scale=3.0)
# PCA
pca = PCA(data.shape[1])
pca.train(data)
data_pca = pca.project(data)
# Display results
# For better visualization the principal components are rescaled
scale_factor = 3
# Figure 1 - Data with estimated principal components
vis.figure(0, figsize=[7, 7])
vis.title("Data with estimated principal components")
vis.plot_2d_data(data)
vis.plot_2d_weights(scale_factor*pca.projection_matrix)
vis.axis('equal')
vis.axis([-4, 4, -4, 4])
# Figure 2 - Data with estimated principal components in projected space
vis.figure(2, figsize=[7, 7])
vis.title("Data with estimated principal components in projected space")
vis.plot_2d_data(data_pca)
vis.plot_2d_weights(scale_factor*pca.project(pca.projection_matrix.T))
vis.axis('equal')
vis.axis([-4, 4, -4, 4])
# PCA with whitening
pca = PCA(data.shape[1], whiten=True)
pca.train(data)
str(numx.mean(ica.log_likelihood(data=whitened_data))))
print "Amari distanca between true mixing matrix and estimated mixing matrix: " + str(
vis.calculate_amari_distance(zca.project(mixing_matrix.T),
ica.projection_matrix.T))
# For better visualization the principal components are rescaled
scale_factor = 3
# Display results: the matrices are normalized such that the
# column norm equals the scale factor
# Figure 1 - Data and mixing matrix
vis.figure(0, figsize=[7, 7])
vis.title("Data and mixing matrix")
vis.plot_2d_data(data)
vis.plot_2d_weights(
numxext.resize_norms(mixing_matrix, norm=scale_factor, axis=0))
vis.axis('equal')
vis.axis([-4, 4, -4, 4])
# Figure 2 - Data and mixing matrix in whitened space
vis.figure(1, figsize=[7, 7])
vis.title("Data and mixing matrix in whitened space")
vis.plot_2d_data(whitened_data)
vis.plot_2d_weights(
numxext.resize_norms(zca.project(mixing_matrix.T).T,
norm=scale_factor,
axis=0))
vis.axis('equal')
vis.axis([-4, 4, -4, 4])