def test_matmul(self):
self._skip_if_tests_to_skip_contains("matmul")
for use_placeholder in False, True:
for shape in self._shapes_to_test:
for dtype in self._dtypes_to_test:
for adjoint in False, True:
for adjoint_arg in False, True:
with self.test_session(graph=ops.Graph()) as sess:
sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
operator, mat, feed_dict = self._operator_and_mat_and_feed_dict(
shape, dtype, use_placeholder=use_placeholder)
x = self._make_x(operator, adjoint=adjoint)
# If adjoint_arg, compute A X^H^H = A X.
if adjoint_arg:
op_matmul = operator.matmul(
linear_operator_util.matrix_adjoint(x),
adjoint=adjoint, adjoint_arg=adjoint_arg)
else:
op_matmul = operator.matmul(x, adjoint=adjoint)
mat_matmul = math_ops.matmul(mat, x, adjoint_a=adjoint)
if not use_placeholder:
self.assertAllEqual(
op_matmul.get_shape(), mat_matmul.get_shape())
op_matmul_v, mat_matmul_v = sess.run(
[op_matmul, mat_matmul], feed_dict=feed_dict)
self.assertAC(op_matmul_v, mat_matmul_v)
def testNonBatchMatrix(self):
a = [[1, 2, 3j], [4, 5, -6j]] # Shape (2, 3)
expected = [[1, 4], [2, 5], [-3j, 6j]] # Shape (3, 2)
with self.test_session():
a_adj = linear_operator_util.matrix_adjoint(a)
self.assertEqual((3, 2), a_adj.get_shape())
self.assertAllClose(expected, a_adj.eval())
def test_solve(self):
self._skip_if_tests_to_skip_contains("solve")
for use_placeholder in False, True:
for shape in self._shapes_to_test:
for dtype in self._dtypes_to_test:
for adjoint in False, True:
for adjoint_arg in False, True:
with self.test_session(graph=ops.Graph()) as sess:
sess.graph.seed = random_seed.DEFAULT_GRAPH_SEED
operator, mat, feed_dict = self._operator_and_mat_and_feed_dict(
shape, dtype, use_placeholder=use_placeholder)
rhs = self._make_rhs(operator, adjoint=adjoint)
# If adjoint_arg, solve A X = (rhs^H)^H = rhs.
if adjoint_arg:
op_solve = operator.solve(
linear_operator_util.matrix_adjoint(rhs),
adjoint=adjoint, adjoint_arg=adjoint_arg)
else:
op_solve = operator.solve(
rhs, adjoint=adjoint, adjoint_arg=adjoint_arg)
mat_solve = linalg_ops.matrix_solve(mat, rhs, adjoint=adjoint)
if not use_placeholder:
self.assertAllEqual(
op_solve.get_shape(), mat_solve.get_shape())
op_solve_v, mat_solve_v = sess.run([op_solve, mat_solve],
feed_dict=feed_dict)
self.assertAC(op_solve_v, mat_solve_v)
def _matmul(self, x, adjoint=False, adjoint_arg=False):
# Note that adjoint has no effect since this matrix is self-adjoint.
x = linear_operator_util.matrix_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)
def _assert_self_adjoint(self):
dense = self._get_cached_dense_matrix()
logging.warn(
"Using (possibly slow) default implementation of assert_self_adjoint."
" Requires conversion to a dense matrix.")
return check_ops.assert_equal(
dense,
linear_operator_util.matrix_adjoint(dense),
message="Matrix was not equal to its adjoint.")
def testBatchMatrix(self):
matrix_0 = [[1j, 2, 3], [4, 5, 6]]
matrix_0_a = [[-1j, 4], [2, 5], [3, 6]]
matrix_1 = [[11, 22, 33], [44, 55, 66j]]
matrix_1_a = [[11, 44], [22, 55], [33, -66j]]
batch_matrix = [matrix_0, matrix_1] # Shape (2, 2, 3)
expected_adj = [matrix_0_a, matrix_1_a] # Shape (2, 3, 2)
with self.test_session():
matrix_adj = linear_operator_util.matrix_adjoint(batch_matrix)
self.assertEqual((2, 3, 2), matrix_adj.get_shape())
self.assertAllEqual(expected_adj, matrix_adj.eval())
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
rhs = linear_operator_util.matrix_adjoint(rhs) if adjoint_arg else rhs
if adjoint:
matrix = self._multiplier_matrix_conj
else:
matrix = self._multiplier_matrix
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 / matrix
def _matmul(self, x, adjoint=False, adjoint_arg=False):
x = linear_operator_util.matrix_adjoint(x) if adjoint_arg else x
if adjoint:
matrix = self._multiplier_matrix_conj
else:
matrix = self._multiplier_matrix
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 * matrix
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
"""Default implementation of _solve."""
if self.is_square is False:
raise NotImplementedError(
"Solve is not yet implemented for non-square operators.")
logging.warn(
"Using (possibly slow) default implementation of solve."
" Requires conversion to a dense matrix and O(N^3) operations.")
rhs = linear_operator_util.matrix_adjoint(rhs) if adjoint_arg else rhs
if self._can_use_cholesky():
return linalg_ops.cholesky_solve(self._get_cached_chol(), rhs)
return linalg_ops.matrix_solve(self._get_cached_dense_matrix(),
rhs,
adjoint=adjoint)
def _solve(self, rhs, adjoint=False, adjoint_arg=False):
rhs = linear_operator_util.matrix_adjoint(rhs) if adjoint_arg else rhs
return linalg_ops.matrix_triangular_solve(
self._tril, rhs, lower=True, adjoint=adjoint)
def _solve(self, rhs, adjoint=False, adjoint_arg=False): diag_term = math_ops.conj(self._diag) if adjoint else self._diag rhs = linear_operator_util.matrix_adjoint(rhs) if adjoint_arg else rhs inv_diag_mat = array_ops.expand_dims(1. / diag_term, -1) return rhs * inv_diag_mat
def _matmul(self, x, adjoint=False, adjoint_arg=False): diag_term = math_ops.conj(self._diag) if adjoint else self._diag x = linear_operator_util.matrix_adjoint(x) if adjoint_arg else x diag_mat = array_ops.expand_dims(diag_term, -1) return diag_mat * x
