import pylab; from sklearn import datasets,mixture
from sklearn.model_selection import train_test_split
n_samples=1000,cluster_std=.5,
centers=[[1,1],[-1,-1],[1,-1],[-1,1]])
X_train,X_test,y_train,y_test=\
train_test_split(bd[0],bd[1],test_size=.2,random_state=1)
usl=mixture.BayesianGaussianMixture(n_components=4,n_init=4)
usl.fit(X_train,y_train); y_predict=usl.predict(X_test)
pylab.figure(figsize=(6,5)); pylab.grid()
pylab.scatter(X_test[:,0],X_test[:,1],c=y_test,
s=50,alpha=.5,cmap=pylab.cm.winter)
pylab.scatter(X_test[:,0]+.03,X_test[:,1]+.03,c=y_predict,
s=30,alpha=.5,cmap=pylab.cm.cool)
pylab.scatter([1,-1,1,-1],[1,-1,-1,1],c='#3636ff',
marker='*',s=300,alpha=.7)
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