* Verified StructuredDotGradCSR (everything looks ok). Added more loops around

theano/sparse/basic.py

@@ -1193,7 +1193,7 @@ class StructuredDotGradCSR(gof.Op):
             const npy_int32 * __restrict__ indptr = (npy_int32 *)%(_indptr)s->data;
             const npy_int32 * __restrict__ indices = (npy_int32 *)%(_indices)s->data;
 
-            // loop over rows
+            // loop over columns of sparse matrix
             for (npy_int32 i = 0; i < N; ++i)
             {
                 // for each non-null value in the sparse row

theano/sparse/tests/test_basic.py

@@ -153,15 +153,19 @@ class T_conversion(unittest.TestCase):
 
 import scipy.sparse as sp
 class test_structureddot(unittest.TestCase):
+    def setUp(self):
+        unittest_tools.seed_rng()
 
     def test_structuredot(self):
         bsize = 2
-        spmat = sp.csc_matrix((5,5))
-        spmat[0,1] = 1
-        spmat[0,2] = 2
-        spmat[1,2] = 3
-        spmat[1,4] = 4
-        spmat[3,4] = 5
+       
+        # iterate 10 times just to make sure (cannot get this wrong !)
+        for i in range(10):
+            spmat = sp.csc_matrix((4,6))
+            for i in range(5):
+                x = numpy.floor(numpy.random.rand()*spmat.shape[0])
+                y = numpy.floor(numpy.random.rand()*spmat.shape[1])
+                spmat[x,y] = numpy.random.rand()*10
        
             kerns = tensor.dvector('kerns')
             images = tensor.dmatrix('images')