + """ if not isinstance(node, TreeNode) or not node: return stack: List[TreeNode] = [] @@ -127,6 +227,20 @@ def in_order_iter(node: TreeNode) -> None: def post_order_iter(node: TreeNode) -> None: + """ + >>> root = TreeNode(1) + >>> tree_node2 = TreeNode(2) + >>> tree_node3 = TreeNode(3) + >>> tree_node4 = TreeNode(4) + >>> tree_node5 = TreeNode(5) + >>> tree_node6 = TreeNode(6) + >>> tree_node7 = TreeNode(7) + >>> root.left, root.right = tree_node2, tree_node3 + >>> tree_node2.left, tree_node2.right = tree_node4 , tree_node5 + >>> tree_node3.left, tree_node3.right = tree_node6 , tree_node7 + >>> post_order_iter(root) + 4 5 2 6 7 3 1 + """ if not isinstance(node, TreeNode) or not node: return stack1, stack2 = [], [] @@ -151,6 +265,8 @@ def prompt(s: str = "", width=50, char="*") -> str: if __name__ == "__main__": + import doctest + doctest.testmod() print(prompt("Binary Tree Traversals")) node = build_tree() From 2f8516e561dc07571e48044f75a942db05068fa9 Mon Sep 17 00:00:00 2001 From: Riemann <40825655+anand372@users.noreply.github.com> Date: Wed, 28 Aug 2019 16:26:43 +0530 Subject: [PATCH 154/193] implementation of sorted vector machines (#1156) * svm.py for issue #840 I would like to add the Support Vector Machine algorithm implemented in Python 3.6.7 Requirements: - sklearn * update svm.py * update svm.py * Update and renamed to sorted_vector_machines.py * Updated sorted_vector_machines.py --- machine_learning/sorted_vector_machines.py | 54 ++++++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 machine_learning/sorted_vector_machines.py diff --git a/machine_learning/sorted_vector_machines.py b/machine_learning/sorted_vector_machines.py new file mode 100644 index 000000000000..92fa814c998f --- /dev/null +++ b/machine_learning/sorted_vector_machines.py @@ -0,0 +1,54 @@ +from sklearn.datasets import load_iris +from sklearn import svm +from sklearn.model_selection import train_test_split +import doctest + +# different functions implementing different types of SVM's +def NuSVC(train_x, train_y): + svc_NuSVC = svm.NuSVC() + svc_NuSVC.fit(train_x, train_y) + return svc_NuSVC + + +def Linearsvc(train_x, train_y): + svc_linear = svm.LinearSVC() + svc_linear.fit(train_x, train_y) + return svc_linear + + +def SVC(train_x, train_y): + # svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=None) + # various parameters like "kernal","gamma","C" can effectively tuned for a given machine learning model. + SVC = svm.SVC(gamma="auto") + SVC.fit(train_x, train_y) + return SVC + + +def test(X_new): + """ + 3 test cases to be passed + an array containing the sepal length (cm), sepal width (cm),petal length (cm),petal width (cm) + based on which the target name will be predicted + >>> test([1,2,1,4]) + 'virginica' + >>> test([5, 2, 4, 1]) + 'versicolor' + >>> test([6,3,4,1]) + 'versicolor' + + """ + iris = load_iris() + # splitting the dataset to test and train + train_x, test_x, train_y, test_y = train_test_split( + iris["data"], iris["target"], random_state=4 + ) + # any of the 3 types of SVM can be used + # current_model=SVC(train_x, train_y) + # current_model=NuSVC(train_x, train_y) + current_model = Linearsvc(train_x, train_y) + prediction = current_model.predict([X_new]) + return iris["target_names"][prediction][0] + + +if __name__ == "__main__": + doctest.testmod() From 82a079c209bf7836baade1aa4e7a6e3a85b740a2 Mon Sep 17 00:00:00 2001 From: Harshil Date: Wed, 28 Aug 2019 17:16:12 +0200 Subject: [PATCH 155/193] add .github/stale.yml (#1158) --- .github/stale.yml | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 .github/stale.yml diff --git a/.github/stale.yml b/.github/stale.yml new file mode 100644 index 000000000000..6af2a10216b8 --- /dev/null +++ b/.github/stale.yml @@ -0,0 +1,18 @@ +# Number of days of inactivity before an issue becomes stale +daysUntilStale: 14 +# Number of days of inactivity before a stale issue is closed +daysUntilClose: 7 +# Issues with these labels will never be considered stale +exemptLabels: + - bug + - help wanted + - OK to merge +# Label to use when marking an issue as stale +staleLabel: wontfix +# Comment to post when marking an issue as stale. Set to `false` to disable +markComment: > + This issue has been automatically marked as stale because it has not had + recent activity. It will be closed if no further activity occurs. Thank you + for your contributions. +# Comment to post when closing a stale issue. Set to `false` to disable +closeComment: false \ No newline at end of file From d327f107022a51f8ae75436d6dae20fda34622ee Mon Sep 17 00:00:00 2001 From: Rohit Gupta Date: Thu, 29 Aug 2019 00:53:42 +0530 Subject: [PATCH 156/193] Update stale.yml --- .github/stale.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/stale.yml b/.github/stale.yml index 6af2a10216b8..70032115fc2c 100644 --- a/.github/stale.yml +++ b/.github/stale.yml @@ -1,5 +1,5 @@ # Number of days of inactivity before an issue becomes stale -daysUntilStale: 14 +daysUntilStale: 30 # Number of days of inactivity before a stale issue is closed daysUntilClose: 7 # Issues with these labels will never be considered stale @@ -15,4 +15,4 @@ markComment: > recent activity. It will be closed if no further activity occurs. Thank you for your contributions. # Comment to post when closing a stale issue. Set to `false` to disable -closeComment: false \ No newline at end of file +closeComment: true From d4151bd5164d99a8184a4d842fe5e36305716dbc Mon Sep 17 00:00:00 2001 From: Rwithik Manoj Date: Sat, 31 Aug 2019 17:10:50 +0530 Subject: [PATCH 157/193] Fix possible error in longest_common_subsequence.py (#1163) The comparison at line 53 was not checking if (j > 0). --- dynamic_programming/longest_common_subsequence.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dynamic_programming/longest_common_subsequence.py b/dynamic_programming/longest_common_subsequence.py index 7447a0cc7810..d39485408988 100644 --- a/dynamic_programming/longest_common_subsequence.py +++ b/dynamic_programming/longest_common_subsequence.py @@ -50,7 +50,7 @@ def longest_common_subsequence(x: str, y: str): seq = "" i, j = m, n - while i > 0 and i > 0: + while i > 0 and j > 0: if x[i - 1] == y[j - 1]: match = 1 else: From d567a9eb8c1bf00e7bc5a007f21e3c977d7f83ec Mon Sep 17 00:00:00 2001 From: b63 <52578583+b63@users.noreply.github.com> Date: Sun, 1 Sep 2019 01:07:31 -0500 Subject: [PATCH 158/193] solution to problem 551 from project euler (#1164) * solution to problem 551 from project euler * renamed variables, and added more comments to improve readabilty --- project_euler/problem_551/__init__.py | 0 project_euler/problem_551/sol1.py | 204 ++++++++++++++++++++++++++ 2 files changed, 204 insertions(+) create mode 100644 project_euler/problem_551/__init__.py create mode 100644 project_euler/problem_551/sol1.py diff --git a/project_euler/problem_551/__init__.py b/project_euler/problem_551/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_551/sol1.py b/project_euler/problem_551/sol1.py new file mode 100644 index 000000000000..238d7b772190 --- /dev/null +++ b/project_euler/problem_551/sol1.py @@ -0,0 +1,204 @@ +""" +Sum of digits sequence +Problem 551 + +Let a(0), a(1),... be an interger sequence defined by: + a(0) = 1 + for n >= 1, a(n) is the sum of the digits of all preceding terms + +The sequence starts with 1, 1, 2, 4, 8, ... +You are given a(10^6) = 31054319. + +Find a(10^15) +""" + +ks = [k for k in range(2, 20+1)] +base = [10 ** k for k in range(ks[-1] + 1)] +memo = {} + + +def next_term(a_i, k, i, n): + """ + Calculates and updates a_i in-place to either the n-th term or the + smallest term for which c > 10^k when the terms are written in the form: + a(i) = b * 10^k + c + + For any a(i), if digitsum(b) and c have the same value, the difference + between subsequent terms will be the same until c >= 10^k. This difference + is cached to greatly speed up the computation. + + Arguments: + a_i -- array of digits starting from the one's place that represent + the i-th term in the sequence + k -- k when terms are written in the from a(i) = b*10^k + c. + Term are calulcated until c > 10^k or the n-th term is reached. + i -- position along the sequence + n -- term to caluclate up to if k is large enough + + Return: a tuple of difference between ending term and starting term, and + the number of terms calculated. ex. if starting term is a_0=1, and + ending term is a_10=62, then (61, 9) is returned. + """ + # ds_b - digitsum(b) + ds_b = 0 + for j in range(k, len(a_i)): + ds_b += a_i[j] + c = 0 + for j in range(min(len(a_i), k)): + c += a_i[j] * base[j] + + diff, dn = 0, 0 + max_dn = n - i + + sub_memo = memo.get(ds_b) + + if sub_memo != None: + jumps = sub_memo.get(c) + + if jumps != None and len(jumps) > 0: + # find and make the largest jump without going over + max_jump = -1 + for _k in range(len(jumps) - 1, -1, -1): + if jumps[_k][2] <= k and jumps[_k][1] <= max_dn: + max_jump = _k + break + + if max_jump >= 0: + diff, dn, _kk = jumps[max_jump] + # since the difference between jumps is cached, add c + new_c = diff + c + for j in range(min(k, len(a_i))): + new_c, a_i[j] = divmod(new_c, 10) + if new_c > 0: + add(a_i, k, new_c) + + else: + sub_memo[c] = [] + else: + sub_memo = {c: []} + memo[ds_b] = sub_memo + + if dn >= max_dn or c + diff >= base[k]: + return diff, dn + + if k > ks[0]: + while True: + # keep doing smaller jumps + _diff, terms_jumped = next_term(a_i, k - 1, i + dn, n) + diff += _diff + dn += terms_jumped + + if dn >= max_dn or c + diff >= base[k]: + break + else: + # would be too small a jump, just compute sequential terms instead + _diff, terms_jumped = compute(a_i, k, i + dn, n) + diff += _diff + dn += terms_jumped + + jumps = sub_memo[c] + + # keep jumps sorted by # of terms skipped + j = 0 + while j < len(jumps): + if jumps[j][1] > dn: + break + j += 1 + + # cache the jump for this value digitsum(b) and c + sub_memo[c].insert(j, (diff, dn, k)) + return (diff, dn) + + +def compute(a_i, k, i, n): + """ + same as next_term(a_i, k, i, n) but computes terms without memoizing results. + """ + if i >= n: + return 0, i + if k > len(a_i): + a_i.extend([0 for _ in range(k - len(a_i))]) + + # note: a_i -> b * 10^k + c + # ds_b -> digitsum(b) + # ds_c -> digitsum(c) + start_i = i + ds_b, ds_c, diff = 0, 0, 0 + for j in range(len(a_i)): + if j >= k: + ds_b += a_i[j] + else: + ds_c += a_i[j] + + while i < n: + i += 1 + addend = ds_c + ds_b + diff += addend + ds_c = 0 + for j in range(k): + s = a_i[j] + addend + addend, a_i[j] = divmod(s, 10) + + ds_c += a_i[j] + + if addend > 0: + break + + if addend > 0: + add(a_i, k, addend) + return diff, i - start_i + + +def add(digits, k, addend): + """ + adds addend to digit array given in digits + starting at index k + """ + for j in range(k, len(digits)): + s = digits[j] + addend + if s >= 10: + quotient, digits[j] = divmod(s, 10) + addend = addend // 10 + quotient + else: + digits[j] = s + addend = addend // 10 + + if addend == 0: + break + + while addend > 0: + addend, digit = divmod(addend, 10) + digits.append(digit) + + +def solution(n): + """ + returns n-th term of sequence + + >>> solution(10) + 62 + + >>> solution(10**6) + 31054319 + + >>> solution(10**15) + 73597483551591773 + """ + + digits = [1] + i = 1 + dn = 0 + while True: + diff, terms_jumped = next_term(digits, 20, i + dn, n) + dn += terms_jumped + if dn == n - i: + break + + a_n = 0 + for j in range(len(digits)): + a_n += digits[j] * 10 ** j + return a_n + + +if __name__ == "__main__": + print(solution(10 ** 15)) From 9492e7af7cb24bb2fbe9e814c2e96dde96e95909 Mon Sep 17 00:00:00 2001 From: McDic Date: Tue, 3 Sep 2019 16:02:53 +0900 Subject: [PATCH 159/193] Created Sherman Morrison method (#1162) * Created Sherman Morrison * Added docstring for class * Updated Sherman morrison 1. Added docstring tests 2. Tweaked __str__() using join 3. Added __repr__() 4. Changed index validation to be independent method * Applied cclauss's point 1. Reduced line length for __str__() 2. Removed parens for assert --- matrix/sherman_morrison.py | 255 +++++++++++++++++++++++++++++++++++++ 1 file changed, 255 insertions(+) create mode 100644 matrix/sherman_morrison.py diff --git a/matrix/sherman_morrison.py b/matrix/sherman_morrison.py new file mode 100644 index 000000000000..0d49d78509be --- /dev/null +++ b/matrix/sherman_morrison.py @@ -0,0 +1,255 @@ +class Matrix: + """ + + Matrix structure. + """ + + def __init__(self, row: int, column: int, default_value: float = 0): + """ + + Initialize matrix with given size and default value. + + Example: + >>> a = Matrix(2, 3, 1) + >>> a + Matrix consist of 2 rows and 3 columns + [1, 1, 1] + [1, 1, 1] + """ + + self.row, self.column = row, column + self.array = [[default_value for c in range(column)] for r in range(row)] + + def __str__(self): + """ + + Return string representation of this matrix. + """ + + # Prefix + s = "Matrix consist of %d rows and %d columns\n" % (self.row, self.column) + + # Make string identifier + max_element_length = 0 + for row_vector in self.array: + for obj in row_vector: + max_element_length = max(max_element_length, len(str(obj))) + string_format_identifier = "%%%ds" % (max_element_length,) + + # Make string and return + def single_line(row_vector): + nonlocal string_format_identifier + line = "[" + line += ", ".join(string_format_identifier % (obj,) for obj in row_vector) + line += "]" + return line + s += "\n".join(single_line(row_vector) for row_vector in self.array) + return s + + def __repr__(self): return str(self) + + def validateIndices(self, loc: tuple): + """ + + Check if given indices are valid to pick element from matrix. + + Example: + >>> a = Matrix(2, 6, 0) + >>> a.validateIndices((2, 7)) + False + >>> a.validateIndices((0, 0)) + True + """ + if not(isinstance(loc, (list, tuple)) and len(loc) == 2): return False + elif not(0 <= loc[0] < self.row and 0 <= loc[1] < self.column): return False + else: return True + + def __getitem__(self, loc: tuple): + """ + + Return array[row][column] where loc = (row, column). + + Example: + >>> a = Matrix(3, 2, 7) + >>> a[1, 0] + 7 + """ + assert self.validateIndices(loc) + return self.array[loc[0]][loc[1]] + + def __setitem__(self, loc: tuple, value: float): + """ + + Set array[row][column] = value where loc = (row, column). + + Example: + >>> a = Matrix(2, 3, 1) + >>> a[1, 2] = 51 + >>> a + Matrix consist of 2 rows and 3 columns + [ 1, 1, 1] + [ 1, 1, 51] + """ + assert self.validateIndices(loc) + self.array[loc[0]][loc[1]] = value + + def __add__(self, another): + """ + + Return self + another. + + Example: + >>> a = Matrix(2, 1, -4) + >>> b = Matrix(2, 1, 3) + >>> a+b + Matrix consist of 2 rows and 1 columns + [-1] + [-1] + """ + + # Validation + assert isinstance(another, Matrix) + assert self.row == another.row and self.column == another.column + + # Add + result = Matrix(self.row, self.column) + for r in range(self.row): + for c in range(self.column): + result[r,c] = self[r,c] + another[r,c] + return result + + def __neg__(self): + """ + + Return -self. + + Example: + >>> a = Matrix(2, 2, 3) + >>> a[0, 1] = a[1, 0] = -2 + >>> -a + Matrix consist of 2 rows and 2 columns + [-3, 2] + [ 2, -3] + """ + + result = Matrix(self.row, self.column) + for r in range(self.row): + for c in range(self.column): + result[r,c] = -self[r,c] + return result + + def __sub__(self, another): return self + (-another) + + def __mul__(self, another): + """ + + Return self * another. + + Example: + >>> a = Matrix(2, 3, 1) + >>> a[0,2] = a[1,2] = 3 + >>> a * -2 + Matrix consist of 2 rows and 3 columns + [-2, -2, -6] + [-2, -2, -6] + """ + + if isinstance(another, (int, float)): # Scalar multiplication + result = Matrix(self.row, self.column) + for r in range(self.row): + for c in range(self.column): + result[r,c] = self[r,c] * another + return result + elif isinstance(another, Matrix): # Matrix multiplication + assert(self.column == another.row) + result = Matrix(self.row, another.column) + for r in range(self.row): + for c in range(another.column): + for i in range(self.column): + result[r,c] += self[r,i] * another[i,c] + return result + else: raise TypeError("Unsupported type given for another (%s)" % (type(another),)) + + def transpose(self): + """ + + Return self^T. + + Example: + >>> a = Matrix(2, 3) + >>> for r in range(2): + ... for c in range(3): + ... a[r,c] = r*c + ... + >>> a.transpose() + Matrix consist of 3 rows and 2 columns + [0, 0] + [0, 1] + [0, 2] + """ + + result = Matrix(self.column, self.row) + for r in range(self.row): + for c in range(self.column): + result[c,r] = self[r,c] + return result + + def ShermanMorrison(self, u, v): + """ + + Apply Sherman-Morrison formula in O(n^2). + To learn this formula, please look this: https://en.wikipedia.org/wiki/Sherman%E2%80%93Morrison_formula + This method returns (A + uv^T)^(-1) where A^(-1) is self. Returns None if it's impossible to calculate. + Warning: This method doesn't check if self is invertible. + Make sure self is invertible before execute this method. + + Example: + >>> ainv = Matrix(3, 3, 0) + >>> for i in range(3): ainv[i,i] = 1 + ... + >>> u = Matrix(3, 1, 0) + >>> u[0,0], u[1,0], u[2,0] = 1, 2, -3 + >>> v = Matrix(3, 1, 0) + >>> v[0,0], v[1,0], v[2,0] = 4, -2, 5 + >>> ainv.ShermanMorrison(u, v) + Matrix consist of 3 rows and 3 columns + [ 1.2857142857142856, -0.14285714285714285, 0.3571428571428571] + [ 0.5714285714285714, 0.7142857142857143, 0.7142857142857142] + [ -0.8571428571428571, 0.42857142857142855, -0.0714285714285714] + """ + + # Size validation + assert isinstance(u, Matrix) and isinstance(v, Matrix) + assert self.row == self.column == u.row == v.row # u, v should be column vector + assert u.column == v.column == 1 # u, v should be column vector + + # Calculate + vT = v.transpose() + numerator_factor = (vT * self * u)[0, 0] + 1 + if numerator_factor == 0: return None # It's not invertable + return self - ((self * u) * (vT * self) * (1.0 / numerator_factor)) + +# Testing +if __name__ == "__main__": + + def test1(): + # a^(-1) + ainv = Matrix(3, 3, 0) + for i in range(3): ainv[i,i] = 1 + print("a^(-1) is %s" % (ainv,)) + # u, v + u = Matrix(3, 1, 0) + u[0,0], u[1,0], u[2,0] = 1, 2, -3 + v = Matrix(3, 1, 0) + v[0,0], v[1,0], v[2,0] = 4, -2, 5 + print("u is %s" % (u,)) + print("v is %s" % (v,)) + print("uv^T is %s" % (u * v.transpose())) + # Sherman Morrison + print("(a + uv^T)^(-1) is %s" % (ainv.ShermanMorrison(u, v),)) + + def test2(): + import doctest + doctest.testmod() + + test2() \ No newline at end of file From a4ed40be86e79375ea54421fda9d860a3e0526e2 Mon Sep 17 00:00:00 2001 From: Maxwell Aladago Date: Wed, 4 Sep 2019 16:06:44 -0400 Subject: [PATCH 160/193] changing typo (#1168) --- divide_and_conquer/convex_hull.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/divide_and_conquer/convex_hull.py b/divide_and_conquer/convex_hull.py index 42219794aed1..a0c319e766da 100644 --- a/divide_and_conquer/convex_hull.py +++ b/divide_and_conquer/convex_hull.py @@ -7,7 +7,7 @@ Two algorithms have been implemented for the convex hull problem here. 1. A brute-force algorithm which runs in O(n^3) -2. A divide-and-conquer algorithm which runs in O(n^3) +2. A divide-and-conquer algorithm which runs in O(n log(n)) There are other several other algorithms for the convex hull problem which have not been implemented here, yet. From f31a812c468e41c3f5f7f170ae1dd5fa13bae6dd Mon Sep 17 00:00:00 2001 From: KirilBangachev <51961981+KirilBangachev@users.noreply.github.com> Date: Thu, 5 Sep 2019 08:58:38 +0300 Subject: [PATCH 161/193] Add Binomial Heap (#1146) * Binomial Heap Implementation of Binomial Heap. Reference: Advanced Data Structures, Peter Brass * Update binomial_heap.py * Update binomial_heap.py * Update binomial_heap.py - Fuller documentation of binomial heap - Update unit tests - Replace printing method by overwriting __str__() * Update binomial_heap.py - Added more tests - Added to the documentation - Stylistic editing - mergeHeaps now also returns a reference to the merged heap - added a preOrder function that returns a list with the preorder of the heap * Update binomial_heap.py Changed the unit tests structure * Turned the tests into doctests --- data_structures/heap/binomial_heap.py | 442 ++++++++++++++++++++++++++ 1 file changed, 442 insertions(+) create mode 100644 data_structures/heap/binomial_heap.py diff --git a/data_structures/heap/binomial_heap.py b/data_structures/heap/binomial_heap.py new file mode 100644 index 000000000000..bc9cb5145f2e --- /dev/null +++ b/data_structures/heap/binomial_heap.py @@ -0,0 +1,442 @@ +""" + Binomial Heap + + Reference: Advanced Data Structures, Peter Brass +""" + + +class Node: + """ + Node in a doubly-linked binomial tree, containing: + - value + - size of left subtree + - link to left, right and parent nodes + """ + + def __init__(self, val): + self.val = val + # Number of nodes in left subtree + self.left_tree_size = 0 + self.left = None + self.right = None + self.parent = None + + def mergeTrees(self, other): + """ + In-place merge of two binomial trees of equal size. + Returns the root of the resulting tree + """ + assert ( + self.left_tree_size == other.left_tree_size + ), "Unequal Sizes of Blocks" + + if self.val < other.val: + other.left = self.right + other.parent = None + if self.right: + self.right.parent = other + self.right = other + self.left_tree_size = ( + self.left_tree_size * 2 + 1 + ) + return self + else: + self.left = other.right + self.parent = None + if other.right: + other.right.parent = self + other.right = self + other.left_tree_size = ( + other.left_tree_size * 2 + 1 + ) + return other + + +class BinomialHeap: + """ + Min-oriented priority queue implemented with the Binomial Heap data + structure implemented with the BinomialHeap class. It supports: + + - Insert element in a heap with n elemnts: Guaranteed logn, amoratized 1 + - Merge (meld) heaps of size m and n: O(logn + logm) + - Delete Min: O(logn) + - Peek (return min without deleting it): O(1) + + Example: + + Create a random permutation of 30 integers to be inserted and + 19 of them deleted + >>> import numpy as np + >>> permutation = np.random.permutation(list(range(30))) + + Create a Heap and insert the 30 integers + + __init__() test + >>> first_heap = BinomialHeap() + + 30 inserts - insert() test + >>> for number in permutation: + ... first_heap.insert(number) + + Size test + >>> print(first_heap.size) + 30 + + Deleting - delete() test + >>> for i in range(25): + ... print(first_heap.deleteMin(), end=" ") + 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 + + Create a new Heap + >>> second_heap = BinomialHeap() + >>> vals = [17, 20, 31, 34] + >>> for value in vals: + ... second_heap.insert(value) + + + The heap should have the following structure: + + 17 + / \ + # 31 + / \ + 20 34 + / \ / \ + # # # # + + preOrder() test + >>> print(second_heap.preOrder()) + [(17, 0), ('#', 1), (31, 1), (20, 2), ('#', 3), ('#', 3), (34, 2), ('#', 3), ('#', 3)] + + printing Heap - __str__() test + >>> print(second_heap) + 17 + -# + -31 + --20 + ---# + ---# + --34 + ---# + ---# + + mergeHeaps() test + >>> merged = second_heap.mergeHeaps(first_heap) + >>> merged.peek() + 17 + + values in merged heap; (merge is inplace) + >>> while not first_heap.isEmpty(): + ... print(first_heap.deleteMin(), end=" ") + 17 20 25 26 27 28 29 31 34 + + """ + + def __init__( + self, bottom_root=None, min_node=None, heap_size=0 + ): + self.size = heap_size + self.bottom_root = bottom_root + self.min_node = min_node + + def mergeHeaps(self, other): + """ + In-place merge of two binomial heaps. + Both of them become the resulting merged heap + """ + + # Empty heaps corner cases + if other.size == 0: + return + if self.size == 0: + self.size = other.size + self.bottom_root = other.bottom_root + self.min_node = other.min_node + return + # Update size + self.size = self.size + other.size + + # Update min.node + if self.min_node.val > other.min_node.val: + self.min_node = other.min_node + # Merge + + # Order roots by left_subtree_size + combined_roots_list = [] + i, j = self.bottom_root, other.bottom_root + while i or j: + if i and ( + (not j) + or i.left_tree_size < j.left_tree_size + ): + combined_roots_list.append((i, True)) + i = i.parent + else: + combined_roots_list.append((j, False)) + j = j.parent + # Insert links between them + for i in range(len(combined_roots_list) - 1): + if ( + combined_roots_list[i][1] + != combined_roots_list[i + 1][1] + ): + combined_roots_list[i][ + 0 + ].parent = combined_roots_list[i + 1][0] + combined_roots_list[i + 1][ + 0 + ].left = combined_roots_list[i][0] + # Consecutively merge roots with same left_tree_size + i = combined_roots_list[0][0] + while i.parent: + if ( + ( + i.left_tree_size + == i.parent.left_tree_size + ) + and (not i.parent.parent) + ) or ( + i.left_tree_size == i.parent.left_tree_size + and i.left_tree_size + != i.parent.parent.left_tree_size + ): + + # Neighbouring Nodes + previous_node = i.left + next_node = i.parent.parent + + # Merging trees + i = i.mergeTrees(i.parent) + + # Updating links + i.left = previous_node + i.parent = next_node + if previous_node: + previous_node.parent = i + if next_node: + next_node.left = i + else: + i = i.parent + # Updating self.bottom_root + while i.left: + i = i.left + self.bottom_root = i + + # Update other + other.size = self.size + other.bottom_root = self.bottom_root + other.min_node = self.min_node + + # Return the merged heap + return self + + def insert(self, val): + """ + insert a value in the heap + """ + if self.size == 0: + self.bottom_root = Node(val) + self.size = 1 + self.min_node = self.bottom_root + else: + # Create new node + new_node = Node(val) + + # Update size + self.size += 1 + + # update min_node + if val < self.min_node.val: + self.min_node = new_node + # Put new_node as a bottom_root in heap + self.bottom_root.left = new_node + new_node.parent = self.bottom_root + self.bottom_root = new_node + + # Consecutively merge roots with same left_tree_size + while ( + self.bottom_root.parent + and self.bottom_root.left_tree_size + == self.bottom_root.parent.left_tree_size + ): + + # Next node + next_node = self.bottom_root.parent.parent + + # Merge + self.bottom_root = self.bottom_root.mergeTrees( + self.bottom_root.parent + ) + + # Update Links + self.bottom_root.parent = next_node + self.bottom_root.left = None + if next_node: + next_node.left = self.bottom_root + + def peek(self): + """ + return min element without deleting it + """ + return self.min_node.val + + def isEmpty(self): + return self.size == 0 + + def deleteMin(self): + """ + delete min element and return it + """ + # assert not self.isEmpty(), "Empty Heap" + + # Save minimal value + min_value = self.min_node.val + + # Last element in heap corner case + if self.size == 1: + # Update size + self.size = 0 + + # Update bottom root + self.bottom_root = None + + # Update min_node + self.min_node = None + + return min_value + # No right subtree corner case + # The structure of the tree implies that this should be the bottom root + # and there is at least one other root + if self.min_node.right == None: + # Update size + self.size -= 1 + + # Update bottom root + self.bottom_root = self.bottom_root.parent + self.bottom_root.left = None + + # Update min_node + self.min_node = self.bottom_root + i = self.bottom_root.parent + while i: + if i.val < self.min_node.val: + self.min_node = i + i = i.parent + return min_value + # General case + # Find the BinomialHeap of the right subtree of min_node + bottom_of_new = self.min_node.right + bottom_of_new.parent = None + min_of_new = bottom_of_new + size_of_new = 1 + + # Size, min_node and bottom_root + while bottom_of_new.left: + size_of_new = size_of_new * 2 + 1 + bottom_of_new = bottom_of_new.left + if bottom_of_new.val < min_of_new.val: + min_of_new = bottom_of_new + # Corner case of single root on top left path + if (not self.min_node.left) and ( + not self.min_node.parent + ): + self.size = size_of_new + self.bottom_root = bottom_of_new + self.min_node = min_of_new + # print("Single root, multiple nodes case") + return min_value + # Remaining cases + # Construct heap of right subtree + newHeap = BinomialHeap( + bottom_root=bottom_of_new, + min_node=min_of_new, + heap_size=size_of_new, + ) + + # Update size + self.size = self.size - 1 - size_of_new + + # Neighbour nodes + previous_node = self.min_node.left + next_node = self.min_node.parent + + # Initialize new bottom_root and min_node + self.min_node = previous_node or next_node + self.bottom_root = next_node + + # Update links of previous_node and search below for new min_node and + # bottom_root + if previous_node: + previous_node.parent = next_node + + # Update bottom_root and search for min_node below + self.bottom_root = previous_node + self.min_node = previous_node + while self.bottom_root.left: + self.bottom_root = self.bottom_root.left + if self.bottom_root.val < self.min_node.val: + self.min_node = self.bottom_root + if next_node: + next_node.left = previous_node + + # Search for new min_node above min_node + i = next_node + while i: + if i.val < self.min_node.val: + self.min_node = i + i = i.parent + # Merge heaps + self.mergeHeaps(newHeap) + + return min_value + + def preOrder(self): + """ + Returns the Pre-order representation of the heap including + values of nodes plus their level distance from the root; + Empty nodes appear as # + """ + # Find top root + top_root = self.bottom_root + while top_root.parent: + top_root = top_root.parent + # preorder + heap_preOrder = [] + self.