committing half-baked sigmoid optimization that wasnt done for scipy

theano/tensor/nnet/sigm.py

@@ -338,3 +338,47 @@ register_local_1msigmoid = False
 if register_local_1msigmoid:
     opt.register_canonicalize(local_1msigmoid)
 
+if 0:
+    # This code is if'd out because it is not complete,
+    # and it isn't obviously a good idea anyway.
+    # The motivation here was to identify the last exp() node
+    # in the SciPy2010 article, which was not optimized away at the time of publication,
+    # so the example is actually not numerically stable, even though it should be.
+    @opt.register_stabilize
+    @gof.local_optimizer([tensor.mul])
+    def local_sigm_gest(node):
+        print "CANONICALIZE"
+        print sigm_canonicalize(node)
+
+    def sigm_canonicalize(node):
+        add = tensor.add
+        mul = tensor.mul
+        div = tensor.true_div
+
+        if node.op == tensor.add:
+            rval = []
+            for i in node.inputs:
+                rval += sigm_canonicalize(i)
+            return rval
+        if node.op == tensor.mul:
+            rval = sigm_canonicalize(node.inputs[0])
+            for i in node.inputs[1:]:
+                old_rval = rval
+                rval = []
+                for t1 in sigm_canonicalize(i):
+                    for t0 in old_rval:
+                        assert t1.owner.op == div
+                        assert t0.owner.op == div
+                        t0top, t0bot = t0.owner.inputs
+                        t1top, t1bot = t1.owner.inputs
+                        rval.append(div(mul(*(t0top+t1top)), mul(*(t0bot+t1bot))))
+
+                        if len(rval) > 100:
+                            # This loop can be exponentially long.
+                            # aborting
+                            return []
+        elif len(node.outputs)>1:
+            return []
+        else:
+            return [node.outputs[0]]
+