def _operator_and_matrix(self, build_info, dtype, use_placeholder):
shape = list(build_info.shape)
# Either 1 or 2 matrices, depending.
num_operators = rng.randint(low=1, high=3)
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
for _ in range(num_operators)
]
lin_op_matrices = matrices
if use_placeholder:
lin_op_matrices = [
array_ops.placeholder_with_default(
matrix, shape=None) for matrix in matrices]
operator = linalg.LinearOperatorComposition(
[linalg.LinearOperatorFullMatrix(l) for l in lin_op_matrices],
is_square=True)
matmul_order_list = list(reversed(matrices))
mat = matmul_order_list[0]
for other_mat in matmul_order_list[1:]:
mat = math_ops.matmul(other_mat, mat)
return operator, mat
def operator_and_matrix(self,
build_info,
dtype,
use_placeholder,
ensure_self_adjoint_and_pd=False):
shape = list(build_info.shape)
if ensure_self_adjoint_and_pd:
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
else:
matrix = linear_operator_test_util.random_tril_matrix(
shape, dtype, force_well_conditioned=True, remove_upper=True)
lin_op_matrix = matrix
if use_placeholder:
lin_op_matrix = array_ops.placeholder_with_default(matrix, shape=None)
if ensure_self_adjoint_and_pd:
operator = LinearOperatorAdjoint(
linalg.LinearOperatorFullMatrix(
lin_op_matrix, is_positive_definite=True, is_self_adjoint=True))
else:
operator = LinearOperatorAdjoint(
linalg.LinearOperatorLowerTriangular(lin_op_matrix))
return operator, linalg.adjoint(matrix)
def operator_and_matrix(self, build_info, dtype, use_placeholder):
shape = list(build_info.shape)
# Either 1 or 2 matrices, depending.
num_operators = rng.randint(low=1, high=3)
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
for _ in range(num_operators)
]
lin_op_matrices = matrices
if use_placeholder:
lin_op_matrices = [
array_ops.placeholder_with_default(matrix, shape=None)
for matrix in matrices
]
operator = linalg.LinearOperatorComposition(
[linalg.LinearOperatorFullMatrix(l) for l in lin_op_matrices],
is_square=True)
matmul_order_list = list(reversed(matrices))
mat = matmul_order_list[0]
for other_mat in matmul_order_list[1:]:
mat = math_ops.matmul(other_mat, mat)
return operator, mat
def operator_and_matrix(self,
build_info,
dtype,
use_placeholder,
ensure_self_adjoint_and_pd=False):
# Matrix is always symmetric and positive definite in this class.
del ensure_self_adjoint_and_pd
shape = list(build_info.shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
lin_op_matrix = matrix
if use_placeholder:
lin_op_matrix = array_ops.placeholder_with_default(matrix,
shape=None)
operator = linalg.LinearOperatorFullMatrix(lin_op_matrix,
is_square=True,
is_self_adjoint=True,
is_positive_definite=True)
return operator, matrix
def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder):
shape = list(build_info.shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
if use_placeholder:
matrix_ph = array_ops.placeholder(dtype=dtype)
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrix = matrix.eval()
# is_square is auto-set because of self_adjoint/pd.
operator = linalg.LinearOperatorFullMatrix(
matrix_ph, is_self_adjoint=True, is_positive_definite=True)
feed_dict = {matrix_ph: matrix}
else:
operator = linalg.LinearOperatorFullMatrix(
matrix, is_self_adjoint=True, is_positive_definite=True)
feed_dict = None
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated matrix to a numpy array.
