# Copyright 2015 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.
# ==============================================================================
"""Tests for tensorflow.ops.math_ops.matrix_inverse."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np

from tensorflow.python.framework import constant_op
from tensorflow.python.ops import linalg_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test


class InverseOpTest(test.TestCase):

  def _verifyInverse(self, x):
    for np_type in [np.float32, np.float64]:
      for adjoint in False, True:
        y = x.astype(np_type)
        with self.test_session():
          # Verify that x^{-1} * x == Identity matrix.
          inv = linalg_ops.matrix_inverse(y, adjoint=adjoint)
          tf_ans = math_ops.matmul(inv, y, adjoint_b=adjoint)
          np_ans = np.identity(y.shape[-1])
          if x.ndim > 2:
            tiling = list(y.shape)
            tiling[-2:] = [1, 1]
            np_ans = np.tile(np_ans, tiling)
          out = tf_ans.eval()
          self.assertAllClose(np_ans, out)
          self.assertShapeEqual(y, tf_ans)

  def testNonsymmetric(self):
    # 2x2 matrices
    matrix1 = np.array([[1., 2.], [3., 4.]])
    matrix2 = np.array([[1., 3.], [3., 5.]])
    self._verifyInverse(matrix1)
    self._verifyInverse(matrix2)
    # A multidimensional batch of 2x2 matrices
    matrix_batch = np.concatenate(
        [np.expand_dims(matrix1, 0), np.expand_dims(matrix2, 0)])
    matrix_batch = np.tile(matrix_batch, [2, 3, 1, 1])
    self._verifyInverse(matrix_batch)

  def testSymmetricPositiveDefinite(self):
    # 2x2 matrices
    matrix1 = np.array([[2., 1.], [1., 2.]])
    matrix2 = np.array([[3., -1.], [-1., 3.]])
    self._verifyInverse(matrix1)
    self._verifyInverse(matrix2)
    # A multidimensional batch of 2x2 matrices
    matrix_batch = np.concatenate(
        [np.expand_dims(matrix1, 0), np.expand_dims(matrix2, 0)])
    matrix_batch = np.tile(matrix_batch, [2, 3, 1, 1])
    self._verifyInverse(matrix_batch)

  def testNonSquareMatrix(self):
    # When the inverse of a non-square matrix is attempted we should return
    # an error
    with self.assertRaises(ValueError):
      linalg_ops.matrix_inverse(np.array([[1., 2., 3.], [3., 4., 5.]]))

  def testWrongDimensions(self):
    # The input to the inverse should be at least a 2-dimensional tensor.
    tensor3 = constant_op.constant([1., 2.])
    with self.assertRaises(ValueError):
      linalg_ops.matrix_inverse(tensor3)

  def testNotInvertible(self):
    # The input should be invertible.
    with self.test_session():
      with self.assertRaisesOpError("Input is not invertible."):
        # All rows of the matrix below add to zero.
        tensor3 = constant_op.constant(
            [[1., 0., -1.], [-1., 1., 0.], [0., -1., 1.]])
        linalg_ops.matrix_inverse(tensor3).eval()

  def testEmpty(self):
    self._verifyInverse(np.empty([0, 2, 2]))
    self._verifyInverse(np.empty([2, 0, 0]))


if __name__ == "__main__":
  test.main()
