def testClipOrderExecution(self):
a_data = np.asfortranarray(np.random.rand(4, 8))
a = tensor(a_data, chunk_size=3)
b = clip(a, 0.2, 0.8)
res = self.executor.execute_tensor(b, concat=True)[0]
expected = np.clip(a_data, 0.2, 0.8)
np.testing.assert_allclose(res, expected)
self.assertTrue(res.flags['F_CONTIGUOUS'])
self.assertFalse(res.flags['C_CONTIGUOUS'])
def test_clip_order_execution(setup):
a_data = np.asfortranarray(np.random.rand(4, 8))
a = tensor(a_data, chunk_size=3)
b = clip(a, 0.2, 0.8)
res = b.execute().fetch()
expected = np.clip(a_data, 0.2, 0.8)
np.testing.assert_allclose(res, expected)
assert res.flags['F_CONTIGUOUS'] is True
assert res.flags['C_CONTIGUOUS'] is False
def testClipExecution(self):
a_data = np.arange(10)
a = tensor(a_data.copy(), chunk_size=3)
b = clip(a, 1, 8)
res = self.executor.execute_tensor(b, concat=True)[0]
expected = np.clip(a_data, 1, 8)
self.assertTrue(np.array_equal(res, expected))
a = tensor(a_data.copy(), chunk_size=3)
clip(a, 3, 6, out=a)
res = self.executor.execute_tensor(a, concat=True)[0]
expected = np.clip(a_data, 3, 6)
self.assertTrue(np.array_equal(res, expected))
a = tensor(a_data.copy(), chunk_size=3)
a_min_data = np.random.randint(1, 10, size=(10, ))
a_max_data = np.random.randint(1, 10, size=(10, ))
a_min = tensor(a_min_data)
a_max = tensor(a_max_data)
clip(a, a_min, a_max, out=a)
res = self.executor.execute_tensor(a, concat=True)[0]
expected = np.clip(a_data, a_min_data, a_max_data)
self.assertTrue(np.array_equal(res, expected))
with option_context() as options:
options.chunk_size = 3
a = tensor(a_data.copy(), chunk_size=3)
b = clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
res = self.executor.execute_tensor(b, concat=True)[0]
expected = np.clip(a_data, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
self.assertTrue(np.array_equal(res, expected))
# test sparse clip
a_data = sps.csr_matrix([[0, 2, 8], [0, 0, -1]])
a = tensor(a_data, chunk_size=3)
b_data = sps.csr_matrix([[0, 3, 0], [1, 0, -2]])
c = clip(a, b_data, 4)
res = self.executor.execute_tensor(c, concat=True)[0]
expected = np.clip(a_data.toarray(), b_data.toarray(), 4)
self.assertTrue(np.array_equal(res.toarray(), expected))
def test_clip_execution(setup):
a_data = np.arange(10)
a = tensor(a_data.copy(), chunk_size=3)
b = clip(a, 1, 8)
res = b.execute().fetch()
expected = np.clip(a_data, 1, 8)
np.testing.assert_array_equal(res, expected)
a = tensor(a_data.copy(), chunk_size=3)
clip(a, 3, 6, out=a)
res = a.execute().fetch()
expected = np.clip(a_data, 3, 6)
np.testing.assert_array_equal(res, expected)
a = tensor(a_data.copy(), chunk_size=3)
a_min_data = np.random.randint(1, 10, size=(10, ))
a_max_data = np.random.randint(1, 10, size=(10, ))
a_min = tensor(a_min_data)
a_max = tensor(a_max_data)
clip(a, a_min, a_max, out=a)
res = a.execute().fetch()
expected = np.clip(a_data, a_min_data, a_max_data)
np.testing.assert_array_equal(res, expected)
with option_context() as options:
options.chunk_size = 3
a = tensor(a_data.copy(), chunk_size=3)
b = clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
res = b.execute().fetch()
expected = np.clip(a_data, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
np.testing.assert_array_equal(res, expected)
# test sparse clip
a_data = sps.csr_matrix([[0, 2, 8], [0, 0, -1]])
a = tensor(a_data, chunk_size=3)
b_data = sps.csr_matrix([[0, 3, 0], [1, 0, -2]])
c = clip(a, b_data, 4)
res = c.execute().fetch()
expected = np.clip(a_data.toarray(), b_data.toarray(), 4)
np.testing.assert_array_equal(res, expected)