__traversal(top_root, heap_preOrder) + return heap_preOrder + + def __traversal(self, curr_node, preorder, level=0): + """ + Pre-order traversal of nodes + """ + if curr_node: + preorder.append((curr_node.val, level)) + self.__traversal( + curr_node.left, preorder, level + 1 + ) + self.__traversal( + curr_node.right, preorder, level + 1 + ) + else: + preorder.append(("#", level)) + + def __str__(self): + """ + Overwriting str for a pre-order print of nodes in heap; + Performance is poor, so use only for small examples + """ + if self.isEmpty(): + return "" + preorder_heap = self.preOrder() + + return "\n".join( + ("-" * level + str(value)) + for value, level in preorder_heap + ) + + +# Unit Tests +if __name__ == "__main__": + import doctest + + doctest.testmod() From 2dfe01e4d8e4e84fbe3b04da08e5dfd18e17410a Mon Sep 17 00:00:00 2001 From: Maxwell Aladago Date: Thu, 5 Sep 2019 02:22:06 -0400 Subject: [PATCH 162/193] Fully refactored the rod cutting module. (#1169) * changing typo * fully refactored the rod-cutting module * more documentations * rewording --- dynamic_programming/rod_cutting.py | 230 +++++++++++++++++++++++------ 1 file changed, 183 insertions(+), 47 deletions(-) diff --git a/dynamic_programming/rod_cutting.py b/dynamic_programming/rod_cutting.py index c3111dcfc8a1..5b52eaca7c89 100644 --- a/dynamic_programming/rod_cutting.py +++ b/dynamic_programming/rod_cutting.py @@ -1,57 +1,193 @@ -from typing import List +""" +This module provides two implementations for the rod-cutting problem: +1. A naive recursive implementation which has an exponential runtime +2. Two dynamic programming implementations which have quadratic runtime -def rod_cutting(prices: List[int],length: int) -> int: +The rod-cutting problem is the problem of finding the maximum possible revenue +obtainable from a rod of length ``n`` given a list of prices for each integral piece +of the rod. The maximum revenue can thus be obtained by cutting the rod and selling the +pieces separately or not cutting it at all if the price of it is the maximum obtainable. + +""" + + +def naive_cut_rod_recursive(n: int, prices: list): + """ + Solves the rod-cutting problem via naively without using the benefit of dynamic programming. + The results is the same sub-problems are solved several times leading to an exponential runtime + + Runtime: O(2^n) + + Arguments + ------- + n: int, the length of the rod + prices: list, the prices for each piece of rod. ``p[i-i]`` is the + price for a rod of length ``i`` + + Returns + ------- + The maximum revenue obtainable for a rod of length n given the list of prices for each piece. + + Examples + -------- + >>> naive_cut_rod_recursive(4, [1, 5, 8, 9]) + 10 + >>> naive_cut_rod_recursive(10, [1, 5, 8, 9, 10, 17, 17, 20, 24, 30]) + 30 + """ + + _enforce_args(n, prices) + if n == 0: + return 0 + max_revue = float("-inf") + for i in range(1, n + 1): + max_revue = max(max_revue, prices[i - 1] + naive_cut_rod_recursive(n - i, prices)) + + return max_revue + + +def top_down_cut_rod(n: int, prices: list): """ - Given a rod of length n and array of prices that indicate price at each length. - Determine the maximum value obtainable by cutting up the rod and selling the pieces - - >>> rod_cutting([1,5,8,9],4) + Constructs a top-down dynamic programming solution for the rod-cutting problem + via memoization. This function serves as a wrapper for _top_down_cut_rod_recursive + + Runtime: O(n^2) + + Arguments + -------- + n: int, the length of the rod + prices: list, the prices for each piece of rod. ``p[i-i]`` is the + price for a rod of length ``i`` + + Note + ---- + For convenience and because Python's lists using 0-indexing, length(max_rev) = n + 1, + to accommodate for the revenue obtainable from a rod of length 0. + + Returns + ------- + The maximum revenue obtainable for a rod of length n given the list of prices for each piece. + + Examples + ------- + >>> top_down_cut_rod(4, [1, 5, 8, 9]) 10 - >>> rod_cutting([1,1,1],3) - 3 - >>> rod_cutting([1,2,3], -1) - Traceback (most recent call last): - ValueError: Given integer must be greater than 1, not -1 - >>> rod_cutting([1,2,3], 3.2) - Traceback (most recent call last): - TypeError: Must be int, not float - >>> rod_cutting([], 3) - Traceback (most recent call last): - AssertionError: prices list is shorted than length: 3 - - - - Args: - prices: list indicating price at each length, where prices[0] = 0 indicating rod of zero length has no value - length: length of rod - - Returns: - Maximum revenue attainable by cutting up the rod in any way. - """ - - prices.insert(0, 0) - if not isinstance(length, int): - raise TypeError('Must be int, not {0}'.format(type(length).__name__)) - if length < 0: - raise ValueError('Given integer must be greater than 1, not {0}'.format(length)) - assert len(prices) - 1 >= length, "prices list is shorted than length: {0}".format(length) - - return rod_cutting_recursive(prices, length) - -def rod_cutting_recursive(prices: List[int],length: int) -> int: - #base case - if length == 0: + >>> top_down_cut_rod(10, [1, 5, 8, 9, 10, 17, 17, 20, 24, 30]) + 30 + """ + _enforce_args(n, prices) + max_rev = [float("-inf") for _ in range(n + 1)] + return _top_down_cut_rod_recursive(n, prices, max_rev) + + +def _top_down_cut_rod_recursive(n: int, prices: list, max_rev: list): + """ + Constructs a top-down dynamic programming solution for the rod-cutting problem + via memoization. + + Runtime: O(n^2) + + Arguments + -------- + n: int, the length of the rod + prices: list, the prices for each piece of rod. ``p[i-i]`` is the + price for a rod of length ``i`` + max_rev: list, the computed maximum revenue for a piece of rod. + ``max_rev[i]`` is the maximum revenue obtainable for a rod of length ``i`` + + Returns + ------- + The maximum revenue obtainable for a rod of length n given the list of prices for each piece. + """ + if max_rev[n] >= 0: + return max_rev[n] + elif n == 0: return 0 - value = float('-inf') - for firstCutLocation in range(1,length+1): - value = max(value, prices[firstCutLocation]+rod_cutting_recursive(prices,length - firstCutLocation)) - return value + else: + max_revenue = float("-inf") + for i in range(1, n + 1): + max_revenue = max(max_revenue, prices[i - 1] + _top_down_cut_rod_recursive(n - i, prices, max_rev)) + + max_rev[n] = max_revenue + + return max_rev[n] + + +def bottom_up_cut_rod(n: int, prices: list): + """ + Constructs a bottom-up dynamic programming solution for the rod-cutting problem + + Runtime: O(n^2) + + Arguments + ---------- + n: int, the maximum length of the rod. + prices: list, the prices for each piece of rod. ``p[i-i]`` is the + price for a rod of length ``i`` + + Returns + ------- + The maximum revenue obtainable from cutting a rod of length n given + the prices for each piece of rod p. + + Examples + ------- + >>> bottom_up_cut_rod(4, [1, 5, 8, 9]) + 10 + >>> bottom_up_cut_rod(10, [1, 5, 8, 9, 10, 17, 17, 20, 24, 30]) + 30 + """ + _enforce_args(n, prices) + + # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of length 0. + max_rev = [float("-inf") for _ in range(n + 1)] + max_rev[0] = 0 + + for i in range(1, n + 1): + max_revenue_i = max_rev[i] + for j in range(1, i + 1): + max_revenue_i = max(max_revenue_i, prices[j - 1] + max_rev[i - j]) + + max_rev[i] = max_revenue_i + + return max_rev[n] + + +def _enforce_args(n: int, prices: list): + """ + Basic checks on the arguments to the rod-cutting algorithms + + n: int, the length of the rod + prices: list, the price list for each piece of rod. + + Throws ValueError: + + if n is negative or there are fewer items in the price list than the length of the rod + """ + if n < 0: + raise ValueError(f"n must be greater than or equal to 0. Got n = {n}") + + if n > len(prices): + raise ValueError(f"Each integral piece of rod must have a corresponding " + f"price. Got n = {n} but length of prices = {len(prices)}") def main(): - assert rod_cutting([1,5,8,9,10,17,17,20,24,30],10) == 30 - # print(rod_cutting([],0)) + prices = [6, 10, 12, 15, 20, 23] + n = len(prices) + + # the best revenue comes from cutting the rod into 6 pieces, each + # of length 1 resulting in a revenue of 6 * 6 = 36. + expected_max_revenue = 36 + + max_rev_top_down = top_down_cut_rod(n, prices) + max_rev_bottom_up = bottom_up_cut_rod(n, prices) + max_rev_naive = naive_cut_rod_recursive(n, prices) + + assert expected_max_revenue == max_rev_top_down + assert max_rev_top_down == max_rev_bottom_up + assert max_rev_bottom_up == max_rev_naive + if __name__ == '__main__': main() - From ab25079e168aa6de61bfd082af5a056b8ee50b43 Mon Sep 17 00:00:00 2001 From: Jai Kumar Dewani Date: Fri, 6 Sep 2019 14:32:37 +0530 Subject: [PATCH 163/193] Update DIRECTORY (#1161) * Update DIRECTORY * Updated DIRECTORY * Fixed bug in directory build and re-build the directory.md * fixed url issue * fixed indentation in Directory.md --- DIRECTORY.md | 622 +++++++--------------------------- scripts/build_directory_md.py | 6 +- 2 files changed, 117 insertions(+), 511 deletions(-) diff --git a/DIRECTORY.md b/DIRECTORY.md index 80bf64ef4c30..248fe7b9eaa6 100644 --- a/DIRECTORY.md +++ b/DIRECTORY.md @@ -1,805 +1,411 @@ ## Arithmetic Analysis - * [bisection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/bisection.py) - * [in static equilibrium](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/in_static_equilibrium.py) - * [intersection](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/intersection.py) - * [lu decomposition](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/lu_decomposition.py) - * [newton method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_method.py) - * [newton raphson method](https://github.com/TheAlgorithms/Python/blob/master/arithmetic_analysis/newton_raphson_method.py) - ## Backtracking - * [all combinations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_combinations.py) - * [all permutations](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_permutations.py) - * [all subsequences](https://github.com/TheAlgorithms/Python/blob/master/backtracking/all_subsequences.py) - * [minimax](https://github.com/TheAlgorithms/Python/blob/master/backtracking/minimax.py) - * [n queens](https://github.com/TheAlgorithms/Python/blob/master/backtracking/n_queens.py) - * [sudoku](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sudoku.py) - * [sum of subsets](https://github.com/TheAlgorithms/Python/blob/master/backtracking/sum_of_subsets.py) - ## Boolean Algebra - * [quine mc cluskey](https://github.com/TheAlgorithms/Python/blob/master/boolean_algebra/quine_mc_cluskey.py) - ## Ciphers - * [affine cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/affine_cipher.py) - * [atbash](https://github.com/TheAlgorithms/Python/blob/master/ciphers/atbash.py) - * [base16](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base16.py) - * [base32](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base32.py) - * [base64 cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base64_cipher.py) - * [base85](https://github.com/TheAlgorithms/Python/blob/master/ciphers/base85.py) - * [brute force caesar cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/brute_force_caesar_cipher.py) - * [caesar cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/caesar_cipher.py) - * [cryptomath module](https://github.com/TheAlgorithms/Python/blob/master/ciphers/cryptomath_module.py) - * [elgamal key generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/elgamal_key_generator.py) - * [hill cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/hill_cipher.py) - * [morse code implementation](https://github.com/TheAlgorithms/Python/blob/master/ciphers/morse_code_implementation.py) - * [onepad cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/onepad_cipher.py) - * [playfair cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/playfair_cipher.py) - * [rabin miller](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rabin_miller.py) - * [rot13](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rot13.py) - * [rsa cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_cipher.py) - * [rsa key generator](https://github.com/TheAlgorithms/Python/blob/master/ciphers/rsa_key_generator.py) - * [simple substitution cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/simple_substitution_cipher.py) - * [trafid cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/trafid_cipher.py) - * [transposition cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher.py) - * [transposition cipher encrypt decrypt file](https://github.com/TheAlgorithms/Python/blob/master/ciphers/transposition_cipher_encrypt_decrypt_file.py) - * [vigenere cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/vigenere_cipher.py) - * [xor cipher](https://github.com/TheAlgorithms/Python/blob/master/ciphers/xor_cipher.py) - ## Compression - * [burrows wheeler](https://github.com/TheAlgorithms/Python/blob/master/compression/burrows_wheeler.py) - * [huffman](https://github.com/TheAlgorithms/Python/blob/master/compression/huffman.py) - * [peak signal to noise ratio](https://github.com/TheAlgorithms/Python/blob/master/compression/peak_signal_to_noise_ratio.py) - ## Conversions - * [decimal to binary](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_binary.py) - * [decimal to hexadecimal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_hexadecimal.py) - * [decimal to octal](https://github.com/TheAlgorithms/Python/blob/master/conversions/decimal_to_octal.py) - ## Data Structures - * Binary Tree - - * [avl tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/avl_tree.py) - - * [basic binary tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/basic_binary_tree.py) - - * [binary search tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/binary_search_tree.py) - - * [fenwick tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/fenwick_tree.py) - - * [lazy segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lazy_segment_tree.py) - - * [lca](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/lca.py) - - * [red black tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/red_black_tree.py) - - * [segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/segment_tree.py) - - * [treap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_tree/treap.py) - + * [avl tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/avl_tree.py) + * [basic binary tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/basic_binary_tree.py) + * [binary search tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/binary_search_tree.py) + * [fenwick tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/fenwick_tree.py) + * [lazy segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/lazy_segment_tree.py) + * [lca](https://github.com/TheAlgorithms/Python/blob/master/data_structures/lca.py) + * [red black tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/red_black_tree.py) + * [segment tree](https://github.com/TheAlgorithms/Python/blob/master/data_structures/segment_tree.py) + * [treap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/treap.py) * Hashing - - * [double hash](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/double_hash.py) - - * [hash table](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table.py) - - * [hash table with linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/hash_table_with_linked_list.py) - - * Number Theory - - * [prime numbers](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/number_theory/prime_numbers.py) - - * [quadratic probing](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hashing/quadratic_probing.py) - + * [double hash](https://github.com/TheAlgorithms/Python/blob/master/data_structures/double_hash.py) + * [hash table](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hash_table.py) + * [hash table with linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/hash_table_with_linked_list.py) + * Number Theory + * [prime numbers](https://github.com/TheAlgorithms/Python/blob/master/data_structures/prime_numbers.py) + * [quadratic probing](https://github.com/TheAlgorithms/Python/blob/master/data_structures/quadratic_probing.py) * Heap - - * [heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap/heap.py) - + * [heap](https://github.com/TheAlgorithms/Python/blob/master/data_structures/heap.py) * Linked List - - * [doubly linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/doubly_linked_list.py) - - * [is palindrome](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/is_palindrome.py) - - * [singly linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/singly_linked_list.py) - - * [swap nodes](https://github.com/TheAlgorithms/Python/blob/master/data_structures/linked_list/swap_nodes.py) - + * [doubly linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/doubly_linked_list.py) + * [is palindrome](https://github.com/TheAlgorithms/Python/blob/master/data_structures/is_palindrome.py) + * [singly linked list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/singly_linked_list.py) + * [swap nodes](https://github.com/TheAlgorithms/Python/blob/master/data_structures/swap_nodes.py) * Queue - - * [double ended queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/double_ended_queue.py) - - * [queue on list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_list.py) - - * [queue on pseudo stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue/queue_on_pseudo_stack.py) - + * [double ended queue](https://github.com/TheAlgorithms/Python/blob/master/data_structures/double_ended_queue.py) + * [queue on list](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue_on_list.py) + * [queue on pseudo stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/queue_on_pseudo_stack.py) * Stacks - - * [balanced parentheses](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/balanced_parentheses.py) - - * [infix to postfix conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_postfix_conversion.py) - - * [infix to prefix conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/infix_to_prefix_conversion.py) - - * [next greater element](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/next_greater_element.py) - - * [postfix evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/postfix_evaluation.py) - - * [stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stack.py) - - * [stock span problem](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stacks/stock_span_problem.py) - + * [balanced parentheses](https://github.com/TheAlgorithms/Python/blob/master/data_structures/balanced_parentheses.py) + * [infix to postfix conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/infix_to_postfix_conversion.py) + * [infix to prefix conversion](https://github.com/TheAlgorithms/Python/blob/master/data_structures/infix_to_prefix_conversion.py) + * [next greater element](https://github.com/TheAlgorithms/Python/blob/master/data_structures/next_greater_element.py) + * [postfix evaluation](https://github.com/TheAlgorithms/Python/blob/master/data_structures/postfix_evaluation.py) + * [stack](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stack.py) + * [stock span problem](https://github.com/TheAlgorithms/Python/blob/master/data_structures/stock_span_problem.py) * Trie - - * [trie](https://github.com/TheAlgorithms/Python/blob/master/data_structures/trie/trie.py) - + * [trie](https://github.com/TheAlgorithms/Python/blob/master/data_structures/trie.py) ## Digital Image Processing - - * [change contrast](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_contrast.py) - + * [change contrast](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/change_contrast.py) * Edge Detection - - * [canny](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/edge_detection/canny.py) - + * [canny](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/canny.py) * Filters - - * [convolve](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/convolve.py) - - * [gaussian filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/gaussian_filter.py) - - * [median filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/median_filter.py) - - * [sobel filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/filters/sobel_filter.py) - - * [test digital image processing](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/test_digital_image_processing.py) - + * [convolve](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/convolve.py) + * [gaussian filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/gaussian_filter.py) + * [median filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/median_filter.py) + * [sobel filter](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/sobel_filter.py) + * [test digital image processing](https://github.com/TheAlgorithms/Python/blob/master/digital_image_processing/test_digital_image_processing.py) ## Divide And Conquer - * [closest pair of points](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/closest_pair_of_points.py) - + * [convex hull](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/convex_hull.py) + * [inversions](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/inversions.py) * [max subarray sum](https://github.com/TheAlgorithms/Python/blob/master/divide_and_conquer/max_subarray_sum.py) - ## Dynamic Programming - * [abbreviation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/abbreviation.py) - * [bitmask](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/bitmask.py) - * [climbing stairs](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/climbing_stairs.py) - * [coin change](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/coin_change.py) - * [edit distance](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/edit_distance.py) - * [factorial](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/factorial.py) - * [fast fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fast_fibonacci.py) - * [fibonacci](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fibonacci.py) - * [floyd warshall](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/floyd_warshall.py) - * [fractional knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/fractional_knapsack.py) - * [integer partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/integer_partition.py) - * [k means clustering tensorflow](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/k_means_clustering_tensorflow.py) - * [knapsack](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/knapsack.py) - * [longest common subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_common_subsequence.py) - * [longest increasing subsequence](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence.py) - * [longest increasing subsequence o(nlogn)](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_increasing_subsequence_o(nlogn).py) - * [longest sub array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/longest_sub_array.py) - * [matrix chain order](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/matrix_chain_order.py) - * [max sub array](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/max_sub_array.py) - * [minimum partition](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/minimum_partition.py) - * [rod