def _to_dense(self):
if self.diagonals_format == _MATRIX:
return self.diagonals
if self.diagonals_format == _COMPACT:
return gen_array_ops.matrix_diag_v3(
self.diagonals,
k=(-1, 1),
num_rows=-1,
num_cols=-1,
align='LEFT_RIGHT',
padding_value=0.)
diagonals = [
ops.convert_to_tensor_v2_with_dispatch(d) for d in self.diagonals
]
diagonals = array_ops.stack(diagonals, axis=-2)
return gen_array_ops.matrix_diag_v3(
diagonals,
k=(-1, 1),
num_rows=-1,
num_cols=-1,
align='LEFT_RIGHT',
padding_value=0.)
def build_operator_and_matrix(
self, build_info, dtype, use_placeholder,
ensure_self_adjoint_and_pd=False,
diagonals_format='sequence'):
shape = list(build_info.shape)
# Ensure that diagonal has large enough values. If we generate a
# self adjoint PD matrix, then the diagonal will be dominant guaranteeing
# positive definitess.
diag = linear_operator_test_util.random_sign_uniform(
shape[:-1], minval=4., maxval=6., dtype=dtype)
# We'll truncate these depending on the format
subdiag = linear_operator_test_util.random_sign_uniform(
shape[:-1], minval=1., maxval=2., dtype=dtype)
if ensure_self_adjoint_and_pd:
# Abs on complex64 will result in a float32, so we cast back up.
diag = math_ops.cast(math_ops.abs(diag), dtype=dtype)
# The first element of subdiag is ignored. We'll add a dummy element
# to superdiag to pad it.
superdiag = math_ops.conj(subdiag)
superdiag = manip_ops.roll(superdiag, shift=-1, axis=-1)
else:
superdiag = linear_operator_test_util.random_sign_uniform(
shape[:-1], minval=1., maxval=2., dtype=dtype)
matrix_diagonals = array_ops.stack(
[superdiag, diag, subdiag], axis=-2)
matrix = gen_array_ops.matrix_diag_v3(
matrix_diagonals,
k=(-1, 1),
num_rows=-1,
num_cols=-1,
align='LEFT_RIGHT',
padding_value=0.)
if diagonals_format == 'sequence':
diagonals = [superdiag, diag, subdiag]
elif diagonals_format == 'compact':
diagonals = array_ops.stack([superdiag, diag, subdiag], axis=-2)
elif diagonals_format == 'matrix':
diagonals = matrix
lin_op_diagonals = diagonals
if use_placeholder:
if diagonals_format == 'sequence':
lin_op_diagonals = [array_ops.placeholder_with_default(
d, shape=None) for d in lin_op_diagonals]
else:
lin_op_diagonals = array_ops.placeholder_with_default(
lin_op_diagonals, shape=None)
operator = linalg_lib.LinearOperatorTridiag(
diagonals=lin_op_diagonals,
diagonals_format=diagonals_format,
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