Merge pull request #3847 from jojolalpin/master

theano/sparse/opt.py

@@ -348,8 +348,8 @@ class StructuredDotCSC(gof.Op):
             // pointers to access actual data in the arrays passed as params.
             dtype_%(z)s*     __restrict__ Dz   = (dtype_%(z)s*)PyArray_DATA(%(z)s);
             const dtype_%(a_val)s* __restrict__ Dval = (dtype_%(a_val)s*)PyArray_DATA(%(a_val)s);
-            const npy_int32 * __restrict__ Dind = (npy_int32*)PyArray_DATA(%(a_ind)s;
-            const npy_int32 * __restrict__ Dptr = (npy_int32*)PyArray_DATA(%(a_ptr)s;
+            const npy_int32 * __restrict__ Dind = (npy_int32*)PyArray_DATA(%(a_ind)s);
+            const npy_int32 * __restrict__ Dptr = (npy_int32*)PyArray_DATA(%(a_ptr)s);
 
             //npy_intp nnz = PyArray_DIMS(%(a_ind)s)[0];
 

theano/sparse/tests/test_opt.py

@@ -152,3 +152,20 @@ def test_local_dense_from_sparse_sparse_from_dense():
         # We should just have a deep copy.
         assert len(f.maker.fgraph.apply_nodes) == 1
         f([[1, 2], [3, 4]])
+
+def test_sd_csc():
+
+    A = sp.rand(4, 5, density=0.60, format='csc', dtype=numpy.float32)
+    b = numpy.random.rand(5,2).astype(numpy.float32)
+    target = A*b
+    
+    a_val = theano.tensor.as_tensor_variable(A.data)
+    a_ind = theano.tensor.as_tensor_variable(A.indices)
+    a_ptr = theano.tensor.as_tensor_variable(A.indptr)
+    nrows = theano.tensor.as_tensor_variable(numpy.int32(A.shape[0]))
+    b = theano.tensor.as_tensor_variable(b)
+    
+    res = theano.sparse.opt.sd_csc(a_val, a_ind, a_ptr, nrows, b).eval()
+    
+    assert (res==target).all()
+