Merge pull request #1 from JacobLee121/JacobLee121-patch-plot

[JacobLee121]

coding: utf-8

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from pandas import *

from future import division

import csv
with open ("E:/6Machine Learning/Deep learning/ML2017/hw1.1/hw1/data/train.csv", "r") as csvfile:
read = csv.DictReader(csvfile)
price = [row["price"] for row in read]
p = [int(item) for item in price]
a = len(price)
ss = len(p)
with open("E:/6Machine Learning/Deep learning/ML2017/hw1.1/hw1/data/train.csv", "r") as csvfile:
read = csv.DictReader(csvfile)
sqft_living=[row["sqft_living"] for row in read]
#"""convert the data type from str to int"""
s_l = [int(item) for item in sqft_living]
b=len(sqft_living)
bb=len(s_l)

plt.figure(1)

plt.plot(price, sqftliving)

plt.show()

print(bb)
print(ss)
print(p)
print(s_l)
f1=plt.figure(1)
plt.subplot(211)
plt.scatter(p,s_l)
plt.subplot(212)
plt.scatter(s_l,p)
plt.show()
def sum_of_squares(v):
"""computes the sum of squared element in v"""
return sum(v_i ** 2 for v_i in v)
sum_of_squares(p)
print(sum)
def difference_quotient(f,x,h):
return (f(x+h)-f(x))/h
"""estimate_gradeient"""
def step(v,direction, step_size):
"""move step_size in the dirction from v"""
return [v_i + step_size * direction_i
for v_i, direction_i in zip(v,direction)]