transformed_distribution_test.py 文件源码

def testShapeChangingBijector(self):
    with self.test_session():
      softmax = bs.SoftmaxCentered()
      standard_normal = ds.Normal(loc=0., scale=1.)
      multi_logit_normal = self._cls()(
          distribution=standard_normal,
          bijector=softmax)
      x = [[-np.log(3.), 0.],
           [np.log(3), np.log(5)]]
      y = softmax.forward(x).eval()
      expected_log_pdf = (stats.norm(loc=0., scale=1.).logpdf(x) -
                          np.sum(np.log(y), axis=-1))
      self.assertAllClose(expected_log_pdf,
                          multi_logit_normal.log_prob(y).eval())
      self.assertAllClose(
          [1, 2, 3, 2],
          array_ops.shape(multi_logit_normal.sample([1, 2, 3])).eval())
      self.assertAllEqual([2], multi_logit_normal.get_event_shape())
      self.assertAllEqual([2], multi_logit_normal.event_shape().eval())