Fix test with all/any, re-enable it

theano/tensor/tests/test_elemwise.py

@@ -315,9 +315,10 @@ class test_CAReduce(unittest_tools.InferShapeTester):
                 else:
                     self.fail()
             else:
-                #numpy.{all,any} return bool type.
+                # numpy.{all,any} return bool type,
+                # but theano ops return an int8 array instead
                 if scalar_op in [scalar.and_, scalar.or_]:
-                    zv = numpy.asarray(zv, dtype=dtype)
+                    zv = numpy.asarray(zv, dtype='int8')
                 if test_nan:
                     self.assertTrue(theano.tensor.TensorType.values_eq(f(xv),
                                                                        zv),
@@ -357,10 +358,6 @@ class test_CAReduce(unittest_tools.InferShapeTester):
             self.with_linker(gof.PerformLinker(), scalar.and_, dtype=dtype)
             self.with_linker(gof.PerformLinker(), scalar.xor, dtype=dtype)
 
-    @dec.knownfailureif(
-        True,
-        ("When there is nan in the input of CAReduce,"
-         " we don't have a good output. "))
     def test_perform_nan(self):
         for dtype in ["floatX", "complex64", "complex128"]:
             self.with_linker(gof.PerformLinker(), scalar.add, dtype=dtype,
@@ -372,12 +369,8 @@ class test_CAReduce(unittest_tools.InferShapeTester):
             self.with_linker(gof.PerformLinker(), scalar.minimum, dtype=dtype,
                              test_nan=True)
             self.with_linker(gof.PerformLinker(), scalar.or_, dtype=dtype,
-                             test_nan=True)
-            self.with_linker(gof.PerformLinker(), scalar.and_, dtype=dtype,
-                             test_nan=True)
-            self.with_linker(gof.PerformLinker(), or_, dtype=dtype,
                              test_nan=True, tensor_op=tensor.any)
-            self.with_linker(gof.PerformLinker(), and_, dtype=dtype,
+            self.with_linker(gof.PerformLinker(), scalar.and_, dtype=dtype,
                              test_nan=True, tensor_op=tensor.all)
 
     def test_c(self):