Additional correctins on class Unique

theano/tensor/extra_ops.py

 import numpy as np
 import numpy
 import warnings
-import theano
 
+import theano
 from theano.tensor import basic
 from theano.tensor import nlinalg
-
 from theano import gof, scalar
 from theano.gradient import DisconnectedType
 tensor = basic
@@ -1014,29 +1013,24 @@ class Unique(theano.Op):
                  return_counts=False):
         self.return_index = return_index
         self.return_inverse = return_inverse
-        self.return_counts = return_counts
-        if self.return_counts == True and np.__version__ < "1.9.0" :
+        self.return_counts = return_counts   
+        numpy_ver = [int(n) for n in numpy.__version__.split('.')[:2]]
+        if self.return_counts == True and bool(numpy_ver < [1, 9]) :
             raise RuntimeError(
                 "Numpy version = " + np.__version__ +
                 ". Option 'return_counts=True' works starting"
                 " from version 1.9.0.")
 
     def make_node(self, x):
-        x = basic.as_tensor_variable(x)
-        #x = x.flatten()
-        
-        outputs = []
-        # output0 = basic.TensorType(broadcastable=[False], dtype=x.dtype)()
-        output0 = x.flatten().type()
-        outputs.append(output0)
+        x = basic.as_tensor_variable(x)        
+        outputs = [basic.TensorType(broadcastable=[False], dtype=x.dtype)()]
         typ = basic.TensorType(broadcastable=[False], dtype='int64')
         if self.return_index :
             outputs.append(typ())
         if self.return_inverse :
             outputs.append(typ())
         if self.return_counts :
-            outputs.append(typ())
-            
+            outputs.append(typ())            
         return theano.Apply(self, [x], outputs)
 
     def perform(self, node, inputs, output_storage):
@@ -1061,7 +1055,7 @@ class Unique(theano.Op):
     def infer_shape(self, node, i0_shapes):
         ret = node.fgraph.shape_feature.default_infer_shape(node, i0_shapes)
         if self.return_inverse :
-            shape = (np.prod(i0_shapes[0]), )
+            shape = (basic.prod(i0_shapes[0]), )
             if self.return_index :
                 ret[2] = shape
                 return ret

theano/tensor/tests/test_extra_ops.py

@@ -2,10 +2,9 @@ from nose.plugins.attrib import attr
 import numpy as np
 import numpy
 import unittest
-import theano
 
+import theano
 from theano.tests import unittest_tools as utt
-
 from theano.tensor.extra_ops import (CumsumOp, cumsum, CumprodOp, cumprod,
                                      BinCountOp, bincount, DiffOp, diff,
                                      squeeze, compress, RepeatOp, repeat,
@@ -671,7 +670,7 @@ class test_Unique(utt.InferShapeTester):
                     Unique(True), 
                     Unique(False, True), 
                     Unique(True, True)]
-        if np.__version__ >= "1.9.0" :
+        if bool(numpy_ver >= [1, 9]) :
             self.ops.extend([
                         Unique(False, False, True), 
                         Unique(True, False, True), 
@@ -689,7 +688,7 @@ class test_Unique(utt.InferShapeTester):
                               np.unique(inp, True), 
                               np.unique(inp, False, True), 
                               np.unique(inp, True, True)]
-        if np.__version__ >= "1.9.0" :
+        if bool(numpy_ver >= [1, 9]) :
             list_outs_expected.extend([
                                 np.unique(inp, False, False, True), 
                                 np.unique(inp, True, False, True), 
@@ -698,11 +697,8 @@ class test_Unique(utt.InferShapeTester):
         for op, outs_expected in zip(self.ops, list_outs_expected) :
             f = theano.function(inputs=[x], outputs=op(x, return_list=True))
             outs = f(inp)
-            print outs
             # Compare the result computed to the expected value.
             for out, out_exp in zip(outs, outs_expected):
-                print out
-                print out_exp
                 utt.assert_allclose(out, out_exp)
         
     def test_basic_matrix(self):            
@@ -715,7 +711,7 @@ class test_Unique(utt.InferShapeTester):
                               np.unique(inp, True),
                               np.unique(inp, False, True),
                               np.unique(inp, True, True)]
-        if np.__version__ >= "1.9.0" :
+        if bool(numpy_ver >= [1, 9]) :
             list_outs_expected.extend([
                                 np.unique(inp, False, False, True),
                                 np.unique(inp, True, False, True),
@@ -724,11 +720,8 @@ class test_Unique(utt.InferShapeTester):
         for op, outs_expected in zip(self.ops, list_outs_expected):
             f = theano.function(inputs=[x], outputs=op(x, return_list=True))
             outs = f(inp)
-            print outs
             # Compare the result computed to the expected value.
             for out, out_exp in zip(outs, outs_expected):
-                print out
-                print out_exp
                 utt.assert_allclose(out, out_exp)
         
     def test_infer_shape_vector(self):