some doc for RandomFunction

tensor_random.py

@@ -10,6 +10,12 @@ from copy import copy
 class RandomFunction(gof.Op):
 
     def __init__(self, fn, outtype, *args, **kwargs):
+        """
+        fn: a random function with the same signature as functions in numpy.random.RandomState
+        outtype: the type of the output
+        args: a list of default arguments for the function
+        kwargs: if the 'inplace' key is there, its value will be used to determine if the op operates inplace or not
+        """
         self.fn = fn
         self.outtype = outtype
         self.args = map(tensor.as_tensor, args)
@@ -18,6 +24,13 @@ class RandomFunction(gof.Op):
             self.destroy_map = {0: [0]}
 
     def make_node(self, r, shape, *args):
+        """
+        in: r -> RandomState (gof.generic),
+            shape -> lvector
+            args -> the arguments expected by the numpy function
+        out: r2 -> the new RandomState (gof.generic)
+             out -> the random numbers we generated
+        """
         args = map(tensor.as_tensor, args)
         shape = tensor.as_tensor(shape)
         assert shape.type == tensor.lvector
@@ -52,6 +65,17 @@ class RandomFunction(gof.Op):
 
 
 def random_function(fn, dtype, *rfargs, **rfkwargs):
+    """
+    Returns a wrapper around RandomFunction which automatically infers the number 
+    of dimensions of the output from the given shape. If the shape cannot be inferred,
+    the user can give an integer as first argument, which will be interpreted as the 
+    number of dimensions.
+
+    The number of dimensions for the following shape arguments can be inferred:
+    - shape(x)
+    - make_lvector(x, y, z, ...)
+    - constants
+    """
     def f(ndim, *args, **kwargs):
         if isinstance(ndim, int):
             r, shape, args = args[0], args[1], args[2:]