def nucnorm(self, x):
"""Calculate nuclear norm
This method returns the nuclear norm error of the deconvolved data in
matrix form
Parameters
----------
x : np.ndarray
Deconvolved data array
Returns
-------
float nuclear norm value
"""
x_prime = cube2matrix(x)
nuc_norm = nuclear_norm(x_prime)
if self.print_cost:
print ' - NUCLEAR NORM:', nuc_norm
return nuc_norm
def nucnorm(self, x):
if self.data_format == 'map':
x_prime = map2matrix(x)
else:
x_prime = cube2matrix(x)
nuc_norm = nuclear_norm(x_prime)
if self.print_cost:
print ' - NUCLEAR NORM:', nuc_norm
return nuc_norm
def descent(self, tolerance=1e-4, max_iter=150):
self.operator.get_spec_rad()
cost0 = 0.0
for i in range(max_iter):
grad = self.operator.grad_step(self.algorithm.data_rec, self.data)
self.algorithm.check_threshold(i, grad)
self.algorithm.update(grad, self.operator.inv_spec_rad)
l2norm0 = norm(self.data - pca_convolve(self.algorithm.data_rec_prev, self.operator.psf_pcs, self.operator.psf_coef), 2)
l2norm1 = norm(self.data - pca_convolve(self.algorithm.data_rec, self.operator.psf_pcs, self.operator.psf_coef), 2)
if l2norm1 > l2norm0:
self.algorithm.speed_switch(False)
nuc_norm = nuclear_norm(map2matrix(self.algorithm.data_rec,
self.layout))
l2norm = norm(self.data - pca_convolve(self.algorithm.data_rec,
self.operator.psf_pcs, self.operator.psf_coef), 2)
cost = (0.5 * l2norm ** 2 + self.algorithm.thresh * nuc_norm)
print ' - i:', i, cost, l2norm, nuc_norm
if np.abs(cost - cost0) < tolerance:
print ' - Gradient Descent converged after %d iterations!' % \
(i + 1)
print ' - Final cost, l2norm, nuc_norm:', cost, l2norm, nuc_norm
break
elif i == max_iter - 1:
print ' - Gradient Descent did not converge after %d iterations!' \
% max_iter
print ' - Final cost, l2norm, nuc_norm:', cost, l2norm, nuc_norm
cost0 = cost
return self.algorithm.data_rec
