add forward logdet jacobian

diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py
index 2d49b42..63c8f1f 100644 (file)
--- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py
@@ -43,7 +43,7 @@ class OrderedBijectorTest(test.TestCase):
       self.assertAllClose(y, ordered.forward(x).eval())
       self.assertAllClose(x, ordered.inverse(y).eval())
       self.assertAllClose(
-          -np.sum(y[..., 1:], axis=-1),
+          np.sum(np.asarray(y)[..., 1:], axis=-1),
           ordered.inverse_log_det_jacobian(y, event_ndims=1).eval(),
           atol=0.,
           rtol=1e-7)
@@ -66,7 +66,7 @@ class OrderedBijectorTest(test.TestCase):
       self.assertAllClose(real_x, ordered.inverse(y).eval(
           feed_dict={y: real_y}))
       self.assertAllClose(
-          -np.sum(y[..., 1:], axis=-1),
+          np.sum(np.asarray(real_y)[..., 1:], axis=-1),
           ordered.inverse_log_det_jacobian(y, event_ndims=1).eval(
               feed_dict={y: real_y}),
           atol=0.,
@@ -96,14 +96,8 @@ class OrderedBijectorTest(test.TestCase):
   def testBijectiveAndFinite(self):
     with self.test_session():
       ordered = Ordered()
-      x = np.linspace(-50, 50, num=10).reshape(5, 2).astype(np.float32)
-      # Make y values on the simplex with a wide range.
-      y_0 = np.ones(5).astype(np.float32)
-      y_1 = (1e-5 * rng.rand(5)).astype(np.float32)
-      y_2 = (1e1 * rng.rand(5)).astype(np.float32)
-      y = np.array([y_0, y_1, y_2])
-      y /= y.sum(axis=0)
-      y = y.T  # y.shape = [5, 3]
+      x = np.sort(rng.randn(3, 10), axis=-1).astype(np.float32)
+      y = (rng.randn(3, 10)).astype(np.float32)
       assert_bijective_and_finite(ordered, x, y, event_ndims=1)
 
 

diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/ordered.py b/tensorflow/contrib/distributions/python/ops/bijectors/ordered.py
index 64cf2e6..b2959cc 100644 (file)
--- a/tensorflow/contrib/distributions/python/ops/bijectors/ordered.py
+++ b/tensorflow/contrib/distributions/python/ops/bijectors/ordered.py
@@ -24,7 +24,6 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import nn_ops
 from tensorflow.python.ops.distributions import bijector
 
 
@@ -37,8 +36,9 @@ class Ordered(bijector.Bijector):
   """Bijector which maps a tensor x_k that has increasing elements in the last
   dimension to an unconstrained tensor y_k.
 
-  The inverse of the bijector applied to a normal random vector `X ~ N(0, 1)`
-  gives back a sorted random vector with the same distribution `Y ~ N(0, 1)`
+  The inverse of the bijector applied to a normal random vector `y ~ N(0, 1)`
+  gives back a sorted random vector with the same distribution `x ~ N(0, 1)`
+  where `x = sort(y)`
 
   On the last dimension of the tensor, Ordered bijector performs:
   `y[0] = x[0]`
@@ -47,11 +47,11 @@ class Ordered(bijector.Bijector):
   Example Use:
 
   ```python
-  bijector.Ordered().forward(tf.log([2, 3, 4]))
-  # Result: [0.6931472, 3.6931472, 7.693147]
+  bijector.Ordered().forward([2, 3, 4])
+  # Result: [2., 0., 0.]
 
-  bijector.Ordered().inverse([0.2, 0.3, 0.4])
-  # Result: tf.log([2, 3, 4])
+  bijector.Ordered().inverse([0.06428002, -1.07774478, -0.71530371])
+  # Result: [0.06428002, 0.40464228, 0.8936858]
   ```
   """
 
@@ -105,7 +105,9 @@ class Ordered(bijector.Bijector):
     return math_ops.reduce_sum(y[..., 1:], axis=-1)
 
   def _forward_log_det_jacobian(self, x):
-    pass
+    return -math_ops.reduce_sum(
+      math_ops.log(x[..., 1:] - x[..., :-1]),
+      axis=-1)
 
   def _maybe_assert_valid_x(self, x):
     if not self.validate_args: