# Copyright 2017 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 MixtureSameFamily distribution."""

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

import numpy as np

from tensorflow.contrib.distributions.python.ops import mixture_same_family as mixture_same_family_lib
from tensorflow.contrib.distributions.python.ops import mvn_diag as mvn_diag_lib
from tensorflow.contrib.distributions.python.ops import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.distributions import bernoulli as bernoulli_lib
from tensorflow.python.ops.distributions import categorical as categorical_lib
from tensorflow.python.ops.distributions import normal as normal_lib
from tensorflow.python.platform import test


class MixtureSameFamilyTest(test_util.VectorDistributionTestHelpers,
                            test.TestCase):

  def testSampleAndLogProbUnivariateShapes(self):
    with self.test_session():
      gm = mixture_same_family_lib.MixtureSameFamily(
          mixture_distribution=categorical_lib.Categorical(probs=[0.3, 0.7]),
          components_distribution=normal_lib.Normal(
              loc=[-1., 1], scale=[0.1, 0.5]))
      x = gm.sample([4, 5], seed=42)
      log_prob_x = gm.log_prob(x)
      self.assertEqual([4, 5], x.shape)
      self.assertEqual([4, 5], log_prob_x.shape)

  def testSampleAndLogProbShapesBroadcastMix(self):
    mix_probs = np.float32([.3, .7])
    bern_probs = np.float32([[.4, .6], [.25, .75]])
    with self.test_session():
      bm = mixture_same_family_lib.MixtureSameFamily(
          mixture_distribution=categorical_lib.Categorical(probs=mix_probs),
          components_distribution=bernoulli_lib.Bernoulli(probs=bern_probs))
      x = bm.sample([4, 5], seed=42)
      log_prob_x = bm.log_prob(x)
      x_ = x.eval()
      self.assertEqual([4, 5, 2], x.shape)
      self.assertEqual([4, 5, 2], log_prob_x.shape)
      self.assertAllEqual(
          np.ones_like(x_, dtype=np.bool), np.logical_or(x_ == 0., x_ == 1.))

  def testSampleAndLogProbMultivariateShapes(self):
    with self.test_session():
      gm = mixture_same_family_lib.MixtureSameFamily(
          mixture_distribution=categorical_lib.Categorical(probs=[0.3, 0.7]),
          components_distribution=mvn_diag_lib.MultivariateNormalDiag(
              loc=[[-1., 1], [1, -1]], scale_identity_multiplier=[1., 0.5]))
      x = gm.sample([4, 5], seed=42)
      log_prob_x = gm.log_prob(x)
      self.assertEqual([4, 5, 2], x.shape)
      self.assertEqual([4, 5], log_prob_x.shape)

  def testSampleAndLogProbBatchMultivariateShapes(self):
    with self.test_session():
      gm = mixture_same_family_lib.MixtureSameFamily(
          mixture_distribution=categorical_lib.Categorical(probs=[0.3, 0.7]),
          components_distribution=mvn_diag_lib.MultivariateNormalDiag(
              loc=[[[-1., 1],
                    [1, -1]],
                   [[0., 1],
                    [1, 0]]],
              scale_identity_multiplier=[1., 0.5]))
      x = gm.sample([4, 5], seed=42)
      log_prob_x = gm.log_prob(x)
      self.assertEqual([4, 5, 2, 2], x.shape)
      self.assertEqual([4, 5, 2], log_prob_x.shape)

  def testSampleConsistentLogProb(self):
    with self.test_session() as sess:
      gm = mixture_same_family_lib.MixtureSameFamily(
          mixture_distribution=categorical_lib.Categorical(probs=[0.3, 0.7]),
          components_distribution=mvn_diag_lib.MultivariateNormalDiag(
              loc=[[-1., 1], [1, -1]], scale_identity_multiplier=[1., 0.5]))
      # Ball centered at component0's mean.
      self.run_test_sample_consistent_log_prob(
          sess, gm, radius=1., center=[-1., 1], rtol=0.02)
      # Larger ball centered at component1's mean.
      self.run_test_sample_consistent_log_prob(
          sess, gm, radius=1., center=[1., -1], rtol=0.02)

  def testLogCdf(self):
    with self.test_session() as sess:
      gm = mixture_same_family_lib.MixtureSameFamily(
          mixture_distribution=categorical_lib.Categorical(probs=[0.3, 0.7]),
          components_distribution=normal_lib.Normal(
              loc=[-1., 1], scale=[0.1, 0.5]))
      x = gm.sample(10, seed=42)
      actual_log_cdf = gm.log_cdf(x)
      expected_log_cdf = math_ops.reduce_logsumexp(
          (gm.mixture_distribution.logits +
           gm.components_distribution.log_cdf(x[..., array_ops.newaxis])),
          axis=1)
      actual_log_cdf_, expected_log_cdf_ = sess.run([
          actual_log_cdf, expected_log_cdf])
      self.assertAllClose(actual_log_cdf_, expected_log_cdf_,
                          rtol=1e-6, atol=0.0)

  def testSampleConsistentMeanCovariance(self):
    with self.test_session() as sess:
      gm = mixture_same_family_lib.MixtureSameFamily(
          mixture_distribution=categorical_lib.Categorical(probs=[0.3, 0.7]),
          components_distribution=mvn_diag_lib.MultivariateNormalDiag(
              loc=[[-1., 1], [1, -1]], scale_identity_multiplier=[1., 0.5]))
      self.run_test_sample_consistent_mean_covariance(sess, gm)

  def testVarianceConsistentCovariance(self):
    with self.test_session() as sess:
      gm = mixture_same_family_lib.MixtureSameFamily(
          mixture_distribution=categorical_lib.Categorical(probs=[0.3, 0.7]),
          components_distribution=mvn_diag_lib.MultivariateNormalDiag(
              loc=[[-1., 1], [1, -1]], scale_identity_multiplier=[1., 0.5]))
      cov_, var_ = sess.run([gm.covariance(), gm.variance()])
      self.assertAllClose(cov_.diagonal(), var_, atol=0.)


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