add test

tests/inferences/test_inference_auto_transform.py

@@ -0,0 +1,44 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import edward as ed
+import numpy as np
+import tensorflow as tf
+
+from edward.models import Gamma, Normal
+
+
+class test_inference_auto_transform_class(tf.test.TestCase):
+
+  def test_auto_transform_true(self):
+    with self.test_session():
+      x = Gamma(2.0, 2.0)
+      qx = Normal(loc=tf.Variable(tf.random_normal([])),
+                  scale=tf.nn.softplus(tf.Variable(tf.random_normal([]))))
+
+      inference = ed.KLqp({x: qx})
+      inference.initialize(auto_transform=True, n_samples=5, n_iter=150)
+      tf.global_variables_initializer().run()
+      for _ in range(inference.n_iter):
+        info_dict = inference.update()
+
+      self.assertAllClose(info_dict['loss'], 0.0, rtol=0.2, atol=0.2)
+
+  def test_auto_transform_false(self):
+    with self.test_session():
+      x = Gamma(2.0, 2.0)
+      qx = Normal(loc=tf.Variable(tf.random_normal([])),
+                  scale=tf.nn.softplus(tf.Variable(tf.random_normal([]))))
+
+      inference = ed.KLqp({x: qx})
+      inference.initialize(auto_transform=False, n_samples=5, n_iter=150)
+      tf.global_variables_initializer().run()
+      for _ in range(inference.n_iter):
+        info_dict = inference.update()
+
+      self.assertAllEqual(info_dict['loss'], np.nan)
+
+if __name__ == '__main__':
+  ed.set_seed(124125)
+  tf.test.main()