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
