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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops.linalg import linalg as linalg_lib
from tensorflow.python.ops.linalg import linear_operator_test_util
from tensorflow.python.platform import test
rng = np.random.RandomState(2016)
class LinearOperatorZerosTest(
linear_operator_test_util.SquareLinearOperatorDerivedClassTest):
"""Most tests done in the base class LinearOperatorDerivedClassTest."""
@property
def _tests_to_skip(self):
return ["log_abs_det", "solve", "solve_with_broadcast"]
@property
def _operator_build_infos(self):
build_info = linear_operator_test_util.OperatorBuildInfo
return [
build_info((1, 1)),
build_info((1, 3, 3)),
build_info((3, 4, 4)),
build_info((2, 1, 4, 4))]
def _operator_and_matrix(self, build_info, dtype, use_placeholder):
del use_placeholder
shape = list(build_info.shape)
assert shape[-1] == shape[-2]
batch_shape = shape[:-2]
num_rows = shape[-1]
operator = linalg_lib.LinearOperatorZeros(
num_rows, batch_shape=batch_shape, dtype=dtype)
matrix = array_ops.zeros(shape=shape, dtype=dtype)
return operator, matrix
def test_assert_positive_definite(self):
operator = linalg_lib.LinearOperatorZeros(num_rows=2)
with self.assertRaisesOpError("non-positive definite"):
operator.assert_positive_definite()
def test_assert_non_singular(self):
with self.assertRaisesOpError("non-invertible"):
operator = linalg_lib.LinearOperatorZeros(num_rows=2)
operator.assert_non_singular()
def test_assert_self_adjoint(self):
with self.cached_session():
operator = linalg_lib.LinearOperatorZeros(num_rows=2)
operator.assert_self_adjoint().run() # Should not fail
def test_non_scalar_num_rows_raises_static(self):
with self.assertRaisesRegexp(ValueError, "must be a 0-D Tensor"):
linalg_lib.LinearOperatorZeros(num_rows=[2])
with self.assertRaisesRegexp(ValueError, "must be a 0-D Tensor"):
linalg_lib.LinearOperatorZeros(num_rows=2, num_columns=[2])
def test_non_integer_num_rows_raises_static(self):
with self.assertRaisesRegexp(TypeError, "must be integer"):
linalg_lib.LinearOperatorZeros(num_rows=2.)
with self.assertRaisesRegexp(TypeError, "must be integer"):
linalg_lib.LinearOperatorZeros(num_rows=2, num_columns=2.)
def test_negative_num_rows_raises_static(self):
with self.assertRaisesRegexp(ValueError, "must be non-negative"):
linalg_lib.LinearOperatorZeros(num_rows=-2)
with self.assertRaisesRegexp(ValueError, "must be non-negative"):
linalg_lib.LinearOperatorZeros(num_rows=2, num_columns=-2)
def test_non_1d_batch_shape_raises_static(self):
with self.assertRaisesRegexp(ValueError, "must be a 1-D"):
linalg_lib.LinearOperatorZeros(num_rows=2, batch_shape=2)
def test_non_integer_batch_shape_raises_static(self):
with self.assertRaisesRegexp(TypeError, "must be integer"):
linalg_lib.LinearOperatorZeros(num_rows=2, batch_shape=[2.])
def test_negative_batch_shape_raises_static(self):
with self.assertRaisesRegexp(ValueError, "must be non-negative"):
linalg_lib.LinearOperatorZeros(num_rows=2, batch_shape=[-2])
def test_non_scalar_num_rows_raises_dynamic(self):
with self.cached_session():
num_rows = array_ops.placeholder(dtypes.int32)
operator = linalg_lib.LinearOperatorZeros(
num_rows, assert_proper_shapes=True)
with self.assertRaisesOpError("must be a 0-D Tensor"):
operator.to_dense().eval(feed_dict={num_rows: [2]})
def test_negative_num_rows_raises_dynamic(self):
with self.cached_session():
n = array_ops.placeholder(dtypes.int32)
operator = linalg_lib.LinearOperatorZeros(
num_rows=n, assert_proper_shapes=True)
with self.assertRaisesOpError("must be non-negative"):
operator.to_dense().eval(feed_dict={n: -2})
operator = linalg_lib.LinearOperatorZeros(
num_rows=2, num_columns=n, assert_proper_shapes=True)
with self.assertRaisesOpError("must be non-negative"):
operator.to_dense().eval(feed_dict={n: -2})
def test_non_1d_batch_shape_raises_dynamic(self):
with self.cached_session():
batch_shape = array_ops.placeholder(dtypes.int32)
operator = linalg_lib.LinearOperatorZeros(
num_rows=2, batch_shape=batch_shape, assert_proper_shapes=True)
with self.assertRaisesOpError("must be a 1-D"):
operator.to_dense().eval(feed_dict={batch_shape: 2})
def test_negative_batch_shape_raises_dynamic(self):
with self.cached_session():
batch_shape = array_ops.placeholder(dtypes.int32)
operator = linalg_lib.LinearOperatorZeros(
num_rows=2, batch_shape=batch_shape, assert_proper_shapes=True)
with self.assertRaisesOpError("must be non-negative"):
operator.to_dense().eval(feed_dict={batch_shape: [-2]})
def test_wrong_matrix_dimensions_raises_static(self):
operator = linalg_lib.LinearOperatorZeros(num_rows=2)
x = rng.randn(3, 3).astype(np.float32)
with self.assertRaisesRegexp(ValueError, "Dimensions.*not compatible"):
operator.matmul(x)
def test_wrong_matrix_dimensions_raises_dynamic(self):
num_rows = array_ops.placeholder(dtypes.int32)
x = array_ops.placeholder(dtypes.float32)
with self.cached_session():
operator = linalg_lib.LinearOperatorZeros(
num_rows, assert_proper_shapes=True)
y = operator.matmul(x)
with self.assertRaisesOpError("Incompatible.*dimensions"):
y.eval(feed_dict={num_rows: 2, x: rng.rand(3, 3)})
def test_is_x_flags(self):
# The is_x flags are by default all True.
operator = linalg_lib.LinearOperatorZeros(num_rows=2)
self.assertFalse(operator.is_positive_definite)
self.assertFalse(operator.is_non_singular)
self.assertTrue(operator.is_self_adjoint)
class LinearOperatorZerosNotSquareTest(
linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest):
def _operator_and_matrix(self, build_info, dtype, use_placeholder):
del use_placeholder
shape = list(build_info.shape)
batch_shape = shape[:-2]
num_rows = shape[-2]
num_columns = shape[-1]
operator = linalg_lib.LinearOperatorZeros(
num_rows, num_columns, is_square=False, is_self_adjoint=False,
batch_shape=batch_shape, dtype=dtype)
matrix = array_ops.zeros(shape=shape, dtype=dtype)
return operator, matrix
if __name__ == "__main__":
test.main()
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