fixed stuff

_test_compile.py

@@ -6,75 +6,8 @@ from compile import *
 from scalar import *
 import tensor
 
-# class Double(gof.result.Result):
 
-#     def __init__(self, data, name = "oignon"):
-#         assert isinstance(data, float)
-#         gof.result.Result.__init__(self, role = None, name = name)
-#         self.data = data
-
-#     def __str__(self):
-#         return self.name
-
-#     def __repr__(self):
-#         return self.name
-
-#     def __copy__(self):
-#         return self.__class__(self.data, self.name)
-
-# class MyOp(gof.op.Op):
-
-#     nin = -1
-
-#     def __init__(self, *inputs):
-#         assert len(inputs) == self.nin
-#         for input in inputs:
-#             if not isinstance(input, Double):
-#                 raise Exception("Error 1")
-#         self.inputs = inputs
-#         self.outputs = [Double(0.0, self.__class__.__name__ + "_R")]
-    
-#     def perform(self):
-#         self.outputs[0].data = self.impl(*[input.data for input in self.inputs])
-
-
-# class Unary(MyOp):
-#     nin = 1
-
-# class Binary(MyOp):
-#     nin = 2
-
-        
-# class Add(Binary):
-#     def impl(self, x, y):
-#         return x + y
-        
-# class Sub(Binary):
-#     def impl(self, x, y):
-#         return x - y
-        
-# class Mul(Binary):
-#     def impl(self, x, y):
-#         return x * y
-        
-# class Div(Binary):
-#     def impl(self, x, y):
-#         return x / y
-
-
-# def env(inputs, outputs, validate = True, features = []):
-#     return gof.env.Env(inputs, outputs, features = features, consistency_check = validate)
-# def perform_linker(env):
-#     lnk = gof.link.PerformLinker(env)
-#     return lnk
-
-# def graph1(): # (x+y) * (x/z)
-#     x = gof.modes.build(Double(1.0, 'x'))
-#     y = gof.modes.build(Double(3.0, 'y'))
-#     z = gof.modes.build(Double(4.0, 'z'))
-
-#     o = Mul(Add(x, y).out, Div(x, z).out).out
-#     return [x,y,z], [o]
+PatternOptimizer = lambda p1, p2, ign=True: gof.OpKeyOptimizer(gof.PatternSub(p1, p2), ignore_newtrees=ign)
 
 
 def graph1(): # (x+y) * (x/z)
@@ -90,23 +23,8 @@ class T_Function(unittest.TestCase):
         p = function(gi, go, optimizer = None, linker = 'py')
         self.failUnless(p(1.0,3.0,4.0) == 1.0)
 
-#     def test_link_noopt(self):
-#         gi, go = graph1()
-#         fn, i, o = perform_linker(env(gi, go)).make_thunk(True)
-#         fn()
-#         self.failUnless(go[0].data == 1.0)
-
-#     def test_link_opt(self):
-#         opt = gof.opt.PatternOptimizer((Div, '1', '2'), (Div, '2', '1'))
-#         gi, go = graph1()
-#         e = env(gi, go)
-#         opt.optimize(e)
-#         fn, i, o = perform_linker(e).make_thunk(True)
-#         fn()
-#         self.failUnless(go[0].data == 16.0)
-
     def test_opt(self):
-        opt = gof.opt.PatternOptimizer((div, '1', '2'), (div, '2', '1'))
+        opt = PatternOptimizer((div, '1', '2'), (div, '2', '1'))
         gi, go = graph1()
         p = function(gi,go, optimizer=opt.optimize, linker = 'py')
         self.failUnless(p(1.,3.,4.) == 16.0)
@@ -116,7 +34,7 @@ class T_Function(unittest.TestCase):
             x, y, z = floats('xyz')
             o = mul(add(x, y), div(x, z))
             return [x,y,z], [o, o.owner.inputs[1]]
-        opt = gof.opt.PatternOptimizer((div, '1', '2'), (div, '2', '1'))
+        opt = PatternOptimizer((div, '1', '2'), (div, '2', '1'))
         gi, go = graph2()
         p = function(gi,go, optimizer=opt.optimize)
         a,b = p(1.,3.,4.)
@@ -211,46 +129,6 @@ class T_fast_compute(unittest.TestCase):
         assert compile._fcache[(e, )]() == 14.0
         