mat = ops.convert_to_tensor(matrix)
return operator, mat, feed_dict
def _operator_and_matrix(self,
build_info,
dtype,
use_placeholder,
ensure_self_adjoint_and_pd=False):
shape = list(build_info.shape)
if ensure_self_adjoint_and_pd:
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
else:
matrix = linear_operator_test_util.random_tril_matrix(
shape, dtype, force_well_conditioned=True, remove_upper=True)
lin_op_matrix = matrix
if use_placeholder:
lin_op_matrix = array_ops.placeholder_with_default(matrix,
shape=None)
if ensure_self_adjoint_and_pd:
operator = LinearOperatorInversion(
linalg.LinearOperatorFullMatrix(lin_op_matrix,
is_positive_definite=True,
is_self_adjoint=True))
else:
operator = LinearOperatorInversion(
linalg.LinearOperatorLowerTriangular(lin_op_matrix))
return operator, linalg.inv(matrix)
def _operator_and_matrix(self, build_info, dtype, use_placeholder):
shape = list(build_info.shape)
expected_factors = build_info.__dict__["factors"]
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
block_shape, dtype, force_well_conditioned=True)
for block_shape in expected_factors
]
lin_op_matrices = matrices
if use_placeholder:
lin_op_matrices = [
array_ops.placeholder_with_default(m, shape=None) for m in matrices]
operator = kronecker.LinearOperatorKronecker(
[linalg.LinearOperatorFullMatrix(
l, is_square=True) for l in lin_op_matrices])
matrices = linear_operator_util.broadcast_matrix_batch_dims(matrices)
kronecker_dense = _kronecker_dense(matrices)
if not use_placeholder:
kronecker_dense.set_shape(shape)
return operator, kronecker_dense
def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
shape = list(shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype)
if use_placeholder:
matrix_ph = array_ops.placeholder(dtype=dtype)
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrix = matrix.eval()
operator = linalg.LinearOperatorFullMatrix(matrix_ph,
is_square=True)
feed_dict = {matrix_ph: matrix}
else:
# is_square should be auto-detected here.
operator = linalg.LinearOperatorFullMatrix(matrix)
feed_dict = None
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated matrix to a numpy array.
mat = ops.convert_to_tensor(matrix)
return operator, mat, feed_dict
def _operator_and_mat_and_feed_dict(self, build_info, dtype,
use_placeholder):
shape = list(build_info.shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
if use_placeholder:
matrix_ph = array_ops.placeholder(dtype=dtype)
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrix = matrix.eval()
# is_square is auto-set because of self_adjoint/pd.
operator = linalg.LinearOperatorFullMatrix(
matrix_ph, is_self_adjoint=True, is_positive_definite=True)
feed_dict = {matrix_ph: matrix}
else:
operator = linalg.LinearOperatorFullMatrix(
matrix, is_self_adjoint=True, is_positive_definite=True)
feed_dict = None
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated matrix to a numpy array.
mat = ops.convert_to_tensor(matrix)
return operator, mat, feed_dict
def operator_and_matrix(self,
build_info,
dtype,
use_placeholder,
ensure_self_adjoint_and_pd=False):
shape = list(build_info.shape)
# Either 1 or 2 matrices, depending.
num_operators = rng.randint(low=1, high=3)
if ensure_self_adjoint_and_pd:
