from numpy import concatenate,column_stack,row_stack import numpy as np import matplotlib.pyplot as plt #%matplotlib inline from sklearn.datasets.samples_generator import make_blobs # X為樣本特征，Y為樣本簇類別， 共1000個樣本，每個樣本4個特征，共4個簇，簇中心在[-1,-1], [0,0],[1,1], [2,2]， 簇方差分別為[0.4, 0.2, 0.2] X, y = make_blobs(n_samples=1000, centers=[[-1,-1], [0,0], [1,1]] ,cluster_std=[0.4, 0.2, 0.2], random_state =9) plt.scatter(X[:, 0], X[:, 1], marker='o') plt.show()from sklearn.cluster import KMeans y_pred = KMeans(n_clusters=3, random_state=9).fit_predict(X) plt.scatter(X[:, 0], X[:, 1], c=y_pred) plt.show()from sklearn import metrics print(metrics.calinski_harabaz_score(X, y_pred)) yy=np.array([y_pred])un=np.hstack((X,yy.T))print(un)print('\n') A_1=['0','0','0'] A_2=['1','1','1'] A_3=['2','2','2'] for i in range(yy.shape[1]):if un[i][2]==0:A_1=row_stack((A_1,un[i])) elif un[i][2]==1:A_2=row_stack((A_2,un[i])) elif un[i][2]==2:A_3=row_stack((A_3,un[i])) print(A_1,'\n','A_1 have ',A_1.shape[0],'element') print(A_2,'\n','A_2 have ',A_2.shape[0],'element') print(A_3,'\n','A_3 have ',A_3.shape[0],'element')

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