eigpca

PCA via eigen-decomposition of the covariance/correlation matrix.

Install

pip install eigpca

Example

from eigpca import PCA
from sklearn.datasets import load_iris

X = load_iris().data
pca = PCA()

pca.fit(X)
pca.transform(X, n_components=2)

Scree Plot

pca.plot(y="eig")

Y axes is proportion of variance

pca.plot(y="pov")