def test_permutation_execute(setup):
rs = tensor.random.RandomState(0)
x = rs.permutation(10)
res = x.execute().fetch()
assert not np.all(res[:-1] < res[1:])
np.testing.assert_array_equal(np.sort(res), np.arange(10))
arr = from_ndarray([1, 4, 9, 12, 15], chunk_size=2)
x = rs.permutation(arr)
res = x.execute().fetch()
assert not np.all(res[:-1] < res[1:])
np.testing.assert_array_equal(np.sort(res), np.asarray([1, 4, 9, 12, 15]))
arr = from_ndarray(np.arange(48).reshape(12, 4), chunk_size=2)
# axis = 0
x = rs.permutation(arr)
res = x.execute().fetch()
assert not np.all(res[:-1] < res[1:])
np.testing.assert_array_equal(np.sort(res, axis=0),
np.arange(48).reshape(12, 4))
# axis != 0
x2 = rs.permutation(arr, axis=1)
res = x2.execute().fetch()
assert not np.all(res[:, :-1] < res[:, 1:])
np.testing.assert_array_equal(np.sort(res, axis=1),
np.arange(48).reshape(12, 4))
def testPermutationExecute(self):
x = tensor.random.permutation(10)
res = self.executor.execute_tensor(x, concat=True)[0]
self.assertFalse(np.all(res[:-1] < res[1:]))
np.testing.assert_array_equal(np.sort(res), np.arange(10))
arr = from_ndarray([1, 4, 9, 12, 15], chunk_size=2)
x = tensor.random.permutation(arr)
res = self.executor.execute_tensor(x, concat=True)[0]
self.assertFalse(np.all(res[:-1] < res[1:]))
np.testing.assert_array_equal(np.sort(res),
np.asarray([1, 4, 9, 12, 15]))
arr = from_ndarray(np.arange(48).reshape(12, 4), chunk_size=2)
# axis = 0
x = tensor.random.permutation(arr)
res = self.executor.execute_tensor(x, concat=True)[0]
self.assertFalse(np.all(res[:-1] < res[1:]))
np.testing.assert_array_equal(np.sort(res, axis=0),
np.arange(48).reshape(12, 4))
# axis != 0
x2 = tensor.random.permutation(arr, axis=1)
res = self.executor.execute_tensor(x2, concat=True)[0]
self.assertFalse(np.all(res[:, :-1] < res[:, 1:]))
np.testing.assert_array_equal(np.sort(res, axis=1),
np.arange(48).reshape(12, 4))
def testRandom(self):
arr = rand(2, 3)
self.assertIsNotNone(arr.dtype)
arr = beta(1, 2, chunk_size=2).tiles()
self.assertEqual(arr.shape, ())
self.assertEqual(len(arr.chunks), 1)
self.assertEqual(arr.chunks[0].shape, ())
self.assertEqual(arr.chunks[0].op.dtype, np.dtype('f8'))
arr = beta([1, 2], [3, 4], chunk_size=2).tiles()
self.assertEqual(arr.shape, (2, ))
self.assertEqual(len(arr.chunks), 1)
self.assertEqual(arr.chunks[0].shape, (2, ))
self.assertEqual(arr.chunks[0].op.dtype, np.dtype('f8'))
arr = beta([[2, 3]],
from_ndarray([[4, 6], [5, 2]], chunk_size=2),
chunk_size=1,
size=(3, 2, 2)).tiles()
self.assertEqual(arr.shape, (3, 2, 2))
self.assertEqual(len(arr.chunks), 12)
self.assertEqual(arr.chunks[0].op.dtype, np.dtype('f8'))
def testBetaExecute(self):
arr = tensor.random.beta(1, 2, chunk_size=2).tiles()
arr.chunks[0].op._seed = 0
self.assertEqual(self.executor.execute_tensor(arr)[0], np.random.RandomState(0).beta(1, 2))
arr = tensor.random.beta([1, 2], [3, 4], chunk_size=2).tiles()
arr.chunks[0].op._seed = 0
self.assertTrue(np.array_equal(self.executor.execute_tensor(arr)[0],
np.random.RandomState(0).beta([1, 2], [3, 4])))
