def extraction(self, data):
# extracting 40 features using sparse filtering
sp = SparseFiltering(self.num_feature_extract)
feature_set = sp.fit_transform(data)
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# ax.scatter(feature_set[:, 0], feature_set[:, 1], feature_set[:, 2], c=label[:, 0])
# ax.set_xlabel('feature 1')
# ax.set_ylabel('feature 2')
# ax.set_zlabel('feature 3')
# plt.title('SP visulization')
# plt.show()
return feature_set
faces_centered.reshape(n_samples, 64, 64) # Reshaping to 64*64 pixel
print("Dataset consists of %d faces" % n_samples)
###############################################################################
# Extract n_patches patches randomly from each image
patches = [
extract_patches_2d(faces_centered[i], (patch_width, patch_width),
max_patches=n_patches,
random_state=i) for i in range(n_samples)
]
patches = np.array(patches).reshape(-1, patch_width * patch_width)
###############################################################################
estimator = \
SparseFiltering(n_features=n_features, maxfun=maxfun, iprint=iprint)
features = estimator.fit_transform(patches)
# #############################################################################
# Plot weights of features
pl.figure(0, figsize=(12, 10))
pl.subplots_adjust(left=0.01,
bottom=0.01,
right=0.99,
top=0.95,
wspace=0.1,
hspace=0.4)
for i in range(estimator.w_.shape[0]):
pl.subplot(int(np.sqrt(n_features)), int(np.sqrt(n_features)), i + 1)
pl.pcolor(estimator.w_[i].reshape(patch_width, patch_width),
cmap=pl.cm.gray)
