def testCosOrderExecution(self):
data = np.asfortranarray(np.random.rand(3, 5))
x = tensor(data, chunk_size=2)
t = cos(x)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_allclose(res, np.cos(data))
self.assertFalse(res.flags['C_CONTIGUOUS'])
self.assertTrue(res.flags['F_CONTIGUOUS'])
t2 = cos(x, order='C')
res2 = self.executor.execute_tensor(t2, concat=True)[0]
np.testing.assert_allclose(res2, np.cos(data, order='C'))
self.assertTrue(res2.flags['C_CONTIGUOUS'])
self.assertFalse(res2.flags['F_CONTIGUOUS'])
def test_cos_order_execution(setup):
data = np.asfortranarray(np.random.rand(3, 5))
x = tensor(data, chunk_size=2)
t = cos(x)
res = t.execute().fetch()
np.testing.assert_allclose(res, np.cos(data))
assert res.flags['C_CONTIGUOUS'] is False
assert res.flags['F_CONTIGUOUS'] is True
t2 = cos(x, order='C')
res2 = t2.execute().fetch()
np.testing.assert_allclose(res2, np.cos(data, order='C'))
assert res2.flags['C_CONTIGUOUS'] is True
assert res2.flags['F_CONTIGUOUS'] is False
def testCupyExecution(self):
a_data = np.random.rand(10, 10)
b_data = np.random.rand(10, 10)
a = tensor(a_data, gpu=True, chunk_size=3)
b = tensor(b_data, gpu=True, chunk_size=3)
res_binary = self.executor.execute_tensor((a + b), concat=True)[0]
np.testing.assert_array_equal(res_binary.get(), (a_data + b_data))
res_unary = self.executor.execute_tensor(cos(a), concat=True)[0]
np.testing.assert_array_almost_equal(res_unary.get(), np.cos(a_data))
def test_cupy_execution(setup):
a_data = np.random.rand(10, 10)
b_data = np.random.rand(10, 10)
a = tensor(a_data, gpu=True, chunk_size=3)
b = tensor(b_data, gpu=True, chunk_size=3)
res_binary = (a + b).execute().fetch()
np.testing.assert_array_equal(res_binary.get(), (a_data + b_data))
res_unary = cos(a).execute().fetch()
np.testing.assert_array_almost_equal(res_unary.get(), np.cos(a_data))
def testCos(self):
t1 = tensor([[0, 1, 0], [1, 0, 0]], chunk_size=2).tosparse()
t = cos(t1)
self.assertTrue(t.issparse())
self.assertIs(type(t), SparseTensor)
def test_cos():
t1 = tensor([[0, 1, 0], [1, 0, 0]], chunk_size=2).tosparse()
t = cos(t1)
assert t.issparse() is True
assert type(t) is SparseTensor
