def _check_domain_range_possibly_add_asserts(self):
"""Static check of init arg `num_rows`, possibly add asserts."""
# Possibly add asserts.
if self._assert_proper_shapes:
self._num_rows = control_flow_ops.with_dependencies([
check_ops.assert_rank(
self._num_rows,
0,
message="Argument num_rows must be a 0-D Tensor."),
check_ops.assert_non_negative(
self._num_rows,
message="Argument num_rows must be non-negative."),
], self._num_rows)
self._num_columns = control_flow_ops.with_dependencies([
check_ops.assert_rank(
self._num_columns,
0,
message="Argument num_columns must be a 0-D Tensor."),
check_ops.assert_non_negative(
self._num_columns,
message="Argument num_columns must be non-negative."),
], self._num_columns)
# Static checks.
if not np.issubdtype(self._num_rows.dtype, np.integer):
raise TypeError("Argument num_rows must be integer type. Found:"
" %s" % self._num_rows)
if not np.issubdtype(self._num_columns.dtype, np.integer):
raise TypeError(
"Argument num_columns must be integer type. Found:"
" %s" % self._num_columns)
num_rows_static = self._num_rows_static
num_columns_static = self._num_columns_static
if num_rows_static is not None:
if num_rows_static.ndim != 0:
raise ValueError(
"Argument num_rows must be a 0-D Tensor. Found:"
" %s" % num_rows_static)
if num_rows_static < 0:
raise ValueError(
"Argument num_rows must be non-negative. Found:"
" %s" % num_rows_static)
if num_columns_static is not None:
if num_columns_static.ndim != 0:
raise ValueError(
"Argument num_columns must be a 0-D Tensor. Found:"
" %s" % num_columns_static)
if num_columns_static < 0:
raise ValueError(
"Argument num_columns must be non-negative. Found:"
" %s" % num_columns_static)
def _check_batch_shape_possibly_add_asserts(self):
"""Static check of init arg `batch_shape`, possibly add asserts."""
if self._batch_shape_arg is None:
return
# Possibly add asserts
if self._assert_proper_shapes:
self._batch_shape_arg = control_flow_ops.with_dependencies([
check_ops.assert_rank(
self._batch_shape_arg,
1,
message="Argument batch_shape must be a 1-D Tensor."),
check_ops.assert_non_negative(
self._batch_shape_arg,
message="Argument batch_shape must be non-negative."),
], self._batch_shape_arg)
# Static checks
if not np.issubdtype(self._batch_shape_arg.dtype, np.integer):
raise TypeError(
"Argument batch_shape must be integer type. Found:"
" %s" % self._batch_shape_arg)
if self._batch_shape_static is None:
return # Cannot do any other static checks.
if self._batch_shape_static.ndim != 1:
raise ValueError(
"Argument batch_shape must be a 1-D Tensor. Found:"
" %s" % self._batch_shape_static)
if np.any(self._batch_shape_static < 0):
raise ValueError(
"Argument batch_shape must be non-negative. Found:"
"%s" % self._batch_shape_static)
def _matmul(self, x, adjoint=False, adjoint_arg=False):
if self._assert_proper_shapes:
x = linalg.adjoint(x) if adjoint_arg else x
aps = linear_operator_util.assert_compatible_matrix_dimensions(
self, x)
x = control_flow_ops.with_dependencies([aps], x)
if self.is_square:
# Note that adjoint has no effect since this matrix is self-adjoint.
if adjoint_arg:
output_shape = array_ops.concat([
array_ops.shape(x)[:-2],
[array_ops.shape(x)[-1],
array_ops.shape(x)[-2]]
],
axis=0)
else:
output_shape = array_ops.shape(x)
return self._possibly_broadcast_batch_shape(
array_ops.zeros(shape=output_shape, dtype=x.dtype))
x_shape = array_ops.shape(x)
n = self._num_columns if adjoint else self._num_rows
m = x_shape[-2] if adjoint_arg else x_shape[-1]
output_shape = array_ops.concat([x_shape[:-2], [n, m]], axis=0)
zeros = array_ops.zeros(shape=output_shape, dtype=x.dtype)
return self._possibly_broadcast_batch_shape(zeros)
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
rhs = linalg.adjoint(rhs) if adjoint_arg else rhs
if self._assert_proper_shapes:
aps = linear_operator_util.assert_compatible_matrix_dimensions(
self, rhs)
rhs = control_flow_ops.with_dependencies([aps], rhs)
return rhs / self._make_multiplier_matrix(conjugate=adjoint)
def _matmul(self, x, adjoint=False, adjoint_arg=False):
x = linalg.adjoint(x) if adjoint_arg else x
if self._assert_proper_shapes:
aps = linear_operator_util.assert_compatible_matrix_dimensions(
self, x)
x = control_flow_ops.with_dependencies([aps], x)
return x * self._make_multiplier_matrix(conjugate=adjoint)
def _matmul(self, x, adjoint=False, adjoint_arg=False):
# Note that adjoint has no effect since this matrix is self-adjoint.
x = linalg.adjoint(x) if adjoint_arg else x
if self._assert_proper_shapes:
aps = linear_operator_util.assert_compatible_matrix_dimensions(
self, x)
x = control_flow_ops.with_dependencies([aps], x)
return self._possibly_broadcast_batch_shape(x)