pep8

theano/sparse/tests/test_sp2.py

@@ -152,7 +152,9 @@ class _HVStackTester(utt.InferShapeTester):
                         allow_input_downcast=True)
 
                     tested = f(*blocks)
-                    expected = self.expected_f(blocks, format=out_f, dtype=dtype)
+                    expected = self.expected_f(blocks,
+                                               format=out_f,
+                                               dtype=dtype)
 
                     assert np.allclose(tested.toarray(), expected.toarray())
                     assert tested.format == expected.format
@@ -248,73 +250,73 @@ class AddSSDataTester(utt.InferShapeTester):
                                structured=True)
 
 
-class test_structured_add_s_v(unittest.TestCase):
+class StructuredAddSVTester(unittest.TestCase):
     def setUp(self):
         utt.seed_rng()
 
     def test_structured_add_s_v_grad(self):
         sp_types = {'csc': sp.csc_matrix,
             'csr': sp.csr_matrix}
-        
+
         for format in ['csr', 'csc']:
             for dtype in ['float32', 'float64']:
                 spmat = sp_types[format](random_lil((4, 3), dtype, 3))
                 mat = np.asarray(np.random.rand(3), dtype=dtype)
-                
+
                 theano.sparse.verify_grad_sparse(S2.structured_add_s_v,
                     [spmat, mat], structured=True)
-    
+
     def test_structured_add_s_v(self):
         sp_types = {'csc': sp.csc_matrix,
             'csr': sp.csr_matrix}
-        
+
         for format in ['csr', 'csc']:
             for dtype in ['float32', 'float64']:
                 x = theano.sparse.SparseType(format, dtype=dtype)()
                 y = tensor.vector(dtype=dtype)
                 f = theano.function([x, y], S2.structured_add_s_v(x, y))
-                
+
                 spmat = sp_types[format](random_lil((4, 3), dtype, 3))
                 spones = spmat.copy()
                 spones.data = np.ones_like(spones.data)
                 mat = np.asarray(np.random.rand(3), dtype=dtype)
-                
+
                 out = f(spmat, mat)
-                
+
                 assert np.allclose(out.toarray(), spones.multiply(spmat + mat))
 
 
-class test_mul_s_v(unittest.TestCase):
+class MulSVTester(unittest.TestCase):
     def setUp(self):
         utt.seed_rng()
 
     def test_structured_add_s_v_grad(self):
         sp_types = {'csc': sp.csc_matrix,
             'csr': sp.csr_matrix}
-        
+
         for format in ['csr', 'csc']:
             for dtype in ['float32', 'float64']:
                 spmat = sp_types[format](random_lil((4, 3), dtype, 3))
                 mat = np.asarray(np.random.rand(3), dtype=dtype)
-                
+
                 theano.sparse.verify_grad_sparse(S2.mul_s_v,
                     [spmat, mat], structured=True)
-    
+
     def test_mul_s_v(self):
         sp_types = {'csc': sp.csc_matrix,
             'csr': sp.csr_matrix}
-        
+
         for format in ['csr', 'csc']:
             for dtype in ['float32', 'float64']:
                 x = theano.sparse.SparseType(format, dtype=dtype)()
                 y = tensor.vector(dtype=dtype)
                 f = theano.function([x, y], S2.mul_s_v(x, y))
-                
+
                 spmat = sp_types[format](random_lil((4, 3), dtype, 3))
                 mat = np.asarray(np.random.rand(3), dtype=dtype)
-                
+
                 out = f(spmat, mat)
-                
+
                 assert np.allclose(out.toarray(), spmat.toarray() * mat)
 
 if __name__ == '__main__':