# print(pca.n_components_) # print(pca.explained_variance_ratio_[0:3]) print(pca.reconstruction_err_) fig = plt.figure(figsize=(20, 20)) ax = fig.add_subplot(111, projection='3d') plt.scatter(datat[:, 0], datat[:, 1], zs=datat[:, 2], c=labels, marker='o') plt.show() plt.close() #km = KMeans(n_clusters=5) km = DPGMM(n_components=7, covariance_type='tied') clabels = km.fit_predict(datat) # for ex, lab in zip(exid, clabels): # print(ex, lab) fig = plt.figure(figsize=(20, 20)) ax = fig.add_subplot(111, projection='3d') plt.scatter(datat[:, 0], datat[:, 1], zs=datat[:, 2], c=clabels, marker='o') plt.show() plt.close()
