def test_abs(self):
a = ht.array([1.0, 1.0j, 1 + 1j, -2 + 2j, 3 - 3j])
absolute = ht.absolute(a)
res = torch.abs(a.larray)
self.assertIs(absolute.device, self.device)
self.assertIs(absolute.dtype, ht.float)
self.assertEqual(absolute.shape, (5,))
self.assertTrue(torch.equal(absolute.larray, res))
a = ht.array([1.0, 1.0j, 1 + 1j, -2 + 2j, 3 - 3j], split=0)
absolute = ht.absolute(a)
res = torch.abs(a.larray)
self.assertIs(absolute.device, self.device)
self.assertIs(absolute.dtype, ht.float)
self.assertEqual(absolute.shape, (5,))
self.assertTrue(torch.equal(absolute.larray, res))
a = ht.array(
[[1.0, 1.0j], [1 + 1j, -2 + 2j], [3 - 3j, -4 - 4j]], split=1, dtype=ht.complex128
)
absolute = ht.absolute(a)
res = torch.abs(a.larray)
self.assertIs(absolute.device, self.device)
self.assertIs(absolute.dtype, ht.double)
self.assertEqual(absolute.shape, (3, 2))
self.assertTrue(torch.equal(absolute.larray, res))
def test_abs(self):
float32_tensor = ht.arange(-10, 10, dtype=ht.float32, split=0)
absolute_values = ht.abs(float32_tensor)
# basic absolute test
self.assertIsInstance(absolute_values, ht.tensor)
self.assertEqual(absolute_values.dtype, ht.float32)
self.assertEqual(absolute_values.sum(axis=0), 100)
# check whether output works
output_tensor = ht.zeros(20, split=0)
self.assertEqual(output_tensor.sum(axis=0), 0)
ht.absolute(float32_tensor, out=output_tensor)
self.assertEqual(output_tensor.sum(axis=0), 100)
# dtype parameter
int64_tensor = ht.arange(-10, 10, dtype=ht.int64)
absolute_values = ht.abs(int64_tensor, dtype=ht.float32)
self.assertIsInstance(absolute_values, ht.tensor)
self.assertEqual(absolute_values.sum(axis=0), 100)
self.assertEqual(absolute_values.dtype, ht.float32)
self.assertEqual(absolute_values._tensor__array.dtype, torch.float32)
# exceptions
with self.assertRaises(TypeError):
ht.absolute('hello')
with self.assertRaises(TypeError):
float32_tensor.abs(out=1)
with self.assertRaises(TypeError):
float32_tensor.absolute(out=float32_tensor, dtype=3.2)
def test_abs(self):
# for abs==absolute
float32_tensor = ht.arange(-10, 10, dtype=ht.float32, split=0)
absolute_values = ht.abs(float32_tensor)
# for fabs
int8_tensor_fabs = ht.arange(-10.5, 10.5, dtype=ht.int8, split=0)
int8_absolute_values_fabs = ht.fabs(int8_tensor_fabs)
int16_tensor_fabs = ht.arange(-10.5, 10.5, dtype=ht.int16, split=0)
int16_absolute_values_fabs = ht.fabs(int16_tensor_fabs)
int32_tensor_fabs = ht.arange(-10.5, 10.5, dtype=ht.int32, split=0)
int32_absolute_values_fabs = ht.fabs(int32_tensor_fabs)
int64_tensor_fabs = ht.arange(-10.5, 10.5, dtype=ht.int64, split=0)
int64_absolute_values_fabs = ht.fabs(int64_tensor_fabs)
float32_tensor_fabs = ht.arange(-10.5, 10.5, dtype=ht.float32, split=0)
float32_absolute_values_fabs = ht.fabs(float32_tensor_fabs)
float64_tensor_fabs = ht.arange(-10.5, 10.5, dtype=ht.float64, split=0)
float64_absolute_values_fabs = ht.fabs(float64_tensor_fabs)
# basic absolute test
self.assertIsInstance(absolute_values, ht.DNDarray)
self.assertEqual(absolute_values.dtype, ht.float32)
self.assertEqual(absolute_values.sum(axis=0), 100)
# for fabs
self.assertEqual(int8_absolute_values_fabs.sum(axis=0), 100.0)
self.assertEqual(int16_absolute_values_fabs.sum(axis=0), 100.0)
self.assertEqual(int32_absolute_values_fabs.sum(axis=0), 100.0)
self.assertEqual(int64_absolute_values_fabs.sum(axis=0), 100.0)
self.assertEqual(float32_absolute_values_fabs.sum(axis=0), 110.5)
self.assertEqual(float64_absolute_values_fabs.sum(axis=0), 110.5)
# check whether output works
# for abs==absolute
output_tensor = ht.zeros(20, split=0)
self.assertEqual(output_tensor.sum(axis=0, keepdim=True), 0)
ht.absolute(float32_tensor, out=output_tensor)
self.assertEqual(output_tensor.sum(axis=0), 100)
# for fabs
output_tensor_fabs = ht.zeros(21, split=0)
self.assertEqual(output_tensor_fabs.sum(axis=0), 0)
ht.fabs(float32_tensor_fabs, out=output_tensor_fabs)
self.assertEqual(output_tensor_fabs.sum(axis=0), 110.5)
# dtype parameter
# for abs==absolute
int64_tensor = ht.arange(-10, 10, dtype=ht.int64)
absolute_values = ht.abs(int64_tensor, dtype=ht.float32)
self.assertIsInstance(absolute_values, ht.DNDarray)
self.assertEqual(absolute_values.sum(axis=0), 100)
self.assertEqual(absolute_values.dtype, ht.float32)
self.assertEqual(absolute_values._DNDarray__array.dtype, torch.float32)
# for fabs
self.assertEqual(int8_absolute_values_fabs.dtype, ht.float32)
self.assertEqual(int16_absolute_values_fabs.dtype, ht.float32)
self.assertEqual(int32_absolute_values_fabs.dtype, ht.float32)
self.assertEqual(int64_absolute_values_fabs.dtype, ht.float64)
self.assertEqual(float32_absolute_values_fabs.dtype, ht.float32)
self.assertEqual(float64_absolute_values_fabs.dtype, ht.float64)
# exceptions
# for abs==absolute
with self.assertRaises(TypeError):
ht.absolute("hello")
with self.assertRaises(TypeError):
float32_tensor.abs(out=1)
with self.assertRaises(TypeError):
float32_tensor.absolute(out=float32_tensor, dtype=3.2)
# for fabs
with self.assertRaises(TypeError):
ht.fabs("hello")
with self.assertRaises(TypeError):
float32_tensor_fabs.fabs(out=1)
# test with unsplit tensor
# for fabs
float32_unsplit_tensor_fabs = ht.arange(-10.5, 10.5, dtype=ht.float32)
float32_unsplit_absolute_values_fabs = ht.fabs(
float32_unsplit_tensor_fabs)
self.assertEqual(float32_unsplit_absolute_values_fabs.sum(), 110.5)
self.assertEqual(float32_unsplit_absolute_values_fabs.dtype,
ht.float32)