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- import numpy as np
- import matplotlib.pyplot as plt
- from sklearn.cluster import KMeans
- # Generate data
- class1 = np.random.randn(1000, 2)
- class2 = np.random.randn(1000, 2)
- class2[:,0] = 4+class2[:,0]
- class2[:,1] = 4+class2[:,1]
- class3 = np.random.randn(1000, 2)
- class3[:,0] = -4+class3[:,0]
- class3[:,1] = 4+class3[:,1]
- data = np.append( class1, class2, axis= 0)
- data = np.append( data, class3, axis= 0)
- print(data.shape)
- # Plot the data
- plt.scatter(data[:,0], data[:,1])
- plt.show()
- # Cluster
- kmeans = KMeans(n_clusters=1, random_state=0, verbose = 1).fit(data)
- # Plot clustered results
- plt.scatter(data[:,0], data[:,1], c=kmeans.labels_)
- plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], c = 'r')
- plt.show()
- # Show the cluster centers
- print(kmeans.cluster_centers_)
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