# The random PD matrices are also symmetric. Here we are computing
# A @ A ... @ A. Since A is symmetric and PD, so are any powers of it.
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
] * num_operators
else:
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
for _ in range(num_operators)
]
lin_op_matrices = matrices
if use_placeholder:
lin_op_matrices = [
array_ops.placeholder_with_default(matrix, shape=None)
for matrix in matrices
]
operator = linalg.LinearOperatorComposition(
[linalg.LinearOperatorFullMatrix(l) for l in lin_op_matrices],
is_positive_definite=True if ensure_self_adjoint_and_pd else None,
is_self_adjoint=True if ensure_self_adjoint_and_pd else None,
is_square=True)
matmul_order_list = list(reversed(matrices))
mat = matmul_order_list[0]
for other_mat in matmul_order_list[1:]:
mat = math_ops.matmul(other_mat, mat)
return operator, mat
def operator_and_matrix(
self, shape_info, dtype, use_placeholder,
ensure_self_adjoint_and_pd=False):
expected_blocks = (
shape_info.__dict__["blocks"] if "blocks" in shape_info.__dict__
else [[list(shape_info.shape)]])
matrices = []
for i, row_shapes in enumerate(expected_blocks):
row = []
for j, block_shape in enumerate(row_shapes):
if i == j: # operator is on the diagonal
row.append(
linear_operator_test_util.random_positive_definite_matrix(
block_shape, dtype, force_well_conditioned=True))
else:
row.append(
linear_operator_test_util.random_normal(block_shape, dtype=dtype))
matrices.append(row)
lin_op_matrices = matrices
if use_placeholder:
lin_op_matrices = [[
array_ops.placeholder_with_default(
matrix, shape=None) for matrix in row] for row in matrices]
operator = block_lower_triangular.LinearOperatorBlockLowerTriangular(
[[linalg.LinearOperatorFullMatrix( # pylint:disable=g-complex-comprehension
l,
is_square=True,
is_self_adjoint=True if ensure_self_adjoint_and_pd else None,
is_positive_definite=True if ensure_self_adjoint_and_pd else None)
for l in row] for row in lin_op_matrices])
# Should be auto-set.
self.assertTrue(operator.is_square)
# Broadcast the shapes.
expected_shape = list(shape_info.shape)
broadcasted_matrices = linear_operator_util.broadcast_matrix_batch_dims(
[op for row in matrices for op in row]) # pylint: disable=g-complex-comprehension
matrices = [broadcasted_matrices[i * (i + 1) // 2:(i + 1) * (i + 2) // 2]
for i in range(len(matrices))]
block_lower_triangular_dense = _block_lower_triangular_dense(
expected_shape, matrices)
if not use_placeholder:
block_lower_triangular_dense.set_shape(expected_shape)
return operator, block_lower_triangular_dense
def _operator_and_matrix(self, build_info, dtype, use_placeholder):
shape = list(build_info.shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype)
lin_op_matrix = matrix
if use_placeholder:
lin_op_matrix = array_ops.placeholder_with_default(matrix, shape=None)
operator = linalg.LinearOperatorFullMatrix(lin_op_matrix, is_square=True)
return operator, matrix
def test_tape_safe(self):
matrices = []
for _ in range(4):
matrices.append(variables_module.Variable(
linear_operator_test_util.random_positive_definite_matrix(
[2, 2], dtype=dtypes.float32, force_well_conditioned=True)))
operator = block_diag.LinearOperatorBlockDiag(
[linalg.LinearOperatorFullMatrix(
matrix, is_self_adjoint=True,
is_positive_definite=True) for matrix in matrices],
is_self_adjoint=True,
is_positive_definite=True,
)
self.check_tape_safe(operator)
def _operator_and_matrix(self, build_info, dtype, use_placeholder):
shape = list(build_info.shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype)
lin_op_matrix = matrix
if use_placeholder:
lin_op_matrix = array_ops.placeholder_with_default(matrix,
shape=None)
operator = linalg.LinearOperatorFullMatrix(lin_op_matrix,
is_square=True)
return operator, matrix
def _operator_and_matrix(self,
build_info,
dtype,
use_placeholder,
ensure_self_adjoint_and_pd=False):
shape = list(build_info.shape)
expected_blocks = (build_info.__dict__["blocks"]
if "blocks" in build_info.__dict__ else [shape])
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
block_shape, dtype, force_well_conditioned=True)
for block_shape in expected_blocks
]
lin_op_matrices = matrices
if use_placeholder:
lin_op_matrices = [
array_ops.placeholder_with_default(matrix, shape=None)
for matrix in matrices
]
operator = block_diag.LinearOperatorBlockDiag([
linalg.LinearOperatorFullMatrix(
l,
is_square=True,
is_self_adjoint=True if ensure_self_adjoint_and_pd else None,
is_positive_definite=True
if ensure_self_adjoint_and_pd else None)
for l in lin_op_matrices
])
# Should be auto-set.
self.assertTrue(operator.is_square)