cutting](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/rod_cutting.py) - * [subset generation](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/subset_generation.py) - * [sum of subset](https://github.com/TheAlgorithms/Python/blob/master/dynamic_programming/sum_of_subset.py) - ## File Transfer - * [recieve file](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/recieve_file.py) - * [send file](https://github.com/TheAlgorithms/Python/blob/master/file_transfer/send_file.py) - ## Graphs - * [a star](https://github.com/TheAlgorithms/Python/blob/master/graphs/a_star.py) - * [articulation points](https://github.com/TheAlgorithms/Python/blob/master/graphs/articulation_points.py) - * [basic graphs](https://github.com/TheAlgorithms/Python/blob/master/graphs/basic_graphs.py) - * [bellman ford](https://github.com/TheAlgorithms/Python/blob/master/graphs/bellman_ford.py) - * [bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs.py) - * [bfs shortest path](https://github.com/TheAlgorithms/Python/blob/master/graphs/bfs_shortest_path.py) - * [breadth first search](https://github.com/TheAlgorithms/Python/blob/master/graphs/breadth_first_search.py) - * [check bipartite graph bfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_bfs.py) - * [check bipartite graph dfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/check_bipartite_graph_dfs.py) - * [depth first search](https://github.com/TheAlgorithms/Python/blob/master/graphs/depth_first_search.py) - * [dfs](https://github.com/TheAlgorithms/Python/blob/master/graphs/dfs.py) - * [dijkstra](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra.py) - * [dijkstra 2](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_2.py) - * [dijkstra algorithm](https://github.com/TheAlgorithms/Python/blob/master/graphs/dijkstra_algorithm.py) - * [directed and undirected (weighted) graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/directed_and_undirected_(weighted)_graph.py) - * [edmonds karp multiple source and sink](https://github.com/TheAlgorithms/Python/blob/master/graphs/edmonds_karp_multiple_source_and_sink.py) - * [eulerian path and circuit for undirected graph](https://github.com/TheAlgorithms/Python/blob/master/graphs/eulerian_path_and_circuit_for_undirected_graph.py) - * [even tree](https://github.com/TheAlgorithms/Python/blob/master/graphs/even_tree.py) - * [finding bridges](https://github.com/TheAlgorithms/Python/blob/master/graphs/finding_bridges.py) - * [graph list](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_list.py) - * [graph matrix](https://github.com/TheAlgorithms/Python/blob/master/graphs/graph_matrix.py) - * [graphs floyd warshall](https://github.com/TheAlgorithms/Python/blob/master/graphs/graphs_floyd_warshall.py) - * [kahns algorithm long](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_long.py) - * [kahns algorithm topo](https://github.com/TheAlgorithms/Python/blob/master/graphs/kahns_algorithm_topo.py) - * [minimum spanning tree kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal.py) - * [minimum spanning tree prims](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims.py) - * [multi hueristic astar](https://github.com/TheAlgorithms/Python/blob/master/graphs/multi_hueristic_astar.py) - * [page rank](https://github.com/TheAlgorithms/Python/blob/master/graphs/page_rank.py) - * [prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/prim.py) - * [scc kosaraju](https://github.com/TheAlgorithms/Python/blob/master/graphs/scc_kosaraju.py) - * [tarjans scc](https://github.com/TheAlgorithms/Python/blob/master/graphs/tarjans_scc.py) - ## Hashes - * [chaos machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/chaos_machine.py) - * [enigma machine](https://github.com/TheAlgorithms/Python/blob/master/hashes/enigma_machine.py) - * [md5](https://github.com/TheAlgorithms/Python/blob/master/hashes/md5.py) - * [sha1](https://github.com/TheAlgorithms/Python/blob/master/hashes/sha1.py) - ## Linear Algebra - * Src - - * [lib](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/lib.py) - - * [python-polynom-for-points](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/python-polynom-for-points.py) - - * [tests](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/src/tests.py) - + * [lib](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/lib.py) + * [polynom-for-points](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/polynom-for-points.py) + * [tests](https://github.com/TheAlgorithms/Python/blob/master/linear_algebra/tests.py) ## Machine Learning - * [decision tree](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/decision_tree.py) - * [gradient descent](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_descent.py) - * [k means clust](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/k_means_clust.py) - * [knn sklearn](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/knn_sklearn.py) - * [linear regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/linear_regression.py) - * [logistic regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/logistic_regression.py) - * [naive bayes](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/naive_bayes.ipynb) - * Random Forest Classification - - * [random forest classification](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classification/random_forest_classification.py) - - * [random forest classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classification/random_forest_classifier.ipynb) - + * [random forest classification](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classification.py) + * [random forest classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_classifier.ipynb) * Random Forest Regression - - * [random forest regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression/random_forest_regression.ipynb) - - * [random forest regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression/random_forest_regression.py) - + * [random forest regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression.ipynb) + * [random forest regression](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/random_forest_regression.py) * [reuters one vs rest classifier](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/reuters_one_vs_rest_classifier.ipynb) - * [scoring functions](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/scoring_functions.py) - + * [sorted vector machines](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/sorted_vector_machines.py) ## Maths - * [3n+1](https://github.com/TheAlgorithms/Python/blob/master/maths/3n+1.py) - * [abs](https://github.com/TheAlgorithms/Python/blob/master/maths/abs.py) - * [abs max](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_max.py) - * [abs min](https://github.com/TheAlgorithms/Python/blob/master/maths/abs_min.py) - * [average mean](https://github.com/TheAlgorithms/Python/blob/master/maths/average_mean.py) - * [average median](https://github.com/TheAlgorithms/Python/blob/master/maths/average_median.py) - * [basic maths](https://github.com/TheAlgorithms/Python/blob/master/maths/basic_maths.py) - * [binary exponentiation](https://github.com/TheAlgorithms/Python/blob/master/maths/binary_exponentiation.py) - * [collatz sequence](https://github.com/TheAlgorithms/Python/blob/master/maths/collatz_sequence.py) - * [extended euclidean algorithm](https://github.com/TheAlgorithms/Python/blob/master/maths/extended_euclidean_algorithm.py) - * [factorial python](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_python.py) - * [factorial recursive](https://github.com/TheAlgorithms/Python/blob/master/maths/factorial_recursive.py) - * [fermat little theorem](https://github.com/TheAlgorithms/Python/blob/master/maths/fermat_little_theorem.py) - * [fibonacci](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci.py) - * [fibonacci sequence recursion](https://github.com/TheAlgorithms/Python/blob/master/maths/fibonacci_sequence_recursion.py) - * [find lcm](https://github.com/TheAlgorithms/Python/blob/master/maths/find_lcm.py) - * [find max](https://github.com/TheAlgorithms/Python/blob/master/maths/find_max.py) - * [find min](https://github.com/TheAlgorithms/Python/blob/master/maths/find_min.py) - * [gaussian](https://github.com/TheAlgorithms/Python/blob/master/maths/gaussian.py) - * [greater common divisor](https://github.com/TheAlgorithms/Python/blob/master/maths/greater_common_divisor.py) - * [is square free](https://github.com/TheAlgorithms/Python/blob/master/maths/is_square_free.py) - * [largest of very large numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/largest_of_very_large_numbers.py) - * [lucas lehmer primality test](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_lehmer_primality_test.py) - * [lucas series](https://github.com/TheAlgorithms/Python/blob/master/maths/lucas_series.py) - * [mobius function](https://github.com/TheAlgorithms/Python/blob/master/maths/mobius_function.py) - * [modular exponential](https://github.com/TheAlgorithms/Python/blob/master/maths/modular_exponential.py) - * [newton raphson](https://github.com/TheAlgorithms/Python/blob/master/maths/newton_raphson.py) - * [prime check](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_check.py) - * [prime factors](https://github.com/TheAlgorithms/Python/blob/master/maths/prime_factors.py) - * [quadratic equations complex numbers](https://github.com/TheAlgorithms/Python/blob/master/maths/quadratic_equations_complex_numbers.py) - * [segmented sieve](https://github.com/TheAlgorithms/Python/blob/master/maths/segmented_sieve.py) - * [sieve of eratosthenes](https://github.com/TheAlgorithms/Python/blob/master/maths/sieve_of_eratosthenes.py) - * [simpson rule](https://github.com/TheAlgorithms/Python/blob/master/maths/simpson_rule.py) - * [test prime check](https://github.com/TheAlgorithms/Python/blob/master/maths/test_prime_check.py) - * [trapezoidal rule](https://github.com/TheAlgorithms/Python/blob/master/maths/trapezoidal_rule.py) - * [volume](https://github.com/TheAlgorithms/Python/blob/master/maths/volume.py) - * [zellers congruence](https://github.com/TheAlgorithms/Python/blob/master/maths/zellers_congruence.py) - ## Matrix - * [matrix operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/matrix_operation.py) - * [nth fibonacci using matrix exponentiation](https://github.com/TheAlgorithms/Python/blob/master/matrix/nth_fibonacci_using_matrix_exponentiation.py) - * [rotate matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/rotate_matrix.py) - * [searching in sorted matrix](https://github.com/TheAlgorithms/Python/blob/master/matrix/searching_in_sorted_matrix.py) - * [spiral print](https://github.com/TheAlgorithms/Python/blob/master/matrix/spiral_print.py) - * Tests - - * [test matrix operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/tests/test_matrix_operation.py) - + * [test matrix operation](https://github.com/TheAlgorithms/Python/blob/master/matrix/test_matrix_operation.py) ## Networking Flow - * [ford fulkerson](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/ford_fulkerson.py) - * [minimum cut](https://github.com/TheAlgorithms/Python/blob/master/networking_flow/minimum_cut.py) - ## Neural Network - * [back propagation neural network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/back_propagation_neural_network.py) - * [convolution neural network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/convolution_neural_network.py) - * [fully connected neural network](https://github.com/TheAlgorithms/Python/blob/master/neural_network/fully_connected_neural_network.ipynb) - * [perceptron](https://github.com/TheAlgorithms/Python/blob/master/neural_network/perceptron.py) - ## Other - * [anagrams](https://github.com/TheAlgorithms/Python/blob/master/other/anagrams.py) - * [binary exponentiation](https://github.com/TheAlgorithms/Python/blob/master/other/binary_exponentiation.py) - * [binary exponentiation 2](https://github.com/TheAlgorithms/Python/blob/master/other/binary_exponentiation_2.py) - * [detecting english programmatically](https://github.com/TheAlgorithms/Python/blob/master/other/detecting_english_programmatically.py) - * [euclidean gcd](https://github.com/TheAlgorithms/Python/blob/master/other/euclidean_gcd.py) - * [fischer yates shuffle](https://github.com/TheAlgorithms/Python/blob/master/other/fischer_yates_shuffle.py) - * [food wastage analysis from 1961-2013 fao](https://github.com/TheAlgorithms/Python/blob/master/other/food_wastage_analysis_from_1961-2013_fao.ipynb) - * [frequency finder](https://github.com/TheAlgorithms/Python/blob/master/other/frequency_finder.py) - * [game of life](https://github.com/TheAlgorithms/Python/blob/master/other/game_of_life.py) - * [linear congruential generator](https://github.com/TheAlgorithms/Python/blob/master/other/linear_congruential_generator.py) - * [nested brackets](https://github.com/TheAlgorithms/Python/blob/master/other/nested_brackets.py) - * [palindrome](https://github.com/TheAlgorithms/Python/blob/master/other/palindrome.py) - * [password generator](https://github.com/TheAlgorithms/Python/blob/master/other/password_generator.py) - * [primelib](https://github.com/TheAlgorithms/Python/blob/master/other/primelib.py) - * [sierpinski triangle](https://github.com/TheAlgorithms/Python/blob/master/other/sierpinski_triangle.py) - * [tower of hanoi](https://github.com/TheAlgorithms/Python/blob/master/other/tower_of_hanoi.py) - * [two sum](https://github.com/TheAlgorithms/Python/blob/master/other/two_sum.py) - * [word patterns](https://github.com/TheAlgorithms/Python/blob/master/other/word_patterns.py) - ## Project Euler - * Problem 01 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol2.py) - - * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol3.py) - - * [sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol4.py) - - * [sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol5.py) - - * [sol6](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_01/sol6.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol3.py) + * [sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol4.py) + * [sol5](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol5.py) + * [sol6](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol6.py) * Problem 02 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol2.py) - - * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol3.py) - - * [sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_02/sol4.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol3.py) + * [sol4](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol4.py) * Problem 03 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_03/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_03/sol2.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) * Problem 04 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_04/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_04/sol2.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) * Problem 05 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_05/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_05/sol2.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) * Problem 06 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_06/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_06/sol2.py) - - * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_06/sol3.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol3.py) * Problem 07 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_07/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_07/sol2.py) - - * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_07/sol3.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol3.py) * Problem 08 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_08/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_08/sol2.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) * Problem 09 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_09/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_09/sol2.py) - - * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_09/sol3.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol3.py) * Problem 10 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_10/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_10/sol2.py) - - * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_10/sol3.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) + * [sol3](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol3.py) * Problem 11 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_11/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_11/sol2.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) * Problem 12 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_12/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_12/sol2.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) * Problem 13 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_13/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 14 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_14/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_14/sol2.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) * Problem 15 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_15/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 16 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_16/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_16/sol2.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) * Problem 17 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_17/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * Problem 18 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 19 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_19/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 20 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_20/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_20/sol2.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) * Problem 21 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_21/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 22 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_22/sol2.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) * Problem 234 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_234/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 24 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_24/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 25 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_25/sol1.py) - - * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_25/sol2.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * [sol2](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol2.py) * Problem 28 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_28/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 29 - - * [solution](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_29/solution.py) - + * [solution](https://github.com/TheAlgorithms/Python/blob/master/project_euler/solution.py) * Problem 31 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_31/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 36 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_36/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 40 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_40/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 48 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_48/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 52 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_52/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 53 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_53/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) + * Problem 56 + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) * Problem 76 - - * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/problem_76/sol1.py) - + * [sol1](https://github.com/TheAlgorithms/Python/blob/master/project_euler/sol1.py) ## Searches - * [binary search](https://github.com/TheAlgorithms/Python/blob/master/searches/binary_search.py) - * [interpolation search](https://github.com/TheAlgorithms/Python/blob/master/searches/interpolation_search.py) - * [jump search](https://github.com/TheAlgorithms/Python/blob/master/searches/jump_search.py) - * [linear search](https://github.com/TheAlgorithms/Python/blob/master/searches/linear_search.py) - * [quick select](https://github.com/TheAlgorithms/Python/blob/master/searches/quick_select.py) - * [sentinel linear search](https://github.com/TheAlgorithms/Python/blob/master/searches/sentinel_linear_search.py) - * [tabu search](https://github.com/TheAlgorithms/Python/blob/master/searches/tabu_search.py) - * [ternary search](https://github.com/TheAlgorithms/Python/blob/master/searches/ternary_search.py) - ## Sorts - * [bitonic sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bitonic_sort.py) - * [bogo sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bogo_sort.py) - * [bubble sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bubble_sort.py) - * [bucket sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/bucket_sort.py) - * [cocktail shaker sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cocktail_shaker_sort.py) - * [comb sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/comb_sort.py) - * [counting sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/counting_sort.py) - * [cycle sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/cycle_sort.py) - * [external sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/external_sort.py) - * [gnome sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/gnome_sort.py) - * [heap sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/heap_sort.py) - * [insertion sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/insertion_sort.py) - * [merge sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort.py) - * [merge sort fastest](https://github.com/TheAlgorithms/Python/blob/master/sorts/merge_sort_fastest.py) - * [odd even transposition parallel](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_parallel.py) - * [odd even transposition single threaded](https://github.com/TheAlgorithms/Python/blob/master/sorts/odd_even_transposition_single_threaded.py) - * [pancake sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pancake_sort.py) - * [pigeon sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/pigeon_sort.py) - * [quick sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort.py) - * [quick sort 3 partition](https://github.com/TheAlgorithms/Python/blob/master/sorts/quick_sort_3_partition.py) - * [radix sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/radix_sort.py) - * [random normal distribution quicksort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_normal_distribution_quicksort.py) - * [random pivot quick sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/random_pivot_quick_sort.py) - * [selection sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/selection_sort.py) - * [shell sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/shell_sort.py) - * [tim sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tim_sort.py) - * [topological sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/topological_sort.py) - * [tree sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/tree_sort.py) - * [wiggle sort](https://github.com/TheAlgorithms/Python/blob/master/sorts/wiggle_sort.py) - ## Strings - * [boyer moore search](https://github.com/TheAlgorithms/Python/blob/master/strings/boyer_moore_search.py) - * [knuth morris pratt](https://github.com/TheAlgorithms/Python/blob/master/strings/knuth_morris_pratt.py) - * [levenshtein distance](https://github.com/TheAlgorithms/Python/blob/master/strings/levenshtein_distance.py) - * [manacher](https://github.com/TheAlgorithms/Python/blob/master/strings/manacher.py) - * [min cost string conversion](https://github.com/TheAlgorithms/Python/blob/master/strings/min_cost_string_conversion.py) - * [naive string search](https://github.com/TheAlgorithms/Python/blob/master/strings/naive_string_search.py) - * [rabin karp](https://github.com/TheAlgorithms/Python/blob/master/strings/rabin_karp.py) - ## Traversals - * [binary tree traversals](https://github.com/TheAlgorithms/Python/blob/master/traversals/binary_tree_traversals.py) + diff --git a/scripts/build_directory_md.py b/scripts/build_directory_md.py index 2ebd445b3667..b39edca6c933 100755 --- a/scripts/build_directory_md.py +++ b/scripts/build_directory_md.py @@ -14,7 +14,7 @@ def good_filepaths(top_dir: str = ".") -> Iterator[str]: continue if os.path.splitext(filename)[1] in (".py", ".ipynb"): yield os.path.join(dirpath, filename).lstrip("./") - + def md_prefix(i): return f"{i * ' '}*" if i else "##" @@ -25,7 +25,7 @@ def print_path(old_path: str, new_path: str) -> str: for i, new_part in enumerate(new_path.split(os.sep)): if i + 1 > len(old_parts) or old_parts[i] != new_part: if new_part: - print(f"{md_prefix(i)} {new_part.replace('_', ' ').title()}") + print(f"{md_prefix(i-1)} {new_part.replace('_', ' ').title()}") return new_path @@ -36,7 +36,7 @@ def print_directory_md(top_dir: str = ".") -> None: if filepath != old_path: old_path = print_path(old_path, filepath) indent = (filepath.count(os.sep) + 1) if filepath else 0 - url = "/".join((URL_BASE, filepath, filename)).replace(" ", "%20") + url = "/".join((URL_BASE, filepath.split(os.sep)[1], filename)).replace(" ", "%20") filename = os.path.splitext(filename.replace("_", " "))[0] print(f"{md_prefix(indent)} [{filename}]({url})") From a41a14f9d89b665178f08535f128ba14652ce449 Mon Sep 17 00:00:00 2001 From: KirilBangachev <51961981+KirilBangachev@users.noreply.github.com> Date: Fri, 6 Sep 2019 12:06:56 +0300 Subject: [PATCH 164/193] Add radix2 FFT (#1166) * Add radix2 FFT Created a dynamic implementation of the radix - 2 Fast Fourier Transform for fast polynomial multiplication. Reference: https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm#The_radix-2_DIT_case * Rename radix2_FFT.py to radix2_fft.py * Update radix2_fft printing Improved the printing method with f.prefix and String.join() * __str__ method update * Turned the tests into doctests --- maths/radix2_fft.py | 222 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 222 insertions(+) create mode 100644 maths/radix2_fft.py diff --git a/maths/radix2_fft.py b/maths/radix2_fft.py new file mode 100644 index 000000000000..c7ffe96528b4 --- /dev/null +++ b/maths/radix2_fft.py @@ -0,0 +1,222 @@ +""" +Fast Polynomial Multiplication using radix-2 fast Fourier Transform. +""" + +import mpmath # for roots of unity +import numpy as np + + +class FFT: + """ + Fast Polynomial Multiplication using radix-2 fast Fourier Transform. + + Reference: + https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm#The_radix-2_DIT_case + + For polynomials of degree m and n the algorithms has complexity + O(n*logn + m*logm) + + The main part of the algorithm is split in two parts: + 1) __DFT: We compute the discrete fourier transform (DFT) of A and B using a + bottom-up dynamic approach - + 2) __multiply: Once we obtain the DFT of A*B, we can similarly + invert it to obtain A*B + + The class FFT takes two polynomials A and B with complex coefficients as arguments; + The two polynomials should be represented as a sequence of coefficients starting + from the free term. Thus, for instance x + 2*x^3 could be represented as + [0,1,0,2] or (0,1,0,2). The constructor adds some zeros at the end so that the + polynomials have the same length which is a power of 2 at least the length of + their product. + + Example: + + Create two polynomials as sequences + >>> A = [0, 1, 0, 2] # x+2x^3 + >>> B = (2, 3, 4, 0) # 2+3x+4x^2 + + Create an FFT object with them + >>> x = FFT(A, B) + + Print product + >>> print(x.product) # 2x + 3x^2 + 8x^3 + 4x^4 + 6x^5 + [(-0+0j), (2+0j), (3+0j), (8+0j), (6+0j), (8+0j)] + + __str__ test + >>> print(x) + A = 0*x^0 + 1*x^1 + 2*x^0 + 3*x^2 + B = 0*x^2 + 1*x^3 + 2*x^4 + A*B = 0*x^(-0+0j) + 1*x^(2+0j) + 2*x^(3+0j) + 3*x^(8+0j) + 4*x^(6+0j) + 5*x^(8+0j) + """ + + def __init__(self, polyA=[0], polyB=[0]): + # Input as list + self.polyA = list(polyA)[:] + self.polyB = list(polyB)[:] + + # Remove leading zero coefficients + while self.polyA[-1] == 0: + self.polyA.pop() + self.len_A = len(self.polyA) + + while self.polyB[-1] == 0: + self.polyB.pop() + self.len_B = len(self.polyB) + + # Add 0 to make lengths equal a power of 2 + self.C_max_length = int( + 2 + ** np.ceil( + np.log2( + len(self.polyA) + len(self.polyB) - 1 + ) + ) + ) + + while len(self.polyA) < self.C_max_length: + self.polyA.append(0) + while len(self.polyB) < self.C_max_length: + self.polyB.append(0) + # A complex root used for the fourier transform + self.root = complex( + mpmath.root(x=1, n=self.C_max_length, k=1) + ) + + # The product + self.product = self.