File Content: plot_affinity_propagation.py """ ================================================= Demo of affinity propagation clustering algorithm ================================================= Reference: Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages Between Data Points", Science Feb. 2007 """ print(__doc__) from sklearn.cluster import AffinityPropagation from sklearn import metrics from sklearn.datasets.samples_generator import make_blobs ############################################################################## # Generate sample data centers = [[1, 1], [-1, -1], [1, -1]] X, labels_true = make_blobs(n_samples=300, centers=centers, cluster_std=0.5, random_state=0) ############################################################################## # Compute Affinity Propagation af = AffinityPropagation(preference=-50).fit(X) cluster_centers_indices = af.cluster_centers_indices_ labels = af.labels_ n_clusters_ = len(cluster_centers_indices) print('Estimated number of clusters: %d' % n_clusters_) print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels)) print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels)) print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels)) print("Adjusted Rand Index: %0.3f" % metrics.adjusted_rand_score(labels_true, labels)) print("Adjusted Mutual Information: %0.3f" % metrics.adjusted_mutual_info_score(labels_true, labels)) print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X, labels, metric='sqeuclidean')) ############################################################################## # Plot result import matplotlib.pyplot as plt from itertools import cycle plt.close('all') plt.figure(1) plt.clf() colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk') for k, col in zip(range(n_clusters_), colors): class_members = labels == k cluster_center = X[cluster_centers_indices[k]] plt.plot(X[class_members, 0], X[class_members, 1], col + '.') plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14) for x in X[class_members]: plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col) plt.title('Estimated number of clusters: %d' % n_clusters_) plt.show() Submit