# Broadcast the shapes.
expected_shape = list(build_info.shape)
matrices = linear_operator_util.broadcast_matrix_batch_dims(matrices)
block_diag_dense = _block_diag_dense(expected_shape, matrices)
if not use_placeholder:
block_diag_dense.set_shape(
expected_shape[:-2] + [expected_shape[-1], expected_shape[-1]])
return operator, block_diag_dense
def _operator_and_mat_and_feed_dict(self, build_info, dtype,
use_placeholder):
shape = list(build_info.shape)
expected_blocks = (build_info.__dict__["blocks"]
if "blocks" in build_info.__dict__ else [shape])
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
block_shape, dtype, force_well_conditioned=True)
for block_shape in expected_blocks
]
if use_placeholder:
matrices_ph = [
array_ops.placeholder(dtype=dtype) for _ in expected_blocks
]
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrices = self.evaluate(matrices)
operator = block_diag.LinearOperatorBlockDiag([
linalg.LinearOperatorFullMatrix(m_ph, is_square=True)
for m_ph in matrices_ph
],
is_square=True)
feed_dict = {m_ph: m for (m_ph, m) in zip(matrices_ph, matrices)}
else:
operator = block_diag.LinearOperatorBlockDiag([
linalg.LinearOperatorFullMatrix(m, is_square=True)
for m in matrices
])
feed_dict = None
# Should be auto-set.
self.assertTrue(operator.is_square)
# Broadcast the shapes.
expected_shape = list(build_info.shape)
matrices = linear_operator_util.broadcast_matrix_batch_dims(matrices)
block_diag_dense = _block_diag_dense(expected_shape, matrices)
if not use_placeholder:
block_diag_dense.set_shape(
expected_shape[:-2] + [expected_shape[-1], expected_shape[-1]])
return operator, block_diag_dense, feed_dict
def test_convert_variables_to_tensors(self):
matrices = []
for _ in range(3):
matrices.append(variables_module.Variable(
linear_operator_test_util.random_positive_definite_matrix(
[3, 3], dtype=dtypes.float32, force_well_conditioned=True)))
operator = block_diag.LinearOperatorBlockDiag(
[linalg.LinearOperatorFullMatrix(
matrix, is_self_adjoint=True,
is_positive_definite=True) for matrix in matrices],
is_self_adjoint=True,
is_positive_definite=True,
)
with self.cached_session() as sess:
sess.run([x.initializer for x in operator.variables])
self.check_convert_variables_to_tensors(operator)
def _operator_and_mat_and_feed_dict(self, build_info, dtype, use_placeholder):
shape = list(build_info.shape)
expected_blocks = (
build_info.__dict__["blocks"] if "blocks" in build_info.__dict__
else [shape])
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
block_shape, dtype, force_well_conditioned=True)
for block_shape in expected_blocks
]
if use_placeholder:
matrices_ph = [
array_ops.placeholder(dtype=dtype) for _ in expected_blocks
]
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrices = self.evaluate(matrices)
operator = block_diag.LinearOperatorBlockDiag(
[linalg.LinearOperatorFullMatrix(
m_ph, is_square=True) for m_ph in matrices_ph],
is_square=True)
feed_dict = {m_ph: m for (m_ph, m) in zip(matrices_ph, matrices)}
else:
operator = block_diag.LinearOperatorBlockDiag(
[linalg.LinearOperatorFullMatrix(
m, is_square=True) for m in matrices])
feed_dict = None
# Should be auto-set.
self.assertTrue(operator.is_square)
