paulaceccon
commented
5 years ago Using the updated code, the same incorrect result is obtained.
From the code, after scaling the data:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(pca_data)
data_scaled_test = scaler.transform(pca_data)
fpc = pca(data_scaled_test, 2)
fpc [[0.7071067811865476, 0.7071067811865476], [0.7071067811865475, 0.7071067811865475]]
From sklearn API:
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
pca.fit(data_scaled_test)
pca.components_array([[-0.70710678, -0.70710678], [ 0.70710678, -0.70710678]])
Why does the PCA example returns components with the opposite sign of the ones from sklearn PCA? Also, when I try to standardize the data and use the code, the components obtained through PCA are the same, which doesn't make sense. Notebook with examples attached. PrincipalComponentAnalysis.ipynb.zip