plot_samples(S / S.std())
plt.title('True Independent Sources')
axis_list = [pca.components_.T, ica.mixing_]
plt.subplot(3, 2, 2)
plot_samples(X / np.std(X), axis_list=axis_list)
legend = plt.legend(['PCA', 'ICA'], loc='upper right')
legend.set_zorder(100)
plt.title('Observations')
plt.subplot(3, 2, 3)
plot_samples(S_pca_ / np.std(S_pca_, axis=0))
plt.title('PCA recovered signals')
plt.subplot(3, 2, 4)
plot_samples(S_ica_ / np.std(S_ica_))
plt.title('ICA recovered signals')
plt.subplot(3, 2, 5)
plot_samples(ica._fit(X, True))
plt.title('ICA sources')
plt.subplot(3, 2, 6)
Sss = np.genfromtxt('ica_res.txt', delimiter=' ')
plot_samples(Sss)
plt.title('My ICA sources')
plt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.36)
plt.show()