# Broadcast the shapes.
expected_shape = list(build_info.shape)
matrices = linear_operator_util.broadcast_matrix_batch_dims(matrices)
block_diag_dense = _block_diag_dense(expected_shape, matrices)
if not use_placeholder:
block_diag_dense.set_shape(
expected_shape[:-2] + [expected_shape[-1], expected_shape[-1]])
return operator, block_diag_dense, feed_dict
def operator_and_matrix(
self, shape_info, dtype, use_placeholder,
ensure_self_adjoint_and_pd=False):
shape = list(shape_info.shape)
expected_blocks = (
shape_info.__dict__["blocks"] if "blocks" in shape_info.__dict__
else [shape])
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
block_shape, dtype, force_well_conditioned=True)
for block_shape in expected_blocks
]
lin_op_matrices = matrices
if use_placeholder:
lin_op_matrices = [
array_ops.placeholder_with_default(
matrix, shape=None) for matrix in matrices]
operator = block_diag.LinearOperatorBlockDiag(
[linalg.LinearOperatorFullMatrix(
l,
is_square=True,
is_self_adjoint=True if ensure_self_adjoint_and_pd else None,
is_positive_definite=True if ensure_self_adjoint_and_pd else None)
for l in lin_op_matrices])
# Should be auto-set.
self.assertTrue(operator.is_square)
# Broadcast the shapes.
expected_shape = list(shape_info.shape)
matrices = linear_operator_util.broadcast_matrix_batch_dims(matrices)
block_diag_dense = _block_diag_dense(expected_shape, matrices)
if not use_placeholder:
block_diag_dense.set_shape(
expected_shape[:-2] + [expected_shape[-1], expected_shape[-1]])
return operator, block_diag_dense
def _operator_and_mat_and_feed_dict(self, build_info, dtype,
use_placeholder):
sess = ops.get_default_session()
shape = list(build_info.shape)
# Either 1 or 2 matrices, depending.
num_operators = rng.randint(low=1, high=3)
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
for _ in range(num_operators)
]
if use_placeholder:
matrices_ph = [
array_ops.placeholder(dtype=dtype)
for _ in range(num_operators)
]
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrices = sess.run(matrices)
operator = linalg.LinearOperatorComposition([
linalg.LinearOperatorFullMatrix(m_ph) for m_ph in matrices_ph
],
is_square=True)
feed_dict = {m_ph: m for (m_ph, m) in zip(matrices_ph, matrices)}
else:
operator = linalg.LinearOperatorComposition(
[linalg.LinearOperatorFullMatrix(m) for m in matrices])
feed_dict = None
# Should be auto-set.
self.assertTrue(operator.is_square)
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated each matrix to a numpy array.
matmul_order_list = list(reversed(matrices))
mat = ops.convert_to_tensor(matmul_order_list[0])
for other_mat in matmul_order_list[1:]:
mat = math_ops.matmul(other_mat, mat)
return operator, mat, feed_dict
def _operator_and_matrix(
self, build_info, dtype, use_placeholder,
ensure_self_adjoint_and_pd=False):
shape = list(build_info.shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype)
lin_op_matrix = matrix
if use_placeholder:
lin_op_matrix = array_ops.placeholder_with_default(matrix, shape=None)
# Set the hints to none to test non-symmetric PD code paths.
operator = linalg.LinearOperatorFullMatrix(
lin_op_matrix,
is_square=True,
is_self_adjoint=True if ensure_self_adjoint_and_pd else None,
is_positive_definite=True if ensure_self_adjoint_and_pd else None)
return operator, matrix
def _operator_and_matrix(
self, build_info, dtype, use_placeholder,
ensure_self_adjoint_and_pd=False):
shape = list(build_info.shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype)
lin_op_matrix = matrix
if use_placeholder:
lin_op_matrix = array_ops.placeholder_with_default(matrix, shape=None)
# Set the hints to none to test non-symmetric PD code paths.
operator = linalg.LinearOperatorFullMatrix(
lin_op_matrix,
is_square=True,
is_self_adjoint=True if ensure_self_adjoint_and_pd else None,
is_positive_definite=True if ensure_self_adjoint_and_pd else None)
return operator, matrix
def operator_and_matrix(self,
build_info,
dtype,
use_placeholder,
ensure_self_adjoint_and_pd=False):
