more backport to python 2.4

theano/tensor/basic.py

@@ -2185,6 +2185,10 @@ class Split(Op):
 
     def perform(self, node, (x, axis, splits), outputs):
         """WRITEME"""
+        #in python 2.4, x.shape[numpy.asarray(1)] don't work.
+        if sys.version_info[0:2]==(2, 4) and axis.size==1:
+          axis=int(axis)
+        
         try:
             len_along_axis = x.shape[axis]
         except :

theano/tensor/tests/test_merge.py

@@ -44,7 +44,9 @@ class MyOp(Op):
         return self is other or isinstance(other, MyOp) and self.x is not None and self.x == other.x
 
     def __hash__(self):
-        return self.x if self.x is not None else id(self)
+        if self.x is not None:
+            return self.x
+        else: return id(self)
 op1 = MyOp('Op1')
 
 

theano/tensor/tests/test_naacl09.py

@@ -3,7 +3,6 @@
 import theano
 from theano import tensor as T
 from theano.tensor import nnet as NN
-import numpy as N
 from theano.compile import module
 from theano.compile.mode import default_mode
 from theano import tensor as T, sparse as S
@@ -88,7 +87,9 @@ class QuadraticDenoisingAA(module.Module):
             self.qfilters = [(q) for q in _qfilters]
 
         #self.w1 = theano.Member(T.matrix('w1')) if _w1 is None else theano.Member(_w1)
-        self.w1 = (T.matrix('w1')) if _w1 is None else (_w1)
+        if _w1 is None:
+            self.w1 = (T.matrix('w1'))
+        else: self.w1 = (_w1)
         if _w2 is None:
             if not tie_weights:
                 #self.w2 = theano.Member(T.matrix())
@@ -99,9 +100,13 @@ class QuadraticDenoisingAA(module.Module):
             #self.w2 = theano.Member(_w2)
             self.w2 = (_w2)
         #self.b1 = theano.Member(T.vector('b1')) if _b1 is None else theano.Member(_b1)
-        self.b1 = (T.vector('b1')) if _b1 is None else (_b1)
+        if _b1 is None:
+            self.b1 = (T.vector('b1'))
+        else: self.b1 = (_b1)
         #self.b2 = theano.Member(T.vector('b2')) if _b2 is None else theano.Member(_b2)
-        self.b2 = (T.vector('b2')) if _b2 is None else (_b2)
+        if _b2 is None:
+            self.b2 = (T.vector('b2'))
+        else: self.b2 = (_b2)
 
 #        # REGULARIZATION COST
 #        self.regularization = self.build_regularization()
@@ -273,16 +278,26 @@ class Module_Nclass(module.FancyModule):
         super(Module_Nclass, self).__init__() #boilerplate
 
         #self.x = module.Member(x) if x is not None else T.matrix('input')
-        self.x = (x) if x is not None else T.matrix('input')
+        if x is not None:
+            self.x = (x)
+        else: self.x = T.matrix('input')
         #self.targ = module.Member(targ) if targ is not None else T.lvector()
-        self.targ = (targ) if targ is not None else T.lvector()
+        if targ is not None:
+            self.targ = (targ)
+        else: self.targ = T.lvector()
 
         #self.w = module.Member(w) if w is not None else module.Member(T.dmatrix())
-        self.w = (w) if w is not None else (T.dmatrix())
+        if w is not None:
+            self.w = (w)
+        else: self.w = (T.dmatrix())
         #self.b = module.Member(b) if b is not None else module.Member(T.dvector())
-        self.b = (b) if b is not None else (T.dvector())
+        if b is not None:
+            self.b = (b)
+        else: self.b = (T.dvector())
         #self.lr = module.Member(lr) if lr is not None else module.Member(T.dscalar())
-        self.lr = (lr) if lr is not None else (T.dscalar())
+        if lr is not None:
+            self.lr = (lr)
+        else: self.lr = (T.dscalar())
 
         self.params = [p for p in [self.w, self.b] if p.owner is None]
 
@@ -445,7 +460,7 @@ class ConvolutionalMLP(module.FancyModule):
             assert (i.w2 == self.input_representations[0].w2).all()
             assert (i.b1 == self.input_representations[0].b1).all()
             assert (i.b2 == self.input_representations[0].b2).all()
-            assert all((a==b).all() for a, b in zip(i.qfilters, self.input_representations[0].qfilters)) 
+            assert N.all((a==b).all() for a, b in zip(i.qfilters, self.input_representations[0].qfilters)) 
 
         self.hidden.initialize(input_size=(len(self.inputs) * self.input_representation_size),
                 hidden_size=self.hidden_representation_size, noise_level=noise_level,
@@ -507,7 +522,9 @@ def test_naacl_model(iters_per_unsup=3, iters_per_sup=3,
     import time
     t = time.time()
     
-    mode = theano.Mode(linker='c|py', optimizer=optimizer) if optimizer else default_mode
+    if optimizer:
+        mode = theano.Mode(linker='c|py', optimizer=optimizer)
+    else: mode = default_mode
     if realistic:
         m = create_realistic(compile_mode=mode)
     else:

theano/tensor/tests/test_opt.py

@@ -1100,7 +1100,9 @@ class test_fusion(unittest.TestCase):
                     print 'key, old val, new val, diff'
                     for key in set(d.keys()+nd.keys()):
                         if d.has_key(key) and nd.has_key(key) and nd[key]!=d[key]:
-                            print key, d.get(key),nd.get(key), nd[key]-d[key] if d.has_key(key) and nd.has_key(key) else None
+                            print key, d.get(key),nd.get(key),
+                            if d.has_key(key) and nd.has_key(key): print nd[key]-d[key]
+                            else: print None
                     gc.collect();gc.collect();gc.collect()
                     d=nd
             
@@ -1115,8 +1117,10 @@ class test_fusion(unittest.TestCase):
                             cmp = not isinstance(x, list)
                             if not cmp and x:
                                 cmp=x[0].__class__.__name__!='array_converter'
-                                cmp=x[0]!='Option' if cmp else cmp
-                                cmp=x[0]!=270 if cmp else cmp
+                                if cmp:
+                                    cmp=x[0]!='Option'
+                                if cmp:
+                                    cmp=x[0]!=270
                                 cmp=False
                             if cmp and x in dl:
                                 nb+=1