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Kernel PCA for QML kernels (ndarray)
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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 |
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