ajout de la fonction illustrationKmeans necessaire dans le cours sur la...

ajout de la fonction illustrationKmeans necessaire dans le cours sur la classification pour visualiser etape par etape

illustrationKmeans.py

+from copy import deepcopy
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+
+def dist(a, b, axe = 1):
+    return np.linalg.norm(a - b, axis=axe)
+
+
+def illustrationKmeans(X,k, xlabel = 'x', ylabel = 'y'):
+    colors = ['r', 'g', 'b', 'y', 'c', 'm']
+
+    # X coordinates of random centroids
+    C_x = np.random.uniform(np.min(X[:,0]), np.max(X[:,0]), size=k)
+    # Y coordinates of random centroids
+    C_y = np.random.uniform(np.min(X[:,1]), np.max(X[:,1]), size=k)
+    C = np.array(list(zip(C_x, C_y)), dtype=np.float32)
+
+
+
+    # To store the value of centroids when it updates
+    C_old = np.zeros(C.shape)
+    # Cluster Lables(0, 1, 2)
+    clusters = np.zeros(len(X))
+    # Error func. - Distance between new centroids and old centroids
+    error = dist(C, C_old, None)
+    # Loop will run till the error becomes zero
+    while error != 0:
+        # Assigning each value to its closest cluster
+        for i in range(len(X)):
+            distances = dist(X[i], C)
+            cluster = np.argmin(distances)
+            clusters[i] = cluster
+        # Storing the old centroid values
+        C_old = deepcopy(C)
+        # Finding the new centroids by taking the average value
+        fig, ax = plt.subplots()
+        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)
+            points2 = np.array([X[j] for j in range(len(X)) if clusters[j] == i])
+            ax.scatter(points2[:, 0], points2[:, 1], s=100, c=colors[i])
+            plt.xlabel(xlabel)
+            plt.ylabel(ylabel)
+        error = dist(C, C_old, None)
+        ax.scatter(C[:, 0], C[:, 1], marker='*', s=200, c='#050505')
+        plt.show()
+
+
+
+