Principal component analysis in Python

Question

I\'d like to use principal component analysis (PCA) for dimensionality reduction. Does numpy or scipy already have it, or do I have to roll my own using numpy.linalg.eigh?<

Answer 1

You can use sklearn:

import sklearn.decomposition as deco
import numpy as np

x = (x - np.mean(x, 0)) / np.std(x, 0) # You need to normalize your data first
pca = deco.PCA(n_components) # n_components is the components number after reduction
x_r = pca.fit(x).transform(x)
print ('explained variance (first %d components): %.2f'%(n_components, sum(pca.explained_variance_ratio_)))