transformed_distribution_test.py 文件源码

def testTransformedDistribution(self):
    g = ops.Graph()
    with g.as_default():
      mu = 3.0
      sigma = 2.0
      # Note: the Jacobian callable only works for this example; more generally
      # you may or may not need a reduce_sum.
      log_normal = self._cls()(
          distribution=ds.Normal(loc=mu, scale=sigma),
          bijector=bs.Exp(event_ndims=0))
      sp_dist = stats.lognorm(s=sigma, scale=np.exp(mu))

      # sample
      sample = log_normal.sample(100000, seed=235)
      self.assertAllEqual([], log_normal.get_event_shape())
      with self.test_session(graph=g):
        self.assertAllEqual([], log_normal.event_shape().eval())
        self.assertAllClose(
            sp_dist.mean(), np.mean(sample.eval()), atol=0.0, rtol=0.05)

      # pdf, log_pdf, cdf, etc...
      # The mean of the lognormal is around 148.
      test_vals = np.linspace(0.1, 1000., num=20).astype(np.float32)
      for func in [[log_normal.log_prob, sp_dist.logpdf],
                   [log_normal.prob, sp_dist.pdf],
                   [log_normal.log_cdf, sp_dist.logcdf],
                   [log_normal.cdf, sp_dist.cdf],
                   [log_normal.survival_function, sp_dist.sf],
                   [log_normal.log_survival_function, sp_dist.logsf]]:
        actual = func[0](test_vals)
        expected = func[1](test_vals)
        with self.test_session(graph=g):
          self.assertAllClose(expected, actual.eval(), atol=0, rtol=0.01)