quantized_distribution_test.py 文件源码

def testNormalLogProbWithCutoffs(self):
    # At integer values, the result should be the same as the standard normal.
    with self.test_session():
      qdist = distributions.QuantizedDistribution(
          distribution=distributions.Normal(
              loc=0., scale=1.),
          lower_cutoff=-2.,
          upper_cutoff=2.)
      sm_normal = stats.norm(0., 1.)
      # These cutoffs create partitions of the real line, and indices:
      # (-inf, -2](-2, -1](-1, 0](0, 1](1, inf)
      #        -2      -1      0     1     2
      # Test interval (-inf, -2], <--> index -2.
      self.assertAllClose(
          np.log(sm_normal.cdf(-2)), qdist.log_prob(-2.).eval(), atol=0)
      # Test interval (-2, -1], <--> index -1.
      self.assertAllClose(
          np.log(sm_normal.cdf(-1) - sm_normal.cdf(-2)),
          qdist.log_prob(-1.).eval(),
          atol=0)
      # Test interval (-1, 0], <--> index 0.
      self.assertAllClose(
          np.log(sm_normal.cdf(0) - sm_normal.cdf(-1)),
          qdist.log_prob(0.).eval(),
          atol=0)
      # Test interval (1, inf), <--> index 2.
      self.assertAllClose(
          np.log(1. - sm_normal.cdf(1)), qdist.log_prob(2.).eval(), atol=0)