We have 2 implementations of PCA :
- one based on Jacobi Transformation of the covariance matrix
- another one based on SVD.
They give the same results but the result are different from the one you can find with sci-kit learn in Python:
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=2)
pca.fit(X)
pca.components_
pca.transform(X)
pca.components_ returns :
array([[-0.83849224, -0.54491354],
[ 0.54491354, -0.83849224]])pca.transform(X) returns:
array([[ 1.38340578, 0.2935787 ],
[ 2.22189802, -0.25133484],
[ 3.6053038 , 0.04224385],
[-1.38340578, -0.2935787 ],
[-2.22189802, 0.25133484],
[-3.6053038 , -0.04224385]])I try to implement a flipsvd method like this one : https://github.com/scikit-learn/scikit-learn/blob/4c65d8e615c9331d37cbb6225c5b67c445a5c959/sklearn/utils/extmath.py#L609
but fails until now.
Please have a look to tests of PMPrincipalComponentAnalyserTest.
We have 2 implementations of PCA :
They give the same results but the result are different from the one you can find with sci-kit learn in Python:
pca.components_returns :pca.transform(X)returns:I try to implement a flipsvd method like this one : https://github.com/scikit-learn/scikit-learn/blob/4c65d8e615c9331d37cbb6225c5b67c445a5c959/sklearn/utils/extmath.py#L609
but fails until now.
Please have a look to tests of
PMPrincipalComponentAnalyserTest.