Thank you, Nicolas.
Huan
On Fri, Feb 15, 2019 at 7:52 PM Huan Tran <hua...@gmail.com> wrote:
> Dear community,
>
> I did a very small pca analysis on a 3D data to print out the
> explained_variance. I found that with scikit-learn 0.18.1 AND 0.20.2, the
> results are significantly different. In particular, for 0.18.1 I got
> +3.875925353581E+00 +3.270175297443E+00 +2.207814537475E+00
>
> and with 0.20.2, I got
> +4.651110424297E+00 +3.924210356932E+00 +2.649377444970E+00
>
> Could anyone has a hint on what is going on? FYI, my data and code are
> enclosed. Many thanks.
>
> Huan
>
> My data is
>
> -3.117642E+00, 1.453819E+00, -7.952874E-02
> 3.081224E+00, 1.453819E+00, -7.952874E-02
> 1.376932E-01, -2.491454E+00, -1.908521E-01
> 9.578602E-02, 3.632759E+00, -1.908521E-01
> -1.238644E-01, 5.396424E-02, -3.147031E+00
> 6.335262E-01, 1.393937E+00, 2.500474E+00
>
> and my code is
>
> import pandas as pd
> import numpy as np
> from sklearn import decomposition
>
> df = pd.read_csv('data', delimiter=',', header=None)
> data = np.array(df)
>
> X = data[:,:]
> data_size = X.shape[0]
> feature_dim = X.shape[1]
>
> print X
>
> pca = decomposition.PCA(n_components=feature_dim)
> X_transformed = pca.fit_transform(X)
> print "%+4.12E %+4.12E %+4.12E" %(pca.explained_variance_[0],
> pca.explained_variance_[1], pca.explained_variance_[2])
>
>
>
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