Add new RandomVariable Op and optimizations

doc/library/scan.txt

@@ -273,7 +273,7 @@ the following:
    bvis = theano.shared(bvis_values)
    bhid = theano.shared(bhid_values)
 
-   trng = tt.shared_randomstreams.RandomStreams(1234)
+   trng = tt.random.utils.RandomStream(1234)
 
    def OneStep(vsample) :
        hmean = tt.nnet.sigmoid(theano.dot(vsample, W) + bhid)
@@ -354,7 +354,7 @@ updated:
     bvis = theano.shared(bvis_values)
     bhid = theano.shared(bhid_values)
 
-    trng = tt.shared_randomstreams.RandomStreams(1234)
+    trng = tt.random.utils.RandomStream(1234)
 
     # OneStep, with explicit use of the shared variables (W, bvis, bhid)
     def OneStep(vsample, W, bvis, bhid):

doc/library/tensor/index.txt

@@ -9,8 +9,8 @@
    :synopsis: symbolic types and operations for n-dimensional arrays.
 .. moduleauthor:: LISA
 
-Theano's strength is in expressing symbolic calculations involving tensors.  
-There are many types of symbolic expressions for tensors. 
+Theano's strength is in expressing symbolic calculations involving tensors.
+There are many types of symbolic expressions for tensors.
 They are grouped into the following sections:
 
 
@@ -19,8 +19,7 @@ They are grouped into the following sections:
 
     basic
     nnet/index
-    raw_random
-    shared_randomstreams
+    random/index
     signal/index
     utils
     elemwise

doc/library/tensor/random/basic.txt

@@ -0,0 +1,45 @@
+
+.. _libdoc_tensor_random:
+
+=============================================
+:mod:`random` -- Low-level random numbers
+=============================================
+
+.. module:: theano.tensor.random
+   :synopsis: symbolic random variables
+.. moduleauthor:: pymc-team
+
+
+The `theano.tensor.random` module provides random-number drawing functionality
+that closely resembles the `numpy.random` module.
+
+Reference
+=========
+
+.. class:: RandomStream()
+
+   A helper class that tracks changes in a shared ``numpy.random.RandomState``
+   and behaves like ``numpy.random.RandomState`` by managing access
+   to `RandomVariable`s.  For example:
+
+   .. testcode:: constructors
+
+      from theano.tensor.random.utils import RandomStream
+
+      rng = RandomStream()
+      sample = rng.normal(0, 1, size=(2, 2))
+
+.. class:: RandomStateType(gof.Type)
+
+    A `Type` for variables that will take ``numpy.random.RandomState``
+    values.
+
+.. function:: random_state_type(name=None)
+
+    Return a new Variable whose ``.type`` is ``random_state_type``.
+
+.. class:: RandomVariable(gof.Op)
+
+    `Op` that draws random numbers from a `numpy.random.RandomState` object.
+    This `Op` is parameterized to draw numbers from many possible
+    distributions.

doc/library/tensor/shared_randomstreams.txt → doc/library/tensor/random/utils.txt

@@ -1,10 +1,10 @@
-.. _libdoc_tensor_shared_randomstreams:
+.. _libdoc_tensor_random_utils:
 
 ======================================================
-:mod:`shared_randomstreams` -- Friendly random numbers
+:mod:`utils` -- Friendly random numbers
 ======================================================
 
-.. module:: theano.tensor.shared_randomstreams
+.. module:: theano.tensor.random.utils
    :platform: Unix, Windows
    :synopsis: symbolic random variables
 .. moduleauthor:: LISA
@@ -27,17 +27,15 @@ For an example of how to use random numbers, see
 Reference
 =========
 
-.. class:: RandomStreams(raw_random.RandomStreamsBase)
+.. class:: RandomStream()
 
-    This is a symbolic stand-in for ``numpy.random.RandomState``. 
-    Random variables of various distributions are instantiated by calls to
-    parent class :class:`raw_random.RandomStreamsBase`.
+    This is a symbolic stand-in for ``numpy.random.RandomState``.
 
     .. method:: updates()
 
         :returns: a list of all the (state, new_state) update pairs for the
           random variables created by this object
-          
+
         This can be a convenient shortcut to enumerating all the random
         variables in a large graph in the ``update`` parameter of function.
 
@@ -60,21 +58,4 @@ Reference
 
     .. method:: uniform, normal, binomial, multinomial, random_integers, ...
 
-        See :class:`raw_random.RandomStreamsBase`.
-
-.. class:: RandomVariable(object)
-
-    .. attribute:: rng
-
-        The shared variable whose ``.value`` is the numpy RandomState
-        generator feeding this random variable.
-
-    .. attribute:: update
-
-        A pair
-        whose first element is a shared variable whose value is a numpy RandomState,
-        and whose second element is an [symbolic] expression for the next value of that
-        RandomState after drawing samples.
-        Including this pair in the``updates`` list to function will cause the
-        function to update the random number generator feeding this variable.
-
+        See :class:`basic.RandomVariable`.

doc/nextml2015/presentation.tex

@@ -866,7 +866,7 @@ \subsection{Scan}
 b_sym = tt.vector("b_sym")
 
 # define shared random stream
-trng = tt.shared_randomstreams.RandomStreams(1234)
+trng = tt.random.utils.RandomStream(1234)
 d=trng.binomial(size=W[1].shape)
 \end{lstlisting}
 \end{frame}