__multiply() + + # Discrete fourier transform of A and B + def __DFT(self, which): + if which == "A": + dft = [[x] for x in self.polyA] + else: + dft = [[x] for x in self.polyB] + # Corner case + if len(dft) <= 1: + return dft[0] + # + next_ncol = self.C_max_length // 2 + while next_ncol > 0: + new_dft = [[] for i in range(next_ncol)] + root = self.root ** next_ncol + + # First half of next step + current_root = 1 + for j in range( + self.C_max_length // (next_ncol * 2) + ): + for i in range(next_ncol): + new_dft[i].append( + dft[i][j] + + current_root + * dft[i + next_ncol][j] + ) + current_root *= root + # Second half of next step + current_root = 1 + for j in range( + self.C_max_length // (next_ncol * 2) + ): + for i in range(next_ncol): + new_dft[i].append( + dft[i][j] + - current_root + * dft[i + next_ncol][j] + ) + current_root *= root + # Update + dft = new_dft + next_ncol = next_ncol // 2 + return dft[0] + + # multiply the DFTs of A and B and find A*B + def __multiply(self): + dftA = self.__DFT("A") + dftB = self.__DFT("B") + inverseC = [ + [ + dftA[i] * dftB[i] + for i in range(self.C_max_length) + ] + ] + del dftA + del dftB + + # Corner Case + if len(inverseC[0]) <= 1: + return inverseC[0] + # Inverse DFT + next_ncol = 2 + while next_ncol <= self.C_max_length: + new_inverseC = [[] for i in range(next_ncol)] + root = self.root ** (next_ncol // 2) + current_root = 1 + # First half of next step + for j in range(self.C_max_length // next_ncol): + for i in range(next_ncol // 2): + # Even positions + new_inverseC[i].append( + ( + inverseC[i][j] + + inverseC[i][ + j + + self.C_max_length + // next_ncol + ] + ) + / 2 + ) + # Odd positions + new_inverseC[i + next_ncol // 2].append( + ( + inverseC[i][j] + - inverseC[i][ + j + + self.C_max_length + // next_ncol + ] + ) + / (2 * current_root) + ) + current_root *= root + # Update + inverseC = new_inverseC + next_ncol *= 2 + # Unpack + inverseC = [ + round(x[0].real, 8) + round(x[0].imag, 8) * 1j + for x in inverseC + ] + + # Remove leading 0's + while inverseC[-1] == 0: + inverseC.pop() + return inverseC + + # Overwrite __str__ for print(); Shows A, B and A*B + def __str__(self): + A = "A = " + " + ".join( + f"{coef}*x^{i}" + for coef, i in enumerate( + self.polyA[: self.len_A] + ) + ) + B = "B = " + " + ".join( + f"{coef}*x^{i}" + for coef, i in enumerate( + self.polyB[: self.len_B] + ) + ) + C = "A*B = " + " + ".join( + f"{coef}*x^{i}" + for coef, i in enumerate(self.product) + ) + + return "\n".join((A, B, C)) + + +# Unit tests +if __name__ == "__main__": + import doctest + + doctest.testmod() From 5b483be73b95b9e5a39c122734ca4eff0da593cd Mon Sep 17 00:00:00 2001 From: Jasper <46252815+jasper256@users.noreply.github.com> Date: Sun, 8 Sep 2019 01:07:14 -0400 Subject: [PATCH 165/193] Added OOP approach to matrices (#1165) * Added OOP aproach to matrices * created methods for minors, cofactors, and determinants and added corresponding doctests * Added methods for adjugate, inverse, and identity (along with corresponding doctests) to matrix_class.py A small bug persists that causes the doctest to fail. After a couple Matrix objects are printed, the next one is printed in a different format. * formatted matrix_class.py with python/black * implemented negation and exponentiation as well as corresponding doctests in matrix_class.py. Also implemented eq and ne comparison operations * changed __str__ method in matrix_class.py to align with numpy standard and fixed bug in cofactors method * removed property decorators from several methods in matrix_class.py --- matrix/matrix_class.py | 364 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 364 insertions(+) create mode 100644 matrix/matrix_class.py diff --git a/matrix/matrix_class.py b/matrix/matrix_class.py new file mode 100644 index 000000000000..2cd43fc9ca8e --- /dev/null +++ b/matrix/matrix_class.py @@ -0,0 +1,364 @@ +# An OOP aproach to representing and manipulating matrices + + +class Matrix: + """ + Matrix object generated from a 2D array where each element is an array representing a row. + Rows can contain type int or float. + Common operations and information available. + >>> rows = [ + ... [1, 2, 3], + ... [4, 5, 6], + ... [7, 8, 9] + ... ] + >>> matrix = Matrix(rows) + >>> print(matrix) + [[1. 2. 3.] + [4. 5. 6.] + [7. 8. 9.]] + + Matrix rows and columns are available as 2D arrays + >>> print(matrix.rows) + [[1, 2, 3], [4, 5, 6], [7, 8, 9]] + >>> print(matrix.columns()) + [[1, 4, 7], [2, 5, 8], [3, 6, 9]] + + Order is returned as a tuple + >>> matrix.order + (3, 3) + + Squareness and invertability are represented as bool + >>> matrix.is_square + True + >>> matrix.is_invertable() + False + + Identity, Minors, Cofactors and Adjugate are returned as Matrices. Inverse can be a Matrix or Nonetype + >>> print(matrix.identity()) + [[1. 0. 0.] + [0. 1. 0.] + [0. 0. 1.]] + >>> print(matrix.minors()) + [[-3. -6. -3.] + [-6. -12. -6.] + [-3. -6. -3.]] + >>> print(matrix.cofactors()) + [[-3. 6. -3.] + [6. -12. 6.] + [-3. 6. -3.]] + >>> print(matrix.adjugate()) # won't be apparent due to the nature of the cofactor matrix + [[-3. 6. -3.] + [6. -12. 6.] + [-3. 6. -3.]] + >>> print(matrix.inverse()) + None + + Determinant is an int, float, or Nonetype + >>> matrix.determinant() + 0 + + Negation, scalar multiplication, addition, subtraction, multiplication and exponentiation are available and all return a Matrix + >>> print(-matrix) + [[-1. -2. -3.] + [-4. -5. -6.] + [-7. -8. -9.]] + >>> matrix2 = matrix * 3 + >>> print(matrix2) + [[3. 6. 9.] + [12. 15. 18.] + [21. 24. 27.]] + >>> print(matrix + matrix2) + [[4. 8. 12.] + [16. 20. 24.] + [28. 32. 36.]] + >>> print(matrix - matrix2) + [[-2. -4. -6.] + [-8. -10. -12.] + [-14. -16. -18.]] + >>> print(matrix ** 3) + [[468. 576. 684.] + [1062. 1305. 1548.] + [1656. 2034. 2412.]] + + Matrices can also be modified + >>> matrix.add_row([10, 11, 12]) + >>> print(matrix) + [[1. 2. 3.] + [4. 5. 6.] + [7. 8. 9.] + [10. 11. 12.]] + >>> matrix2.add_column([8, 16, 32]) + >>> print(matrix2) + [[3. 6. 9. 8.] + [12. 15. 18. 16.] + [21. 24. 27. 32.]] + >>> print(matrix * matrix2) + [[90. 108. 126. 136.] + [198. 243. 288. 304.] + [306. 378. 450. 472.] + [414. 513. 612. 640.]] + + """ + + def __init__(self, rows): + error = TypeError( + "Matrices must be formed from a list of zero or more lists containing at least one and the same number of values, \ + each of which must be of type int or float" + ) + if len(rows) != 0: + cols = len(rows[0]) + if cols == 0: + raise error + for row in rows: + if not len(row) == cols: + raise error + for value in row: + if not isinstance(value, (int, float)): + raise error + self.rows = rows + else: + self.rows = [] + + # MATRIX INFORMATION + def columns(self): + return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))] + + @property + def num_rows(self): + return len(self.rows) + + @property + def num_columns(self): + return len(self.rows[0]) + + @property + def order(self): + return (self.num_rows, self.num_columns) + + @property + def is_square(self): + if self.order[0] == self.order[1]: + return True + return False + + def identity(self): + values = [ + [0 if column_num != row_num else 1 for column_num in range(self.num_rows)] + for row_num in range(self.num_rows) + ] + return Matrix(values) + + def determinant(self): + if not self.is_square: + return None + if self.order == (0, 0): + return 1 + if self.order == (1, 1): + return self.rows[0][0] + if self.order == (2, 2): + return (self.rows[0][0] * self.rows[1][1]) - ( + self.rows[0][1] * self.rows[1][0] + ) + else: + return sum( + [ + self.rows[0][column] * self.cofactors().rows[0][column] + for column in range(self.num_columns) + ] + ) + + def is_invertable(self): + if self.determinant(): + return True + return False + + def get_minor(self, row, column): + values = [ + [ + self.rows[other_row][other_column] + for other_column in range(self.num_columns) + if other_column != column + ] + for other_row in range(self.num_rows) + if other_row != row + ] + return Matrix(values).determinant() + + def get_cofactor(self, row, column): + if (row + column) % 2 == 0: + return self.get_minor(row, column) + return -1 * self.get_minor(row, column) + + def minors(self): + return Matrix( + [ + [self.get_minor(row, column) for column in range(self.num_columns)] + for row in range(self.num_rows) + ] + ) + + def cofactors(self): + return Matrix( + [ + [ + self.minors().rows[row][column] + if (row + column) % 2 == 0 + else self.minors().rows[row][column] * -1 + for column in range(self.minors().num_columns) + ] + for row in range(self.minors().num_rows) + ] + ) + + def adjugate(self): + values = [ + [self.cofactors().rows[column][row] for column in range(self.num_columns)] + for row in range(self.num_rows) + ] + return Matrix(values) + + def inverse(self): + if not self.is_invertable(): + return None + return self.adjugate() * (1 / self.determinant()) + + def __repr__(self): + return str(self.rows) + + def __str__(self): + if self.num_rows == 0: + return "[]" + if self.num_rows == 1: + return "[[" + ". ".join(self.rows[0]) + "]]" + return ( + "[" + + "\n ".join( + [ + "[" + ". ".join([str(value) for value in row]) + ".]" + for row in self.rows + ] + ) + + "]" + ) + + # MATRIX MANIPULATION + def add_row(self, row, position=None): + type_error = TypeError("Row must be a list containing all ints and/or floats") + if not isinstance(row, list): + raise type_error + for value in row: + if not isinstance(value, (int, float)): + raise type_error + if len(row) != self.num_columns: + raise ValueError( + "Row must be equal in length to the other rows in the matrix" + ) + if position is None: + self.rows.append(row) + else: + self.rows = self.rows[0:position] + [row] + self.rows[position:] + + def add_column(self, column, position=None): + type_error = TypeError( + "Column must be a list containing all ints and/or floats" + ) + if not isinstance(column, list): + raise type_error + for value in column: + if not isinstance(value, (int, float)): + raise type_error + if len(column) != self.num_rows: + raise ValueError( + "Column must be equal in length to the other columns in the matrix" + ) + if position is None: + self.rows = [self.rows[i] + [column[i]] for i in range(self.num_rows)] + else: + self.rows = [ + self.rows[i][0:position] + [column[i]] + self.rows[i][position:] + for i in range(self.num_rows) + ] + + # MATRIX OPERATIONS + def __eq__(self, other): + if not isinstance(other, Matrix): + raise TypeError("A Matrix can only be compared with another Matrix") + if self.rows == other.rows: + return True + return False + + def __ne__(self, other): + if self == other: + return False + return True + + def __neg__(self): + return self * -1 + + def __add__(self, other): + if self.order != other.order: + raise ValueError("Addition requires matrices of the same order") + return Matrix( + [ + [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)] + for i in range(self.num_rows) + ] + ) + + def __sub__(self, other): + if self.order != other.order: + raise ValueError("Subtraction requires matrices of the same order") + return Matrix( + [ + [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)] + for i in range(self.num_rows) + ] + ) + + def __mul__(self, other): + if not isinstance(other, (int, float, Matrix)): + raise TypeError( + "A Matrix can only be multiplied by an int, float, or another matrix" + ) + if type(other) in (int, float): + return Matrix([[element * other for element in row] for row in self.rows]) + if type(other) is Matrix: + if self.num_columns != other.num_rows: + raise ValueError( + "The number of columns in the first matrix must be equal to the number of rows in the second" + ) + return Matrix( + [ + [Matrix.dot_product(row, column) for column in other.columns()] + for row in self.rows + ] + ) + + def __pow__(self, other): + if not isinstance(other, int): + raise TypeError("A Matrix can only be raised to the power of an int") + if not self.is_square: + raise ValueError("Only square matrices can be raised to a power") + if other == 0: + return self.identity() + if other < 0: + if self.is_invertable: + return self.inverse() ** (-other) + raise ValueError( + "Only invertable matrices can be raised to a negative power" + ) + result = self + for i in range(other - 1): + result *= self + return result + + @classmethod + def dot_product(cls, row, column): + return sum([row[i] * column[i] for i in range(len(row))]) + + +if __name__ == "__main__": + import doctest + + test = doctest.testmod() + print(test) From 3c3f92db530e3ae0e594cd1d5c4e034cc23defc9 Mon Sep 17 00:00:00 2001 From: Simon Landry Date: Sun, 8 Sep 2019 04:40:07 -0400 Subject: [PATCH 166/193] Add problem 67 solution (#1170) --- project_euler/problem_67/__init__.py | 0 project_euler/problem_67/sol1.py | 49 +++++++++++++ project_euler/problem_67/triangle.txt | 100 ++++++++++++++++++++++++++ 3 files changed, 149 insertions(+) create mode 100644 project_euler/problem_67/__init__.py create mode 100644 project_euler/problem_67/sol1.py create mode 100644 project_euler/problem_67/triangle.txt diff --git a/project_euler/problem_67/__init__.py b/project_euler/problem_67/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/project_euler/problem_67/sol1.py b/project_euler/problem_67/sol1.py new file mode 100644 index 000000000000..2da757e303aa --- /dev/null +++ b/project_euler/problem_67/sol1.py @@ -0,0 +1,49 @@ +""" +Problem Statement: +By starting at the top of the triangle below and moving to adjacent numbers on +the row below, the maximum total from top to bottom is 23. +3 +7 4 +2 4 6 +8 5 9 3 +That is, 3 + 7 + 4 + 9 = 23. +Find the maximum total from top to bottom in triangle.txt (right click and +'Save Link/Target As...'), a 15K text file containing a triangle with +one-hundred rows. +""" +import os + + +def solution(): + """ + Finds the maximum total in a triangle as described by the problem statement + above. + + >>> solution() + 7273 + """ + script_dir = os.path.dirname(os.path.realpath(__file__)) + triangle = os.path.join(script_dir, 'triangle.txt') + + with open(triangle, 'r') as f: + triangle = f.readlines() + + a = map(lambda x: x.rstrip('\r\n').split(' '), triangle) + a = list(map(lambda x: list(map(lambda y: int(y), x)), a)) + + for i in range(1, len(a)): + for j in range(len(a[i])): + if j != len(a[i - 1]): + number1 = a[i - 1][j] + else: + number1 = 0 + if j > 0: + number2 = a[i - 1][j - 1] + else: + number2 = 0 + a[i][j] += max(number1, number2) + return max(a[-1]) + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_67/triangle.txt b/project_euler/problem_67/triangle.txt new file mode 100644 index 000000000000..00aa2bc6382d --- /dev/null +++ b/project_euler/problem_67/triangle.txt @@ -0,0 +1,100 @@ +59 +73 41 +52 40 09 +26 53 06 34 +10 51 87 86 81 +61 95 66 57 25 68 +90 81 80 38 92 67 73 +30 28 51 76 81 18 75 44 +84 14 95 87 62 81 17 78 58 +21 46 71 58 02 79 62 39 31 09 +56 34 35 53 78 31 81 18 90 93 15 +78 53 04 21 84 93 32 13 97 11 37 51 +45 03 81 79 05 18 78 86 13 30 63 99 95 +39 87 96 28 03 38 42 17 82 87 58 07 22 57 +06 17 51 17 07 93 09 07 75 97 95 78 87 08 53 +67 66 59 60 88 99 94 65 55 77 55 34 27 53 78 28 +76 40 41 04 87 16 09 42 75 69 23 97 30 60 10 79 87 +12 10 44 26 21 36 32 84 98 60 13 12 36 16 63 31 91 35 +70 39 06 05 55 27 38 48 28 22 34 35 62 62 15 14 94 89 86 +66 56 68 84 96 21 34 34 34 81 62 40 65 54 62 05 98 03 02 60 +38 89 46 37 99 54 34 53 36 14 70 26 02 90 45 13 31 61 83 73 47 +36 10 63 96 60 49 41 05 37 42 14 58 84 93 96 17 09 43 05 43 06 59 +66 57 87 57 61 28 37 51 84 73 79 15 39 95 88 87 43 39 11 86 77 74 18 +54 42 05 79 30 49 99 73 46 37 50 02 45 09 54 52 27 95 27 65 19 45 26 45 +71 39 17 78 76 29 52 90 18 99 78 19 35 62 71 19 23 65 93 85 49 33 75 09 02 +33 24 47 61 60 55 32 88 57 55 91 54 46 57 07 77 98 52 80 99 24 25 46 78 79 05 +92 09 13 55 10 67 26 78 76 82 63 49 51 31 24 68 05 57 07 54 69 21 67 43 17 63 12 +24 59 06 08 98 74 66 26 61 60 13 03 09 09 24 30 71 08 88 70 72 70 29 90 11 82 41 34 +66 82 67 04 36 60 92 77 91 85 62 49 59 61 30 90 29 94 26 41 89 04 53 22 83 41 09 74 90 +48 28 26 37 28 52 77 26 51 32 18 98 79 36 62 13 17 08 19 54 89 29 73 68 42 14 08 16 70 37 +37 60 69 70 72 71 09 59 13 60 38 13 57 36 09 30 43 89 30 39 15 02 44 73 05 73 26 63 56 86 12 +55 55 85 50 62 99 84 77 28 85 03 21 27 22 19 26 82 69 54 04 13 07 85 14 01 15 70 59 89 95 10 19 +04 09 31 92 91 38 92 86 98 75 21 05 64 42 62 84 36 20 73 42 21 23 22 51 51 79 25 45 85 53 03 43 22 +75 63 02 49 14 12 89 14 60 78 92 16 44 82 38 30 72 11 46 52 90 27 08 65 78 03 85 41 57 79 39 52 33 48 +78 27 56 56 39 13 19 43 86 72 58 95 39 07 04 34 21 98 39 15 39 84 89 69 84 46 37 57 59 35 59 50 26 15 93 +42 89 36 27 78 91 24 11 17 41 05 94 07 69 51 96 03 96 47 90 90 45 91 20 50 56 10 32 36 49 04 53 85 92 25 65 +52 09 61 30 61 97 66 21 96 92 98 90 06 34 96 60 32 69 68 33 75 84 18 31 71 50 84 63 03 03 19 11 28 42 75 45 45 +61 31 61 68 96 34 49 39 05 71 76 59 62 67 06 47 96 99 34 21 32 47 52 07 71 60 42 72 94 56 82 83 84 40 94 87 82 46 +01 20 60 14 17 38 26 78 66 81 45 95 18 51 98 81 48 16 53 88 37 52 69 95 72 93 22 34 98 20 54 27 73 61 56 63 60 34 63 +93 42 94 83 47 61 27 51 79 79 45 01 44 73 31 70 83 42 88 25 53 51 30 15 65 94 80 44 61 84 12 77 02 62 02 65 94 42 14 94 +32 73 09 67 68 29 74 98 10 19 85 48 38 31 85 67 53 93 93 77 47 67 39 72 94 53 18 43 77 40 78 32 29 59 24 06 02 83 50 60 66 +32 01 44 30 16 51 15 81 98 15 10 62 86 79 50 62 45 60 70 38 31 85 65 61 64 06 69 84 14 22 56 43 09 48 66 69 83 91 60 40 36 61 +92 48 22 99 15 95 64 43 01 16 94 02 99 19 17 69 11 58 97 56 89 31 77 45 67 96 12 73 08 20 36 47 81 44 50 64 68 85 40 81 85 52 09 +91 35 92 45 32 84 62 15 19 64 21 66 06 01 52 80 62 59 12 25 88 28 91 50 40 16 22 99 92 79 87 51 21 77 74 77 07 42 38 42 74 83 02 05 +46 19 77 66 24 18 05 32 02 84 31 99 92 58 96 72 91 36 62 99 55 29 53 42 12 37 26 58 89 50 66 19 82 75 12 48 24 87 91 85 02 07 03 76 86 +99 98 84 93 07 17 33 61 92 20 66 60 24 66 40 30 67 05 37 29 24 96 03 27 70 62 13 04 45 47 59 88 43 20 66 15 46 92 30 04 71 66 78 70 53 99 +67 60 38 06 88 04 17 72 10 99 71 07 42 25 54 05 26 64 91 50 45 71 06 30 67 48 69 82 08 56 80 67 18 46 66 63 01 20 08 80 47 07 91 16 03 79 87 +18 54 78 49 80 48 77 40 68 23 60 88 58 80 33 57 11 69 55 53 64 02 94 49 60 92 16 35 81 21 82 96 25 24 96 18 02 05 49 03 50 77 06 32 84 27 18 38 +68 01 50 04 03 21 42 94 53 24 89 05 92 26 52 36 68 11 85 01 04 42 02 45 15 06 50 04 53 73 25 74 81 88 98 21 67 84 79 97 99 20 95 04 40 46 02 58 87 +94 10 02 78 88 52 21 03 88 60 06 53 49 71 20 91 12 65 07 49 21 22 11 41 58 99 36 16 09 48 17 24 52 36 23 15 72 16 84 56 02 99 43 76 81 71 29 39 49 17 +64 39 59 84 86 16 17 66 03 09 43 06 64 18 63 29 68 06 23 07 87 14 26 35 17 12 98 41 53 64 78 18 98 27 28 84 80 67 75 62 10 11 76 90 54 10 05 54 41 39 66 +43 83 18 37 32 31 52 29 95 47 08 76 35 11 04 53 35 43 34 10 52 57 12 36 20 39 40 55 78 44 07 31 38 26 08 15 56 88 86 01 52 62 10 24 32 05 60 65 53 28 57 99 +03 50 03 52 07 73 49 92 66 80 01 46 08 67 25 36 73 93 07 42 25 53 13 96 76 83 87 90 54 89 78 22 78 91 73 51 69 09 79 94 83 53 09 40 69 62 10 79 49 47 03 81 30 +71 54 73 33 51 76 59 54 79 37 56 45 84 17 62 21 98 69 41 95 65 24 39 37 62 03 24 48 54 64 46 82 71 78 33 67 09 16 96 68 52 74 79 68 32 21 13 78 96 60 09 69 20 36 +73 26 21 44 46 38 17 83 65 98 07 23 52 46 61 97 33 13 60 31 70 15 36 77 31 58 56 93 75 68 21 36 69 53 90 75 25 82 39 50 65 94 29 30 11 33 11 13 96 02 56 47 07 49 02 +76 46 73 30 10 20 60 70 14 56 34 26 37 39 48 24 55 76 84 91 39 86 95 61 50 14 53 93 64 67 37 31 10 84 42 70 48 20 10 72 60 61 84 79 69 65 99 73 89 25 85 48 92 56 97 16 +03 14 80 27 22 30 44 27 67 75 79 32 51 54 81 29 65 14 19 04 13 82 04 91 43 40 12 52 29 99 07 76 60 25 01 07 61 71 37 92 40 47 99 66 57 01 43 44 22 40 53 53 09 69 26 81 07 +49 80 56 90 93 87 47 13 75 28 87 23 72 79 32 18 27 20 28 10 37 59 21 18 70 04 79 96 03 31 45 71 81 06 14 18 17 05 31 50 92 79 23 47 09 39 47 91 43 54 69 47 42 95 62 46 32 85 +37 18 62 85 87 28 64 05 77 51 47 26 30 65 05 70 65 75 59 80 42 52 25 20 44 10 92 17 71 95 52 14 77 13 24 55 11 65 26 91 01 30 63 15 49 48 41 17 67 47 03 68 20 90 98 32 04 40 68 +90 51 58 60 06 55 23 68 05 19 76 94 82 36 96 43 38 90 87 28 33 83 05 17 70 83 96 93 06 04 78 47 80 06 23 84 75 23 87 72 99 14 50 98 92 38 90 64 61 58 76 94 36 66 87 80 51 35 61 38 +57 95 64 06 53 36 82 51 40 33 47 14 07 98 78 65 39 58 53 06 50 53 04 69 40 68 36 69 75 78 75 60 03 32 39 24 74 47 26 90 13 40 44 71 90 76 51 24 36 50 25 45 70 80 61 80 61 43 90 64 11 +18 29 86 56 68 42 79 10 42 44 30 12 96 18 23 18 52 59 02 99 67 46 60 86 43 38 55 17 44 93 42 21 55 14 47 34 55 16 49 24 23 29 96 51 55 10 46 53 27 92 27 46 63 57 30 65 43 27 21 20 24 83 +81 72 93 19 69 52 48 01 13 83 92 69 20 48 69 59 20 62 05 42 28 89 90 99 32 72 84 17 08 87 36 03 60 31 36 36 81 26 97 36 48 54 56 56 27 16 91 08 23 11 87 99 33 47 02 14 44 73 70 99 43 35 33 +90 56 61 86 56 12 70 59 63 32 01 15 81 47 71 76 95 32 65 80 54 70 34 51 40 45 33 04 64 55 78 68 88 47 31 47 68 87 03 84 23 44 89 72 35 08 31 76 63 26 90 85 96 67 65 91 19 14 17 86 04 71 32 95 +37 13 04 22 64 37 37 28 56 62 86 33 07 37 10 44 52 82 52 06 19 52 57 75 90 26 91 24 06 21 14 67 76 30 46 14 35 89 89 41 03 64 56 97 87 63 22 34 03 79 17 45 11 53 25 56 96 61 23 18 63 31 37 37 47 +77 23 26 70 72 76 77 04 28 64 71 69 14 85 96 54 95 48 06 62 99 83 86 77 97 75 71 66 30 19 57 90 33 01 60 61 14 12 90 99 32 77 56 41 18 14 87 49 10 14 90 64 18 50 21 74 14 16 88 05 45 73 82 47 74 44 +22 97 41 13 34 31 54 61 56 94 03 24 59 27 98 77 04 09 37 40 12 26 87 09 71 70 07 18 64 57 80 21 12 71 83 94 60 39 73 79 73 19 97 32 64 29 41 07 48 84 85 67 12 74 95 20 24 52 41 67 56 61 29 93 35 72 69 +72 23 63 66 01 11 07 30 52 56 95 16 65 26 83 90 50 74 60 18 16 48 43 77 37 11 99 98 30 94 91 26 62 73 45 12 87 73 47 27 01 88 66 99 21 41 95 80 02 53 23 32 61 48 32 43 43 83 14 66 95 91 19 81 80 67 25 88 +08 62 32 18 92 14 83 71 37 96 11 83 39 99 05 16 23 27 10 67 02 25 44 11 55 31 46 64 41 56 44 74 26 81 51 31 45 85 87 09 81 95 22 28 76 69 46 48 64 87 67 76 27 89 31 11 74 16 62 03 60 94 42 47 09 34 94 93 72 +56 18 90 18 42 17 42 32 14 86 06 53 33 95 99 35 29 15 44 20 49 59 25 54 34 59 84 21 23 54 35 90 78 16 93 13 37 88 54 19 86 67 68 55 66 84 65 42 98 37 87 56 33 28 58 38 28 38 66 27 52 21 81 15 08 22 97 32 85 27 +91 53 40 28 13 34 91 25 01 63 50 37 22 49 71 58 32 28 30 18 68 94 23 83 63 62 94 76 80 41 90 22 82 52 29 12 18 56 10 08 35 14 37 57 23 65 67 40 72 39 93 39 70 89 40 34 07 46 94 22 20 05 53 64 56 30 05 56 61 88 27 +23 95 11 12 37 69 68 24 66 10 87 70 43 50 75 07 62 41 83 58 95 93 89 79 45 39 02 22 05 22 95 43 62 11 68 29 17 40 26 44 25 71 87 16 70 85 19 25 59 94 90 41 41 80 61 70 55 60 84 33 95 76 42 63 15 09 03 40 38 12 03 32 +09 84 56 80 61 55 85 97 16 94 82 94 98 57 84 30 84 48 93 90 71 05 95 90 73 17 30 98 40 64 65 89 07 79 09 19 56 36 42 30 23 69 73 72 07 05 27 61 24 31 43 48 71 84 21 28 26 65 65 59 65 74 77 20 10 81 61 84 95 08 52 23 70 +47 81 28 09 98 51 67 64 35 51 59 36 92 82 77 65 80 24 72 53 22 07 27 10 21 28 30 22 48 82 80 48 56 20 14 43 18 25 50 95 90 31 77 08 09 48 44 80 90 22 93 45 82 17 13 96 25 26 08 73 34 99 06 49 24 06 83 51 40 14 15 10 25 01 +54 25 10 81 30 64 24 74 75 80 36 75 82 60 22 69 72 91 45 67 03 62 79 54 89 74 44 83 64 96 66 73 44 30 74 50 37 05 09 97 70 01 60 46 37 91 39 75 75 18 58 52 72 78 51 81 86 52 08 97 01 46 43 66 98 62 81 18 70 93 73 08 32 46 34 +96 80 82 07 59 71 92 53 19 20 88 66 03 26 26 10 24 27 50 82 94 73 63 08 51 33 22 45 19 13 58 33 90 15 22 50 36 13 55 06 35 47 82 52 33 61 36 27 28 46 98 14 73 20 73 32 16 26 80 53 47 66 76 38 94 45 02 01 22 52 47 96 64 58 52 39 +88 46 23 39 74 63 81 64 20 90 33 33 76 55 58 26 10 46 42 26 74 74 12 83 32 43 09 02 73 55 86 54 85 34 28 23 29 79 91 62 47 41 82 87 99 22 48 90 20 05 96 75 95 04 43 28 81 39 81 01 28 42 78 25 39 77 90 57 58 98 17 36 73 22 63 74 51 +29 39 74 94 95 78 64 24 38 86 63 87 93 06 70 92 22 16 80 64 29 52 20 27 23 50 14 13 87 