Mahi

PYTHON/k_means_clustering.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_blobs
+
+X,y = make_blobs(n_samples = 500,n_features = 2,centers = 3,random_state = 23)
+
+fig = plt.figure(0)
+plt.grid(True)
+plt.scatter(X[:,0],X[:,1])
+plt.show()
+
+k = 3
+
+clusters = {}
+np.random.seed(23)
+
+for idx in range(k):
+    center = 2*(2*np.random.random((X.shape[1],))-1)
+    points = []
+    cluster = {
+        'center' : center,
+        'points' : []
+    }
+
+    clusters[idx] = cluster
+
+clusters
+
+plt.scatter(X[:,0],X[:,1])
+plt.grid(True)
+for i in clusters:
+    center = clusters[i]['center']
+    plt.scatter(center[0],center[1],marker = '*',c = 'red')
+plt.show()
+
+
+def distance(p1,p2):
+    return np.sqrt(np.sum((p1-p2)**2))
+
+def assign_clusters(X, clusters):
+    for idx in range(X.shape[0]):
+        dist = []
+
+        curr_x = X[idx]
+
+        for i in range(k):
+            dis = distance(curr_x,clusters[i]['center'])
+            dist.append(dis)
+        curr_cluster = np.argmin(dist)
+        clusters[curr_cluster]['points'].append(curr_x)
+    return clusters
+
+#Implementing the M-Step
+def update_clusters(X, clusters):
+    for i in range(k):
+        points = np.array(clusters[i]['points'])
+        if points.shape[0] > 0:
+            new_center = points.mean(axis =0)
+            clusters[i]['center'] = new_center
+
+            clusters[i]['points'] = []
+    return clusters
+
+def pred_cluster(X, clusters):
+    pred = []
+    for i in range(X.shape[0]):
+        dist = []
+        for j in range(k):
+            dist.append(distance(X[i],clusters[j]['center']))
+        pred.append(np.argmin(dist))
+    return pred  
+
+clusters = assign_clusters(X,clusters)
+clusters = update_clusters(X,clusters)
+pred = pred_cluster(X,clusters) 
+
+plt.scatter(X[:,0],X[:,1],c = pred)
+for i in clusters:
+    center = clusters[i]['center']
+    plt.scatter(center[0],center[1],marker = '^',c = 'red')
+plt.show()
+