monte_carlo_test.py 文件源码

def test_normal_distribution_second_moment_estimated_correctly(self):
    # Test the importance sampled estimate against an analytical result.
    n = int(1e6)
    with self.test_session():
      mu_p = constant_op.constant([0.0, 0.0], dtype=dtypes.float64)
      mu_q = constant_op.constant([-1.0, 1.0], dtype=dtypes.float64)
      sigma_p = constant_op.constant([1.0, 2 / 3.], dtype=dtypes.float64)
      sigma_q = constant_op.constant([1.0, 1.0], dtype=dtypes.float64)
      p = distributions.Normal(loc=mu_p, scale=sigma_p)
      q = distributions.Normal(loc=mu_q, scale=sigma_q)

      # Compute E_p[X^2].
      # Should equal [1, (2/3)^2]
      log_e_x2 = monte_carlo.expectation_importance_sampler_logspace(
          log_f=lambda x: math_ops.log(math_ops.square(x)),
          log_p=p.log_prob,
          sampling_dist_q=q,
          n=n,
          seed=42)
      e_x2 = math_ops.exp(log_e_x2)

      # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
      # pass.
      self.assertEqual(p.get_batch_shape(), e_x2.get_shape())
      self.assertAllClose([1., (2 / 3.)**2], e_x2.eval(), rtol=0.02)