def global_transform(X_batch, pad_data, encoder_, n_atoms, nonneg):
X_batch = X_batch.swapaxes(0, 1).swapaxes(1, 2)
h, w, n = X_batch.shape
# Fourier transform
X_new = _cdl.patches_to_vectors(X_batch, pad_data=pad_data)
# Encode
X_new = encoder_(X_new)
X_new = _cdl.real2_to_complex(X_new)
X_new = X_new.reshape((-1, X_new.shape[1] * n_atoms), order='F')
X_new = _cdl.complex_to_real2(X_new)
X_new = _cdl.vectors_to_patches(X_new, w, pad_data=pad_data, real=True)
X_new = X_new.reshape((X_new.shape[0], X_new.shape[1], n_atoms, n),
order='F')
X_new = X_new.swapaxes(3, 0).swapaxes(3, 2).swapaxes(3, 1)
if nonneg:
X_new = np.maximum(X_new, 0.0)
return X_new
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 global_transform(X_batch, pad_data, encoder_, n_atoms, nonneg):
X_batch = X_batch.swapaxes(0, 1).swapaxes(1, 2)
h, w, n = X_batch.shape
# Fourier transform
X_new = _cdl.patches_to_vectors(X_batch, pad_data=pad_data)
# Encode
X_new = encoder_(X_new)
X_new = _cdl.real2_to_complex(X_new)
X_new = X_new.reshape( (-1, X_new.shape[1] * n_atoms), order='F')
X_new = _cdl.complex_to_real2(X_new)
X_new = _cdl.vectors_to_patches(X_new, w,
pad_data=pad_data,
real=True)
X_new = X_new.reshape( (X_new.shape[0], X_new.shape[1],
n_atoms, n),
order='F')
X_new = X_new.swapaxes(3, 0).swapaxes(3, 2).swapaxes(3, 1)
if nonneg:
X_new = np.maximum(X_new, 0.0)
return X_new
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 data_generator(self, X_full):
'''Make a CDL data generator from an input array
Arguments
---------
X_full -- (ndarray) n-by-h-by-w data array
Returns
-------
batch_gen -- (generator) batches of CDL-transformed input data
the generator will loop infinitely
'''
# 1. initialize the RNG
if type(self.random_state) is int:
random.seed(self.random_state)
elif self.random_state is not None:
random.setstate(self.random_state)
n = X_full.shape[0]
indices = range(n)
while True:
if self.shuffle:
random.shuffle(indices)
for i in range(0, n, self.chunk_size):
if i + self.chunk_size > n:
break
X = X_full[i:i+self.chunk_size]
# Swap the axes around
X = X.swapaxes(0, 1).swapaxes(1, 2)
# X is now h-*-w-by-n
yield _cdl.patches_to_vectors(X, pad_data=self.pad_data)
def data_generator(self, X_full):
'''Make a CDL data generator from an input array
Arguments
---------
X_full -- (ndarray) n-by-h-by-w data array
Returns
-------
batch_gen -- (generator) batches of CDL-transformed input data
the generator will loop infinitely
'''
# 1. initialize the RNG
if type(self.random_state) is int:
random.seed(self.random_state)
elif self.random_state is not None:
random.setstate(self.random_state)
n = X_full.shape[0]
indices = range(n)
while True:
if self.shuffle:
random.shuffle(indices)
for i in range(0, n, self.chunk_size):
if i + self.chunk_size > n:
break
X = X_full[i:i + self.chunk_size]
# Swap the axes around
X = X.swapaxes(0, 1).swapaxes(1, 2)
# X is now h-*-w-by-n
yield _cdl.patches_to_vectors(X, pad_data=self.pad_data)
