update edward/inferences/

edward/inferences/gan_inference.py

@@ -33,10 +33,10 @@ def __init__(self, data, discriminator):
 
     Notes
     -----
-    ``GANInference`` does not support model wrappers or latent
-    variable inference. Note that GAN-style training also samples from
-    the prior: this does not work well for latent variables that are
-    shared across many data points (global variables).
+    ``GANInference`` does not support latent variable inference. Note
+    that GAN-style training also samples from the prior: this does not
+    work well for latent variables that are shared across many data
+    points (global variables).
 
     In building the computation graph for inference, the
     discriminator's parameters can be accessed with the variable scope
@@ -55,7 +55,7 @@ def __init__(self, data, discriminator):
       raise NotImplementedError()
 
     self.discriminator = discriminator
-    super(GANInference, self).__init__(None, data, model_wrapper=None)
+    super(GANInference, self).__init__(None, data)
 
   def initialize(self, optimizer=None, optimizer_d=None,
                  global_step=None, global_step_d=None, var_list=None,

edward/inferences/hmc.py

@@ -125,32 +125,28 @@ def _log_joint(self, z_sample):
     z_sample : dict
       Latent variable keys to samples.
     """
-    if self.model_wrapper is None:
-      self.scope_iter += 1
-      scope = 'inference_' + str(id(self)) + '/' + str(self.scope_iter)
-      # Form dictionary in order to replace conditioning on prior or
-      # observed variable with conditioning on a specific value.
-      dict_swap = z_sample.copy()
-      for x, qx in six.iteritems(self.data):
-        if isinstance(x, RandomVariable):
-          if isinstance(qx, RandomVariable):
-            qx_copy = copy(qx, scope=scope)
-            dict_swap[x] = qx_copy.value()
-          else:
-            dict_swap[x] = qx
-
-      log_joint = 0.0
-      for z in six.iterkeys(self.latent_vars):
-        z_copy = copy(z, dict_swap, scope=scope)
-        log_joint += tf.reduce_sum(z_copy.log_prob(dict_swap[z]))
-
-      for x in six.iterkeys(self.data):
-        if isinstance(x, RandomVariable):
-          x_copy = copy(x, dict_swap, scope=scope)
-          log_joint += tf.reduce_sum(x_copy.log_prob(dict_swap[x]))
-    else:
-      x = self.data
-      log_joint = self.model_wrapper.log_prob(x, z_sample)
+    self.scope_iter += 1
+    scope = 'inference_' + str(id(self)) + '/' + str(self.scope_iter)
+    # Form dictionary in order to replace conditioning on prior or
+    # observed variable with conditioning on a specific value.
+    dict_swap = z_sample.copy()
+    for x, qx in six.iteritems(self.data):
+      if isinstance(x, RandomVariable):
+        if isinstance(qx, RandomVariable):
+          qx_copy = copy(qx, scope=scope)
+          dict_swap[x] = qx_copy.value()
+        else:
+          dict_swap[x] = qx
+
+    log_joint = 0.0
+    for z in six.iterkeys(self.latent_vars):
+      z_copy = copy(z, dict_swap, scope=scope)
+      log_joint += tf.reduce_sum(z_copy.log_prob(dict_swap[z]))
+
+    for x in six.iterkeys(self.data):
+      if isinstance(x, RandomVariable):
+        x_copy = copy(x, dict_swap, scope=scope)
+        log_joint += tf.reduce_sum(x_copy.log_prob(dict_swap[x]))
 
     return log_joint
 

edward/inferences/implicit_klqp.py

@@ -80,7 +80,7 @@ def __init__(self, latent_vars, data=None, discriminator=None,
     self.discriminator = discriminator
     self.global_vars = global_vars
     # call grandparent's method; avoid parent (GANInference)
-    super(GANInference, self).__init__(latent_vars, data, model_wrapper=None)
+    super(GANInference, self).__init__(latent_vars, data)
 
   def initialize(self, ratio_loss='log', *args, **kwargs):
     """Initialization.