15 72 96 81 22 08 49 72 30 70 24 79 31 16 64 59 21 89 34 96 91 48 76 43 53 88 01 57 80 23 81 90 79 58 01 80 87 17 99 86 90 72 63 32 69 14 28 88 69 +37 17 71 95 56 93 71 35 43 45 04 98 92 94 84 96 11 30 31 27 31 60 92 03 48 05 98 91 86 94 35 90 90 08 48 19 33 28 68 37 59 26 65 96 50 68 22 07 09 49 34 31 77 49 43 06 75 17 81 87 61 79 52 26 27 72 29 50 07 98 86 01 17 10 46 64 24 18 56 +51 30 25 94 88 85 79 91 40 33 63 84 49 67 98 92 15 26 75 19 82 05 18 78 65 93 61 48 91 43 59 41 70 51 22 15 92 81 67 91 46 98 11 11 65 31 66 10 98 65 83 21 05 56 05 98 73 67 46 74 69 34 08 30 05 52 07 98 32 95 30 94 65 50 24 63 28 81 99 57 +19 23 61 36 09 89 71 98 65 17 30 29 89 26 79 74 94 11 44 48 97 54 81 55 39 66 69 45 28 47 13 86 15 76 74 70 84 32 36 33 79 20 78 14 41 47 89 28 81 05 99 66 81 86 38 26 06 25 13 60 54 55 23 53 27 05 89 25 23 11 13 54 59 54 56 34 16 24 53 44 06 +13 40 57 72 21 15 60 08 04 19 11 98 34 45 09 97 86 71 03 15 56 19 15 44 97 31 90 04 87 87 76 08 12 30 24 62 84 28 12 85 82 53 99 52 13 94 06 65 97 86 09 50 94 68 69 74 30 67 87 94 63 07 78 27 80 36 69 41 06 92 32 78 37 82 30 05 18 87 99 72 19 99 +44 20 55 77 69 91 27 31 28 81 80 27 02 07 97 23 95 98 12 25 75 29 47 71 07 47 78 39 41 59 27 76 13 15 66 61 68 35 69 86 16 53 67 63 99 85 41 56 08 28 33 40 94 76 90 85 31 70 24 65 84 65 99 82 19 25 54 37 21 46 33 02 52 99 51 33 26 04 87 02 08 18 96 +54 42 61 45 91 06 64 79 80 82 32 16 83 63 42 49 19 78 65 97 40 42 14 61 49 34 04 18 25 98 59 30 82 72 26 88 54 36 21 75 03 88 99 53 46 51 55 78 22 94 34 40 68 87 84 25 30 76 25 08 92 84 42 61 40 38 09 99 40 23 29 39 46 55 10 90 35 84 56 70 63 23 91 39 +52 92 03 71 89 07 09 37 68 66 58 20 44 92 51 56 13 71 79 99 26 37 02 06 16 67 36 52 58 16 79 73 56 60 59 27 44 77 94 82 20 50 98 33 09 87 94 37 40 83 64 83 58 85 17 76 53 02 83 52 22 27 39 20 48 92 45 21 09 42 24 23 12 37 52 28 50 78 79 20 86 62 73 20 59 +54 96 80 15 91 90 99 70 10 09 58 90 93 50 81 99 54 38 36 10 30 11 35 84 16 45 82 18 11 97 36 43 96 79 97 65 40 48 23 19 17 31 64 52 65 65 37 32 65 76 99 79 34 65 79 27 55 33 03 01 33 27 61 28 66 08 04 70 49 46 48 83 01 45 19 96 13 81 14 21 31 79 93 85 50 05 +92 92 48 84 59 98 31 53 23 27 15 22 79 95 24 76 05 79 16 93 97 89 38 89 42 83 02 88 94 95 82 21 01 97 48 39 31 78 09 65 50 56 97 61 01 07 65 27 21 23 14 15 80 97 44 78 49 35 33 45 81 74 34 05 31 57 09 38 94 07 69 54 69 32 65 68 46 68 78 90 24 28 49 51 45 86 35 +41 63 89 76 87 31 86 09 46 14 87 82 22 29 47 16 13 10 70 72 82 95 48 64 58 43 13 75 42 69 21 12 67 13 64 85 58 23 98 09 37 76 05 22 31 12 66 50 29 99 86 72 45 25 10 28 19 06 90 43 29 31 67 79 46 25 74 14 97 35 76 37 65 46 23 82 06 22 30 76 93 66 94 17 96 13 20 72 +63 40 78 08 52 09 90 41 70 28 36 14 46 44 85 96 24 52 58 15 87 37 05 98 99 39 13 61 76 38 44 99 83 74 90 22 53 80 56 98 30 51 63 39 44 30 91 91 04 22 27 73 17 35 53 18 35 45 54 56 27 78 48 13 69 36 44 38 71 25 30 56 15 22 73 43 32 69 59 25 93 83 45 11 34 94 44 39 92 +12 36 56 88 13 96 16 12 55 54 11 47 19 78 17 17 68 81 77 51 42 55 99 85 66 27 81 79 93 42 65 61 69 74 14 01 18 56 12 01 58 37 91 22 42 66 83 25 19 04 96 41 25 45 18 69 96 88 36 93 10 12 98 32 44 83 83 04 72 91 04 27 73 07 34 37 71 60 59 31 01 54 54 44 96 93 83 36 04 45 +30 18 22 20 42 96 65 79 17 41 55 69 94 81 29 80 91 31 85 25 47 26 43 49 02 99 34 67 99 76 16 14 15 93 08 32 99 44 61 77 67 50 43 55 87 55 53 72 17 46 62 25 50 99 73 05 93 48 17 31 70 80 59 09 44 59 45 13 74 66 58 94 87 73 16 14 85 38 74 99 64 23 79 28 71 42 20 37 82 31 23 +51 96 39 65 46 71 56 13 29 68 53 86 45 33 51 49 12 91 21 21 76 85 02 17 98 15 46 12 60 21 88 30 92 83 44 59 42 50 27 88 46 86 94 73 45 54 23 24 14 10 94 21 20 34 23 51 04 83 99 75 90 63 60 16 22 33 83 70 11 32 10 50 29 30 83 46 11 05 31 17 86 42 49 01 44 63 28 60 07 78 95 40 +44 61 89 59 04 49 51 27 69 71 46 76 44 04 09 34 56 39 15 06 94 91 75 90 65 27 56 23 74 06 23 33 36 69 14 39 05 34 35 57 33 22 76 46 56 10 61 65 98 09 16 69 04 62 65 18 99 76 49 18 72 66 73 83 82 40 76 31 89 91 27 88 17 35 41 35 32 51 32 67 52 68 74 85 80 57 07 11 62 66 47 22 67 +65 37 19 97 26 17 16 24 24 17 50 37 64 82 24 36 32 11 68 34 69 31 32 89 79 93 96 68 49 90 14 23 04 04 67 99 81 74 70 74 36 96 68 09 64 39 88 35 54 89 96 58 66 27 88 97 32 14 06 35 78 20 71 06 85 66 57 02 58 91 72 05 29 56 73 48 86 52 09 93 22 57 79 42 12 01 31 68 17 59 63 76 07 77 +73 81 14 13 17 20 11 09 01 83 08 85 91 70 84 63 62 77 37 07 47 01 59 95 39 69 39 21 99 09 87 02 97 16 92 36 74 71 90 66 33 73 73 75 52 91 11 12 26 53 05 26 26 48 61 50 90 65 01 87 42 47 74 35 22 73 24 26 56 70 52 05 48 41 31 18 83 27 21 39 80 85 26 08 44 02 71 07 63 22 05 52 19 08 20 +17 25 21 11 72 93 33 49 64 23 53 82 03 13 91 65 85 02 40 05 42 31 77 42 05 36 06 54 04 58 07 76 87 83 25 57 66 12 74 33 85 37 74 32 20 69 03 97 91 68 82 44 19 14 89 28 85 85 80 53 34 87 58 98 88 78 48 65 98 40 11 57 10 67 70 81 60 79 74 72 97 59 79 47 30 20 54 80 89 91 14 05 33 36 79 39 +60 85 59 39 60 07 57 76 77 92 06 35 15 72 23 41 45 52 95 18 64 79 86 53 56 31 69 11 91 31 84 50 44 82 22 81 41 40 30 42 30 91 48 94 74 76 64 58 74 25 96 57 14 19 03 99 28 83 15 75 99 01 89 85 79 50 03 95 32 67 44 08 07 41 62 64 29 20 14 76 26 55 48 71 69 66 19 72 44 25 14 01 48 74 12 98 07 +64 66 84 24 18 16 27 48 20 14 47 69 30 86 48 40 23 16 61 21 51 50 26 47 35 33 91 28 78 64 43 68 04 79 51 08 19 60 52 95 06 68 46 86 35 97 27 58 04 65 30 58 99 12 12 75 91 39 50 31 42 64 70 04 46 07 98 73 98 93 37 89 77 91 64 71 64 65 66 21 78 62 81 74 42 20 83 70 73 95 78 45 92 27 34 53 71 15 +30 11 85 31 34 71 13 48 05 14 44 03 19 67 23 73 19 57 06 90 94 72 57 69 81 62 59 68 88 57 55 69 49 13 07 87 97 80 89 05 71 05 05 26 38 40 16 62 45 99 18 38 98 24 21 26 62 74 69 04 85 57 77 35 58 67 91 79 79 57 86 28 66 34 72 51 76 78 36 95 63 90 08 78 47 63 45 31 22 70 52 48 79 94 15 77 61 67 68 +23 33 44 81 80 92 93 75 94 88 23 61 39 76 22 03 28 94 32 06 49 65 41 34 18 23 08 47 62 60 03 63 33 13 80 52 31 54 73 43 70 26 16 69 57 87 83 31 03 93 70 81 47 95 77 44 29 68 39 51 56 59 63 07 25 70 07 77 43 53 64 03 94 42 95 39 18 01 66 21 16 97 20 50 90 16 70 10 95 69 29 06 25 61 41 26 15 59 63 35 From 030600f9b3c4f9265fcaed97ae81b41ad2d7f7f4 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Tue, 10 Sep 2019 07:49:07 +0200 Subject: [PATCH 167/193] Update matrix_class.py (#1175) --- matrix/matrix_class.py | 45 +++++++++++++++--------------------------- 1 file changed, 16 insertions(+), 29 deletions(-) diff --git a/matrix/matrix_class.py b/matrix/matrix_class.py index 2cd43fc9ca8e..c82fb2cf6464 100644 --- a/matrix/matrix_class.py +++ b/matrix/matrix_class.py @@ -102,15 +102,15 @@ class Matrix: def __init__(self, rows): error = TypeError( - "Matrices must be formed from a list of zero or more lists containing at least one and the same number of values, \ - each of which must be of type int or float" + "Matrices must be formed from a list of zero or more lists containing at least " + "one and the same number of values, each of which must be of type int or float." ) if len(rows) != 0: cols = len(rows[0]) if cols == 0: raise error for row in rows: - if not len(row) == cols: + if len(row) != cols: raise error for value in row: if not isinstance(value, (int, float)): @@ -137,9 +137,7 @@ def order(self): @property def is_square(self): - if self.order[0] == self.order[1]: - return True - return False + return self.order[0] == self.order[1] def identity(self): values = [ @@ -168,9 +166,7 @@ def determinant(self): ) def is_invertable(self): - if self.determinant(): - return True - return False + return bool(self.determinant()) def get_minor(self, row, column): values = [ @@ -218,9 +214,8 @@ def adjugate(self): return Matrix(values) def inverse(self): - if not self.is_invertable(): - return None - return self.adjugate() * (1 / self.determinant()) + determinant = self.determinant() + return None if not determinant else self.adjugate() * (1 / determinant) def __repr__(self): return str(self.rows) @@ -283,14 +278,10 @@ def add_column(self, column, position=None): def __eq__(self, other): if not isinstance(other, Matrix): raise TypeError("A Matrix can only be compared with another Matrix") - if self.rows == other.rows: - return True - return False + return self.rows == other.rows def __ne__(self, other): - if self == other: - return False - return True + return not self == other def __neg__(self): return self * -1 @@ -316,23 +307,20 @@ def __sub__(self, other): ) def __mul__(self, other): - if not isinstance(other, (int, float, Matrix)): - raise TypeError( - "A Matrix can only be multiplied by an int, float, or another matrix" - ) - if type(other) in (int, float): + if isinstance(other, (int, float)): return Matrix([[element * other for element in row] for row in self.rows]) - if type(other) is Matrix: + elif isinstance(other, Matrix): if self.num_columns != other.num_rows: - raise ValueError( - "The number of columns in the first matrix must be equal to the number of rows in the second" - ) + raise ValueError("The number of columns in the first matrix must " + "be equal to the number of rows in the second") return Matrix( [ [Matrix.dot_product(row, column) for column in other.columns()] for row in self.rows ] ) + else: + raise TypeError("A Matrix can only be multiplied by an int, float, or another matrix") def __pow__(self, other): if not isinstance(other, int): @@ -360,5 +348,4 @@ def dot_product(cls, row, column): if __name__ == "__main__": import doctest - test = doctest.testmod() - print(test) + doctest.testmod() From 47d17951b830f23fe7cddffa9c24431c29cfea74 Mon Sep 17 00:00:00 2001 From: Kiril Bangachev <51961981+KirilBangachev@users.noreply.github.com> Date: Fri, 13 Sep 2019 07:13:55 -0400 Subject: [PATCH 168/193] Add Kth lexicographic permutation (#1179) * Add Kth lexicographic permutation Function that computes the kth lexicographic permtation of 0,1,2,...,n-1 in O(n^2) time * Update kth_lexicographic_permutation.py Addressed requested changes --- maths/kth_lexicographic_permutation.py | 40 ++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 maths/kth_lexicographic_permutation.py diff --git a/maths/kth_lexicographic_permutation.py b/maths/kth_lexicographic_permutation.py new file mode 100644 index 000000000000..1820be7274e3 --- /dev/null +++ b/maths/kth_lexicographic_permutation.py @@ -0,0 +1,40 @@ +def kthPermutation(k, n): + """ + Finds k'th lexicographic permutation (in increasing order) of + 0,1,2,...n-1 in O(n^2) time. + + Examples: + First permutation is always 0,1,2,...n + >>> kthPermutation(0,5) + [0, 1, 2, 3, 4] + + The order of permutation of 0,1,2,3 is [0,1,2,3], [0,1,3,2], [0,2,1,3], + [0,2,3,1], [0,3,1,2], [0,3,2,1], [1,0,2,3], [1,0,3,2], [1,2,0,3], + [1,2,3,0], [1,3,0,2] + >>> kthPermutation(10,4) + [1, 3, 0, 2] + """ + # Factorails from 1! to (n-1)! + factorials = [1] + for i in range(2, n): + factorials.append(factorials[-1] * i) + assert 0 <= k < factorials[-1] * n, "k out of bounds" + + permutation = [] + elements = list(range(n)) + + # Find permutation + while factorials: + factorial = factorials.pop() + number, k = divmod(k, factorial) + permutation.append(elements[number]) + elements.remove(elements[number]) + permutation.append(elements[0]) + + return permutation + + +if __name__ == "__main__": + import doctest + + doctest.testmod() From f8e30cfab1863b4f76415956ee1f10d606c50674 Mon Sep 17 00:00:00 2001 From: "Marvin M. Michum" Date: Fri, 13 Sep 2019 07:40:14 -0400 Subject: [PATCH 169/193] Digital Image Processing Tests (#1178) * add version of smaller image * swap image in tests * edits for image src --- .../image_data/lena_small.jpg | Bin 0 -> 6971 bytes .../test_digital_image_processing.py | 8 ++++---- 2 files changed, 4 insertions(+), 4 deletions(-) create mode 100644 digital_image_processing/image_data/lena_small.jpg diff --git a/digital_image_processing/image_data/lena_small.jpg b/digital_image_processing/image_data/lena_small.jpg new file mode 100644 index 0000000000000000000000000000000000000000..b85144e9f65ca0c115c41e79a6c29c24aa9a322d GIT binary patch literal 6971 zcmbW4cTiJd7v@7RB1lK1NC#=sq(~8wE+9y65s)q-y#+!Mklq9YsUk%KM5Kd+-g^@X zz1Khj5&{Wf@%wgmXZEk%J$K$S_nmw0o#%JYJI{UZ^`Gliz#Sb8Z4Cec0Rcek<^WtT z0Mr14H&X(Ff0^)K5EK2&B*er-#H1vor2iRY6y&626lA2NAuc{W<6~x4_NScOFC||~%gQUheg9coSKrXs)ZEh5-P7CG zKQK5nH9a%?d+yIXd~JPWb8CBNcW)nca{BiSeU7=f{KrKAAo?%XP5v*~|G`Cj!$tV7 z2W0=a2nYji1`#bW36}&ZovI<3ofkc~WCS_G)3oB6P6{3=BP64}_ar3~uk;!p>L1#F z$o}uZBLBb0{ukK)=7IyLhzM?)M??!y0h}+~Ru1``+0txJr;-z=oKFI}!oG7eTEujB z|5S_)5czD(5yd&y_Sgbb>l}bO*2l<}7;KBSK^EO4i#Pl=n9s&1Xe>`?7T`OF%2LQ* zFEmd%rP)&KfT>$L-$(XNU47wyYgK-(X{#^Wz!3VfoiEcMc@0l&<;R4lM8z`f2^Z14 zbD5Y2*8mSxzgVY_gPGPX}aHS@^-_bRI3@5cp!?^dQ z>!35&x_N5^9{d=am+Ezf`<_K@o&5$SrFcq{-G?6>TH)yucP1t^wSTBJ>|Y(w_c^!s zZIP1&_p4piBWhKA{h3)0^bf4t=7&OiCmPFNb|<4`Ts}}NG%eYZ|L=Yq%y1NxjgrH} zb}7`aMByBYZ1z~|<8JwtyWL?aP5c>Sqm9cR%f=eypxySccZ#2 zRIm)*eUwubr!a%Ssjb}-)j6wGnV+>5LRV>tAOF3F@$=yJ6tl9(GPlB3e!T;hunk3? 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Implements issue #973 * Remove wildcard imports --- .travis.yml | 2 +- linear_algebra/src/tests.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index f7a9264803f8..be227df1fdbd 100644 --- a/.travis.yml +++ b/.travis.yml @@ -6,7 +6,7 @@ before_install: pip install --upgrade pip setuptools install: pip install -r requirements.txt before_script: - black --check . || true - - flake8 . --count --select=E9,F401,F63,F7,F82 --show-source --statistics + - flake8 . --count --select=E9,F4,F63,F7,F82 --show-source --statistics script: - scripts/validate_filenames.py # no uppercase, no spaces, in a directory - mypy --ignore-missing-imports . diff --git a/linear_algebra/src/tests.py b/linear_algebra/src/tests.py index a26eb92653e2..afca4ce87117 100644 --- a/linear_algebra/src/tests.py +++ b/linear_algebra/src/tests.py @@ -9,7 +9,7 @@ """ import unittest -from lib import * +from lib import Matrix, Vector, axpy, squareZeroMatrix, unitBasisVector, zeroVector class Test(unittest.TestCase): def test_component(self): From 768700b91f85b21fbb422d9deba6bb69d320e18d Mon Sep 17 00:00:00 2001 From: Sangeet K Date: Fri, 13 Sep 2019 13:24:25 -0700 Subject: [PATCH 171/193] Add delete to trie.py + tests (#1177) * Add delete to trie.py + tests * Minor fixes + tests * Remove noqa comments + modify tests for Travis CI to detect * Minor improvement --- data_structures/trie/trie.py | 84 +++++++++++++++++++++++++++++------- 1 file changed, 68 insertions(+), 16 deletions(-) diff --git a/data_structures/trie/trie.py b/data_structures/trie/trie.py index b6234c6704c6..5a560b97c293 100644 --- a/data_structures/trie/trie.py +++ b/data_structures/trie/trie.py @@ -1,9 +1,8 @@ """ A Trie/Prefix Tree is a kind of search tree used to provide quick lookup of words/patterns in a set of words. A basic Trie however has O(n^2) space complexity -making it impractical in practice. It however provides O(max(search_string, length of longest word)) lookup -time making it an optimal approach when space is not an issue. - +making it impractical in practice. It however provides O(max(search_string, length of longest word)) +lookup time making it an optimal approach when space is not an issue. """ @@ -12,7 +11,7 @@ def __init__(self): self.nodes = dict() # Mapping from char to TrieNode self.is_leaf = False - def insert_many(self, words: [str]): # noqa: E999 This syntax is Python 3 only + def insert_many(self, words: [str]): """ Inserts a list of words into the Trie :param words: list of string words @@ -21,7 +20,7 @@ def insert_many(self, words: [str]): # noqa: E999 This syntax is Python 3 only for word in words: self.insert(word) - def insert(self, word: str): # noqa: E999 This syntax is Python 3 only + def insert(self, word: str): """ Inserts a word into the Trie :param word: word to be inserted @@ -34,7 +33,7 @@ def insert(self, word: str): # noqa: E999 This syntax is Python 3 only curr = curr.nodes[char] curr.is_leaf = True - def find(self, word: str) -> bool: # noqa: E999 This syntax is Python 3 only + def find(self, word: str) -> bool: """ Tries to find word in a Trie :param word: word to look for @@ -47,8 +46,36 @@ def find(self, word: str) -> bool: # noqa: E999 This syntax is Python 3 only curr = curr.nodes[char] return curr.is_leaf + def delete(self, word: str): + """ + Deletes a word in a Trie + :param word: word to delete + :return: None + """ -def print_words(node: TrieNode, word: str): # noqa: E999 This syntax is Python 3 only + def _delete(curr: TrieNode, word: str, index: int): + if index == len(word): + # If word does not exist + if not curr.is_leaf: + return False + curr.is_leaf = False + return len(curr.nodes) == 0 + char = word[index] + char_node = curr.nodes.get(char) + # If char not in current trie node + if not char_node: + return False + # Flag to check if node can be deleted + delete_curr = _delete(char_node, word, index + 1) + if delete_curr: + del curr.nodes[char] + return len(curr.nodes) == 0 + return delete_curr + + _delete(self, word, 0) + + +def print_words(node: TrieNode, word: str): """ Prints all the words in a Trie :param node: root node of Trie @@ -56,20 +83,45 @@ def print_words(node: TrieNode, word: str): # noqa: E999 This syntax is Python :return: None """ if node.is_leaf: - print(word, end=' ') + print(word, end=" ") for key, value in node.nodes.items(): print_words(value, word + key) -def test(): - words = ['banana', 'bananas', 'bandana', 'band', 'apple', 'all', 'beast'] +def test_trie(): + words = "banana bananas bandana band apple all beast".split() root = TrieNode() root.insert_many(words) - # print_words(root, '') - assert root.find('banana') - assert not root.find('bandanas') - assert not root.find('apps') - assert root.find('apple') + # print_words(root, "") + assert all(root.find(word) for word in words) + assert root.find("banana") + assert not root.find("bandanas") + assert not root.find("apps") + assert root.find("apple") + assert root.find("all") + root.delete("all") + assert not root.find("all") + root.delete("banana") + assert not root.find("banana") + assert root.find("bananas") + return True + + +def print_results(msg: str, passes: bool) -> None: + print(str(msg), "works!" if passes else "doesn't work :(") + + +def pytests(): + assert test_trie() + + +def main(): + """ + >>> pytests() + """ + print_results("Testing trie functionality", test_trie()) + -test() +if __name__ == "__main__": + main() From a2b5a90c11ad07f82432fe4b96f6f17bed40e6c8 Mon Sep 17 00:00:00 2001 From: BAKEZQ Date: Wed, 18 Sep 2019 22:01:05 +0800 Subject: [PATCH 172/193] Added sequential minimum optimization algorithm for SVM (#508) * Implementation of sequential minimal optimization algorithm * Update smo.py * Add demonstration of svm partition boundary 1:Use matplotlib show svm's partition boundary 2:Automatically download test dataset * Update smo.py * Update smo.py * Rename smo.py to sequential_minimum_optimization.py * Update doc and simplify the code. Fix filename typo error in doc. Use ternary conditional operator in predict() * Update doc. --- .../sequential_minimum_optimization.py | 526 ++++++++++++++++++ 1 file changed, 526 insertions(+) create mode 100644 machine_learning/sequential_minimum_optimization.py diff --git a/machine_learning/sequential_minimum_optimization.py b/machine_learning/sequential_minimum_optimization.py new file mode 100644 index 000000000000..0b5d788e92e1 --- /dev/null +++ b/machine_learning/sequential_minimum_optimization.py @@ -0,0 +1,526 @@ +# coding: utf-8 +""" + Implementation of sequential minimal optimization(SMO) for support vector machines(SVM). + + Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem + that arises during the training of support vector machines. + It was invented by John Platt in 1998. + +Input: + 0: type: numpy.ndarray. + 1: first column of ndarray must be tags of samples, must be 1 or -1. + 2: rows of ndarray represent samples. + +Usage: + Command: + python3 sequential_minimum_optimization.py + Code: + from sequential_minimum_optimization import SmoSVM, Kernel + + kernel = Kernel(kernel='poly', degree=3., coef0=1., gamma=0.5) + init_alphas = np.zeros(train.shape[0]) + SVM = SmoSVM(train=train, alpha_list=init_alphas, kernel_func=kernel, cost=0.4, b=0.0, tolerance=0.001) + SVM.fit() + predict = SVM.predict(test_samples) + +Reference: + https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/smo-book.pdf + https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf + http://web.cs.iastate.edu/~honavar/smo-svm.pdf +""" + +from __future__ import division + +import os +import sys +import urllib.request + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from sklearn.datasets import make_blobs, make_circles +from sklearn.preprocessing import StandardScaler + +CANCER_DATASET_URL = 'http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data' + + +class SmoSVM(object): + def __init__(self, train, kernel_func, alpha_list=None, cost=0.4, b=0.0, tolerance=0.001, auto_norm=True): + self._init = True + self._auto_norm = auto_norm + self._c = np.float64(cost) + self._b = np.float64(b) + self._tol = np.float64(tolerance) if tolerance > 0.0001 else np.float64(0.001) + + self.tags = train[:, 0] + self.samples = self._norm(train[:, 1:]) if self._auto_norm else train[:, 1:] + self.alphas = alpha_list if alpha_list is not None else np.zeros(train.shape[0]) + self.Kernel = kernel_func + + self._eps = 0.001 + self._all_samples = list(range(self.length)) + self._K_matrix = self._calculate_k_matrix() + self._error = np.zeros(self.length) + self._unbound = [] + + self.choose_alpha = self._choose_alphas() + + # Calculate alphas using SMO algorithsm + def fit(self): + K = self._k + state = None + while True: + + # 1: Find alpha1, alpha2 + try: + i1, i2 = self.choose_alpha.send(state) + state = None + except StopIteration: + print("Optimization done!\r\nEvery sample satisfy the KKT condition!") + break + + # 2: calculate new alpha2 and new alpha1 + y1, y2 = self.tags[i1], self.tags[i2] + a1, a2 = self.alphas[i1].copy(), self.alphas[i2].copy() + e1, e2 = self._e(i1), self._e(i2) + args = (i1, i2, a1, a2, e1, e2, y1, y2) + a1_new, a2_new = self._get_new_alpha(*args) + if not a1_new and not a2_new: + state = False + continue + self.alphas[i1], self.alphas[i2] = a1_new, a2_new + + # 3: update threshold(b) + b1_new = np.float64(-e1 - y1 * K(i1, i1) * (a1_new - a1) - y2 * K(i2, i1) * (a2_new - a2) + self._b) + b2_new = np.float64(-e2 - y2 * K(i2, i2) * (a2_new - a2) - y1 * K(i1, i2) * (a1_new - a1) + self._b) + if 0.0 < a1_new < self._c: + b = b1_new + if 0.0 < a2_new < self._c: + b = b2_new + if not (np.float64(0) < a2_new < self._c) and not (np.float64(0) < a1_new < self._c): + b = (b1_new + b2_new) / 2.0 + b_old = self._b + self._b = b + + # 4: update error value,here we only calculate those non-bound samples' error + self._unbound = [i for i in self._all_samples if self._is_unbound(i)] + for s in self.unbound: + if s == i1 or s == i2: + continue + self._error[s] += y1 * (a1_new - a1) * K(i1, s) + y2 * (a2_new - a2) * K(i2, s) + (self._b - b_old) + + # if i1 or i2 is non-bound,update there error value to zero + if self._is_unbound(i1): + self._error[i1] = 0 + if self._is_unbound(i2): + self._error[i2] = 0 + + # Predict test samles + def predict(self, test_samples, classify=True): + + if test_samples.shape[1] > self.samples.shape[1]: + raise ValueError("Test samples' feature length does not equal to that of train samples") + + if self._auto_norm: + test_samples = self._norm(test_samples) + + results = [] + for test_sample in test_samples: + result = self._predict(test_sample) + if classify: + results.append(1 if result > 0 else -1) + else: + results.append(result) + return np.array(results) + + # Check if alpha violate KKT condition + def _check_obey_kkt(self, index): + alphas = self.alphas + tol = self._tol + r = self._e(index) * self.tags[index] + c = self._c + + return (r < -tol and alphas[index] < c) or (r > tol and alphas[index] > 0.0) + + # Get value calculated from kernel function + def _k(self, i1, i2): + # for test samples,use Kernel function + if isinstance(i2, np.ndarray): + return self.Kernel(self.samples[i1], i2) + # for train samples,Kernel values have been saved in matrix + else: + return self._K_matrix[i1, i2] + + # Get sample's error + def _e(self, index): + """ + Two cases: + 1:Sample[index] is non-bound,Fetch error from list: _error + 2:sample[index] is bound,Use predicted value deduct true value: g(xi) - yi + + """ + # get from error data + if self._is_unbound(index): + return self._error[index] + # get by g(xi) - yi + else: + gx = np.dot(self.alphas * self.tags, self._K_matrix[:, index]) + self._b + yi = self.tags[index] + return gx - yi + + # Calculate Kernel matrix of all possible i1,i2 ,saving time + def _calculate_k_matrix(self): + k_matrix = np.zeros([self.length, self.length]) + for i in self._all_samples: + for j in self._all_samples: + k_matrix[i, j] = np.float64(self.Kernel(self.samples[i, :], self.samples[j, :])) + return k_matrix + + # Predict test sample's tag + def _predict(self, sample): + k = self._k + predicted_value = np.sum( + [self.alphas[i1] * self.tags[i1] * k(i1, sample) for i1 in self._all_samples]) + self._b + return predicted_value + + # Choose alpha1 and alpha2 + def _choose_alphas(self): + locis = yield from self._choose_a1() + if not locis: + return + return locis + + def _choose_a1(self): + """ + Choose first alpha ;steps: + 1:Fisrt loop over all sample + 2:Second loop over all non-bound samples till all non-bound samples does not voilate kkt condition. + 3:Repeat this two process endlessly,till all samples does not voilate kkt condition samples after first loop. + """ + while True: + all_not_obey = True + # all sample + print('scanning all sample!') + for i1 in [i for i in self._all_samples if self._check_obey_kkt(i)]: + all_not_obey = False + yield from self._choose_a2(i1) + + # non-bound sample + print('scanning non-bound sample!') + while True: + not_obey = True + for i1 in [i for i in self._all_samples if self._check_obey_kkt(i) and self._is_unbound(i)]: + not_obey = False + yield from self._choose_a2(i1) + if not_obey: + print('all non-bound samples fit the KKT condition!') + break + if all_not_obey: + print('all samples fit the KKT condition! Optimization done!') + break + return False + + def _choose_a2(self, i1): + """ + Choose the second alpha by using heuristic algorithm ;steps: + 1:Choosed alpha2 which get the maximum step size (|E1 - E2|). + 2:Start in a random point,loop over all non-bound samples till alpha1 and alpha2 are optimized. + 3:Start in a random point,loop over all samples till alpha1 and alpha2 are optimized. + """ + self._unbound = [i for i in self._all_samples if self._is_unbound(i)] + + if len(self.unbound) > 0: + tmp_error = self._error.copy().tolist() + tmp_error_dict = {index: value for index, value in enumerate(tmp_error) if self._is_unbound(index)} + if self._e(i1) >= 0: + i2 = min(tmp_error_dict, key=lambda index: tmp_error_dict[index]) + else: + i2 = max(tmp_error_dict, key=lambda index: tmp_error_dict[index]) + cmd = yield i1, i2 + if cmd is None: + return + + for i2 in np.roll(self.unbound, np.random.choice(self.length)): + cmd = yield i1, i2 + if cmd is None: + return + + for i2 in np.roll(self._all_samples, np.random.choice(self.length)): + cmd = yield i1, i2 + if cmd is None: + return + + # Get the new alpha2 and new alpha1 + def _get_new_alpha(self, i1, i2, a1, a2, e1, e2, y1, y2): + K = self._k + if i1 == i2: + return None, None + + # calculate L and H which bound the new alpha2 + s = y1 * y2 + if s == -1: + L, H = max(0.0, a2 - a1), min(self._c, self._c + a2 - a1) + else: + L, H = max(0.0, a2 + a1 - self._c), min(self._c, a2 + a1) + if L == H: + return None, None + + # calculate eta + k11 = K(i1, i1) + k22 = K(i2, i2) + k12 = K(i1, i2) + eta = k11 + k22 - 2.0 * k12 + + # select the new alpha2 which could get the minimal objectives + if eta > 0.0: + a2_new_unc = a2 + (y2 * (e1 - e2)) / eta + # a2_new has a boundry + if a2_new_unc >= H: + a2_new = H + elif a2_new_unc <= L: + a2_new = L + else: + a2_new = a2_new_unc + else: + b = self._b + l1 = a1 + s * (a2 - L) + h1 = a1 + s * (a2 - H) + + # way 1 + f1 = y1 * (e1 + b) - a1 * K(i1, i1) - s * a2 * K(i1, i2) + f2 = y2 * (e2 + b) - a2 * K(i2, i2) - s * a1 * K(i1, i2) + ol = l1 * f1 + L * f2 + 1 / 2 * l1 ** 2 * K(i1, i1) + 1 / 2 * L ** 2 * K(i2, i2) + s * L * l1 * K(i1, i2) + oh = h1 * f1 + H * f2 + 1 / 2 * h1 ** 2 * K(i1, i1) + 1 / 2 * H ** 2 * K(i2, i2) + s * H * h1 * K(i1, i2) + """ + # way 2 + Use objective function check which alpha2 new could get the minimal objectives + + """ + if ol < (oh - self._eps): + a2_new = L + elif ol > oh + self._eps: + a2_new = H + else: + a2_new = a2 + + # a1_new has a boundry too + a1_new = a1 + s * (a2 - a2_new) + if a1_new < 0: + a2_new += s * a1_new + a1_new = 0 + if a1_new > self._c: + a2_new += s * (a1_new - self._c) + a1_new = self._c + + return a1_new, a2_new + + # Normalise data using min_max way + def _norm(self, data): + if self._init: + self._min = np.min(data, axis=0) + self._max = np.max(data, axis=0) + self._init = False + return (data - self._min) / (self._max - self._min) + else: + return (data - self._min) / (self._max - self._min) + + def _is_unbound(self, index): + if 0.0 < self.alphas[index] < self._c: + return True + else: + return False + + def _is_support(self, index): + if self.alphas[index] > 0: + return True + else: + return False + + @property + def unbound(self): + return self._unbound + + @property + def support(self): + return [i for i in range(self.length) if self._is_support(i)] + + @property + def length(self): + return self.samples.shape[0] + + +class Kernel(object): + def __init__(self, kernel, degree=1.0, coef0=0.0, gamma=1.0): + self.degree = np.float64(degree) + self.coef0 = np.float64(coef0) + self.gamma = np.float64(gamma) + self._kernel_name = kernel + self._kernel = self._get_kernel(kernel_name=kernel) + self._check() + + def _polynomial(self, v1, v2): + return (self.gamma * np.inner(v1, v2) + self.coef0) ** self.degree + + def _linear(self, v1, v2): + return np.inner(v1, v2) + self.coef0 + + def _rbf(self, v1, v2): + return np.exp(-1 * (self.gamma * np.linalg.norm(v1 - v2) ** 2)) + + def _check(self): + if self._kernel == self._rbf: + if self.gamma < 0: + raise ValueError('gamma value must greater than 0') + + def _get_kernel(self, kernel_name): + maps = { + 'linear': self._linear, + 'poly': self._polynomial, + 'rbf': self._rbf + } + return maps[kernel_name] + + def __call__(self, v1, v2): + return self._kernel(v1, v2) + + def __repr__(self): + return self._kernel_name + + +def count_time(func): + def call_func(*args, **kwargs): + import time + start_time = time.time() + func(*args, **kwargs) + end_time = time.time() + print('smo algorithm cost {} seconds'.format(end_time - start_time)) + + return call_func + + +@count_time +def test_cancel_data(): + print('Hello!\r\nStart test svm by smo algorithm!') + # 0: download dataset and load into pandas' dataframe + if not os.path.exists(r'cancel_data.csv'): + request = urllib.request.Request( + CANCER_DATASET_URL, + headers={'User-Agent': 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)'} + ) + response = urllib.request.urlopen(request) + content = response.read().decode('utf-8') + with open(r'cancel_data.csv', 'w') as f: + f.write(content) + + data = pd.read_csv(r'cancel_data.csv', header=None) + + # 1: pre-processing data + del data[data.columns.tolist()[0]] + data = data.dropna(axis=0) + data = data.replace({'M': np.float64(1), 'B': np.float64(-1)}) + samples = np.array(data)[:, :] + + # 2: deviding data into train_data data and test_data data + train_data, test_data = samples[:328, :], samples[328:, :] + test_tags, test_samples = test_data[:, 0], test_data[:, 1:] + + # 3: choose kernel function,and set initial alphas to zero(optional) + mykernel = Kernel(kernel='rbf', degree=5, coef0=1, gamma=0.5) + al = np.zeros(train_data.shape[0]) + + # 4: calculating best alphas using SMO algorithm and predict test_data samples + mysvm = SmoSVM(train=train_data, kernel_func=mykernel, alpha_list=al, cost=0.4, b=0.0, tolerance=0.001) + mysvm.fit() + predict = mysvm.predict(test_samples) + + # 5: check accuracy + score = 0 + test_num = test_tags.shape[0] + for i in range(test_tags.shape[0]): + if test_tags[i] == predict[i]: + score += 1 + print('\r\nall: {}\r\nright: {}\r\nfalse: {}'.format(test_num, score, test_num - score)) + print("Rough Accuracy: {}".format(score / test_tags.shape[0])) + + +def test_demonstration(): + # change stdout + print('\r\nStart plot,please wait!!!') + sys.stdout = open(os.devnull, 'w') + + ax1 = plt.subplot2grid((2, 2), (0, 0)) + ax2 = plt.subplot2grid((2, 2), (0, 1)) + ax3 = plt.subplot2grid((2, 2), (1, 0)) + ax4 = plt.subplot2grid((2, 2), (1, 1)) + ax1.set_title("linear svm,cost:0.1") + test_linear_kernel(ax1, cost=0.1) + ax2.set_title("linear svm,cost:500") + test_linear_kernel(ax2, cost=500) + ax3.set_title("rbf kernel svm,cost:0.1") + test_rbf_kernel(ax3, cost=0.1) + ax4.set_title("rbf kernel svm,cost:500") + test_rbf_kernel(ax4, cost=500) + + sys.stdout = sys.__stdout__ + print("Plot done!!!") + +def test_linear_kernel(ax, cost): + train_x, train_y = make_blobs(n_samples=500, centers=2, + n_features=2, random_state=1) + train_y[train_y == 0] = -1 + scaler = StandardScaler() + train_x_scaled = scaler.fit_transform(train_x, train_y) + train_data = np.hstack((train_y.reshape(500, 1), train_x_scaled)) + mykernel = Kernel(kernel='linear', degree=5, coef0=1, gamma=0.5) + mysvm = SmoSVM(train=train_data, kernel_func=mykernel, cost=cost, tolerance=0.001, auto_norm=False) + mysvm.fit() + plot_partition_boundary(mysvm, train_data, ax=ax) + + +def test_rbf_kernel(ax, cost): + train_x, train_y = make_circles(n_samples=500, noise=0.1, factor=0.1, random_state=1) + train_y[train_y == 0] = -1 + scaler = StandardScaler() + train_x_scaled = scaler.fit_transform(train_x, train_y) + train_data = np.hstack((train_y.reshape(500, 1), train_x_scaled)) + mykernel = Kernel(kernel='rbf', degree=5, coef0=1, gamma=0.5) + mysvm = SmoSVM(train=train_data, kernel_func=mykernel, cost=cost, tolerance=0.001, auto_norm=False) + mysvm.fit() + plot_partition_boundary(mysvm, train_data, ax=ax) + + +def plot_partition_boundary(model, train_data, ax, resolution=100, colors=('b', 'k', 'r')): + """ + We can not get the optimum w of our kernel svm model which is different from linear svm. + For this reason, we generate randomly destributed points with high desity and prediced values of these points are + calculated by using our tained model. Then we could use this prediced values to draw contour map. + And this contour map can represent svm's partition boundary. + + """ + train_data_x = train_data[:, 1] + train_data_y = train_data[:, 2] + train_data_tags = train_data[:, 0] + xrange = np.linspace(train_data_x.min(), train_data_x.max(), resolution) + yrange = np.linspace(train_data_y.min(), train_data_y.max(), resolution) + test_samples = np.array([(x, y) for x in xrange for y in yrange]).reshape(resolution * resolution, 2) + + test_tags = model.predict(test_samples, classify=False) + grid = test_tags.reshape((len(xrange), len(yrange))) + + # Plot contour map which represents the partition boundary + ax.contour(xrange, yrange, np.mat(grid).T, levels=(-1, 0, 1), linestyles=('--', '-', '--'), + linewidths=(1, 1, 1), + colors=colors) + # Plot all train samples + ax.scatter(train_data_x, train_data_y, c=train_data_tags, cmap=plt.cm.Dark2, lw=0, alpha=0.5) + + # Plot support vectors + support = model.support + ax.scatter(train_data_x[support], train_data_y[support], c=train_data_tags[support], cmap=plt.cm.Dark2) + + +if __name__ == '__main__': + test_cancel_data() + test_demonstration() + plt.show() + From 04962c0d17b82f349e0453b06b76d9f594e2b024 Mon Sep 17 00:00:00 2001 From: Denis Trofimov Date: Sat, 21 Sep 2019 17:23:34 +0300 Subject: [PATCH 173/193] Fix lgtm error display #1024 (#1190) * fix: Syntax Error lgtm display in matrix/matrix_operation.py. * Testing for None should use the 'is' operator. * fix: Too many arguments for string format. * fix: supress lgtm alert as false positive. * style: Unnecessary 'pass' statement. * Revert "fix: Syntax Error lgtm display in matrix/matrix_operation.py." This reverts commit 4c629b4ce16a32b99a8127f5553cdb9015bf5e80. --- data_structures/heap/binomial_heap.py | 2 +- divide_and_conquer/convex_hull.py | 2 +- neural_network/convolution_neural_network.py | 3 +-- strings/boyer_moore_search.py | 2 +- 4 files changed, 4 insertions(+), 5 deletions(-) diff --git a/data_structures/heap/binomial_heap.py b/data_structures/heap/binomial_heap.py index bc9cb5145f2e..0154390d7707 100644 --- a/data_structures/heap/binomial_heap.py +++ b/data_structures/heap/binomial_heap.py @@ -307,7 +307,7 @@ def deleteMin(self): # No right subtree corner case # The structure of the tree implies that this should be the bottom root # and there is at least one other root - if self.min_node.right == None: + if self.min_node.right is None: # Update size self.size -= 1 diff --git a/divide_and_conquer/convex_hull.py b/divide_and_conquer/convex_hull.py index a0c319e766da..534ebda2c780 100644 --- a/divide_and_conquer/convex_hull.py +++ b/divide_and_conquer/convex_hull.py @@ -191,7 +191,7 @@ def _validate_input(points): else: raise ValueError("Expecting an iterable of type Point, list or tuple. " "Found objects of type {} instead" - .format(["point", "list", "tuple"], type(points[0]))) + .format(type(points[0]))) elif not hasattr(points, "__iter__"): raise ValueError("Expecting an iterable object " "but got an non-iterable type {}".format(points)) diff --git a/neural_network/convolution_neural_network.py b/neural_network/convolution_neural_network.py index e4dd0a11db9d..786992c054a0 100644 --- a/neural_network/convolution_neural_network.py +++ b/neural_network/convolution_neural_network.py @@ -297,7 +297,6 @@ def convolution(self, data): if __name__ == '__main__': - pass ''' I will put the example on other file -''' + ''' diff --git a/strings/boyer_moore_search.py b/strings/boyer_moore_search.py index 781ff0ca6106..2d67043dc028 100644 --- a/strings/boyer_moore_search.py +++ b/strings/boyer_moore_search.py @@ -70,7 +70,7 @@ def bad_character_heuristic(self): positions.append(i) else: match_index = self.match_in_pattern(self.text[mismatch_index]) - i = mismatch_index - match_index #shifting index + i = mismatch_index - match_index #shifting index lgtm [py/multiple-definition] return positions From 837bfffd991cddb3113139a3d13292b58529f05f Mon Sep 17 00:00:00 2001 From: Holden-Lin Date: Sun, 22 Sep 2019 22:56:32 +0800 Subject: [PATCH 174/193] Rename sorted_vector_machines.py to support_vector_machines.py (#1195) SVM stands for support vector machines. Intuitively, a support vector is the vector right near the decision boundary. --- .../{sorted_vector_machines.py => support_vector_machines.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename machine_learning/{sorted_vector_machines.py => support_vector_machines.py} (100%) diff --git a/machine_learning/sorted_vector_machines.py b/machine_learning/support_vector_machines.py similarity index 100% rename from machine_learning/sorted_vector_machines.py rename to machine_learning/support_vector_machines.py From 01601e6382e92b5a3806fb3a44357460dff97ee7 Mon Sep 17 00:00:00 2001 From: luoheng <1301089462@qq.com> Date: Mon, 23 Sep 2019 11:08:20 +0800 Subject: [PATCH 175/193] Add disjoint set (#1194) * Add disjoint set * disjoint set: add doctest, make code more Pythonic * disjoint set: replace x.p with x.parent * disjoint set: add test and refercence --- data_structures/disjoint_set/disjoint_set.py | 79 ++++++++++++++++++++ 1 file changed, 79 insertions(+) create mode 100644 data_structures/disjoint_set/disjoint_set.py diff --git a/data_structures/disjoint_set/disjoint_set.py b/data_structures/disjoint_set/disjoint_set.py new file mode 100644 index 000000000000..a93b89621c4a --- /dev/null +++ b/data_structures/disjoint_set/disjoint_set.py @@ -0,0 +1,79 @@ +""" + disjoint set + Reference: https://en.wikipedia.org/wiki/Disjoint-set_data_structure +""" + + +class Node: + def __init__(self, data): + self.data = data + + +def make_set(x): + """ + make x as a set. + """ + # rank is the distance from x to its' parent + # root's rank is 0 + x.rank = 0 + x.parent = x + + +def union_set(x, y): + """ + union two sets. + set with bigger rank should be parent, so that the + disjoint set tree will be more flat. + """ + x, y = find_set(x), find_set(y) + if x.rank > y.rank: + y.parent = x + else: + x.parent = y + if x.rank == y.rank: + y.rank += 1 + + +def find_set(x): + """ + return the parent of x + """ + if x != x.parent: + x.parent = find_set(x.parent) + return x.parent + + +def find_python_set(node: Node) -> set: + """ + Return a Python Standard Library set that contains i. + """ + sets = ({0, 1, 2}, {3, 4, 5}) + for s in sets: + if node.data in s: + return s + raise ValueError(f"{node.data} is not in {sets}") + + +def test_disjoint_set(): + """ + >>> test_disjoint_set() + """ + vertex = [Node(i) for i in range(6)] + for v in vertex: + make_set(v) + + union_set(vertex[0], vertex[1]) + union_set(vertex[1], vertex[2]) + union_set(vertex[3], vertex[4]) + union_set(vertex[3], vertex[5]) + + for node0 in vertex: + for node1 in vertex: + if find_python_set(node0).isdisjoint(find_python_set(node1)): + assert find_set(node0) != find_set(node1) + else: + assert find_set(node0) == find_set(node1) + + +if __name__ == "__main__": + test_disjoint_set() From e40d4a25f9b7a58d08fdc5d247a25cb226a46d21 Mon Sep 17 00:00:00 2001 From: Aniruddha Bhattacharjee Date: Wed, 25 Sep 2019 23:38:45 +0530 Subject: [PATCH 176/193] Added Matrix Exponentiation (#1203) * Added the matrix_exponentiation.py file in maths directory * Implemented the requested changes * Update matrix_exponentiation.py --- maths/matrix_exponentiation.py | 99 ++++++++++++++++++++++++++++++++++ 1 file changed, 99 insertions(+) create mode 100644 maths/matrix_exponentiation.py diff --git a/maths/matrix_exponentiation.py b/maths/matrix_exponentiation.py new file mode 100644 index 000000000000..ee9e1757687c --- /dev/null +++ b/maths/matrix_exponentiation.py @@ -0,0 +1,99 @@ +"""Matrix Exponentiation""" + +import timeit + +""" +Matrix Exponentiation is a technique to solve linear recurrences in logarithmic time. +You read more about it here: +http://zobayer.blogspot.com/2010/11/matrix-exponentiation.html +https://www.hackerearth.com/practice/notes/matrix-exponentiation-1/ +""" + + +class Matrix(object): + def __init__(self, arg): + if isinstance(arg, list): # Initialzes a matrix identical to the one provided. + self.t = arg + self.n = len(arg) + else: # Initializes a square matrix of the given size and set the values to zero. + self.n = arg + self.t = [[0 for _ in range(self.n)] for _ in range(self.n)] + + def __mul__(self, b): + matrix = Matrix(self.n) + for i in range(self.n): + for j in range(self.n): + for k in range(self.n): + matrix.t[i][j] += self.t[i][k] * b.t[k][j] + return matrix + + +def modular_exponentiation(a, b): + matrix = Matrix([[1, 0], [0, 1]]) + while b > 0: + if b & 1: + matrix *= a + a *= a + b >>= 1 + return matrix + + +def fibonacci_with_matrix_exponentiation(n, f1, f2): + # Trivial Cases + if n == 1: + return f1 + elif n == 2: + return f2 + matrix = Matrix([[1, 1], [1, 0]]) + matrix = modular_exponentiation(matrix, n - 2) + return f2 * matrix.t[0][0] + f1 * matrix.t[0][1] + + +def simple_fibonacci(n, f1, f2): + # Trival Cases + if n == 1: + return f1 + elif n == 2: + return f2 + + fn_1 = f1 + fn_2 = f2 + n -= 2 + + while n > 0: + fn_1, fn_2 = fn_1 + fn_2, fn_1 + n -= 1 + + return fn + + +def matrix_exponentiation_time(): + setup = """ +from random import randint +from __main__ import fibonacci_with_matrix_exponentiation +""" + code = "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)" + exec_time = timeit.timeit(setup=setup, stmt=code, number=100) + print("With matrix exponentiation the average execution time is ", exec_time / 100) + return exec_time + + +def simple_fibonacci_time(): + setup = """ +from random import randint +from __main__ import simple_fibonacci +""" + code = "simple_fibonacci(randint(1,70000), 1, 1)" + exec_time = timeit.timeit(setup=setup, stmt=code, number=100) + print("Without matrix exponentiation the average execution time is ", + exec_time / 100) + return exec_time + + +def main(): + matrix_exponentiation_time() + simple_fibonacci_time() + + +if __name__ == "__main__": + main() From 6ac7b1387f9b2c2fa428bbe2bd26d3e087e41b23 Mon Sep 17 00:00:00 2001 From: Raj Date: Wed, 25 Sep 2019 15:03:31 -0700 Subject: [PATCH 177/193] Min head with decrease key functionality (#1202) * Min head with decrease key functionality * doctest added * __str__ changed as per Python convention * edits in doctest * get_value by key added * __getitem__ added --- data_structures/heap/min_heap.py | 169 +++++++++++++++++++++++++++++++ 1 file changed, 169 insertions(+) create mode 100644 data_structures/heap/min_heap.py diff --git a/data_structures/heap/min_heap.py b/data_structures/heap/min_heap.py new file mode 100644 index 000000000000..6184d83be774 --- /dev/null +++ b/data_structures/heap/min_heap.py @@ -0,0 +1,169 @@ +# Min head data structure +# with decrease key functionality - in O(log(n)) time + + +class Node: + def __init__(self, name, val): + self.name = name + self.val = val + + def __str__(self): + return f"{self.__class__.__name__}({self.name}, {self.val})" + + def __lt__(self, other): + return self.val < other.val + + +class MinHeap: + """ + >>> r = Node("R", -1) + >>> b = Node("B", 6) + >>> a = Node("A", 3) + >>> x = Node("X", 1) + >>> e = Node("E", 4) + >>> print(b) + Node(B, 6) + >>> myMinHeap = MinHeap([r, b, a, x, e]) + >>> myMinHeap.decrease_key(b, -17) + >>> print(b) + Node(B, -17) + >>> print(myMinHeap["B"]) + -17 + """ + + def __init__(self, array): + self.idx_of_element = {} + self.heap_dict = {} + self.heap = self.build_heap(array) + + def __getitem__(self, key): + return self.get_value(key) + + def get_parent_idx(self, idx): + return (idx - 1) // 2 + + def get_left_child_idx(self, idx): + return idx * 2 + 1 + + def get_right_child_idx(self, idx): + return idx * 2 + 2 + + def get_value(self, key): + return self.heap_dict[key] + + def build_heap(self, array): + lastIdx = len(array) - 1 + startFrom = self.get_parent_idx(lastIdx) + + for idx, i in enumerate(array): + self.idx_of_element[i] = idx + self.heap_dict[i.name] = i.val + + for i in range(startFrom, -1, -1): + self.sift_down(i, array) + return array + + # this is min-heapify method + def sift_down(self, idx, array): + while True: + l = self.get_left_child_idx(idx) + r = self.get_right_child_idx(idx) + + smallest = idx + if l < len(array) and array[l] < array[idx]: + smallest = l + if r < len(array) and array[r] < array[smallest]: + smallest = r + + if smallest != idx: + array[idx], array[smallest] = array[smallest], array[idx] + self.idx_of_element[array[idx]], self.idx_of_element[ + array[smallest] + ] = ( + self.idx_of_element[array[smallest]], + self.idx_of_element[array[idx]], + ) + idx = smallest + else: + break + + def sift_up(self, idx): + p = self.get_parent_idx(idx) + while p >= 0 and self.heap[p] > self.heap[idx]: + self.heap[p], self.heap[idx] = self.heap[idx], self.heap[p] + self.idx_of_element[self.heap[p]], self.idx_of_element[self.heap[idx]] = ( + self.idx_of_element[self.heap[idx]], + self.idx_of_element[self.heap[p]], + ) + idx = p + p = self.get_parent_idx(idx) + + def peek(self): + return self.heap[0] + + def remove(self): + self.heap[0], self.heap[-1] = self.heap[-1], self.heap[0] + self.idx_of_element[self.heap[0]], self.idx_of_element[self.heap[-1]] = ( + self.idx_of_element[self.heap[-1]], + self.idx_of_element[self.heap[0]], + ) + + x = self.heap.pop() + del self.idx_of_element[x] + self.sift_down(0, self.heap) + return x + + def insert(self, node): + self.heap.append(node) + self.idx_of_element[node] = len(self.heap) - 1 + self.heap_dict[node.name] = node.val + self.sift_up(len(self.heap) - 1) + + def is_empty(self): + return True if len(self.heap) == 0 else False + + def decrease_key(self, node, newValue): + assert ( + self.heap[self.idx_of_element[node]].val > newValue + ), "newValue must be less that current value" + node.val = newValue + self.heap_dict[node.name] = newValue + self.sift_up(self.idx_of_element[node]) + + +## USAGE + +r = Node("R", -1) +b = Node("B", 6) +a = Node("A", 3) +x = Node("X", 1) +e = Node("E", 4) + +# Use one of these two ways to generate Min-Heap + +# Generating Min-Heap from array +myMinHeap = MinHeap([r, b, a, x, e]) + +# Generating Min-Heap by Insert method +# myMinHeap.insert(a) +# myMinHeap.insert(b) +# myMinHeap.insert(x) +# myMinHeap.insert(r) +# myMinHeap.insert(e) + +# Before +print("Min Heap - before decrease key") +for i in myMinHeap.heap: + print(i) + +print("Min Heap - After decrease key of node [B -> -17]") +myMinHeap.decrease_key(b, -17) + +# After +for i in myMinHeap.heap: + print(i) + +if __name__ == "__main__": + import doctest + + doctest.testmod() From 2375bfbee59f091435818d850e5057fdd50ffe19 Mon Sep 17 00:00:00 2001 From: Charitoc <37042130+Charitoc@users.noreply.github.com> Date: Thu, 26 Sep 2019 18:19:01 +0300 Subject: [PATCH 178/193] Adding stooge sort (#1206) * Adding stooge sort * Updated doctest * Just added underscore in the name --- sorts/stooge_sort.py | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 sorts/stooge_sort.py diff --git a/sorts/stooge_sort.py b/sorts/stooge_sort.py new file mode 100644 index 000000000000..d2325abc9b38 --- /dev/null +++ b/sorts/stooge_sort.py @@ -0,0 +1,34 @@ +def stooge_sort(arr): + """ + >>> arr = [2, 4, 5, 3, 1] + >>> stooge_sort(arr) + >>> print(arr) + [1, 2, 3, 4, 5] + """ + stooge(arr,0,len(arr)-1) + + +def stooge(arr, i, h): + + + if i >= h: + return + + # If first element is smaller than the last then swap them + if arr[i]>arr[h]: + arr[i], arr[h] = arr[h], arr[i] + + # If there are more than 2 elements in the array + if h-i+1 > 2: + t = (int)((h-i+1)/3) + + # Recursively sort first 2/3 elements + stooge(arr, i, (h-t)) + + # Recursively sort last 2/3 elements + stooge(arr, i+t, (h)) + + # Recursively sort first 2/3 elements + stooge(arr, i, (h-t)) + + From a79fc2b92ad2c36e77d1c1696a2a0bc746ef9fea Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Thu, 26 Sep 2019 17:32:04 +0200 Subject: [PATCH 179/193] Fix the build typo: fn --> fn1 (#1205) --- maths/matrix_exponentiation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/maths/matrix_exponentiation.py b/maths/matrix_exponentiation.py index ee9e1757687c..f80f6c3cad5e 100644 --- a/maths/matrix_exponentiation.py +++ b/maths/matrix_exponentiation.py @@ -64,7 +64,7 @@ def simple_fibonacci(n, f1, f2): fn_1, fn_2 = fn_1 + fn_2, fn_1 n -= 1 - return fn + return fn_1 def matrix_exponentiation_time(): From 4617aa78b2a61fcfebc136f7fc217ea980205067 Mon Sep 17 00:00:00 2001 From: Kaushik Amar Das Date: Sun, 29 Sep 2019 14:14:41 +0530 Subject: [PATCH 180/193] DBSCAN algorithm (#1207) * Added dbscan in two formats. A jupyter notebook file for the storytelling and a .py file for people that just want to look at the code. The code in both is essentially the same. With a few things different in the .py file for plotting the clusters. * fixed LGTM problems * Some requested changes implemented. Still need to do docstring * implememted all changes as requested --- machine_learning/dbscan/dbscan.ipynb | 376 +++++++++++++++++++++++++++ machine_learning/dbscan/dbscan.py | 271 +++++++++++++++++++ 2 files changed, 647 insertions(+) create mode 100644 machine_learning/dbscan/dbscan.ipynb create mode 100644 machine_learning/dbscan/dbscan.py diff --git a/machine_learning/dbscan/dbscan.ipynb b/machine_learning/dbscan/dbscan.ipynb new file mode 100644 index 000000000000..603a4cd405b9 --- /dev/null +++ b/machine_learning/dbscan/dbscan.ipynb @@ -0,0 +1,376 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DBSCAN\n", + "This implementation and notebook is inspired from the original DBSCAN algorithm and article as given in \n", + "[DBSCAN Wikipedia](https://en.wikipedia.org/wiki/DBSCAN).\n", + "\n", + "Stands for __Density-based spatial clustering of applications with noise__ . \n", + "\n", + "DBSCAN is clustering algorithm that tries to captures the intuition that if two points belong to the same cluster they should be close to one another. It does so by finding regions that are densely packed together, i.e, the points that have many close neighbours.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### When to use ?\n", + "\n", + "1. You need a robust clustering algorithm.\n", + "2. You don't know how many clusters there are in the dataset\n", + "3. You find it difficult to guess the number of clusters there are just by eyeballing the dataset.