Merge pull request #1910 from nouiz/split_0idx

theano/sparse/tests/test_basic.py

@@ -770,7 +770,7 @@ class test_comparison(unittest.TestCase):
         y = theano.tensor.matrix()
 
         m1 = sp.csc_matrix((2, 2), dtype=theano.config.floatX)
-        m2 = numpy.asarray([[0, 0], [0, 0]])
+        m2 = numpy.asarray([[0, 0], [0, 0]], dtype=theano.config.floatX)
 
         for func in self.testsDic:
 

theano/tensor/basic.py

@@ -3150,8 +3150,9 @@ class Split(Op):
         if numpy.sum(splits) != len_along_axis:
             raise ValueError('The splits sum to %s, expected %s' %
                              (numpy.sum(splits), len_along_axis))
-        if not python_all(splits):
-            raise ValueError('Cannot have a split of zero.')
+        if python_any([nb < 0 for nb in splits]):
+            raise ValueError('Split: you try to make an ndarray with'
+                             'negative number of elements.')
 
         # Checking is done, let's roll the splitting algorithm!
         # Basically we step along the given axis of x, extracting

theano/tensor/tests/test_basic.py

@@ -2343,6 +2343,7 @@ def test_batched_dot():
 
     assert result.shape[0] == first_mat_val.shape[0]
 
+
 def test_batched_tensordot():
     first = theano.tensor.tensor4("first")
     second = theano.tensor.tensor4("second")
@@ -2364,10 +2365,10 @@ def test_batched_tensordot():
     second_mat_val = numpy.random.rand(10, 4).astype(config.floatX)
     result_fn = theano.function([first_mat, second_mat], output)
     result = result_fn(first_mat_val, second_mat_val)
-    print(result.shape)
     assert result.shape[0] == first_mat_val.shape[0]
     assert len(result.shape) == 1
 
+
 def test_tensor_values_eq_approx():
     #test, inf, -inf and nan equal themself
     a = numpy.asarray([-numpy.inf, -1, 0, 1, numpy.inf, numpy.nan])
@@ -3145,15 +3146,12 @@ class T_Join_and_Split(unittest.TestCase):
         b_v = numpy.random.rand(4)
         f = theano.function([a, b], [Ha, Hb])
         Ha_v, Hb_v = f(a_v, b_v)
-        print Ha_v
-        print Hb_v
         # The Hessian is always a matrix full of 0
         assert Ha_v.shape == (4, 4)
         assert Hb_v.shape == (4, 4)
         assert numpy.allclose(Ha_v, 0.)
         assert numpy.allclose(Hb_v, 0.)
 
-
     def test_join_concatenate_one_element(self):
         ''' Fast test of concatenate as this is an alias for join.
         also test that we remove the Join op if there is only 1 input'''
@@ -3547,6 +3545,26 @@ class T_Join_and_Split(unittest.TestCase):
         m = self.shared(rng.rand(4, 4).astype(self.floatX))
         self.assertRaises(TypeError, self.join_op(), 0, v, m)
 
+    def test_split_0elem(self):
+        rng = numpy.random.RandomState(seed=utt.fetch_seed())
+        m = self.shared(rng.rand(4, 6).astype(self.floatX))
+        o = self.split_op(2)(m, 0, [4, 0])
+        f = function([], o, mode=self.mode)
+        assert any([isinstance(node.op, self.split_op)
+                    for node in f.maker.fgraph.toposort()])
+        o1, o2 = f()
+        assert numpy.allclose(o1, m.get_value(borrow=True))
+        assert numpy.allclose(o2, m.get_value(borrow=True)[4:])
+
+    def test_split_neg(self):
+        rng = numpy.random.RandomState(seed=utt.fetch_seed())
+        m = self.shared(rng.rand(4, 6).astype(self.floatX))
+        o = self.split_op(2)(m, 0, [5, -1])
+        f = function([], o, mode=self.mode)
+        assert any([isinstance(node.op, self.split_op)
+                    for node in f.maker.fgraph.toposort()])
+        self.assertRaises(ValueError, f)
+
 
 class test_comparison(unittest.TestCase):
     """Test <, >, <=, >=, == and !=