data, _ = generate_2d_mixtures(num_samples=50000,
mean=0.0,
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
"Amari distanca between true mixing matrix and GRBM weight vector 2 and 4: "
+ str(
vis.calculate_amari_distance(zca.project(mixing_matrix.T),
numx.vstack(
(rbm.w.T[1:2], rbm.w.T[3:4])))))
print(
"Amari distanca between true mixing matrix and GRBM weight vector 3 and 4: "
+ str(
vis.calculate_amari_distance(zca.project(mixing_matrix.T),
numx.vstack(
(rbm.w.T[2:3], rbm.w.T[3:4])))))
# Display results
# create a new figure of size 5x5
vis.figure(0, figsize=[7, 7])
vis.title("P(x)")
# plot the data
vis.plot_2d_data(whitened_data)
# plot weights
vis.plot_2d_weights(rbm.w, rbm.bv)
# pass our P(x) as function to plotting function
vis.plot_2d_contour(lambda v: numx.exp(rbm.log_probability_v(logZ, v)))
# No inconsistent scaling
vis.axis('equal')
# Set size of the plot
vis.axis([-5, 5, -5, 5])
# Do the sam efor the LOG-Plot
# create a new figure of size 5x5
vis.figure(1, figsize=[7, 7])