Update sparse test to test more combinations.

theano/sparse/tests/test_basic.py

@@ -347,24 +347,24 @@ class test_structureddot(unittest.TestCase):
     def test_dot_sparse_sparse(self):
         #test dot for 2 input sparse matrix
         sparse_dtype = 'float64'
-        for sparse_format in ['csc','csr']:
-            a = SparseType(sparse_format, dtype=sparse_dtype)()
-            b = SparseType(sparse_format, dtype=sparse_dtype)()
-            d = theano.dot(a,b)
-            f = theano.function([a,b], theano.Out(d, borrow=True))
-            topo = f.maker.env.toposort()
-            for M,N,K,nnz in [(4,3,2,3),
-                              (40,30,20,3),
-                              (40,30,20,30),
-                              (400,3000,200,6000),
-                              ]:
-                if sparse_format == 'csc':
-                    spmat = sp.csc_matrix(random_lil((M,N), sparse_dtype, nnz))
-                    spmat2 = sp.csc_matrix(random_lil((N,K), sparse_dtype, nnz))
-                elif sparse_format == 'csr':
-                    spmat = sp.csr_matrix(random_lil((M,N), sparse_dtype, nnz))
-                    spmat2 = sp.csr_matrix(random_lil((N,K), sparse_dtype, nnz))
-                f(spmat,spmat2)
+        sp_mat = {'csc':sp.csc_matrix,
+                  'csr':sp.csr_matrix}
+
+        for sparse_format_a in ['csc','csr']:
+            for sparse_format_b in ['csc', 'csr']:
+                a = SparseType(sparse_format_a, dtype=sparse_dtype)()
+                b = SparseType(sparse_format_b, dtype=sparse_dtype)()
+                d = theano.dot(a,b)
+                f = theano.function([a,b], theano.Out(d, borrow=True))
+                topo = f.maker.env.toposort()
+                for M,N,K,nnz in [(4,3,2,3),
+                                  (40,30,20,3),
+                                  (40,30,20,30),
+                                  (400,3000,200,6000),
+                                  ]:
+                    a_val = sp_mat[sparse_format_a](random_lil((M,N), sparse_dtype, nnz))
+                    b_val = sp_mat[sparse_format_b](random_lil((N,K), sparse_dtype, nnz))
+                    f(a_val, b_val)
 
     def test_csc_correct_output_faster_than_scipy(self):
         sparse_dtype = 'float64'