def operator_and_matrix(
self, build_info, dtype, use_placeholder,
ensure_self_adjoint_and_pd=False):
shape = list(build_info.shape)
reflection_axis = linear_operator_test_util.random_sign_uniform(
shape[:-1], minval=1., maxval=2., dtype=dtype)
# Make sure unit norm.
reflection_axis = reflection_axis / linalg_ops.norm(
reflection_axis, axis=-1, keepdims=True)
lin_op_reflection_axis = reflection_axis
if use_placeholder:
lin_op_reflection_axis = array_ops.placeholder_with_default(
reflection_axis, shape=None)
operator = householder.LinearOperatorHouseholder(lin_op_reflection_axis)
mat = reflection_axis[..., array_ops.newaxis]
matrix = -2 * linear_operator_util.matmul_with_broadcast(
mat, mat, adjoint_b=True)
matrix = array_ops.matrix_set_diag(
matrix, 1. + array_ops.matrix_diag_part(matrix))
return operator, matrix
def test_tape_safe(self):
reflection_axis = variables_module.Variable([1., 3., 5., 8.])
operator = householder.LinearOperatorHouseholder(reflection_axis)
self.check_tape_safe(
operator,
skip_options=[
# Determinant hard-coded as 1.
CheckTapeSafeSkipOptions.DETERMINANT,
CheckTapeSafeSkipOptions.LOG_ABS_DETERMINANT,
# Trace hard-coded.
CheckTapeSafeSkipOptions.TRACE,
])
def test_householder_inverse_type(self):
reflection_axis = [1., 3., 5., 8.]
operator = householder.LinearOperatorHouseholder(reflection_axis)
self.assertIsInstance(
operator.inverse(), householder.LinearOperatorHouseholder)
def test_scalar_reflection_axis_raises(self):
with self.assertRaisesRegexp(ValueError, "must have at least 1 dimension"):
householder.LinearOperatorHouseholder(1.)
def test_convert_variables_to_tensors(self):
reflection_axis = variables_module.Variable([1., 3., 5., 8.])
operator = householder.LinearOperatorHouseholder(reflection_axis)
with self.cached_session() as sess:
sess.run([reflection_axis.initializer])
self.check_convert_variables_to_tensors(operator)
