manual merge.

doc/README.txt

@@ -73,6 +73,8 @@ Software Requirements
 
 - g++, python-dev (optional, to compile generated C code)
 
+- nose, for testing
+
 -  `psyco <http://psyco.sourceforge.net/>`__ can make your python code much faster, if you are on a 32-bit x86 architecture.  If you use compiled C code, this can be less important.
 
 Downloading Theano
@@ -89,7 +91,7 @@ Get the source and run the tests like this:
     
     hg clone http://pylearn.org/hg/theano theano
     cd theano
-    nosetest
+    nosetests
 
 To update your library to the latest on pylearn.org, change directory (`cd`) to this `theano` folder and type
 

doc/index.txt

@@ -25,7 +25,7 @@ Our project uses the name to honour the ancient Greek mathematician.
 Overview
 ========
 
-**To get up & running quickly** see README_.
+**To get up and running quickly** see README_.
 
 All **documentation** can be reached from the `Theano Project Documentation Overview`_.
 

theano/__init__.py

@@ -44,7 +44,9 @@ from compile import \
     predefined_modes, predefined_linkers, predefined_optimizers, \
     FunctionMaker, function, OpFromGraph, \
     Component, External, Member, KitComponent, Method, \
-    Composite, ComponentList, Module, FancyModule
+    Composite, ComponentList, ComponentDict, Module
+
+FancyModule = Module
 
 from printing import \
     pprint, pp

theano/gof/link.py

@@ -246,6 +246,7 @@ class LocalLinker(Linker):
 
 class PerformLinker(LocalLinker):
     """WRITEME
+
     Basic L{Linker} subclass that calls the perform method on each L{Op} in
     the L{Env} in the order given by L{Env.toposort}.
     """
@@ -254,6 +255,13 @@ class PerformLinker(LocalLinker):
         self.env = None
 
     def accept(self, env, no_recycling = []):
+        """
+        :param env: a PerformLinker can have accepted one Env instance at a time.
+        
+        :param no_recycling: WRITEME
+
+        :returns: self (WHY? Who calls this function?)
+        """
         if self.env is not None and self.env is not env:
             return type(self)().accept(env, no_recycling)
             #raise Exception("Cannot accept from a Linker that is already tied to another Env.")
@@ -262,6 +270,14 @@ class PerformLinker(LocalLinker):
         return self
 
     def make_all(self, profiler = None, input_storage = None, output_storage = None):
+        """
+        :param profiler: WRITEME
+        :param input_storage: WRITEME
+        :param output_storage: WRITEME
+
+        :returns: WRITEME (or see: SOMETHING)
+
+        """
         env = self.env
         order = env.toposort()
         no_recycling = self.no_recycling

theano/printing.py

-
+"""Pretty-printing graphs, and the 'Print' Op.
+"""
 import gof
 from copy import copy
 import sys
 
+from gof import Op, Apply
+
+class Print(Op):
+    """This identity-like Op has the side effect of printing a message followed by its inputs
+    when it runs.
+    """
+    def __init__(self,message=""):
+        self.message=message
+        self.view_map={0:[0]}
+
+    def make_node(self,xin):
+        xout = xin.type.make_result()
+        return Apply(op = self, inputs = [xin], outputs=[xout])
+
+    def perform(self,node,inputs,output_storage):
+        xin, = inputs
+        xout, = output_storage
+        xout[0] = xin
+        print self.message,xin
+
+    def grad(self,input,output_gradients):
+        return output_gradients
 
 class PrinterState(gof.utils.scratchpad):
     
@@ -232,3 +255,4 @@ pprint.assign(lambda pstate, r: hasattr(pstate, 'target') and pstate.target is n
 
 pp = pprint
 
+

theano/tensor/basic.py

@@ -21,7 +21,7 @@ from .. import scalar as scal
 from ..gof.python25 import partial
 
 from .. import compile, printing
-from ..printing import pprint
+from ..printing import pprint, Print
 
 
 ### set up the external interface
@@ -456,10 +456,11 @@ class _tensor_py_operators:
     def __abs__(self): return abs_(self)
     def __neg__(self): return neg(self)
 
-    #CASTS
-    def __int__(self): return AsInt(self).out
-    def __float__(self): return AsInt(self).out
-    def __complex__(self): return AsComplex(self).out
+    #CASTS 
+    #### REMOVED THESE BECAUSE PYTHON appears to require __int__ to return an int. -JB 20081112
+    #def __int__(self): return convert_to_int32(self)
+    #def __float__(self): return convert_to_float64(self)
+    #def __complex__(self): return convert_to_complex128(self)
 
