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# Copyright 2016 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
from tensorflow.contrib import linalg as linalg_lib
from tensorflow.contrib.linalg.python.ops import linear_operator_test_util
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import random_seed
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
linalg = linalg_lib
random_seed.set_random_seed(23)
class SquareLinearOperatorMatrixTest(
linear_operator_test_util.SquareLinearOperatorDerivedClassTest):
"""Most tests done in the base class LinearOperatorDerivedClassTest."""
def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
shape = list(shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(shape,
dtype)
if use_placeholder:
matrix_ph = array_ops.placeholder(dtype=dtype)
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrix = matrix.eval()
operator = linalg.LinearOperatorMatrix(matrix)
feed_dict = {matrix_ph: matrix}
else:
operator = linalg.LinearOperatorMatrix(matrix)
feed_dict = None
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated matrix to a numpy array.
mat = ops.convert_to_tensor(matrix)
return operator, mat, feed_dict
def test_is_x_flags(self):
# Matrix with two positive eigenvalues.
matrix = [[1., 0.], [1., 11.]]
operator = linalg.LinearOperatorMatrix(
matrix,
is_positive_definite=True,
is_non_singular=True,
is_self_adjoint=False)
self.assertTrue(operator.is_positive_definite)
self.assertTrue(operator.is_non_singular)
self.assertFalse(operator.is_self_adjoint)
class SquareLinearOperatorMatrixSymmetricPositiveDefiniteTest(
linear_operator_test_util.SquareLinearOperatorDerivedClassTest):
"""Most tests done in the base class LinearOperatorDerivedClassTest.
In this test, the operator is constructed with hints that invoke the use of
a Cholesky decomposition for solves/determinant.
"""
def setUp(self):
# Increase from 1e-6 to 1e-5. This reduction in tolerance happens,
# presumably, because we are taking a different code path in the operator
# and the matrix. The operator uses a Choleksy, the matrix uses standard
# solve.
self._atol[dtypes.float32] = 1e-5
self._rtol[dtypes.float32] = 1e-5
self._atol[dtypes.float64] = 1e-10
self._rtol[dtypes.float64] = 1e-10
@property
def _dtypes_to_test(self):
return [dtypes.float32, dtypes.float64]
def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
shape = list(shape)
matrix = linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
if use_placeholder:
matrix_ph = array_ops.placeholder(dtype=dtype)
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrix = matrix.eval()
operator = linalg.LinearOperatorMatrix(
matrix, is_self_adjoint=True, is_positive_definite=True)
feed_dict = {matrix_ph: matrix}
else:
operator = linalg.LinearOperatorMatrix(
matrix, is_self_adjoint=True, is_positive_definite=True)
feed_dict = None
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated matrix to a numpy array.
mat = ops.convert_to_tensor(matrix)
return operator, mat, feed_dict
def test_is_x_flags(self):
# Matrix with two positive eigenvalues.
matrix = [[1., 0.], [0., 7.]]
operator = linalg.LinearOperatorMatrix(
matrix, is_positive_definite=True, is_self_adjoint=True)
self.assertTrue(operator.is_positive_definite)
self.assertTrue(operator.is_self_adjoint)
# Should be auto-set
self.assertTrue(operator.is_non_singular)
self.assertTrue(operator._is_spd)
class NonSquareLinearOperatorMatrixTest(
linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest):
"""Most tests done in the base class LinearOperatorDerivedClassTest."""
def _operator_and_mat_and_feed_dict(self, shape, dtype, use_placeholder):
matrix = linear_operator_test_util.random_normal(shape, dtype=dtype)
if use_placeholder:
matrix_ph = array_ops.placeholder(dtype=dtype)
# Evaluate here because (i) you cannot feed a tensor, and (ii)
# values are random and we want the same value used for both mat and
# feed_dict.
matrix = matrix.eval()
operator = linalg.LinearOperatorMatrix(matrix)
feed_dict = {matrix_ph: matrix}
else:
operator = linalg.LinearOperatorMatrix(matrix)
feed_dict = None
# Convert back to Tensor. Needed if use_placeholder, since then we have
# already evaluated matrix to a numpy array.
mat = ops.convert_to_tensor(matrix)
return operator, mat, feed_dict
def test_is_x_flags(self):
# Matrix with two positive eigenvalues.
matrix = [[3., 0.], [1., 1.]]
operator = linalg.LinearOperatorMatrix(
matrix,
is_positive_definite=True,
is_non_singular=True,
is_self_adjoint=False)
self.assertTrue(operator.is_positive_definite)
self.assertTrue(operator.is_non_singular)
self.assertFalse(operator.is_self_adjoint)
if __name__ == "__main__":
test.main()
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