def test_negative_order(): raw1 = np.random.rand(4, 2) raw2 = np.asfortranarray(np.random.rand(4, 2)) t1 = tensor(raw1) t2 = tensor(raw2) t3 = tensor(raw1) t4 = tensor(raw2) # C assert negative(t1).flags['C_CONTIGUOUS'] == np.negative( raw1).flags['C_CONTIGUOUS'] assert negative(t1).flags['F_CONTIGUOUS'] == np.negative( raw1).flags['F_CONTIGUOUS'] # F assert negative(t2).flags['C_CONTIGUOUS'] == np.negative( raw2).flags['C_CONTIGUOUS'] assert negative(t2).flags['F_CONTIGUOUS'] == np.negative( raw2).flags['F_CONTIGUOUS'] # C + out assert negative(t1, out=t4).flags['C_CONTIGUOUS'] == np.negative( raw1, out=np.empty((4, 2), order='F')).flags['C_CONTIGUOUS'] assert negative(t1, out=t4).flags['F_CONTIGUOUS'] == np.negative( raw1, out=np.empty((4, 2), order='F')).flags['F_CONTIGUOUS'] # F + out assert negative(t2, out=t3).flags['C_CONTIGUOUS'] == np.negative( raw1, out=np.empty((4, 2), order='C')).flags['C_CONTIGUOUS'] assert negative(t2, out=t3).flags['F_CONTIGUOUS'] == np.negative( raw1, out=np.empty((4, 2), order='C')).flags['F_CONTIGUOUS'] with pytest.raises(TypeError): negative(t1, order='B')
def testNegativeOrder(self): raw1 = np.random.rand(4, 2) raw2 = np.asfortranarray(np.random.rand(4, 2)) t1 = tensor(raw1) t2 = tensor(raw2) t3 = tensor(raw1) t4 = tensor(raw2) # C self.assertEqual(negative(t1).flags['C_CONTIGUOUS'], np.negative(raw1).flags['C_CONTIGUOUS']) self.assertEqual(negative(t1).flags['F_CONTIGUOUS'], np.negative(raw1).flags['F_CONTIGUOUS']) # F self.assertEqual(negative(t2).flags['C_CONTIGUOUS'], np.negative(raw2).flags['C_CONTIGUOUS']) self.assertEqual(negative(t2).flags['F_CONTIGUOUS'], np.negative(raw2).flags['F_CONTIGUOUS']) # C + out self.assertEqual(negative(t1, out=t4).flags['C_CONTIGUOUS'], np.negative(raw1, out=np.empty((4, 2), order='F')).flags['C_CONTIGUOUS']) self.assertEqual(negative(t1, out=t4).flags['F_CONTIGUOUS'], np.negative(raw1, out=np.empty((4, 2), order='F')).flags['F_CONTIGUOUS']) # F + out self.assertEqual(negative(t2, out=t3).flags['C_CONTIGUOUS'], np.negative(raw1, out=np.empty((4, 2), order='C')).flags['C_CONTIGUOUS']) self.assertEqual(negative(t2, out=t3).flags['F_CONTIGUOUS'], np.negative(raw1, out=np.empty((4, 2), order='C')).flags['F_CONTIGUOUS']) with self.assertRaises(TypeError): negative(t1, order='B')
def testNegative(self): t1 = tensor([[0, 1, 0], [1, 0, 0]], chunk_size=2).tosparse() t = negative(t1) self.assertTrue(t.issparse()) self.assertIs(type(t), SparseTensor) t.tiles() self.assertTrue(t.chunks[0].op.sparse)
def test_negative(): t1 = tensor([[0, 1, 0], [1, 0, 0]], chunk_size=2).tosparse() t = negative(t1) assert t.op.gpu is False assert t.issparse() is True assert type(t) is SparseTensor t = tile(t) assert t.chunks[0].op.sparse is True