def _inverse_diag(diag_operator):
return linear_operator_diag.LinearOperatorDiag(
1. / diag_operator.diag,
is_non_singular=diag_operator.is_non_singular,
is_self_adjoint=diag_operator.is_self_adjoint,
is_positive_definite=diag_operator.is_positive_definite,
is_square=True)
def _add(self, op1, op2, operator_name, hints):
return linear_operator_diag.LinearOperatorDiag(
diag=op1.diag_part() + op2.diag_part(),
is_non_singular=hints.is_non_singular,
is_self_adjoint=hints.is_self_adjoint,
is_positive_definite=hints.is_positive_definite,
name=operator_name)
def _cholesky_diag(diag_operator):
return linear_operator_diag.LinearOperatorDiag(math_ops.sqrt(
diag_operator.diag),
is_non_singular=True,
is_self_adjoint=True,
is_positive_definite=True,
is_square=True)
def _set_diag_operators(self, diag_update, is_diag_update_positive):
"""Set attributes self._diag_update and self._diag_operator."""
if diag_update is not None:
self._diag_operator = linear_operator_diag.LinearOperatorDiag(
self._diag_update,
is_positive_definite=is_diag_update_positive)
self._diag_inv_operator = linear_operator_diag.LinearOperatorDiag(
1. / self._diag_update,
is_positive_definite=is_diag_update_positive)
else:
if tensor_shape.dimension_value(
_ops.TensorShape(self.u.shape)[-1]) is not None:
r = tensor_shape.dimension_value(
_ops.TensorShape(self.u.shape)[-1])
else:
r = array_ops.shape(self.u)[-1]
self._diag_operator = linear_operator_identity.LinearOperatorIdentity(
num_rows=r, dtype=self.dtype)
self._diag_inv_operator = self._diag_operator
def _matmul_linear_operator_diag(linop_a, linop_b):
return linear_operator_diag.LinearOperatorDiag(
diag=linop_a.diag * linop_b.diag,
is_non_singular=registrations_util.combined_non_singular_hint(
linop_a, linop_b),
is_self_adjoint=registrations_util.
combined_commuting_self_adjoint_hint(linop_a, linop_b),
is_positive_definite=(
registrations_util.combined_commuting_positive_definite_hint(
linop_a, linop_b)),
is_square=True)
def _adjoint_diag(diag_operator):
diag = diag_operator.diag
if np.issubdtype(diag.dtype, np.complexfloating):
diag = math_ops.conj(diag)
return linear_operator_diag.LinearOperatorDiag(
diag=diag,
is_non_singular=diag_operator.is_non_singular,
is_self_adjoint=diag_operator.is_self_adjoint,
is_positive_definite=diag_operator.is_positive_definite,
is_square=True)
def _matmul_linear_operator_diag_scaled_identity_left(linop_scaled_identity,
linop_diag):
return linear_operator_diag.LinearOperatorDiag(
diag=linop_diag.diag * linop_scaled_identity.multiplier,
is_non_singular=registrations_util.combined_non_singular_hint(
linop_diag, linop_scaled_identity),
is_self_adjoint=registrations_util.
combined_commuting_self_adjoint_hint(linop_diag,
linop_scaled_identity),
is_positive_definite=(
registrations_util.combined_commuting_positive_definite_hint(
linop_diag, linop_scaled_identity)),
is_square=True)
