Small adjustments to inclusion of code in ReStructuredText files.

Author: mspandit

code/hmc/hmc.py

@@ -321,15 +321,21 @@ def __init__(self, **kwargs):
         self.__dict__.update(kwargs)
 
     @classmethod
-    def new_from_shared_positions(cls, shared_positions, energy_fn,
-            initial_stepsize=0.01, target_acceptance_rate=.9, n_steps=20,
-            stepsize_dec=0.98,
-            stepsize_min=0.001,
-            stepsize_max=0.25,
-            stepsize_inc=1.02,
- # used in geometric avg. 1.0 would be not moving at all
-            avg_acceptance_slowness=0.9,
-            seed=12345):
+    def new_from_shared_positions(
+        cls, 
+        shared_positions, 
+        energy_fn,
+        initial_stepsize=0.01, 
+        target_acceptance_rate=.9, 
+        n_steps=20,
+        stepsize_dec=0.98,
+        stepsize_min=0.001,
+        stepsize_max=0.25,
+        stepsize_inc=1.02,
+        # used in geometric avg. 1.0 would be not moving at all
+        avg_acceptance_slowness=0.9,
+        seed=12345
+    ):
         """
         :param shared_positions: theano ndarray shared var with
             many particle [initial] positions

doc/DBN.txt

@@ -214,7 +214,7 @@ The few lines of code below constructs the deep belief network :
 
 .. literalinclude:: ../code/DBN.py
   :start-after: # numpy random generator
-  :end-before: #########################
+  :end-before: start-snippet-2
 
 There are two stages in training this network: (1) a layer-wise pre-training and
 (2) a fine-tuning stage.