added tests for CSM op and gradient

theano/sparse/tests/test_basic.py

@@ -22,7 +22,7 @@ from theano.sparse.basic import _is_dense_variable, _is_sparse_variable
 from theano.sparse.basic import verify_grad_sparse
 from theano.sparse import (as_sparse_variable, CSC, CSR, CSM, CSMProperties,
     csm_properties)
-from theano.sparse import SparseType, CSMGrad
+from theano.sparse import SparseType, CSMGrad, CSMGradC
 from theano.sparse import StructuredDot, StructuredDotCSC
 from theano.sparse import StructuredDotGradCSC, StructuredDotGradCSR
 from theano.sparse import AddSS, AddSD, MulSS, MulSD, Transpose, Neg, Remove0
@@ -184,7 +184,7 @@ class SparseInferShapeTester(utt.InferShapeTester):
                                     [out],
                                     [spm.data, spm.indices, spm.indptr,
                                      spm.shape],
-                                    CSMGrad
+                                    (CSMGrad, CSMGradC)
                                    )
 
     def test_transpose(self):
@@ -616,6 +616,45 @@ class test_csm_properties(unittest.TestCase):
                 assert numpy.all(shape == spmat.shape)
 
 
+class test_csm(unittest.TestCase):
+    def setUp(self):
+        utt.seed_rng()
+
+    def test_csm_grad(self):
+        sp_types = {'csc': sp.csc_matrix,
+            'csr': sp.csr_matrix}
+        
+        for format in ['csc', 'csr']:
+            for dtype in ['float32', 'float64']:
+                spmat = sp_types[format](random_lil((4, 3), dtype, 3))
+                
+                verify_grad_sparse(lambda x: CSM(format)(x, spmat.indices,
+                    spmat.indptr, numpy.asarray(spmat.shape, 'int32')),
+                    [spmat.data], structured=True)
+    
+    def test_csm(self):
+        sp_types = {'csc': sp.csc_matrix,
+            'csr': sp.csr_matrix}
+        
+        for format in ['csc', 'csr']:
+            for dtype in ['float32', 'float64']:
+                x = tensor.tensor(dtype=dtype, broadcastable=(False,))
+                y = tensor.ivector()
+                z = tensor.ivector()
+                s = tensor.ivector()
+                f = theano.function([x, y, z, s], CSM(format)(x, y, z, s))
+                
+                spmat = sp_types[format](random_lil((4, 3), dtype, 3))
+                
+                res = f(spmat.data, spmat.indices, spmat.indptr,
+                    numpy.asarray(spmat.shape, 'int32'))
+                
+                assert numpy.all(res.data == spmat.data)
+                assert numpy.all(res.indices == spmat.indices)
+                assert numpy.all(res.indptr == spmat.indptr)
+                assert numpy.all(res.shape == spmat.shape)
+
+
 class test_structureddot(unittest.TestCase):
     def setUp(self):
         utt.seed_rng()