# Kronecker products constructed below will be from symmetric
# positive-definite matrices.
del ensure_self_adjoint_and_pd
shape = list(build_info.shape)
expected_factors = build_info.__dict__["factors"]
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
block_shape, dtype, force_well_conditioned=True)
for block_shape in expected_factors
]
lin_op_matrices = matrices
if use_placeholder:
lin_op_matrices = [
array_ops.placeholder_with_default(m, shape=None)
for m in matrices
]
operator = kronecker.LinearOperatorKronecker([
linalg.LinearOperatorFullMatrix(l,
is_square=True,
is_self_adjoint=True,
is_positive_definite=True)
for l in lin_op_matrices
])
matrices = linear_operator_util.broadcast_matrix_batch_dims(matrices)
kronecker_dense = _kronecker_dense(matrices)
if not use_placeholder:
kronecker_dense.set_shape(shape)
return operator, kronecker_dense
def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
sess = ops.get_default_session()
shape = list(shape)
# Either 1 or 2 matrices, depending.
num_operators = rng.randint(low=1, high=3)
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
for _ in range(num_operators)
]
if use_placeholder:
matrices_ph = [
array_ops.placeholder(dtype=dtype) for _ in range(num_operators)
]
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrices = sess.run(matrices)
operator = linalg.LinearOperatorComposition(
[linalg.LinearOperatorFullMatrix(m_ph) for m_ph in matrices_ph],
is_square=True)
feed_dict = {m_ph: m for (m_ph, m) in zip(matrices_ph, matrices)}
else:
operator = linalg.LinearOperatorComposition(
[linalg.LinearOperatorFullMatrix(m) for m in matrices])
feed_dict = None
# Should be auto-set.
self.assertTrue(operator.is_square)
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated each matrix to a numpy array.
matmul_order_list = list(reversed(matrices))
mat = ops.convert_to_tensor(matmul_order_list[0])
for other_mat in matmul_order_list[1:]:
mat = math_ops.matmul(other_mat, mat)
return operator, mat, feed_dict
def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
shape = list(shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(shape,
dtype)
if use_placeholder:
matrix_ph = array_ops.placeholder(dtype=dtype)
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrix = matrix.eval()
operator = linalg.LinearOperatorFullMatrix(matrix_ph, is_square=True)
feed_dict = {matrix_ph: matrix}
else:
# is_square should be auto-detected here.
operator = linalg.LinearOperatorFullMatrix(matrix)
feed_dict = None
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated matrix to a numpy array.
mat = ops.convert_to_tensor(matrix)
return operator, mat, feed_dict
def _operator_and_matrix(
self, build_info, dtype, use_placeholder,
ensure_self_adjoint_and_pd=False):
# Kronecker products constructed below will be from symmetric
# positive-definite matrices.
del ensure_self_adjoint_and_pd
shape = list(build_info.shape)
expected_factors = build_info.__dict__["factors"]
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
block_shape, dtype, force_well_conditioned=True)
for block_shape in expected_factors
]
lin_op_matrices = matrices
if use_placeholder:
lin_op_matrices = [
array_ops.placeholder_with_default(m, shape=None) for m in matrices]
operator = kronecker.LinearOperatorKronecker(
[linalg.LinearOperatorFullMatrix(
l,
is_square=True,
is_self_adjoint=True,
is_positive_definite=True)
for l in lin_op_matrices])
matrices = linear_operator_util.broadcast_matrix_batch_dims(matrices)
kronecker_dense = _kronecker_dense(matrices)
if not use_placeholder:
kronecker_dense.set_shape(shape)
return operator, kronecker_dense
def _operator_and_matrix(
self, build_info, dtype, use_placeholder,
ensure_self_adjoint_and_pd=False):
# Matrix is always symmetric and positive definite in this class.
del ensure_self_adjoint_and_pd
shape = list(build_info.shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
lin_op_matrix = matrix
if use_placeholder:
lin_op_matrix = array_ops.placeholder_with_default(matrix, shape=None)
operator = linalg.LinearOperatorFullMatrix(
lin_op_matrix,
is_square=True,
is_self_adjoint=True,
is_positive_definite=True)
return operator, matrix
