In https://github.com/rushter/MLAlgorithms/blob/master/mla/pca.py#L48, the variance ratio of a component is calculated as the square of its eigenvalue divided by the sum of all squared eigenvalues.
But according to https://stats.stackexchange.com/questions/31908/what-is-percentage-of-variance-in-pca, the proportion of variance of a component should be its eigenvector divided by the sum of all eigenvectors. Any ideas?
In https://github.com/rushter/MLAlgorithms/blob/master/mla/pca.py#L48, the variance ratio of a component is calculated as the square of its eigenvalue divided by the sum of all squared eigenvalues.
But according to https://stats.stackexchange.com/questions/31908/what-is-percentage-of-variance-in-pca, the proportion of variance of a component should be its eigenvector divided by the sum of all eigenvectors. Any ideas?