def test_driver(self):
for index in range(len(self.penalties)):
solution_vector = np.zeros(self.dictionary.shape[1])
feature_sign_search(self.dictionary, self.signal,
self.penalties[index],
solution=solution_vector)
yield self.check_against_reference, solution_vector, index
yield self.check_zerocoef_optimality_cond, solution_vector, index
yield self.check_nonzero_optimality_cond, solution_vector, index
yield self.check_zeros_against_reference, solution_vector, index
yield self.check_nonzeros_against_reference, solution_vector, index
def test_driver(self):
for index in range(len(self.penalties)):
solution_vector = np.zeros(self.dictionary.shape[1])
feature_sign_search(self.dictionary,
self.signal,
self.penalties[index],
solution=solution_vector)
yield self.check_against_reference, solution_vector, index
yield self.check_zerocoef_optimality_cond, solution_vector, index
yield self.check_nonzero_optimality_cond, solution_vector, index
yield self.check_zeros_against_reference, solution_vector, index
yield self.check_nonzeros_against_reference, solution_vector, index
def test_shape_rank_matches_2d_generated(self):
sparsity = self.penalties[0]
signal = self.signal.reshape(1, -1)
solution = feature_sign_search(self.dictionary, signal, sparsity)
assert solution.ndim == 2
assert solution.shape[0] == 1
assert solution.shape[1] == self.dictionary.shape[1]
def test_solution_identity_2d_provided(self):
sparsity = self.penalties[0]
solution = np.zeros((1, self.dictionary.shape[1]))
signal = self.signal.reshape(1, -1)
newsol = feature_sign_search(self.dictionary, signal, sparsity,
solution=solution)
assert solution is newsol
def test_shape_rank_matches_2d_generated(self):
sparsity = self.penalties[0]
signal = self.signal.reshape(1, -1)
solution = feature_sign_search(self.dictionary, signal, sparsity)
assert solution.ndim == 2
assert solution.shape[0] == 1
assert solution.shape[1] == self.dictionary.shape[1]
def test_solution_identity_1d_provided(self):
sparsity = self.penalties[0]
solution = np.zeros(self.dictionary.shape[1])
newsol = feature_sign_search(self.dictionary,
self.signal,
sparsity,
solution=solution)
assert solution is newsol
def optimize_gamma(self, example):
#variable names chosen to follow the arguments to l1ls_featuresign
Y = N.zeros((self.nvis,))
Y[:] = example
c = (1e-10 + N.square(self.W.get_value(borrow=True) -
example).sum(axis=1))
A = self.W.get_value(borrow=True).T / c
x = feature_sign_search(A, Y, self.coeff)
g = x / c
return g
def optimize_gamma(self, example):
#variable names chosen to follow the arguments to l1ls_featuresign
Y = N.zeros((self.nvis, ))
Y[:] = example
c = (1e-10 +
N.square(self.W.get_value(borrow=True) - example).sum(axis=1))
A = self.W.get_value(borrow=True).T / c
x = feature_sign_search(A, Y, self.coeff)
g = x / c
return g
