Implement Beta.log_cdf and Beta.cdf.

Change: 131537182

2 files changed, 31 insertions, 0 deletions

diff --git a/tensorflow/contrib/distributions/python/kernel_tests/beta_test.py b/tensorflow/contrib/distributions/python/kernel_tests/beta_test.py
index abf56c00dd..a36753d6a1 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/beta_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/beta_test.py
@@ -262,5 +262,30 @@ class BetaTest(tf.test.TestCase):
           stats.beta.mean(a, b)[1, :],
           atol=1e-1)
 
+  def testBetaCdf(self):
+    with self.test_session():
+      shape = (30, 40, 50)
+      for dt in (np.float32, np.float64):
+        a = 10. * np.random.random(shape).astype(dt)
+        b = 10. * np.random.random(shape).astype(dt)
+        x = np.random.random(shape).astype(dt)
+        actual = tf.contrib.distributions.Beta(a, b).cdf(x).eval()
+        self.assertAllEqual(np.ones(shape, dtype=np.bool), 0. <= x)
+        self.assertAllEqual(np.ones(shape, dtype=np.bool), 1. >= x)
+        self.assertAllClose(stats.beta.cdf(x, a, b), actual, rtol=1e-4, atol=0)
+
+  def testBetaLogCdf(self):
+    with self.test_session():
+      shape = (30, 40, 50)
+      for dt in (np.float32, np.float64):
+        a = 10. * np.random.random(shape).astype(dt)
+        b = 10. * np.random.random(shape).astype(dt)
+        x = np.random.random(shape).astype(dt)
+        actual = tf.exp(tf.contrib.distributions.Beta(a, b).log_cdf(x)).eval()
+        self.assertAllEqual(np.ones(shape, dtype=np.bool), 0. <= x)
+        self.assertAllEqual(np.ones(shape, dtype=np.bool), 1. >= x)
+        self.assertAllClose(stats.beta.cdf(x, a, b), actual, rtol=1e-4, atol=0)
+
+
 if __name__ == "__main__":
   tf.test.main()
diff --git a/tensorflow/contrib/distributions/python/ops/beta.py b/tensorflow/contrib/distributions/python/ops/beta.py
index 04f9d9acb9..6024dd9fc2 100644
--- a/tensorflow/contrib/distributions/python/ops/beta.py
+++ b/tensorflow/contrib/distributions/python/ops/beta.py
@@ -196,6 +196,12 @@ class Beta(distribution.Distribution):
   def _prob(self, x):
     return math_ops.exp(self._log_prob(x))
 
+  def _log_cdf(self, x):
+    return math_ops.log(self._cdf(x))
+
+  def _cdf(self, x):
+    return math_ops.betainc(self.a, self.b, x)
+
   def _entropy(self):
     return (math_ops.lgamma(self.a) -
             (self.a - 1.) * math_ops.digamma(self.a) +