Merge pull request #1636 from nouiz/scipy_0.13

theano/sparse/basic.py

@@ -2246,6 +2246,10 @@ class HStack(gof.op.Op):
             assert _is_sparse(b)
         out[0] = scipy.sparse.hstack(block, format=self.format,
                                      dtype=self.dtype)
+        # Some version of scipy (at least 0.14.0.dev-c4314b0)
+        # Do not cast to the wanted dtype.
+        if out[0].dtype != self.dtype:
+            out[0] = out[0].astype(self.dtype)
 
     def grad(self, inputs, (gz, )):
         is_continuous = [(inputs[i].dtype in tensor.continuous_dtypes)
@@ -2321,6 +2325,10 @@ class VStack(HStack):
             assert _is_sparse(b)
         out[0] = scipy.sparse.vstack(block, format=self.format,
                                      dtype=self.dtype)
+        # Some version of scipy (at least 0.14.0.dev-c4314b0)
+        # Do not cast to the wanted dtype.
+        if out[0].dtype != self.dtype:
+            out[0] = out[0].astype(self.dtype)
 
     def grad(self, inputs, (gz, )):
         is_continuous = [(inputs[i].dtype in tensor.continuous_dtypes)

theano/sparse/tests/test_basic.py

@@ -69,14 +69,16 @@ def random_lil(shape, dtype, nnz):
     huge = 2 ** 30
     for k in range(nnz):
         # set non-zeros in random locations (row x, col y)
-        idx = numpy.random.random_integers(huge, size=len(shape)) % shape
+        idx = numpy.random.random_integers(huge, size=2) % shape
         value = numpy.random.rand()
         #if dtype *int*, value will always be zeros!
         if "int" in dtype:
             value = int(value * 100)
+        # The call to tuple is needed as scipy 0.13.1 do not support
+        # ndarray with lenght 2 as idx tuple.
         rval.__setitem__(
-                idx,
-                value)
+            tuple(idx),
+            value)
     return rval