def test_matrix2cube(self):
"""Test matrix2cube."""
npt.assert_array_equal(
transform.matrix2cube(self.matrix, self.cube[0].shape),
self.cube,
err_msg='Incorrect transformation: matrix2cube',
)
def _op_method(self, input_data, extra_factor=1.0, rank=None):
"""Operator.
This method returns the input data after the singular values have been
thresholded.
Parameters
----------
input_data : numpy.ndarray
Input data array
extra_factor : float
Additional multiplication factor (default is ``1.0``)
rank: int, optional
Estimation of the rank to save computation time in standard mode,
if not set an internal estimation is used.
Returns
-------
numpy.ndarray
SVD thresholded data
Raises
------
ValueError
if lowr_type is not in ``{'standard', 'ngole'}``
"""
# Update threshold with extra factor.
threshold = self.thresh * extra_factor
if self.lowr_type == 'standard' and self.rank is None and rank is None:
data_matrix = svd_thresh(
cube2matrix(input_data),
threshold,
thresh_type=self.thresh_type,
)
elif self.lowr_type == 'standard':
data_matrix, update_rank = svd_thresh_coef_fast(
cube2matrix(input_data),
threshold,
n_vals=rank or self.rank,
extra_vals=5,
thresh_type=self.thresh_type,
)
self.rank = update_rank # save for future use
elif self.lowr_type == 'ngole':
data_matrix = svd_thresh_coef(
cube2matrix(input_data),
self.operator,
threshold,
thresh_type=self.thresh_type,
)
else:
raise ValueError('lowr_type should be standard or ngole')
# Return updated data.
return matrix2cube(data_matrix, input_data.shape[1:])
def svd_thresh_coef(input_data, operator, threshold, thresh_type='hard'):
"""Threshold the singular values coefficients.
This method thresholds the input data using singular value decomposition.
Parameters
----------
input_data : numpy.ndarray
Input data array, 2D matrix
operator : class
Operator class instance
threshold : float or numpy.ndarray
Threshold value(s)
thresh_type : {'hard', 'soft'}
Type of noise to be added (default is ``'hard'``)
Returns
-------
numpy.ndarray
Thresholded data
Raises
------
TypeError
If operator not callable
"""
if not callable(operator):
raise TypeError('Operator must be a callable function.')
# Get SVD of data matrix
u_vec, s_values, v_vec = calculate_svd(input_data)
# Diagnalise s
s_values = np.diag(s_values)
# Compute coefficients
a_matrix = np.dot(s_values, v_vec)
# Get the shape of the array
array_shape = np.repeat(np.int(np.sqrt(u_vec.shape[0])), 2)
# Compute threshold matrix.
ti = np.array([
np.linalg.norm(elem)
for elem in operator(matrix2cube(u_vec, array_shape))
])
threshold *= np.repeat(ti, a_matrix.shape[1]).reshape(a_matrix.shape)
# Threshold coefficients.
a_new = thresh(a_matrix, threshold, thresh_type)
# Return the thresholded image.
return np.dot(u_vec, a_new)
def svd_thresh_coef(data, operator, threshold, thresh_type='hard'):
"""Threshold the singular values coefficients
This method thresholds the input data using singular value decomposition
Parameters
----------
data : numpy.ndarray
Input data array, 2D matrix
operator : class
Operator class instance
threshold : float or numpy.ndarray
Threshold value(s)
threshold_type : {'hard', 'soft'}
Type of noise to be added (default is 'hard')
Returns
-------
numpy.ndarray
Thresholded data
Raises
------
ValueError
For invalid string entry for n_pc
"""
if not callable(operator):
raise TypeError('Operator must be a callable function.')
# Get SVD of data matrix
u, s, v = calculate_svd(data)
# Diagnalise s
s = np.diag(s)
# Compute coefficients
a = np.dot(s, v)
# Get the shape of the array
array_shape = np.repeat(np.int(np.sqrt(u.shape[0])), 2)
# Compute threshold matrix.
ti = np.array(
[np.linalg.norm(x) for x in operator(matrix2cube(u, array_shape))])
threshold *= np.repeat(ti, a.shape[1]).reshape(a.shape)
# Threshold coefficients.
a_new = thresh(a, threshold, thresh_type)
# Return the thresholded image.
return np.dot(u, a_new)
def _op_method(self, input_data, extra_factor=1.0):
"""Operator.
This method returns the input data after the singular values have been
thresholded.
Parameters
----------
input_data : numpy.ndarray
Input data array
extra_factor : float
Additional multiplication factor (default is ``1.0``)
Returns
-------
numpy.ndarray
SVD thresholded data
"""
# Update threshold with extra factor.
threshold = self.thresh * extra_factor
if self.lowr_type == 'standard':
data_matrix = svd_thresh(
cube2matrix(input_data),
threshold,
thresh_type=self.thresh_type,
)
elif self.lowr_type == 'ngole':
data_matrix = svd_thresh_coef(
cube2matrix(input_data),
self.operator,
threshold,
thresh_type=self.thresh_type,
)
# Return updated data.
return matrix2cube(data_matrix, input_data.shape[1:])