def __test(d, m):
# Generate a random dictionary
X = numpy.random.randn(2 * d, m)
# Convert to diagonals
Xdiags = _cdl.columns_to_diags(X)
# Normalize
N_Xdiag = _cdl.normalize_dictionary(Xdiags)
# Convert back
N_X = _cdl.diags_to_columns(N_Xdiag)
# 1. verify unit norms
norms = numpy.sum(N_X**2, axis=0)**0.5
assert EQ(norms, numpy.ones_like(norms))
# 2. verify that directions are correct:
# projection onto the normalized basis should equal norm
# of the original basis
norms_orig = numpy.sum(X**2, axis=0)**0.5
projection = numpy.sum(X * N_X, axis=0)
assert EQ(norms_orig, projection)
pass
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 __test(d, m):
# Build a random dictionary in diagonal form
X = numpy.random.randn(2 * d, m)
# Normalize the dictionary and convert back to columns
X_norm = _cdl.diags_to_columns(_cdl.normalize_dictionary(_cdl.columns_to_diags(X)))
# Rescale the normalized dictionary to have some inside, some outside
R = 2.0**numpy.linspace(-4, 4, m)
X_scale = X_norm * R
# Vectorize
V_scale = _cdl.columns_to_vector(X_scale)
# Project
V_proj = _cdl.proj_l2_ball(V_scale, m)
# Rearrange the projected matrix into columns
X_proj = _cdl.vector_to_columns(V_proj, m)
# Compute norms
X_scale_norms = numpy.sum(X_scale**2, axis=0)**0.5
X_proj_norms = numpy.sum(X_proj**2, axis=0)**0.5
Xdot = numpy.sum(X_scale * X_proj, axis=0)
for k in range(m):
# 1. verify norm is at most 1
# allow some fudge factor here for numerical instability
assert X_proj_norms[k] <= 1.0 + 1e-10
# 2. verify that points with R < 1 were untouched
assert R[k] > 1.0 or EQ(X_proj[:, k], X_scale[:, k])
# 3. verify that points with R >= 1.0 preserve direction
assert R[k] < 1.0 or EQ(Xdot[k], X_scale_norms[k])
pass
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 __test(d, m):
X = numpy.random.randn(2 * d, m)
Q = _cdl.columns_to_diags(X)
X_back = _cdl.diags_to_columns(Q)
assert EQ(X, X_back)
pass
