import numpy as np
from sklearn.decomposition import PCA
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
#pca = PCA(n_components=1,svd_solver='full')
pca=PCA(n_components='mle')
pca.fit(X)
print(pca.explained_variance_ratio_) #输出方差占所有特征值的比
print(pca.explained_variance_) #输出特征方差
newA=pca.transform(X)
print newA
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