text: # 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.
  # ==============================================================================
  """Tests for MultivariateNormal."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  from scipy import stats
  from tensorflow.contrib import distributions
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.platform import test
  
  
  ds = distributions
  
  
  class MultivariateNormalDiagTest(test.TestCase):
    """Well tested because this is a simple override of the base class."""
  
    def setUp(self):
      self._rng = np.random.RandomState(42)
  
    def testScalarParams(self):
      mu = -1.
      diag = -5.
      with self.test_session():
        with self.assertRaisesRegexp(ValueError, "at least 1 dimension"):
          ds.MultivariateNormalDiag(mu, diag)
  
    def testVectorParams(self):
      mu = [-1.]
      diag = [-5.]
      with self.test_session():
        dist = ds.MultivariateNormalDiag(mu, diag, validate_args=True)
        self.assertAllEqual([3, 1], dist.sample(3).get_shape())
  
    def testMean(self):
      mu = [-1., 1]
      diag = [1., -5]
      with self.test_session():
        dist = ds.MultivariateNormalDiag(mu, diag, validate_args=True)
        self.assertAllEqual(mu, dist.mean().eval())
  
    def testMeanWithBroadcastLoc(self):
      mu = [-1.]
      diag = [1., -5]
      with self.test_session():
        dist = ds.MultivariateNormalDiag(mu, diag, validate_args=True)
        self.assertAllEqual([-1., -1.], dist.mean().eval())
  
    def testEntropy(self):
      mu = [-1., 1]
      diag = [-1., 5]
      diag_mat = np.diag(diag)
      scipy_mvn = stats.multivariate_normal(mean=mu, cov=diag_mat**2)
      with self.test_session():
        dist = ds.MultivariateNormalDiag(mu, diag, validate_args=True)
        self.assertAllClose(scipy_mvn.entropy(), dist.entropy().eval(), atol=1e-4)
  
    def testSample(self):
      mu = [-1., 1]
      diag = [1., -2]
      with self.test_session():
        dist = ds.MultivariateNormalDiag(mu, diag, validate_args=True)
        samps = dist.sample(int(1e3), seed=0).eval()
        cov_mat = array_ops.matrix_diag(diag).eval()**2
  
        self.assertAllClose(mu, samps.mean(axis=0),
                            atol=0., rtol=0.05)
        self.assertAllClose(cov_mat, np.cov(samps.T),
                            atol=0.05, rtol=0.05)
  
    def testCovariance(self):
      with self.test_session():
        mvn = ds.MultivariateNormalDiag(
            loc=array_ops.zeros([2, 3], dtype=dtypes.float32))
        self.assertAllClose(
            np.diag(np.ones([3], dtype=np.float32)),
            mvn.covariance().eval())
  
        mvn = ds.MultivariateNormalDiag(
            loc=array_ops.zeros([3], dtype=dtypes.float32),
            scale_identity_multiplier=[3., 2.])
        self.assertAllEqual([2], mvn.batch_shape)
        self.assertAllEqual([3], mvn.event_shape)
        self.assertAllClose(
            np.array([[[3., 0, 0],
                       [0, 3, 0],
                       [0, 0, 3]],
                      [[2, 0, 0],
                       [0, 2, 0],
                       [0, 0, 2]]])**2.,
            mvn.covariance().eval())
  
        mvn = ds.MultivariateNormalDiag(
            loc=array_ops.zeros([3], dtype=dtypes.float32),
            scale_diag=[[3., 2, 1], [4, 5, 6]])
        self.assertAllEqual([2], mvn.batch_shape)
        self.assertAllEqual([3], mvn.event_shape)
        self.assertAllClose(
            np.array([[[3., 0, 0],
                       [0, 2, 0],
                       [0, 0, 1]],
                      [[4, 0, 0],
                       [0, 5, 0],
                       [0, 0, 6]]])**2.,
            mvn.covariance().eval())
  
    def testVariance(self):
      with self.test_session():
        mvn = ds.MultivariateNormalDiag(
            loc=array_ops.zeros([2, 3], dtype=dtypes.float32))
        self.assertAllClose(
            np.ones([3], dtype=np.float32),
            mvn.variance().eval())
  
        mvn = ds.MultivariateNormalDiag(
            loc=array_ops.zeros([3], dtype=dtypes.float32),
            scale_identity_multiplier=[3., 2.])
        self.assertAllClose(
            np.array([[3., 3, 3],
                      [2, 2, 2]])**2.,
            mvn.variance().eval())
  
        mvn = ds.MultivariateNormalDiag(
            loc=array_ops.zeros([3], dtype=dtypes.float32),
            scale_diag=[[3., 2, 1],
                        [4, 5, 6]])
        self.assertAllClose(
            np.array([[3., 2, 1],
                      [4, 5, 6]])**2.,
            mvn.variance().eval())
  
    def testStddev(self):
      with self.test_session():
        mvn = ds.MultivariateNormalDiag(
            loc=array_ops.zeros([2, 3], dtype=dtypes.float32))
        self.assertAllClose(
            np.ones([3], dtype=np.float32),
            mvn.stddev().eval())
  
        mvn = ds.MultivariateNormalDiag(
            loc=array_ops.zeros([3], dtype=dtypes.float32),
            scale_identity_multiplier=[3., 2.])
        self.assertAllClose(
            np.array([[3., 3, 3],
                      [2, 2, 2]]),
            mvn.stddev().eval())
  
        mvn = ds.MultivariateNormalDiag(
            loc=array_ops.zeros([3], dtype=dtypes.float32),
            scale_diag=[[3., 2, 1], [4, 5, 6]])
        self.assertAllClose(
            np.array([[3., 2, 1],
                      [4, 5, 6]]),
            mvn.stddev().eval())
  
    def testMultivariateNormalDiagWithSoftplusScale(self):
      mu = [-1.0, 1.0]
      diag = [-1.0, -2.0]
      with self.test_session():
        dist = ds.MultivariateNormalDiagWithSoftplusScale(
            mu, diag, validate_args=True)
        samps = dist.sample(1000, seed=0).eval()
        cov_mat = array_ops.matrix_diag(nn_ops.softplus(diag)).eval()**2
  
        self.assertAllClose(mu, samps.mean(axis=0), atol=0.1)
        self.assertAllClose(cov_mat, np.cov(samps.T), atol=0.1)
  
  
  if __name__ == "__main__":
    test.main()
  