Delete old test.

theano/sparse/sandbox/truedot.py

@@ -220,166 +220,3 @@ class TrueDotTester(utt.InferShapeTester):
                     self.op,
                     data,
                     structured=False)
-
-
-class TrueDotTester2(unittest.TestCase):
-    def setUp(self):
-        numpy.random.seed(44)
-
-    def test_basicSS(self):
-        for mtype in _mtypes:
-            x = as_sparse_variable(mtype((500, 3)))
-            x.data[(10, 1)] = 1
-            x.data[(20, 2)] = 2
-            self.assertTrue(_is_sparse_variable(x))
-
-            xT = x.T
-            self.assertTrue(_is_sparse_variable(xT))
-
-            zop = true_dot(x, xT)
-            self.assertTrue(_is_sparse_variable(zop))
-            z = eval_outputs([zop])
-            self.assertTrue(_is_sparse(z))
-            self.assertTrue(z.shape == (500, 500))
-            self.assertTrue(type(z) is mtype)
-
-            w = mtype((500, 500))
-            w[(10, 10)] = 1
-            w[(20, 20)] = 4
-            self.assertTrue(z.shape == w.shape)
-            self.assertTrue(type(z) == type(w))
-            self.assertTrue(z.dtype == w.dtype)
-
-            #self.assertTrue(z == w)
-            self.assertTrue(abs(z - w).nnz == 0)
-
-            z = z.todense()
-            w = w.todense()
-            self.assertTrue((z == w).all() == True)
-
-    def test_basicSD(self):
-        for mtype in _mtypes:
-            x = as_sparse_variable(mtype((500, 3)))
-            x.data[(10, 1)] = 1
-            x.data[(20, 2)] = 2
-            self.assertTrue(_is_sparse_variable(x))
-
-            y = tensor.as_tensor_variable([[1., 2], [3, 4], [2, 1]])
-            self.assertTrue(_is_dense_variable(y))
-
-            zop = true_dot(x, y)
-            self.assertTrue(_is_sparse_variable(zop))
-            z = eval_outputs([zop])
-            self.assertTrue(_is_sparse(z))
-            self.assertTrue(z.shape == (500, 2))
-            self.assertTrue(type(z) is mtype)
-
-            w = mtype((500, 2))
-            w[(10, 0)] = 3.
-            w[(20, 0)] = 4
-            w[(10, 1)] = 4
-            w[(20, 1)] = 2
-            self.assertTrue(z.shape == w.shape)
-            self.assertTrue(type(z) == type(w))
-            self.assertTrue(z.dtype == w.dtype)
-
-            #self.assertTrue(z == w)
-            self.assertTrue(abs(z - w).nnz == 0)
-
-            z = z.todense()
-            w = w.todense()
-            self.assertTrue((z == w).all() == True)
-
-    def test_basicDS(self):
-        for mtype in _mtypes:
-            x = as_sparse_variable(mtype((500, 3)))
-            x.data[(10, 1)] = 1
-            x.data[(20, 2)] = 2
-            self.assertTrue(_is_sparse_variable(x))
-
-            y = tensor.as_tensor_variable([[1., 2], [3, 4], [2, 1]])
-            self.assertTrue(_is_dense_variable(y))
-
-            x.data = x.data.T
-            y.data = y.data.T
-
-            zop = true_dot(y, x)
-            zop = transpose(true_dot(y, x))
-            self.assertTrue(_is_sparse_variable(zop))
-            z = eval_outputs([zop])
-            self.assertTrue(_is_sparse(z))
-            self.assertTrue(z.shape == (500, 2))
-            # self.assertTrue(type(z) is mtype)
-
-            w = mtype((500, 2))
-            w[(10, 0)] = 3.
-            w[(20, 0)] = 4
-            w[(10, 1)] = 4
-            w[(20, 1)] = 2
-            self.assertTrue(z.shape == w.shape)
-            # Type should switch from csr to csc and vice-versa,
-            # so don't perform this test
-            # self.assertTrue(type(z) == type(w))
-            self.assertTrue(z.dtype == w.dtype)
-
-            # Type should switch from csr to csc and vice-versa,
-            # so don't perform this test
-            # self.assertTrue(z == w)
-            self.assertTrue(abs(z - w).nnz == 0)
-
-            z = z.todense()
-            w = w.todense()
-            self.assertTrue((z == w).all() == True)
-
-    def test_graph_bprop0(self):
-        for mtype in _mtypes:
-            # x = TensorType('float64', broadcastable=[False,False], name='x')
-            x = tensor.matrix('x')
-            w = SparseType(dtype='float64',
-                           format=_mtype_to_str[mtype]).make_variable()
-            xw = dense_from_sparse(true_dot(w, x))
-            y = dense_from_sparse(true_dot(w.T, xw))
-            diff = x - y
-            loss = tensor.sum(tensor.sqr(diff))
-            gw = tensor.grad(loss, w)
-            trainfn = compile.function([x, w], [y, loss, gw])
-
-            x = numpy.asarray([[1., 2], [3, 4], [2, 1]])
-            w = mtype((500, 3))
-            w[(10, 1)] = 1
-            w[(20, 2)] = 2
-            lr = 0.001
-            y, origloss, gw = trainfn(x, w)
-            for epoch in xrange(50):
-                y, loss, gw = trainfn(x, w)
-                w = w - (lr * gw)
-                # print loss
-
-            self.assertTrue(origloss > loss)
-            self.assertTrue('1.05191241115' == str(loss))
-
-    def test_graph_bprop_rand(self):
-        for i in range(10):
-            xorig = numpy.random.rand(3, 2)
-            for mtype in _mtypes:
-                x = tensor.matrix('x')
-                w = SparseType(dtype='float64',
-                               format=_mtype_to_str[mtype]).make_variable()
-                xw = dense_from_sparse(true_dot(w, x))
-                y = dense_from_sparse(true_dot(w.T, xw))
-                diff = x - y
-                loss = tensor.sum(tensor.sqr(diff))
-                gw = tensor.grad(loss, w)
-                trainfn = compile.function([x, w], [y, loss, gw])
-
-                x = xorig
-                w = mtype((500, 3))
-                w[(10, 1)] = 1
-                w[(20, 2)] = 2
-                lr = 0.001
-                y, origloss, gw = trainfn(x, w)
-                for epoch in xrange(50):
-                    y, loss, gw = trainfn(x, w)
-                    w = w - (lr * gw)
-
-                self.assertTrue(origloss > loss)