Made the docs numpydoc compliant.

Modified inference procedure to have a method "is_rbm_compatible" that will raise a NotImplementedError if not appropriate for RBM.
Removed assert for RBM and UpDown inference.

pylearn2/models/dbm/dbm.py

@@ -144,9 +144,14 @@ def energy(self, V, hidden):
 
     def mf(self, *args, **kwargs):
         """
-        .. todo::
+        Mean field inference of model.
 
-            WRITEME
+        Performs the inference procedure on the model.
+
+        Parameters
+        ----------
+        *args: TODO
+        **kwargs: TODO
         """
 
         self.setup_inference_procedure()
@@ -218,7 +223,6 @@ def setup_rng(self):
     def setup_inference_procedure(self):
         """
         Sets up the inference procedure for the DBM.
-        If the number of hidden layers is one, then use UpDown.
         """
         if not hasattr(self, 'inference_procedure') or \
                 self.inference_procedure is None:
@@ -227,11 +231,15 @@ def setup_inference_procedure(self):
             else:
                 self.inference_procedure = WeightDoubling()
             self.inference_procedure.set_dbm(self)
-        elif len(self.hidden_layers) == 1:
-            assert isinstance(self.inference_procedure, UpDown),\
-                "A DBM with a single layer (a.k.a an RBM) should use %r"\
-                "as the inference_procedure, "\
-                "not %r" %(UpDown, type(self.inference_procedure))
+
+        if len(self.hidden_layers) == 1:
+            try:
+                self.inference_procedure.is_rbm_compatible()
+            except NotImplementedError:
+                warnings.warn("Inference procedure %r may have unexpected"
+                              "behavior when used with one hidden layer (RBM)."
+                              "See models/dbn/inference_procedure.py for"
+                              "details." % type(self.inference_procedure))
 
     def setup_sampling_procedure(self):
         """
@@ -294,9 +302,11 @@ def add_layers(self, layers):
 
     def freeze(self, parameter_set):
         """
-        .. todo::
+        Freezes the set of parameters.
 
-            WRITEME
+        Parameters
+        ----------
+        parameter_set: WRITEME
         """
         # patch old pickle files
         if not hasattr(self, 'freeze_set'):
@@ -338,6 +348,11 @@ def get_params(self):
     def set_batch_size(self, batch_size):
         """
         Sets the batch size of the DBM.
+
+        Parameters
+        ----------
+        batch_size: int
+            The batch size
         """
         self.batch_size = batch_size
         self.force_batch_size = batch_size
@@ -387,9 +402,8 @@ def get_lr_scalers(self):
     def get_weights(self):
         """
         Returns the weights of the bottom hidden layer.
-
-        TODO: add visualization of higher levels.
         """
+
         return self.hidden_layers[0].get_weights()
 
     def get_weights_view_shape(self):
@@ -587,7 +601,7 @@ def get_monitoring_channels(self, data):
         This is done through the visible and all of the hidden layers of DBM.
 
         Parameters
-        -----------
+        ----------
         data: tensor-like
             Data from which to evaluate model.
         """
@@ -666,7 +680,7 @@ def reconstruct(self, V):
         Reconstructs an input using inpainting method.
 
         Parameters
-        -----------
+        ----------
         V: tensor-like
             Input sample.
 
@@ -689,20 +703,25 @@ def reconstruct(self, V):
 
     def do_inpainting(self, *args, **kwargs):
         """
-        .. todo::
+        Perform inpainting on model.
 
-            WRITEME
+        Inpainting is defined by the inference procedure.
+
+        Parameters
+        ----------
+        *args: WRITEME
+        **kwargs: WRITEME
         """
         self.setup_inference_procedure()
         return self.inference_procedure.do_inpainting(*args, **kwargs)
 
     def initialize_chains(self, X, Y, theano_rng):
         """
         Function to initialize chains for model when performing the neg phase.
-        TODO: implement when actually getting gradients.
+        TODO: implement in cost functions.
 
         Parameters
-        -----------
+        ----------
         X: tensor-like
             The data. If none, then persistent (TODO)
         Y: tensor-like
@@ -745,6 +764,7 @@ def initialize_chains(self, X, Y, theano_rng):
             num_steps=1)
         return layer_to_chains
 
+
 class RBM(DBM):
     """
     A restricted Boltzmann machine.

pylearn2/models/dbm/inference_procedure.py

@@ -1539,4 +1539,8 @@ def is_rbm_compatible(self):
         """
         Is implemented as UpDown is RBM compatible.
         """
+<<<<<<< HEAD
         return True
+=======
+        return
+>>>>>>> 20fa672... Made the docs numpydoc compliant.