def set_codebook(self, D):
'''Clobber the existing codebook and encoder with a new one.'''
self.components_ = D
extra_args = {}
if self.penalty == 'l1_space':
extra_args['height'] = D.shape[1]
extra_args['width'] = D.shape[2]
extra_args['pad_data'] = self.pad_data
extra_args['nonneg'] = self.nonneg
D = D.swapaxes(0, 1).swapaxes(1, 2)
D = _cdl.patches_to_vectors(D, pad_data=self.pad_data)
D = _cdl.columns_to_diags(D)
self.fft_components_ = D
encoder, D, diagnostics = _cdl.learn_dictionary([],
self.n_atoms,
reg=self.penalty,
alpha=self.alpha,
max_steps=0,
verbose=False,
D=D,
**extra_args)
self.encoder_ = encoder
return self
def set_codebook(self, D):
'''Clobber the existing codebook and encoder with a new one.'''
self.components_ = D
extra_args = {}
if self.penalty == 'l1_space':
extra_args['height'] = D.shape[1]
extra_args['width'] = D.shape[2]
extra_args['pad_data'] = self.pad_data
extra_args['nonneg'] = self.nonneg
D = D.swapaxes(0, 1).swapaxes(1, 2)
D = _cdl.patches_to_vectors(D, pad_data=self.pad_data)
D = _cdl.columns_to_diags(D)
self.fft_components_ = D
encoder, D, diagnostics = _cdl.learn_dictionary(
[],
self.n_atoms,
reg = self.penalty,
alpha = self.alpha,
max_steps = 0,
verbose = False,
D = D,
**extra_args)
self.encoder_ = encoder
return self
def fit(self, X):
'''Fit the model to the data in X.
Parameters
----------
X : array-like, shape [n_samples, n_features_y, n_features_x]
Training data patches.
Returns
-------
self: object
Returns the instance itself.
'''
width = X.shape[2]
extra_args = {}
if self.penalty == 'l1_space':
extra_args['height'] = X.shape[1]
extra_args['width'] = X.shape[2]
extra_args['pad_data'] = self.pad_data
extra_args['nonneg'] = self.nonneg
encoder, D, diagnostics = _cdl.learn_dictionary(
self.data_generator(X),
self.n_atoms,
reg = self.penalty,
alpha = self.alpha,
max_steps = self.n_iter,
verbose = self.verbose,
D = self.fft_components_,
**extra_args)
self.fft_components_ = D
D = _cdl.diags_to_columns(D)
D = _cdl.vectors_to_patches(D, width,
pad_data=self.pad_data)
self.components_ = D.swapaxes(1, 2).swapaxes(0, 1)
self.encoder_ = encoder
self.diagnostics_ = diagnostics
return self
def fit(self, X):
'''Fit the model to the data in X.
Parameters
----------
X : array-like, shape [n_samples, n_features_y, n_features_x]
Training data patches.
Returns
-------
self: object
Returns the instance itself.
'''
width = X.shape[2]
extra_args = {}
if self.penalty == 'l1_space':
extra_args['height'] = X.shape[1]
extra_args['width'] = X.shape[2]
extra_args['pad_data'] = self.pad_data
extra_args['nonneg'] = self.nonneg
encoder, D, diagnostics = _cdl.learn_dictionary(self.data_generator(X),
self.n_atoms,
reg=self.penalty,
alpha=self.alpha,
max_steps=self.n_iter,
verbose=self.verbose,
D=self.fft_components_,
**extra_args)
self.fft_components_ = D
D = _cdl.diags_to_columns(D)
D = _cdl.vectors_to_patches(D, width, pad_data=self.pad_data)
self.components_ = D.swapaxes(1, 2).swapaxes(0, 1)
self.encoder_ = encoder
self.diagnostics_ = diagnostics
return self