Obtain eigen values and vectors from sklearn PCA

Question

How I can get the the eigen values and eigen vectors of the PCA application?

from sklearn.decomposition import PCA
clf=PCA(0.98,whiten=True)      #converse 98%          


        

Answer 1

I used the sklearn PCA function. The return parameters 'components_' is eigen vectors and 'explained_variance_' is eigen values. Below is my test code.

from sklearn.decomposition import PCA
import numpy as np


def main():
    data = np.array([[2.5, 2.4], [0.5, 0.7], [2.2, 2.9], [1.9, 2.2], [3.1, 3.0], [2.3, 2.7], [2, 1.6], [1, 1.1], [1.5, 1.6], [1.1, 0.9]])
    print(data)
    pca = PCA()
    pca.fit(data)

    print(pca.components_)
    print(pca.explained_variance_)



if __name__ == "__main__":
    main()