Kernel PCA for QML kernels (ndarray)

kpca.py

import numpy as np
import scipy


def kpca(K, n=2, centering=True):
    
    assert K.shape[0] == K.shape[1], "Square matrix required for Kernel PCA."
    assert np.allclose(K, K.T, atol=1e-8), "Symmetric matrix required for Kernel PCA."

    # First center kernel.
    K_centered = None

    if (centering):
        z = K.shape[0]
        G = np.ones((z,z))
        G /= z

        K_centered =  K - np.dot(G, K) - np.dot(K, G) + np.dot(np.dot(G, K), G)
    else:
        K_centered = K

    (w, X) = scipy.linalg.eig(K_centered)

    pca = np.transpose(X[:,:n])

    print(pca)
    return pca