digit3_mean.reshape((n_sqrt_features, n_sqrt_features)),
cmap=plt.cm.Greys_r,
interpolation='none')
plt.imshow(eigvecs[:, i].reshape((n_sqrt_features, n_sqrt_features)),
cmap=plt.cm.Greys_r,
interpolation='none')
plt.title('$\lambda$ = {0:3.1e}'.format(eigvals[i]))
# PCA reconstruction
digit3 = digit3_X[0]
plt.subplot(n_figrows, n_figcols, n_figcols + 1)
plt.imshow(
digit3.reshape((n_sqrt_features, n_sqrt_features)),
cmap=plt.cm.Greys_r,
interpolation='none')
plt.title('Origin')
M = [1, 10, 50, 250]
for i, n_components in enumerate(M):
plt.subplot(n_figrows, n_figcols, n_figcols + 1 + i + 1)
pca = PCA(M=n_components)
pca.fit(digit3_X)
reconstructed = pca.reconstruct(digit3)
plt.imshow(
reconstructed.reshape((n_sqrt_features, n_sqrt_features)),
cmap=plt.cm.Greys_r,
interpolation='none')
plt.title('M = {0}'.format(n_components))
plt.show()
