Merge.

theano/tensor/tests/test_basic.py

@@ -806,9 +806,9 @@ ClipTester = makeTester(name='ClipTester',
                                               -1, 1),
                                     correct4=(randint(5, 5).astype('int16'),
                                               -1, 1),
-                                    correct4=(randint(5, 5).astype('int32'),
+                                    correct5=(randint(5, 5).astype('int32'),
                                               -1, 1),
-                                    correct5=(randint(5, 5).astype('int64'),
+                                    correct6=(randint(5, 5).astype('int64'),
                                               -1, 1)),
                         # These don't build -- is this equivalent to marking
                         # them as 'known fail'?
@@ -853,23 +853,31 @@ ALL_DTYPES = ('int8', 'int16', 'int32', 'int64',
 
 def rand_of_dtype(shape, dtype):
     if 'int' in dtype:
-        return randint(shape).astype(dtype)
+        return randint(*shape).astype(dtype)
     elif 'float' in dtype:
-        return rand(shape).astype(dtype)
+        return rand(*shape).astype(dtype)
     elif 'complex' in dtype:
-        return randcomplex(shape).astype(dtype)
+        return randcomplex(*shape).astype(dtype)
     else:
         raise TypeError()
 
 def multi_dtype_tests(shape1, shape2, dtypes=ALL_DTYPES, nameprefix=''):
     for dtype1, dtype2 in combinations(dtypes, 2):
-        name1 = '%s_%s' % (nameprefix, dtype1, dtype2)
-        name2 = '%s_%s' % (nameprefix, dtype2, dtype1)
+        name1 = '%s_%s_%s' % (nameprefix, dtype1, dtype2)
+        name2 = '%s_%s_%s' % (nameprefix, dtype2, dtype1)
         obj1 = rand_of_dtype(shape1, dtype1)
         obj2 = rand_of_dtype(shape2, dtype2)
         yield (name, (obj1, obj2))
         yield (name, (obj2, obj1))
 
+def multi_dtype_cast_tests(shape, dtypes=ALL_DTYPES, nameprefix=''):
+    for dtype1, dtype2 in combinations(dtypes, 2):
+        name1 = '%s_%s_%s' % (nameprefix, dtype1, dtype2)
+        name2 = '%s_%s_%s' % (nameprefix, dtype2, dtype1)
+        obj1 = rand_of_dtype(shape, dtype1)
+        yield (name, (obj1, dtype2))
+        yield (name, (obj2, dtype1))
+
 SecondBroadcastTester = makeTester(
                             name='SecondBroadcastTester',
                             op=second,
@@ -903,6 +911,22 @@ SecondSameRankTester = makeTester(
                             bad_runtime=None
                         )
 
+CastTester = makeTester(
+                name='CastTester',
+                op=cast,
+                expected=lambda x, y: x.astype(y),
+                good=dict(itertools.chain(
+                    multi_dtype_cast_tests((2,)),
+                    [('%s_%s' % (dtype, dtype), rand_of_dtype((2,), dtype), dtype)
+                     for dtype in ALL_DTYPES]
+                )),
+                bad_build=dict(
+                    fail_not_a_real_dtype=((2,), 'blah')
+                ),
+                bad_runtime=None
+            )
+
+
 #TODO: consider moving this function / functionality to gradient.py
 #      rationale: it's tricky, and necessary everytime you want to verify
 #      gradient numerically