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])
data
Description: Binary data
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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