transformed_distribution_test.py 文件源码

def testEntropy(self):
    with self.test_session():
      shift = np.array([[-1, 0, 1], [-1, -2, -3]], dtype=np.float32)
      diag = np.array([[1, 2, 3], [2, 3, 2]], dtype=np.float32)
      actual_mvn_entropy = np.concatenate([
          [stats.multivariate_normal(shift[i], np.diag(diag[i]**2)).entropy()]
          for i in range(len(diag))])
      fake_mvn = self._cls()(
          ds.MultivariateNormalDiag(
              array_ops.zeros_like(shift),
              array_ops.ones_like(diag),
              validate_args=True),
          bs.AffineLinearOperator(
              shift,
              scale=la.LinearOperatorDiag(diag, is_non_singular=True),
              validate_args=True),
          validate_args=True)
      self.assertAllClose(actual_mvn_entropy,
                          fake_mvn.entropy().eval())