Just moved the code for normal closer to uniform since they are very similar

theano/tensor/raw_random.py

@@ -418,6 +418,27 @@ def uniform(random_state, size=None, low=0.0, high=1.0, ndim=None, dtype='floatX
             tensor.TensorType(dtype=out_dtype, broadcastable=bcast) )
     return op(random_state, size, low, high)
 
+
+def normal(random_state, size=None, avg=0.0, std=1.0, ndim=None, dtype=theano.config.floatX):
+    """
+    Sample from a normal distribution centered on avg with
+    the specified standard deviation (std).
+
+    If the size argument is ambiguous on the number of dimensions, ndim
+    may be a plain integer to supplement the missing information.
+
+    If size is None, the output shape will be determined by the shapes
+    of avg and std.
+    """
+    avg = tensor.as_tensor_variable(avg)
+    std = tensor.as_tensor_variable(std)
+    ndim, size, bcast = _infer_ndim_bcast(ndim, size, avg, std)
+    dtype = tensor.scal.upcast(dtype, avg.dtype, std.dtype)
+    op = RandomFunction('normal',
+            tensor.TensorType(dtype=dtype, broadcastable=bcast))
+    return op(random_state, size, avg, std)
+
+
 def binomial(random_state, size=None, n=1, p=0.5, ndim=None, dtype='int64', prob=None):
     """
     Sample n times with probability of success prob for each trial,
@@ -446,25 +467,6 @@ def binomial(random_state, size=None, n=1, p=0.5, ndim=None, dtype='int64', prob
             tensor.TensorType(dtype = dtype, broadcastable = (False,)*ndim) )
     return op(random_state, size, n, p)
 
-def normal(random_state, size=None, avg=0.0, std=1.0, ndim=None, dtype=theano.config.floatX):
-    """
-    Sample from a normal distribution centered on avg with
-    the specified standard deviation (std).
-
-    If the size argument is ambiguous on the number of dimensions, ndim
-    may be a plain integer to supplement the missing information.
-
-    If size is None, the output shape will be determined by the shapes
-    of avg and std.
-    """
-    avg = tensor.as_tensor_variable(avg)
-    std = tensor.as_tensor_variable(std)
-    ndim, size, bcast = _infer_ndim_bcast(ndim, size, avg, std)
-    dtype = tensor.scal.upcast(dtype, avg.dtype, std.dtype)
-    op = RandomFunction('normal',
-            tensor.TensorType(dtype=dtype, broadcastable=bcast))
-    return op(random_state, size, avg, std)
-
 def random_integers_helper(random_state, low, high, size):
     '''
     Helper function to draw random integers.