         
-        
-#     def test_orphans(self):
-#         gi, go = graph1()
-#         opt = None
-#         p0 = function(gi[0:0], go, optimizer = None, linker = 'py')
-
-#         self.failUnless(p0() == 1.0)
-
-#         p3 = Function(gi,go)
-#         p2 = Function(gi[0:2], go)
-#         p1 = Function(gi[0:1], go)
-#         try:
-#             self.failUnless(p3() == 6.0)
-#             self.fail()
-#         except TypeError, e:
-#             self.failUnless(e[0].split()[0:3] == ['Function','call', 'takes'])
-
-#         self.failUnless(p3(1.,3.,4.) == 1.0)
-#         self.failUnless(p2(1.,3.) == 1.0)
-#         self.failUnless(p1(1.,) == 1.0)
-
-
-#     def test_some_constant_outputs(self):
-#         x = gof.modes.build(Double(1.0, 'x'))
-#         y = gof.modes.build(Double(3.0, 'y'))
-#         z = gof.modes.build(Double(4.0, 'z'))
-#         xy = Mul(x,y).out
-#         zz = Mul(z,z).out
-
-#         p0 = Function([x,y], [xy, zz])
-#         self.failUnless(p0(1.,3.) == [3.0,16.0])
-#         self.failUnless(p0(1.5,4.) == [6.0,16.0])
-#         self.failUnless(x.data == 1.0)
-#         self.failUnless(y.data == 3.0)
-#         self.failUnless(z.data == 4.0)
-
-#         p1 = Function([z], [xy, zz],unpack_single=False)
-#         self.failUnless(p1(4.) == [3.0,16.0]) #returns 6.0, 16.10
-#         self.failUnless(p1(5.) == [3.0,25.0])
-
 
 if __name__ == '__main__':
 

_test_elemwise.py

@@ -2,7 +2,7 @@
 import time
 import unittest
 
-from gof import Result, Op, Env
+from gof import Result, Op
 import gof
 
 from scalar import *
@@ -11,6 +11,10 @@ import tensor
 from elemwise import *
 
 
+def Env(i, o):
+    e = gof.Env(i, o)
+    gof.ExpandMacros().optimize(e)
+    return e
 
 class _test_DimShuffle(unittest.TestCase):
 

elemwise.py

@@ -174,7 +174,7 @@ class DimShuffle(Op):
     def make_node(self, input):
         ib = tuple(input.type.broadcastable)
         if not ib == self.input_broadcastable:
-            raise TypeError("The number of dimensions and/or broadcastable pattern of the input is incorrect for this op. Expected %s, got %s." % (ib, self.input_broadcastable))
+            raise TypeError("The number of dimensions and/or broadcastable pattern of the input is incorrect for this op. Expected %s, got %s." % (self.input_broadcastable, ib))
         ob = []
         for value in self.new_order:
             if value == 'x':
@@ -327,10 +327,10 @@ class Elemwise(Op):
 #                 args.append(DimShuffle(input.type.broadcastable, ['x']*difference + range(length))(input))
 #         inputs = args
  
-        try:
-            assert len(set([len(input.type.broadcastable) for input in inputs])) == 1
-        except (AssertionError, AttributeError):
-            raise TypeError("All inputs to a Broadcast subclass must be Tensor instances and their broadcastable fields must all have the same length.", inputs)
+#         try:
+#             assert len(set([len(input.type.broadcastable) for input in inputs])) == 1
+#         except (AssertionError, AttributeError):
+#             raise TypeError("All inputs to a Broadcast subclass must be Tensor instances and their broadcastable fields must all have the same length.", inputs)
 
         out_broadcastables = [[all(bcast) for bcast in zip(*[input.type.broadcastable for input in inputs])]] * shadow.nout
         inplace_pattern = self.inplace_pattern