\n", + "4. The clusters are of arbitrary shapes.\n", + "5. You want to detect outliers/noise." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why DBSCAN ? \n", + "\n", + "This algorithm is way better than other clustering algorithms such as [k-means](https://en.wikipedia.org/wiki/K-means_clustering) whose only job is to find circular blobs. It is smart enough to figure out the number of clusters in the dataset on its own, unlike k-means where you need to specify 'k'. It can also find clusters of arbitrary shapes, not just circular blobs. Its too robust to be affected by outliers (the noise points) and isn't fooled by them, unlike k-means where the entire centroid get pulled thanks to pesky outliers. Plus, you can fine-tune its parameters depending on what you are clustering.\n", + "\n", + "#### Have a look at these [neat animations](https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/) of DBSCAN to see for yourself." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## First lets grab a dataset\n", + "We will take the moons dataset which is pretty good at showing the power of DBSCAN. \n", + "\n", + "Lets generate 200 random points in the shape of two moons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import make_moons\n", + "\n", + "x, label = make_moons(n_samples=200, noise=0.1, random_state=19)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the dataset using matplotlib\n", + "You will observe that the points are in the shape of two crescent moons. \n", + "\n", + "The challenge here is to cluster the two moons. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x[:,0], x[:,1],'ro')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Abstract of the Algorithm\n", + "The DBSCAN algorithm can be abstracted into the following steps:\n", + "\n", + "- Find the points in the $ε$ (eps) neighborhood of every point, and identify the core points with more than min_pts neighbors.\n", + "- Find the connected components of core points on the neighbor graph, ignoring all non-core points.\n", + "- Assign each non-core point to a nearby cluster if the cluster is an $ε$ (eps) neighbor, otherwise assign it to noise.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparing the points\n", + "Initially we label all the points in the dataset as __undefined__ .\n", + "\n", + "__points__ is our database of all points in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "points = { (point[0],point[1]):{'label':'undefined'} for point in x }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def euclidean_distance(q, p):\n", + " \"\"\"\n", + " Calculates the Euclidean distance\n", + " between points P and Q\n", + " \"\"\"\n", + " a = pow((q[0] - p[0]), 2)\n", + " b = pow((q[1] - p[1]), 2)\n", + " return pow((a + b), 0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def find_neighbors(db, q, eps):\n", + " \"\"\"\n", + " Finds all points in the DB that\n", + " are within a distance of eps from Q\n", + " \"\"\"\n", + " return [p for p in db if euclidean_distance(q, p) <= eps]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_cluster(db, clusters):\n", + " \"\"\"\n", + " Extracts all the points in the DB and puts them together\n", + " as seperate clusters and finally plots them\n", + " \"\"\"\n", + " temp = []\n", + " noise = []\n", + " for i in clusters:\n", + " stack = []\n", + " for k, v in db.items():\n", + " if v[\"label\"] == i:\n", + " stack.append(k)\n", + " elif v[\"label\"] == \"noise\":\n", + " noise.append(k)\n", + " temp.append(stack)\n", + "\n", + " color = iter(plt.cm.rainbow(np.linspace(0, 1, len(clusters))))\n", + " for i in range(0, len(temp)):\n", + " c = next(color)\n", + " x = [l[0] for l in temp[i]]\n", + " y = [l[1] for l in temp[i]]\n", + " plt.plot(x, y, \"ro\", c=c)\n", + "\n", + " x = [l[0] for l in noise]\n", + " y = [l[1] for l in noise]\n", + " plt.plot(x, y, \"ro\", c=\"0\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Implementation of DBSCAN\n", + "\n", + "Initialize an empty list, clusters = $[ ]$ and cluster identifier, c = 0\n", + "\n", + "1. For each point p in our database/dict db :\n", + "\n", + " 1.1 Check if p is already labelled. If it's already labelled (means it already been associated to a cluster), continue to the next point,i.e, go to step 1\n", + " \n", + " 1.2. Find the list of neighbors of p , i.e, points that are within a distance of eps from p\n", + " \n", + " 1.3. If p does not have atleast min_pts neighbours, we label it as noise and go back to step 1\n", + " \n", + " 1.4. Initialize the cluster, by incrementing c by 1\n", + " \n", + " 1.5. Append the cluster identifier c to clusters\n", + " \n", + " 1.6. Label p with the cluster identifier c\n", + " \n", + " 1.7 Remove p from the list of neighbors (p will be detected as its own neighbor because it is within eps of itself)\n", + " \n", + " 1.8. Initialize the seed_set as a copy of neighbors\n", + " \n", + " 1.9. While the seed_set is not empty:\n", + " 1.9.1. Removing the 1st point from seed_set and initialise it as q\n", + " 1.9.2. If it's label is noise, label it with c\n", + " 1.9.3. If it's not unlabelled, go back to step 1.9\n", + " 1.9.4. Label q with c\n", + " 1.9.5. Find the neighbours of q \n", + " 1.9.6. If there are atleast min_pts neighbors, append them to the seed_set" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def dbscan(db,eps,min_pts):\n", + " '''\n", + " Implementation of the DBSCAN algorithm\n", + " '''\n", + " clusters = []\n", + " c = 0\n", + " for p in db:\n", + " if db[p][\"label\"] != \"undefined\":\n", + " continue\n", + " neighbors = find_neighbors(db, p, eps)\n", + " if len(neighbors) < min_pts:\n", + " db[p][\"label\"] = \"noise\"\n", + " continue\n", + " c += 1\n", + " clusters.append(c)\n", + " db[p][\"label\"] = c\n", + " neighbors.remove(p)\n", + " seed_set = neighbors.copy()\n", + " while seed_set != []:\n", + " q = seed_set.pop(0)\n", + " if db[q][\"label\"] == \"noise\":\n", + " db[q][\"label\"] = c\n", + " if db[q][\"label\"] != \"undefined\":\n", + " continue\n", + " db[q][\"label\"] = c\n", + " neighbors_n = find_neighbors(db, q, eps)\n", + " if len(neighbors_n) >= min_pts:\n", + " seed_set = seed_set + neighbors_n\n", + " return db, clusters\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets run it!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "eps = 0.25\n", + "min_pts = 12\n", + "\n", + "db,clusters = dbscan(points,eps,min_pts)\n", + "\n", + "plot_cluster(db,clusters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I encourage you to try with different datasets and playing with the values of eps and min_pts.\n", + "\n", + "Also, try kmeans on this dataset and see how it compares to dbscan. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I hope by now you are convinced about about how cool dbscan is. But it has its pitfalls.\n", + "### When NOT to use ?\n", + "\n", + "1. You have a high dimentional dataset. Euclidean distance will fail thanks to '[curse of dimentionality](https://en.wikipedia.org/wiki/Curse_of_dimensionality#Distance_functions)'.\n", + "2. We have used a dict to store the points. So we can't do anything about the order in which the points will be processed. So it's not entirely deterministic.\n", + "3. Won't work well if there are large differences in density. Finding the min_pts and $ε$ combination will be difficult.\n", + "4. Choosing the $ε$ without understanding the data and its scale, might result is poor clustering performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/machine_learning/dbscan/dbscan.py b/machine_learning/dbscan/dbscan.py new file mode 100644 index 000000000000..04fb5f0186e1 --- /dev/null +++ b/machine_learning/dbscan/dbscan.py @@ -0,0 +1,271 @@ +import matplotlib.pyplot as plt +import numpy as np +from sklearn.datasets import make_moons +import warnings + + +def euclidean_distance(q, p): + """ + Calculates the Euclidean distance + between points q and p + + Distance can only be calculated between numeric values + >>> euclidean_distance([1,'a'],[1,2]) + Traceback (most recent call last): + ... + ValueError: Non-numeric input detected + + The dimentions of both the points must be the same + >>> euclidean_distance([1,1,1],[1,2]) + Traceback (most recent call last): + ... + ValueError: expected dimensions to be 2-d, instead got p:3 and q:2 + + Supports only two dimentional points + >>> euclidean_distance([1,1,1],[1,2]) + Traceback (most recent call last): + ... + ValueError: expected dimensions to be 2-d, instead got p:3 and q:2 + + Input should be in the format [x,y] or (x,y) + >>> euclidean_distance(1,2) + Traceback (most recent call last): + ... + TypeError: inputs must be iterable, either list [x,y] or tuple (x,y) + """ + if not hasattr(q, "__iter__") or not hasattr(p, "__iter__"): + raise TypeError("inputs must be iterable, either list [x,y] or tuple (x,y)") + + if isinstance(q, str) or isinstance(p, str): + raise TypeError("inputs cannot be str") + + if len(q) != 2 or len(p) != 2: + raise ValueError( + "expected dimensions to be 2-d, instead got p:{} and q:{}".format( + len(q), len(p) + ) + ) + + for num in q + p: + try: + num = int(num) + except: + raise ValueError("Non-numeric input detected") + + a = pow((q[0] - p[0]), 2) + b = pow((q[1] - p[1]), 2) + return pow((a + b), 0.5) + + +def find_neighbors(db, q, eps): + """ + Finds all points in the db that + are within a distance of eps from Q + + eps value should be a number + >>> find_neighbors({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}}, (2,5),'a') + Traceback (most recent call last): + ... + ValueError: eps should be either int or float + + Q must be a 2-d point as list or tuple + >>> find_neighbors({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}}, 2, 0.5) + Traceback (most recent call last): + ... + TypeError: Q must a 2-dimentional point in the format (x,y) or [x,y] + + Points must be in correct format + >>> find_neighbors([], (2,2) ,0.4) + Traceback (most recent call last): + ... + TypeError: db must be a dict of points in the format {(x,y):{'label':'boolean/undefined'}} + """ + + if not isinstance(eps, (int, float)): + raise ValueError("eps should be either int or float") + + if not hasattr(q, "__iter__"): + raise TypeError("Q must a 2-dimentional point in the format (x,y) or [x,y]") + + if not isinstance(db, dict): + raise TypeError( + "db must be a dict of points in the format {(x,y):{'label':'boolean/undefined'}}" + ) + + return [p for p in db if euclidean_distance(q, p) <= eps] + + +def plot_cluster(db, clusters, ax): + """ + Extracts all the points in the db and puts them together + as seperate clusters and finally plots them + + db cannot be empty + >>> fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(7, 5)) + >>> plot_cluster({},[1,2], axes[1] ) + Traceback (most recent call last): + ... + Exception: db is empty. No points to cluster + + clusters cannot be empty + >>> fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(7, 5)) + >>> plot_cluster({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}},[],axes[1] ) + Traceback (most recent call last): + ... + Exception: nothing to cluster. Empty clusters + + clusters cannot be empty + >>> fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(7, 5)) + >>> plot_cluster({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}},[],axes[1] ) + Traceback (most recent call last): + ... + Exception: nothing to cluster. Empty clusters + + ax must be a plotable + >>> plot_cluster({ (1,2):{'label':'1'}, (2,3):{'label':'2'}},[1,2], [] ) + Traceback (most recent call last): + ... + TypeError: ax must be an slot in a matplotlib figure + """ + if len(db) == 0: + raise Exception("db is empty. No points to cluster") + + if len(clusters) == 0: + raise Exception("nothing to cluster. Empty clusters") + + if not hasattr(ax, "plot"): + raise TypeError("ax must be an slot in a matplotlib figure") + + temp = [] + noise = [] + for i in clusters: + stack = [] + for k, v in db.items(): + if v["label"] == i: + stack.append(k) + elif v["label"] == "noise": + noise.append(k) + temp.append(stack) + + color = iter(plt.cm.rainbow(np.linspace(0, 1, len(clusters)))) + for i in range(0, len(temp)): + c = next(color) + x = [l[0] for l in temp[i]] + y = [l[1] for l in temp[i]] + ax.plot(x, y, "ro", c=c) + + x = [l[0] for l in noise] + y = [l[1] for l in noise] + ax.plot(x, y, "ro", c="0") + + +def dbscan(db, eps, min_pts): + """ + Implementation of the DBSCAN algorithm + + Points must be in correct format + >>> dbscan([], (2,2) ,0.4) + Traceback (most recent call last): + ... + TypeError: db must be a dict of points in the format {(x,y):{'label':'boolean/undefined'}} + + eps value should be a number + >>> dbscan({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}},'a',20 ) + Traceback (most recent call last): + ... + ValueError: eps should be either int or float + + min_pts value should be an integer + >>> dbscan({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}},0.4,20.0 ) + Traceback (most recent call last): + ... + ValueError: min_pts should be int + + db cannot be empty + >>> dbscan({},0.4,20.0 ) + Traceback (most recent call last): + ... + Exception: db is empty, nothing to cluster + + min_pts cannot be negative + >>> dbscan({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}}, 0.4, -20) + Traceback (most recent call last): + ... + ValueError: min_pts or eps cannot be negative + + eps cannot be negative + >>> dbscan({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}},-0.4, 20) + Traceback (most recent call last): + ... + ValueError: min_pts or eps cannot be negative + + """ + if not isinstance(db, dict): + raise TypeError( + "db must be a dict of points in the format {(x,y):{'label':'boolean/undefined'}}" + ) + + if len(db) == 0: + raise Exception("db is empty, nothing to cluster") + + if not isinstance(eps, (int, float)): + raise ValueError("eps should be either int or float") + + if not isinstance(min_pts, int): + raise ValueError("min_pts should be int") + + if min_pts < 0 or eps < 0: + raise ValueError("min_pts or eps cannot be negative") + + if min_pts == 0: + warnings.warn("min_pts is 0. Are you sure you want this ?") + + if eps == 0: + warnings.warn("eps is 0. Are you sure you want this ?") + + clusters = [] + c = 0 + for p in db: + if db[p]["label"] != "undefined": + continue + neighbors = find_neighbors(db, p, eps) + if len(neighbors) < min_pts: + db[p]["label"] = "noise" + continue + c += 1 + clusters.append(c) + db[p]["label"] = c + neighbors.remove(p) + seed_set = neighbors.copy() + while seed_set != []: + q = seed_set.pop(0) + if db[q]["label"] == "noise": + db[q]["label"] = c + if db[q]["label"] != "undefined": + continue + db[q]["label"] = c + neighbors_n = find_neighbors(db, q, eps) + if len(neighbors_n) >= min_pts: + seed_set = seed_set + neighbors_n + return db, clusters + + +if __name__ == "__main__": + + fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(7, 5)) + + x, label = make_moons(n_samples=200, noise=0.1, random_state=19) + + axes[0].plot(x[:, 0], x[:, 1], "ro") + + points = {(point[0], point[1]): {"label": "undefined"} for point in x} + + eps = 0.25 + + min_pts = 12 + + db, clusters = dbscan(points, eps, min_pts) + + plot_cluster(db, clusters, axes[1]) + + plt.show() From 189b35031224e78e10b668e19fc4b2a1966d19c1 Mon Sep 17 00:00:00 2001 From: yijoonsu <44707391+paulo9428@users.noreply.github.com> Date: Mon, 30 Sep 2019 23:27:41 +0900 Subject: [PATCH 181/193] Deque (#1200) * deque add pop * deque add remove --- data_structures/queue/double_ended_queue.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/data_structures/queue/double_ended_queue.py b/data_structures/queue/double_ended_queue.py index a2fc8f66ec22..a3cfa7230710 100644 --- a/data_structures/queue/double_ended_queue.py +++ b/data_structures/queue/double_ended_queue.py @@ -37,3 +37,21 @@ # printing modified deque print("The deque after reversing deque is : ") print(de) + +# get right-end value and eliminate +startValue = de.pop() + +print("The deque after popping value at end is : ") +print(de) + +# get left-end value and eliminate +endValue = de.popleft() + +print("The deque after popping value at start is : ") +print(de) + +# eliminate element searched by value +de.remove(5) + +print("The deque after eliminating element searched by value : ") +print(de) From b738281f2b0cf44257e50b4f21ad74c8cbb1714e Mon Sep 17 00:00:00 2001 From: Shoaib Asgar Date: Tue, 1 Oct 2019 12:28:00 +0530 Subject: [PATCH 182/193] maths-polynomial_evalutation (#1214) * maths-polynomial_evalutation * added doctest and removed redundancy --- maths/polynomial_evaluation.py | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 maths/polynomial_evaluation.py diff --git a/maths/polynomial_evaluation.py b/maths/polynomial_evaluation.py new file mode 100644 index 000000000000..b4f18b9fa106 --- /dev/null +++ b/maths/polynomial_evaluation.py @@ -0,0 +1,25 @@ +def evaluate_poly(poly, x): + """ + Objective: Computes the polynomial function for a given value x. + Returns that value. + Input Prams: + poly: tuple of numbers - value of cofficients + x: value for x in f(x) + Return: value of f(x) + + >>> evaluate_poly((0.0, 0.0, 5.0, 9.3, 7.0), 10) + 79800.0 + """ + + return sum(c*(x**i) for i, c in enumerate(poly)) + + +if __name__ == "__main__": + """ + Example: poly = (0.0, 0.0, 5.0, 9.3, 7.0) # f(x) = 7.0x^4 + 9.3x^3 + 5.0x^2 + x = -13 + print (evaluate_poly(poly, x)) # f(-13) = 7.0(-13)^4 + 9.3(-13)^3 + 5.0(-13)^2 = 180339.9 + """ + poly = (0.0, 0.0, 5.0, 9.3, 7.0) + x = 10 + print(evaluate_poly(poly, x)) From df44d1b703d756f965f0085ac05e91810b8e9c21 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Wed, 2 Oct 2019 18:19:00 +0200 Subject: [PATCH 183/193] Update CONTRIBUTING.md (#1250) * Update CONTRIBUTING.md * Update CONTRIBUTING.md * Add Python type hints and mypy --- CONTRIBUTING.md | 88 ++++++++++++++++++++++++++++--------------------- 1 file changed, 51 insertions(+), 37 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 8c0f54ad528d..8cd03217d51f 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -23,26 +23,38 @@ We are very happy that you consider implementing algorithms and data structure f We appreciate any contribution, from fixing a grammar mistake in a comment to implementing complex algorithms. Please read this section if you are contributing your work. +Your contribution will be tested by our [automated testing on Travis CI](https://travis-ci.org/TheAlgorithms/Python/pull_requests) to save time and mental energy. After you have submitted your pull request, you should see the Travis tests start to run at the bottom of your submission page. If those tests fail, then click on the ___details___ button try to read through the Travis output to understand the failure. If you do not understand, please leave a comment on your submission page and a community member will try to help. + #### Coding Style We want your work to be readable by others; therefore, we encourage you to note the following: -- Please write in Python 3.x. -- Please consider running [__python/black__](https://github.com/python/black) on your Python file(s) before submitting your pull request. This is not a requirement but it does make your code more readable. There are other code formatters (autopep8, yapf) but the __black__ style is now the recommendation of the Python core team. To use it, +- Please write in Python 3.7+. __print()__ is a function in Python 3 so __print "Hello"__ will _not_ work but __print("Hello")__ will. + +- Please focus hard on naming of functions, classes, and variables. Help your reader by using __descriptive names__ that can help you to remove redundant comments. + - Single letter variable names are _old school_ so please avoid them unless their life only spans a few lines. + - Expand acronyms because __gcd()__ is hard to understand but __greatest_common_divisor()__ is not. + - Please follow the [Python Naming Conventions](https://pep8.org/#prescriptive-naming-conventions) so variable_names and function_names should be lower_case, CONSTANTS in UPPERCASE, ClassNames should be CamelCase, etc. + +- We encourage the use of Python [f-strings](https://realpython.com/python-f-strings/#f-strings-a-new-and-improved-way-to-format-strings-in-python) where the make the code easier to read. + +- Please consider running [__psf/black__](https://github.com/python/black) on your Python file(s) before submitting your pull request. This is not yet a requirement but it does make your code more readable and automatically aligns it with much of [PEP 8](https://www.python.org/dev/peps/pep-0008/). There are other code formatters (autopep8, yapf) but the __black__ style is now the recommendation of the Python Core Team. To use it, ```bash pip3 install black # only required the first time - black my-submission.py + black . ``` - All submissions will need to pass the test __flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics__ before they will be accepted so if possible, try this test locally on your Python file(s) before submitting your pull request. + ```bash + pip3 install flake8 # only required the first time + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + ``` -- If you know [PEP 8](https://www.python.org/dev/peps/pep-0008/) already, you will have no problem in coding style, though we do not follow it strictly. Read the remaining section and have fun coding! - -- Always use 4 spaces to indent. +- Original code submission require docstrings or comments to describe your work. -- Original code submission requires comments to describe your work. +- More on docstrings and comments: -- More on comments and docstrings: + If you are using a Wikipedia article or some other source material to create your algorithm, please add the URL in a docstring or comment to help your reader. The following are considered to be bad and may be requested to be improved: @@ -52,34 +64,40 @@ We want your work to be readable by others; therefore, we encourage you to note This is too trivial. Comments are expected to be explanatory. For comments, you can write them above, on or below a line of code, as long as you are consistent within the same piece of code. - *Sometimes, docstrings are avoided.* This will happen if you are using some editors and not careful with indentation: + We encourage you to put docstrings inside your functions but please pay attention to indentation of docstrings. The following is acceptable in this case: ```python + def sumab(a, b): + """ + This function returns the sum of two integers a and b + Return: a + b """ - This function sums a and b - """ - def sum(a, b): return a + b ``` - However, if you insist to use docstrings, we encourage you to put docstrings inside functions. Also, please pay attention to indentation to docstrings. The following is acceptable in this case: +- Write tests (especially [__doctests__](https://docs.python.org/3/library/doctest.html)) to illustrate and verify your work. We highly encourage the use of _doctests on all functions_. ```python def sumab(a, b): """ - This function sums two integers a and b - Return: a + b + This function returns the sum of two integers a and b + Return: a + b + >>> sum(2, 2) + 4 + >>> sum(-2, 3) + 1 + >>> sum(4.9, 6.1) + 10.0 """ return a + b ``` -- `lambda`, `map`, `filter`, `reduce` and complicated list comprehension are welcome and acceptable to demonstrate the power of Python, as long as they are simple enough to read. - - - This is arguable: **write comments** and assign appropriate variable names, so that the code is easy to read! - -- Write tests to illustrate your work. + These doctests will be run by pytest as part of our automated testing so please try to run your doctests locally and make sure that they are found and pass: + ```bash + python3 -m doctest -v my_submission.py + ``` - The following "testing" approaches are **not** encouraged: + The use of the Python builtin __input()__ function is **not** encouraged: ```python input('Enter your input:') @@ -87,34 +105,31 @@ We want your work to be readable by others; therefore, we encourage you to note input = eval(input("Enter your input: ")) ``` - However, if your code uses __input()__ then we encourage you to gracefully deal with leading and trailing whitespace in user input by adding __.strip()__ to the end as in: + However, if your code uses __input()__ then we encourage you to gracefully deal with leading and trailing whitespace in user input by adding __.strip()__ as in: ```python starting_value = int(input("Please enter a starting value: ").strip()) ``` - - Please write down your test case, like the following: - - ```python - def sumab(a, b): - return a + b - # Write tests this way: - print(sumab(1, 2)) # 1+2 = 3 - print(sumab(6, 4)) # 6+4 = 10 - # Or this way: - print("1 + 2 = ", sumab(1, 2)) # 1+2 = 3 - print("6 + 4 = ", sumab(6, 4)) # 6+4 = 10 + + The use of [Python type hints](https://docs.python.org/3/library/typing.html) is encouraged for function parameters and return values. Our automated testing will run [mypy](http://mypy-lang.org) so run that locally before making your submission. +```python +def sumab(a: int, b: int) --> int: + pass ``` - Better yet, if you know how to write [__doctests__](https://docs.python.org/3/library/doctest.html), please consider adding them. +- [__list comprehensions and generators__](https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions) are preferred over the use of `lambda`, `map`, `filter`, `reduce` but the important thing is to demonstrate the power of Python in code that is easy to read and maintain. - Avoid importing external libraries for basic algorithms. Only use those libraries for complicated algorithms. +- If you need a third party module that is not in the file __requirements.txt__, please add it to that file as part of your submission. + #### Other Standard While Submitting Your Work - File extension for code should be `.py`. Jupiter notebook files are acceptable in machine learning algorithms. -- Strictly use snake case (underscore separated) in your file name, as it will be easy to parse in future using scripts. +- Please avoid creating new directories if at all possible. Try to fit your work into the existing directory structue. + +- Strictly use snake_case (underscore_separated) in your