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jaineel.txt

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+just for hactober fest
+please merge this commit

main.py

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+from copy import deepcopy
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+plt.rcParams['figure.figsize'] = (16, 9)
+plt.style.use('ggplot')
+
+data = pd.read_csv('xclara.csv')
+print("Input Data and Shape")
+print(data.shape)
+print(data.head())
+
+f1 = data['V1'].values
+f2 = data['V2'].values
+X = np.array(list(zip(f1, f2)))
+plt.scatter(f1, f2, c='black', s=7)
+
+def dist(a, b, ax=1):
+    return np.linalg.norm(a - b, axis=ax)
+k = 2
+C_x = np.random.randint(0, np.max(X)-20, size=k)
+C_y = np.random.randint(0, np.max(X)-20, size=k)
+C = np.array(list(zip(C_x, C_y)), dtype=np.float32)
+print("Initial Centroids")
+print(C)
+
+plt.scatter(f1, f2, c='#050505', s=7)
+plt.scatter(C_x, C_y, marker='*', s=200, c='g')
+C_old = np.zeros(C.shape)
+clusters = np.zeros(len(X))
+error = dist(C, C_old, None)
+while error != 0:
+    for i in range(len(X)):
+        distances = dist(X[i], C)
+        cluster = np.argmin(distances)
+        clusters[i] = cluster
+    C_old = deepcopy(C)
+    for i in range(k):
+        points = [X[j] for j in range(len(X)) if clusters[j] == i]
+        C[i] = np.mean(points, axis=0)
+    error = dist(C, C_old, None)
+
+colors = ['r', 'g', 'b', 'y', 'c', 'm']
+fig, ax = plt.subplots()
+for i in range(k):
+        points = np.array([X[j] for j in range(len(X)) if clusters[j] == i])
+        ax.scatter(points[:, 0], points[:, 1], s=7, c=colors[i])
+ax.scatter(C[:, 0], C[:, 1], marker='*', s=200, c='#050505')
+