     #COMPARISONS
     def __lt__(self,other): return lt(self, other)
@@ -712,7 +713,7 @@ class Shape(Op):
         x = as_tensor(x)
         return Apply(self, [x], [lvector()])
     def perform(self, node, (x, ), (out, )):
-        out[0] = numpy.asarray(x.shape)
+        out[0] = numpy.asarray(x.shape, dtype = 'int64')
     def grad(self, (x,), (gz,)):
         return [None]
 @_redefine_asRoutine(Shape())
@@ -1012,6 +1013,10 @@ pprint.assign(Sum(), printing.FunctionPrinter('sum'))
 @constructor
 def mean(input, axis = None):
     """WRITEME"""
+    if str(input.dtype).startswith('int'):
+        # we need to cast eventually anyway, and this helps
+        # to prevents overflow
+        input = convert_to_float64(input)
     s = sum(input, axis)
     shp = shape(input)
     if axis is None:
@@ -1589,7 +1594,7 @@ def concatenate(tensor_list, axis=0):
     if not isinstance(tensor_list, (tuple, list)):
         raise TypeError("The 'tensors' argument must be either a tuple "
                 "or a list, make sure you did not forget () or [] around "
-                "arguments of concatenate.", tensors)
+                "arguments of concatenate.", tensor_list)
     return join(axis, *tensor_list)
 
 def get_vector_length(v):

theano/tensor/raw_random.py

@@ -55,8 +55,9 @@ class RandomFunction(gof.Op):
             r = copy(r)
         rout[0] = r
         rval = self.fn(r, *(args + [shape]))
-        if not isinstance(rval, numpy.ndarray):
-            out[0] = numpy.asarray(rval, dtype = node.outputs[0].type.dtype)
+        if not isinstance(rval, numpy.ndarray) \
+               or str(rval.dtype) != node.outputs[1].type.dtype:
+            out[0] = numpy.asarray(rval, dtype = node.outputs[1].type.dtype)
         else:
             out[0] = rval
 
@@ -237,7 +238,7 @@ class RandomKit(SymbolicInputKit):
 rk = RandomKit('rk', 0xBAD5EED)
 
 
-class RModule(compile.FancyModule):
+class RModule(compile.Module):
 
     def __init__(self, components = {}, **kwcomponents):
         super(RModule, self).__init__(components, **kwcomponents)

theano/tensor/tests/_test_xlogx.py

+from xlogx import xlogx
+
+import unittest
+from theano import compile
+from theano import gradient
+
+from theano.tensor import as_tensor
+import theano._test_tensor as TT
+
+import random
+import numpy.random
+
+class T_XlogX(unittest.TestCase):
+    def test0(self):
+        x = as_tensor([1, 0])
+        y = xlogx(x)
+        y = compile.eval_outputs([y])
+        self.failUnless(numpy.all(y == numpy.asarray([0, 0.])))
+    def test1(self):
+        class Dummy(object):
+            def make_node(self, a):
+                return [xlogx(a)[:,2]]
+        TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)])
+
+
+if __name__ == '__main__':
+    unittest.main()

theano/tensor/xlogx.py

+
+import theano
+from theano import tensor, scalar
+import numpy
+
+class XlogX(scalar.UnaryScalarOp):
+    """
+    Compute X * log(X), with special case 0 log(0) = 0.
+    """
+    @staticmethod
+    def st_impl(x):
+        if x == 0.0:
+            return 0.0
+        return x * numpy.log(x)
+    def impl(self, x):
+        return XlogX.st_impl(x)
+    def grad(self, (x,), (gz,)):
+        return [gz * (1 + scalar.log(x))]
+    def c_code(self, node, name, (x,), (z,), sub):
+        if node.inputs[0].type in [scalar.float32, scalar.float64]:
+            return """%(z)s =
+                %(x)s == 0.0
+                ? 0.0
+                : %(x)s * log(%(x)s);""" % locals()
+        raise NotImplementedError('only floatingpoint is implemented')
+scalar_xlogx  = XlogX(scalar.upgrade_to_float, name='scalar_xlogx')
+xlogx = tensor.Elemwise(scalar_xlogx, name='xlogx')
+