def _somp(X_g, D, Gram, n_nonzero_coefs=None):
n_atoms = D.shape[1]
n_group_samples = X_g.shape[1]
Z = np.zeros((n_atoms, n_group_samples))
Dx = np.array([])
Dx = Dx.astype(int)
R = X_g
if n_nonzero_coefs is not None:
tol = 1e-20
def cont_criterion():
not_reached_sparsity = i < n_nonzero_coefs
return (not_reached_sparsity and frobenius_squared(R) > tol)
else:
cont_criterion = lambda: frobenius_squared(R) > tol
i = 0
while (cont_criterion()):
A = fast_dot(D.T, R)
j = np.argmax([norm(A[k, :]) for k in range(n_atoms)])
Dx = np.append(Dx, j)
G = Gram[Dx, :][:, Dx]
G = np.atleast_2d(G)
try:
G_inv = inv(G)
except LinAlgError:
print gram_singular_msg
break
Z[Dx, :] = fast_dot(fast_dot(inv(G_inv), D[:, Dx].T), X_g)
R = X_g - fast_dot(D, Z)
i += 1
return Z
def _somp(X_g, D, Gram, n_nonzero_coefs=None):
n_atoms = D.shape[1]
n_group_samples = X_g.shape[1]
Z = np.zeros((n_atoms, n_group_samples))
Dx = np.array([])
Dx = Dx.astype(int)
R = X_g
if n_nonzero_coefs is not None:
tol = 1e-20
def cont_criterion():
not_reached_sparsity = i < n_nonzero_coefs
return (not_reached_sparsity and frobenius_squared(R) > tol)
else:
cont_criterion = lambda: frobenius_squared(R) > tol
i = 0
while (cont_criterion()):
A = fast_dot(D.T, R)
j = np.argmax([norm(A[k, :]) for k in range(n_atoms)])
Dx = np.append(Dx, j)
G = Gram[Dx, :][:, Dx]
G = np.atleast_2d(G)
try:
G_inv = inv(G)
except LinAlgError:
print gram_singular_msg
break
Z[Dx, :] = fast_dot(fast_dot(inv(G_inv), D[:, Dx].T), X_g)
R = X_g - fast_dot(D, Z)
i += 1
return Z
def cont_criterion():
not_reached_sparsity = i < n_nonzero_coefs
return (not_reached_sparsity and frobenius_squared(R) > tol)
def approx_error_proc(X, Z, D):
error = frobenius_squared(X - fast_dot(D, Z))
return error
def approx_error(D, Z, X, n_jobs=1):
"""computes the approximation error ||X-DZ||_{F}^{2} """
if n_jobs > 1:
set_openblas_threads(n_jobs)
error = frobenius_squared(X - fast_dot(D, Z))
return error
def cont_criterion():
not_reached_sparsity = i < n_nonzero_coefs
return (not_reached_sparsity and frobenius_squared(R) > tol)
def approx_error_proc(X, Z, D):
error = frobenius_squared(X - fast_dot(D, Z))
return error
def approx_error(D, Z, X, n_jobs=1):
"""computes the approximation error ||X-DZ||_{F}^{2} """
if n_jobs > 1:
set_openblas_threads(n_jobs)
error = frobenius_squared(X - fast_dot(D, Z))
return error