1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | import matplotlib.pyplot as plt import seaborn as sns; sns.set() import numpy as np from sklearn.cluster import KMeans from sklearn.datasets.samples_generator import make_blobs X, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0) plt.scatter(X[:, 0], X[:, 1], s=50); kmeans = KMeans(n_clusters=4) kmeans.fit(X) y_kmeans = kmeans.predict(X) plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis') centers = kmeans.cluster_centers_ plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5); plt.show() | cs |