file_name, as it will be easy to parse in future using scripts. If possible, follow the standard *within* the folder you are submitting to. @@ -135,5 +150,4 @@ We want your work to be readable by others; therefore, we encourage you to note - Happy coding! - Writer [@poyea](https://github.com/poyea), Jun 2019. From b8490ed097d8baab3234c54f835b8a7a55454e52 Mon Sep 17 00:00:00 2001 From: Anup Kumar Panwar <1anuppanwar@gmail.com> Date: Thu, 3 Oct 2019 12:47:22 +0530 Subject: [PATCH 184/193] Removed owners from README (#1254) --- README.md | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/README.md b/README.md index d4f4acbadb6d..a5af46ad8505 100644 --- a/README.md +++ b/README.md @@ -10,18 +10,6 @@ These implementations are for learning purposes. They may be less efficient than the implementations in the Python standard library. -## Owners - -Anup Kumar Panwar -  [[Gmail](mailto:1anuppanwar@gmail.com?Subject=The%20Algorithms%20-%20Python) -  [GitHub](https://github.com/anupkumarpanwar) -  [LinkedIn](https://www.linkedin.com/in/anupkumarpanwar/)] - -Chetan Kaushik -  [[Gmail](mailto:dynamitechetan@gmail.com?Subject=The%20Algorithms%20-%20Python) -  [GitHub](https://github.com/dynamitechetan) -  [LinkedIn](https://www.linkedin.com/in/chetankaushik/)] - ## Contribution Guidelines Read our [Contribution Guidelines](CONTRIBUTING.md) before you contribute. From 390feb0b23b4845a7ddfec7afd703aa053d1b224 Mon Sep 17 00:00:00 2001 From: Parth Paradkar Date: Thu, 3 Oct 2019 13:49:11 +0530 Subject: [PATCH 185/193] Add doctests for sorting algorithms (#1263) * doctests and intro docstring added * doctests, docstrings and check for empty collection added * Intro docstring added * python versions reversed --- sorts/pancake_sort.py | 28 +++++++++++++++++++++++----- sorts/pigeon_sort.py | 26 +++++++++++++++++++++++--- 2 files changed, 46 insertions(+), 8 deletions(-) diff --git a/sorts/pancake_sort.py b/sorts/pancake_sort.py index 3b48bc6e46d9..873c14a0a174 100644 --- a/sorts/pancake_sort.py +++ b/sorts/pancake_sort.py @@ -1,9 +1,25 @@ -"""Pancake Sort Algorithm.""" -# Only can reverse array from 0 to i - +""" +This is a pure python implementation of the pancake sort algorithm +For doctests run following command: +python3 -m doctest -v pancake_sort.py +or +python -m doctest -v pancake_sort.py +For manual testing run: +python pancake_sort.py +""" def pancake_sort(arr): - """Sort Array with Pancake Sort.""" + """Sort Array with Pancake Sort. + :param arr: Collection containing comparable items + :return: Collection ordered in ascending order of items + Examples: + >>> pancake_sort([0, 5, 3, 2, 2]) + [0, 2, 2, 3, 5] + >>> pancake_sort([]) + [] + >>> pancake_sort([-2, -5, -45]) + [-45, -5, -2] + """ cur = len(arr) while cur > 1: # Find the maximum number in arr @@ -17,4 +33,6 @@ def pancake_sort(arr): if __name__ == '__main__': - print(pancake_sort([0, 10, 15, 3, 2, 9, 14, 13])) + user_input = input('Enter numbers separated by a comma:\n').strip() + unsorted = [int(item) for item in user_input.split(',')] + print(pancake_sort(unsorted)) diff --git a/sorts/pigeon_sort.py b/sorts/pigeon_sort.py index 5e5afa137685..5417234d331b 100644 --- a/sorts/pigeon_sort.py +++ b/sorts/pigeon_sort.py @@ -1,7 +1,29 @@ ''' This is an implementation of Pigeon Hole Sort. + For doctests run following command: + + python3 -m doctest -v pigeon_sort.py + or + python -m doctest -v pigeon_sort.py + + For manual testing run: + python pigeon_sort.py ''' def pigeon_sort(array): + """ + Implementation of pigeon hole sort algorithm + :param array: Collection of comparable items + :return: Collection sorted in ascending order + >>> pigeon_sort([0, 5, 3, 2, 2]) + [0, 2, 2, 3, 5] + >>> pigeon_sort([]) + [] + >>> pigeon_sort([-2, -5, -45]) + [-45, -5, -2] + """ + if(len(array) == 0): + return array + # Manually finds the minimum and maximum of the array. min = array[0] max = array[0] @@ -37,6 +59,4 @@ def pigeon_sort(array): if __name__ == '__main__': user_input = input('Enter numbers separated by comma:\n') unsorted = [int(x) for x in user_input.split(',')] - sorted = pigeon_sort(unsorted) - - print(sorted) + print(pigeon_sort(unsorted)) From 0e333ae02193fca1046fcd65ff5202eb054cc04e Mon Sep 17 00:00:00 2001 From: William Zhang <39932068+WilliamHYZhang@users.noreply.github.com> Date: Thu, 3 Oct 2019 05:17:30 -0400 Subject: [PATCH 186/193] added bogobogosort (#1258) * added bogobogosort * fix indentation error --- sorts/bogo_bogo_sort.py | 54 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 sorts/bogo_bogo_sort.py diff --git a/sorts/bogo_bogo_sort.py b/sorts/bogo_bogo_sort.py new file mode 100644 index 000000000000..f26a46e78645 --- /dev/null +++ b/sorts/bogo_bogo_sort.py @@ -0,0 +1,54 @@ +""" +Python implementation of bogobogosort, a "sorting algorithm +designed not to succeed before the heat death of the universe +on any sizable list" - https://en.wikipedia.org/wiki/Bogosort. + +Author: WilliamHYZhang +""" + +import random + + +def bogo_bogo_sort(collection): + """ + returns the collection sorted in ascending order + :param collection: list of comparable items + :return: the list sorted in ascending order + + Examples: + >>> bogo_bogo_sort([0, 5, 3, 2, 2]) + [0, 2, 2, 3, 5] + >>> bogo_bogo_sort([-2, -5, -45]) + [-45, -5, -2] + >>> bogo_bogo_sort([420, 69]) + [69, 420] + """ + + def is_sorted(collection): + if len(collection) == 1: + return True + + clone = collection.copy() + while True: + random.shuffle(clone) + ordered = bogo_bogo_sort(clone[:-1]) + if clone[len(clone) - 1] >= max(ordered): + break + + for i in range(len(ordered)): + clone[i] = ordered[i] + + for i in range(len(collection)): + if clone[i] != collection[i]: + return False + return True + + while not is_sorted(collection): + random.shuffle(collection) + return collection + + +if __name__ == "__main__": + user_input = input("Enter numbers separated by a comma:\n").strip() + unsorted = [int(item) for item in user_input.split(",")] + print(bogo_bogo_sort(unsorted)) From 0e2d6b2963deff0a47c97c2b41deb69aed254350 Mon Sep 17 00:00:00 2001 From: Kaushik Amar Das Date: Thu, 3 Oct 2019 20:00:36 +0530 Subject: [PATCH 187/193] adding softmax function (#1267) * adding softmax function * wraped lines as asked --- maths/softmax.py | 56 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) create mode 100644 maths/softmax.py diff --git a/maths/softmax.py b/maths/softmax.py new file mode 100644 index 000000000000..92ff4ca27b88 --- /dev/null +++ b/maths/softmax.py @@ -0,0 +1,56 @@ +""" +This script demonstrates the implementation of the Softmax function. + +Its a function that takes as input a vector of K real numbers, and normalizes +it into a probability distribution consisting of K probabilities proportional +to the exponentials of the input numbers. After softmax, the elements of the +vector always sum up to 1. + +Script inspired from its corresponding Wikipedia article +https://en.wikipedia.org/wiki/Softmax_function +""" + +import numpy as np + + +def softmax(vector): + """ + Implements the softmax function + + Parameters: + vector (np.array,list,tuple): A numpy array of shape (1,n) + consisting of real values or a similar list,tuple + + + Returns: + softmax_vec (np.array): The input numpy array after applying + softmax. + + The softmax vector adds up to one. We need to ceil to mitigate for + precision + >>> np.ceil(np.sum(softmax([1,2,3,4]))) + 1.0 + + >>> vec = np.array([5,5]) + >>> softmax(vec) + array([0.5, 0.5]) + + >>> softmax([0]) + array([1.]) + """ + + # Calculate e^x for each x in your vector where e is Euler's + # number (approximately 2.718) + exponentVector = np.exp(vector) + + # Add up the all the exponentials + sumOfExponents = np.sum(exponentVector) + + # Divide every exponent by the sum of all exponents + softmax_vector = exponentVector / sumOfExponents + + return softmax_vector + + +if __name__ == "__main__": + print(softmax((0,))) From 03aba96c0a28cf69e827b85dac1a463c74930530 Mon Sep 17 00:00:00 2001 From: Shubham garg <42842217+shubhamgarg2000@users.noreply.github.com> Date: Fri, 4 Oct 2019 01:01:11 +0530 Subject: [PATCH 188/193] added defination (#1244) --- arithmetic_analysis/newton_raphson_method.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/arithmetic_analysis/newton_raphson_method.py b/arithmetic_analysis/newton_raphson_method.py index bb6fdd2193ec..d17b57a2e670 100644 --- a/arithmetic_analysis/newton_raphson_method.py +++ b/arithmetic_analysis/newton_raphson_method.py @@ -1,6 +1,7 @@ # Implementing Newton Raphson method in Python # Author: Syed Haseeb Shah (github.com/QuantumNovice) - +#The Newton-Raphson method (also known as Newton's method) is a way to +#quickly find a good approximation for the root of a real-valued function from sympy import diff from decimal import Decimal From f970c730611ea1c4679b5db1708c2f2f2cb8dabc Mon Sep 17 00:00:00 2001 From: Simon Landry Date: Thu, 3 Oct 2019 16:08:25 -0400 Subject: [PATCH 189/193] Add problem 23 solution (#1261) --- project_euler/problem_23/sol1.py | 51 ++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) create mode 100644 project_euler/problem_23/sol1.py diff --git a/project_euler/problem_23/sol1.py b/project_euler/problem_23/sol1.py new file mode 100644 index 000000000000..e76be053040f --- /dev/null +++ b/project_euler/problem_23/sol1.py @@ -0,0 +1,51 @@ +""" +A perfect number is a number for which the sum of its proper divisors is exactly +equal to the number. For example, the sum of the proper divisors of 28 would be +1 + 2 + 4 + 7 + 14 = 28, which means that 28 is a perfect number. + +A number n is called deficient if the sum of its proper divisors is less than n +and it is called abundant if this sum exceeds n. + +As 12 is the smallest abundant number, 1 + 2 + 3 + 4 + 6 = 16, the smallest +number that can be written as the sum of two abundant numbers is 24. By +mathematical analysis, it can be shown that all integers greater than 28123 +can be written as the sum of two abundant numbers. However, this upper limit +cannot be reduced any further by analysis even though it is known that the +greatest number that cannot be expressed as the sum of two abundant numbers +is less than this limit. + +Find the sum of all the positive integers which cannot be written as the sum +of two abundant numbers. +""" + +def solution(limit = 28123): + """ + Finds the sum of all the positive integers which cannot be written as + the sum of two abundant numbers + as described by the statement above. + + >>> solution() + 4179871 + """ + sumDivs = [1] * (limit + 1) + + for i in range(2, int(limit ** 0.5) + 1): + sumDivs[i * i] += i + for k in range(i + 1, limit // i + 1): + sumDivs[k * i] += k + i + + abundants = set() + res = 0 + + for n in range(1, limit + 1): + if sumDivs[n] > n: + abundants.add(n) + + if not any((n - a in abundants) for a in abundants): + res+=n + + return res + + +if __name__ == "__main__": + print(solution()) From d28fc7120281d726741e3001d5750bd737a38c59 Mon Sep 17 00:00:00 2001 From: Simon Landry Date: Thu, 3 Oct 2019 16:47:08 -0400 Subject: [PATCH 190/193] Add problem18 solution (#1260) --- project_euler/problem_18/solution.py | 64 +++++++++++++++++++++++++++ project_euler/problem_18/triangle.txt | 15 +++++++ 2 files changed, 79 insertions(+) create mode 100644 project_euler/problem_18/solution.py create mode 100644 project_euler/problem_18/triangle.txt diff --git a/project_euler/problem_18/solution.py b/project_euler/problem_18/solution.py new file mode 100644 index 000000000000..f9762e8b0176 --- /dev/null +++ b/project_euler/problem_18/solution.py @@ -0,0 +1,64 @@ +""" +By starting at the top of the triangle below and moving to adjacent numbers on +the row below, the maximum total from top to bottom is 23. + +3 +7 4 +2 4 6 +8 5 9 3 + +That is, 3 + 7 + 4 + 9 = 23. + +Find the maximum total from top to bottom of the triangle below: + +75 +95 64 +17 47 82 +18 35 87 10 +20 04 82 47 65 +19 01 23 75 03 34 +88 02 77 73 07 63 67 +99 65 04 28 06 16 70 92 +41 41 26 56 83 40 80 70 33 +41 48 72 33 47 32 37 16 94 29 +53 71 44 65 25 43 91 52 97 51 14 +70 11 33 28 77 73 17 78 39 68 17 57 +91 71 52 38 17 14 91 43 58 50 27 29 48 +63 66 04 68 89 53 67 30 73 16 69 87 40 31 +04 62 98 27 23 09 70 98 73 93 38 53 60 04 23 +""" +import os + + +def solution(): + """ + Finds the maximum total in a triangle as described by the problem statement + above. + + >>> solution() + 1074 + """ + script_dir = os.path.dirname(os.path.realpath(__file__)) + triangle = os.path.join(script_dir, 'triangle.txt') + + with open(triangle, 'r') as f: + triangle = f.readlines() + + a = [[int(y) for y in x.rstrip('\r\n').split(' ')] for x in triangle] + + for i in range(1, len(a)): + for j in range(len(a[i])): + if j != len(a[i - 1]): + number1 = a[i - 1][j] + else: + number1 = 0 + if j > 0: + number2 = a[i - 1][j - 1] + else: + number2 = 0 + a[i][j] += max(number1, number2) + return max(a[-1]) + + +if __name__ == "__main__": + print(solution()) diff --git a/project_euler/problem_18/triangle.txt b/project_euler/problem_18/triangle.txt new file mode 100644 index 000000000000..e236c2ff7ee2 --- /dev/null +++ b/project_euler/problem_18/triangle.txt @@ -0,0 +1,15 @@ +75 +95 64 +17 47 82 +18 35 87 10 +20 04 82 47 65 +19 01 23 75 03 34 +88 02 77 73 07 63 67 +99 65 04 28 06 16 70 92 +41 41 26 56 83 40 80 70 33 +41 48 72 33 47 32 37 16 94 29 +53 71 44 65 25 43 91 52 97 51 14 +70 11 33 28 77 73 17 78 39 68 17 57 +91 71 52 38 17 14 91 43 58 50 27 29 48 +63 66 04 68 89 53 67 30 73 16 69 87 40 31 +04 62 98 27 23 09 70 98 73 93 38 53 60 04 23 From 6e6920866662f314a9cbb125667c64e421046a4b Mon Sep 17 00:00:00 2001 From: Simon Landry Date: Thu, 3 Oct 2019 16:47:39 -0400 Subject: [PATCH 191/193] Add problem 32 solution (#1257) --- project_euler/problem_32/solution.py | 62 ++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 project_euler/problem_32/solution.py diff --git a/project_euler/problem_32/solution.py b/project_euler/problem_32/solution.py new file mode 100644 index 000000000000..fd5178303de3 --- /dev/null +++ b/project_euler/problem_32/solution.py @@ -0,0 +1,62 @@ +""" +We shall say that an n-digit number is pandigital if it makes use of all the +digits 1 to n exactly once; for example, the 5-digit number, 15234, is 1 through +5 pandigital. + +The product 7254 is unusual, as the identity, 39 × 186 = 7254, containing +multiplicand, multiplier, and product is 1 through 9 pandigital. + +Find the sum of all products whose multiplicand/multiplier/product identity can +be written as a 1 through 9 pandigital. + +HINT: Some products can be obtained in more than one way so be sure to only +include it once in your sum. +""" +import itertools + + +def isCombinationValid(combination): + """ + Checks if a combination (a tuple of 9 digits) + is a valid product equation. + + >>> isCombinationValid(('3', '9', '1', '8', '6', '7', '2', '5', '4')) + True + + >>> isCombinationValid(('1', '2', '3', '4', '5', '6', '7', '8', '9')) + False + + """ + return ( + int(''.join(combination[0:2])) * + int(''.join(combination[2:5])) == + int(''.join(combination[5:9])) + ) or ( + int(''.join(combination[0])) * + int(''.join(combination[1:5])) == + int(''.join(combination[5:9])) + ) + + +def solution(): + """ + Finds the sum of all products whose multiplicand/multiplier/product identity + can be written as a 1 through 9 pandigital + + >>> solution() + 45228 + """ + + return sum( + set( + [ + int(''.join(pandigital[5:9])) + for pandigital + in itertools.permutations('123456789') + if isCombinationValid(pandigital) + ] + ) + ) + +if __name__ == "__main__": + print(solution()) From 309204a5813cae19e2397f5aeb27fefb7dfac514 Mon Sep 17 00:00:00 2001 From: Simon Landry Date: Thu, 3 Oct 2019 16:48:53 -0400 Subject: [PATCH 192/193] Add problem 42 solution (#1259) --- project_euler/problem_42/solution.py | 50 ++++++++++++++++++++++++++++ project_euler/problem_42/words.txt | 1 + 2 files changed, 51 insertions(+) create mode 100644 project_euler/problem_42/solution.py create mode 100644 project_euler/problem_42/words.txt diff --git a/project_euler/problem_42/solution.py b/project_euler/problem_42/solution.py new file mode 100644 index 000000000000..ff976545055d --- /dev/null +++ b/project_euler/problem_42/solution.py @@ -0,0 +1,50 @@ +""" +The nth term of the sequence of triangle numbers is given by, tn = ½n(n+1); so +the first ten triangle numbers are: + +1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ... + +By converting each letter in a word to a number corresponding to its +alphabetical position and adding these values we form a word value. For example, +the word value for SKY is 19 + 11 + 25 = 55 = t10. If the word value is a +triangle number then we shall call the word a triangle word. + +Using words.txt (right click and 'Save Link/Target As...'), a 16K text file +containing nearly two-thousand common English words, how many are triangle +words? +""" +import os + + +# Precomputes a list of the 100 first triangular numbers +TRIANGULAR_NUMBERS = [int(0.5 * n * (n + 1)) for n in range(1, 101)] + + +def solution(): + """ + Finds the amount of triangular words in the words file. + + >>> solution() + 162 + """ + script_dir = os.path.dirname(os.path.realpath(__file__)) + wordsFilePath = os.path.join(script_dir, 'words.txt') + + words = '' + with open(wordsFilePath, 'r') as f: + words = f.readline() + + words = list(map(lambda word: word.strip('"'), words.strip('\r\n').split(','))) + words = list( + filter( + lambda word: word in TRIANGULAR_NUMBERS, + map( + lambda word: sum(map(lambda x: ord(x) - 64, word)), + words + ) + ) + ) + return len(words) + +if __name__ == '__main__': + print(solution()) diff --git a/project_euler/problem_42/words.txt b/project_euler/problem_42/words.txt new file mode 100644 index 000000000000..af3aeb42f151 --- /dev/null +++ b/project_euler/problem_42/words.txt @@ -0,0 +1 @@ +"A","ABILITY","ABLE","ABOUT","ABOVE","ABSENCE","ABSOLUTELY","ACADEMIC","ACCEPT","ACCESS","ACCIDENT","ACCOMPANY","ACCORDING","ACCOUNT","ACHIEVE","ACHIEVEMENT","ACID","ACQUIRE","ACROSS","ACT","ACTION","ACTIVE","ACTIVITY","ACTUAL","ACTUALLY","ADD","ADDITION","ADDITIONAL","ADDRESS","ADMINISTRATION","ADMIT","ADOPT","ADULT","ADVANCE","ADVANTAGE","ADVICE","ADVISE","AFFAIR","AFFECT","AFFORD","AFRAID","AFTER","AFTERNOON","AFTERWARDS","AGAIN","AGAINST","AGE","AGENCY","AGENT","AGO","AGREE","AGREEMENT","AHEAD","AID","AIM","AIR","AIRCRAFT","ALL","ALLOW","ALMOST","ALONE","ALONG","ALREADY","ALRIGHT","ALSO","ALTERNATIVE","ALTHOUGH","ALWAYS","AMONG","AMONGST","AMOUNT","AN","ANALYSIS","ANCIENT","AND","ANIMAL","ANNOUNCE","ANNUAL","ANOTHER","ANSWER","ANY","ANYBODY","ANYONE","ANYTHING","ANYWAY","APART","APPARENT","APPARENTLY","APPEAL","APPEAR","APPEARANCE","APPLICATION","APPLY","APPOINT","APPOINTMENT","APPROACH","APPROPRIATE","APPROVE","AREA","ARGUE","ARGUMENT","ARISE","ARM","ARMY","AROUND","ARRANGE","ARRANGEMENT","ARRIVE","ART","ARTICLE","ARTIST","AS","ASK","ASPECT","ASSEMBLY","ASSESS","ASSESSMENT","ASSET","ASSOCIATE","ASSOCIATION","ASSUME","ASSUMPTION","AT","ATMOSPHERE","ATTACH","ATTACK","ATTEMPT","ATTEND","ATTENTION","ATTITUDE","ATTRACT","ATTRACTIVE","AUDIENCE","AUTHOR","AUTHORITY","AVAILABLE","AVERAGE","AVOID","AWARD","AWARE","AWAY","AYE","BABY","BACK","BACKGROUND","BAD","BAG","BALANCE","BALL","BAND","BANK","BAR","BASE","BASIC","BASIS","BATTLE","BE","BEAR","BEAT","BEAUTIFUL","BECAUSE","BECOME","BED","BEDROOM","BEFORE","BEGIN","BEGINNING","BEHAVIOUR","BEHIND","BELIEF","BELIEVE","BELONG","BELOW","BENEATH","BENEFIT","BESIDE","BEST","BETTER","BETWEEN","BEYOND","BIG","BILL","BIND","BIRD","BIRTH","BIT","BLACK","BLOCK","BLOOD","BLOODY","BLOW","BLUE","BOARD","BOAT","BODY","BONE","BOOK","BORDER","BOTH","BOTTLE","BOTTOM","BOX","BOY","BRAIN","BRANCH","BREAK","BREATH","BRIDGE","BRIEF","BRIGHT","BRING","BROAD","BROTHER","BUDGET","BUILD","BUILDING","BURN","BUS","BUSINESS","BUSY","BUT","BUY","BY","CABINET","CALL","CAMPAIGN","CAN","CANDIDATE","CAPABLE","CAPACITY","CAPITAL","CAR","CARD","CARE","CAREER","CAREFUL","CAREFULLY","CARRY","CASE","CASH","CAT","CATCH","CATEGORY","CAUSE","CELL","CENTRAL","CENTRE","CENTURY","CERTAIN","CERTAINLY","CHAIN","CHAIR","CHAIRMAN","CHALLENGE","CHANCE","CHANGE","CHANNEL","CHAPTER","CHARACTER","CHARACTERISTIC","CHARGE","CHEAP","CHECK","CHEMICAL","CHIEF","CHILD","CHOICE","CHOOSE","CHURCH","CIRCLE","CIRCUMSTANCE","CITIZEN","CITY","CIVIL","CLAIM","CLASS","CLEAN","CLEAR","CLEARLY","CLIENT","CLIMB","CLOSE","CLOSELY","CLOTHES","CLUB","COAL","CODE","COFFEE","COLD","COLLEAGUE","COLLECT","COLLECTION","COLLEGE","COLOUR","COMBINATION","COMBINE","COME","COMMENT","COMMERCIAL","COMMISSION","COMMIT","COMMITMENT","COMMITTEE","COMMON","COMMUNICATION","COMMUNITY","COMPANY","COMPARE","COMPARISON","COMPETITION","COMPLETE","COMPLETELY","COMPLEX","COMPONENT","COMPUTER","CONCENTRATE","CONCENTRATION","CONCEPT","CONCERN","CONCERNED","CONCLUDE","CONCLUSION","CONDITION","CONDUCT","CONFERENCE","CONFIDENCE","CONFIRM","CONFLICT","CONGRESS","CONNECT","CONNECTION","CONSEQUENCE","CONSERVATIVE","CONSIDER","CONSIDERABLE","CONSIDERATION","CONSIST","CONSTANT","CONSTRUCTION","CONSUMER","CONTACT","CONTAIN","CONTENT","CONTEXT","CONTINUE","CONTRACT","CONTRAST","CONTRIBUTE","CONTRIBUTION","CONTROL","CONVENTION","CONVERSATION","COPY","CORNER","CORPORATE","CORRECT","COS","COST","COULD","COUNCIL","COUNT","COUNTRY","COUNTY","COUPLE","COURSE","COURT","COVER","CREATE","CREATION","CREDIT","CRIME","CRIMINAL","CRISIS","CRITERION","CRITICAL","CRITICISM","CROSS","CROWD","CRY","CULTURAL","CULTURE","CUP","CURRENT","CURRENTLY","CURRICULUM","CUSTOMER","CUT","DAMAGE","DANGER","DANGEROUS","DARK","DATA","DATE","DAUGHTER","DAY","DEAD","DEAL","DEATH","DEBATE","DEBT","DECADE","DECIDE","DECISION","DECLARE","DEEP","DEFENCE","DEFENDANT","DEFINE","DEFINITION","DEGREE","DELIVER","DEMAND","DEMOCRATIC","DEMONSTRATE","DENY","DEPARTMENT","DEPEND","DEPUTY","DERIVE","DESCRIBE","DESCRIPTION","DESIGN","DESIRE","DESK","DESPITE","DESTROY","DETAIL","DETAILED","DETERMINE","DEVELOP","DEVELOPMENT","DEVICE","DIE","DIFFERENCE","DIFFERENT","DIFFICULT","DIFFICULTY","DINNER","DIRECT","DIRECTION","DIRECTLY","DIRECTOR","DISAPPEAR","DISCIPLINE","DISCOVER","DISCUSS","DISCUSSION","DISEASE","DISPLAY","DISTANCE","DISTINCTION","DISTRIBUTION","DISTRICT","DIVIDE","DIVISION","DO","DOCTOR","DOCUMENT","DOG","DOMESTIC","DOOR","DOUBLE","DOUBT","DOWN","DRAW","DRAWING","DREAM","DRESS","DRINK","DRIVE","DRIVER","DROP","DRUG","DRY","DUE","DURING","DUTY","EACH","EAR","EARLY","EARN","EARTH","EASILY","EAST","EASY","EAT","ECONOMIC","ECONOMY","EDGE","EDITOR","EDUCATION","EDUCATIONAL","EFFECT","EFFECTIVE","EFFECTIVELY","EFFORT","EGG","EITHER","ELDERLY","ELECTION","ELEMENT","ELSE","ELSEWHERE","EMERGE","EMPHASIS","EMPLOY","EMPLOYEE","EMPLOYER","EMPLOYMENT","EMPTY","ENABLE","ENCOURAGE","END","ENEMY","ENERGY","ENGINE","ENGINEERING","ENJOY","ENOUGH","ENSURE","ENTER","ENTERPRISE","ENTIRE","ENTIRELY","ENTITLE","ENTRY","ENVIRONME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From 07f04a2e5523f91954d7c468e94b395457ea3b7d Mon Sep 17 00:00:00 2001 From: Kaushik Amar Das Date: Fri, 4 Oct 2019 13:29:45 +0530 Subject: [PATCH 193/193] adding jaccard similarity (#1270) * adding jaccard similarity * renaming files. zeebus! what an headache --- maths/jaccard_similarity.py | 80 +++++++++++++++++++ .../problem_32/{solution.py => sol32.py} | 0 .../problem_42/{solution.py => solution42.py} | 0 3 files changed, 80 insertions(+) create mode 100644 maths/jaccard_similarity.py rename project_euler/problem_32/{solution.py => sol32.py} (100%) rename project_euler/problem_42/{solution.py => solution42.py} (100%) diff --git a/maths/jaccard_similarity.py b/maths/jaccard_similarity.py new file mode 100644 index 000000000000..4f24d308f340 --- /dev/null +++ b/maths/jaccard_similarity.py @@ -0,0 +1,80 @@ +""" +The Jaccard similarity coefficient is a commonly used indicator of the +similarity between two sets. Let U be a set and A and B be subsets of U, +then the Jaccard index/similarity is defined to be the ratio of the number +of elements of their intersection and the number of elements of their union. + +Inspired from Wikipedia and +the book Mining of Massive Datasets [MMDS 2nd Edition, Chapter 3] + +https://en.wikipedia.org/wiki/Jaccard_index +https://mmds.org + +Jaccard similarity is widely used with MinHashing. +""" + + +def jaccard_similariy(setA, setB, alternativeUnion=False): + """ + Finds the jaccard similarity between two sets. + Essentially, its intersection over union. + + The alternative way to calculate this is to take union as sum of the + number of items in the two sets. This will lead to jaccard similarity + of a set with itself be 1/2 instead of 1. [MMDS 2nd Edition, Page 77] + + Parameters: + :setA (set,list,tuple): A non-empty set/list + :setB (set,list,tuple): A non-empty set/list + :alternativeUnion (boolean): If True, use sum of number of + items as union + + Output: + (float) The jaccard similarity between the two sets. + + Examples: + >>> setA = {'a', 'b', 'c', 'd', 'e'} + >>> setB = {'c', 'd', 'e', 'f', 'h', 'i'} + >>> jaccard_similariy(setA,setB) + 0.375 + + >>> jaccard_similariy(setA,setA) + 1.0 + + >>> jaccard_similariy(setA,setA,True) + 0.5 + + >>> setA = ['a', 'b', 'c', 'd', 'e'] + >>> setB = ('c', 'd', 'e', 'f', 'h', 'i') + >>> jaccard_similariy(setA,setB) + 0.375 + """ + + if isinstance(setA, set) and isinstance(setB, set): + + intersection = len(setA.intersection(setB)) + + if alternativeUnion: + union = len(setA) + len(setB) + else: + union = len(setA.union(setB)) + + return intersection / union + + if isinstance(setA, (list, tuple)) and isinstance(setB, (list, tuple)): + + intersection = [element for element in setA if element in setB] + + if alternativeUnion: + union = len(setA) + len(setB) + else: + union = setA + [element for element in setB if element not in setA] + + return len(intersection) / len(union) + + +if __name__ == "__main__": + + setA = {"a", "b", "c", "d", "e"} + setB = {"c", "d", "e", "f", "h", "i"} + print(jaccard_similariy(setA, setB)) diff --git a/project_euler/problem_32/solution.py b/project_euler/problem_32/sol32.py similarity index 100% rename from project_euler/problem_32/solution.py rename to project_euler/problem_32/sol32.py diff --git a/project_euler/problem_42/solution.py b/project_euler/problem_42/solution42.py similarity index 100% rename from project_euler/problem_42/solution.py rename to project_euler/problem_42/solution42.py