arr = tensor.random.beta([[2, 3]], from_ndarray([[4, 6], [5, 2]], chunk_size=2),
chunk_size=1, size=(3, 2, 2)).tiles()
for c in arr.chunks:
c.op._seed = 0
res = self.executor.execute_tensor(arr, concat=True)[0]
self.assertEqual(res[0, 0, 0], np.random.RandomState(0).beta(2, 4))
self.assertEqual(res[0, 0, 1], np.random.RandomState(0).beta(3, 6))
self.assertEqual(res[0, 1, 0], np.random.RandomState(0).beta(2, 5))
self.assertEqual(res[0, 1, 1], np.random.RandomState(0).beta(3, 2))
arr = tensor.random.RandomState(0).beta([[3, 4]], [[1], [2]], chunk_size=1)
tensor.random.seed(0)
arr2 = tensor.random.beta([[3, 4]], [[1], [2]], chunk_size=1)
self.assertTrue(np.array_equal(self.executor.execute_tensor(arr, concat=True)[0],
self.executor.execute_tensor(arr2, concat=True)[0]))
def test_random():
arr = rand(2, 3)
assert arr.dtype is not None
arr = tile(beta(1, 2, chunk_size=2))
assert arr.shape == ()
assert len(arr.chunks) == 1
assert arr.chunks[0].shape == ()
assert arr.chunks[0].op.dtype == np.dtype('f8')
arr = tile(beta([1, 2], [3, 4], chunk_size=2))
assert arr.shape == (2, )
assert len(arr.chunks) == 1
assert arr.chunks[0].shape == (2, )
assert arr.chunks[0].op.dtype == np.dtype('f8')
arr = tile(
beta([[2, 3]],
from_ndarray([[4, 6], [5, 2]], chunk_size=2),
chunk_size=1,
size=(3, 2, 2)))
assert arr.shape == (3, 2, 2)
assert len(arr.chunks) == 12
assert arr.chunks[0].op.dtype == np.dtype('f8')
def testRandomSerialize(self):
arr = RandomState(0).beta([[2, 3]], from_ndarray([[4, 6], [5, 2]], chunk_size=2),
chunk_size=1, size=(3, 2, 2)).tiles()
chunk = arr.chunks[0]
self.assertEqual(chunk.op.dtype, np.dtype('f8'))
serials = self._pb_serial(chunk)
chunk2 = self._pb_deserial(serials)[chunk.data]
self.assertEqual(chunk.index, chunk2.index)
self.assertEqual(chunk.op.state, chunk2.op.state)
self.assertEqual(chunk.op.seed, chunk2.op.seed)
def testPermutation(self):
x = permutation(10)
self.assertEqual(x.shape, (10, ))
self.assertIsInstance(x.op, TensorPermutation)
x = x.tiles()
self.assertEqual(len(x.chunks), 1)
self.assertIsInstance(x.chunks[0].op, TensorPermutation)
arr = from_ndarray([1, 4, 9, 12, 15], chunk_size=2)
x = permutation(arr)
self.assertEqual(x.shape, (5, ))
self.assertIsInstance(x.op, TensorPermutation)
x = x.tiles()
arr = get_tiled(arr)
self.assertEqual(len(x.chunks), 3)
self.assertTrue(np.isnan(x.chunks[0].shape[0]))
self.assertIs(x.chunks[0].inputs[0].inputs[0].inputs[0],
arr.chunks[0].data)
arr = rand(3, 3, chunk_size=2)
x = permutation(arr)
self.assertEqual(x.shape, (3, 3))
self.assertIsInstance(x.op, TensorPermutation)
x = x.tiles()
arr = get_tiled(arr)
self.assertEqual(len(x.chunks), 4)
self.assertTrue(np.isnan(x.chunks[0].shape[0]))
self.assertEqual(x.chunks[0].shape[1], 2)
self.assertIs(x.cix[0, 0].inputs[0].inputs[0].inputs[0],
arr.cix[0, 0].data)
self.assertIs(x.cix[0, 0].inputs[0].inputs[1].inputs[0],
arr.cix[1, 0].data)
self.assertEqual(x.cix[0, 0].op.seed, x.cix[0, 1].op.seed)
self.assertEqual(x.cix[0, 0].inputs[0].inputs[0].inputs[0].op.seed,
x.cix[1, 0].inputs[0].inputs[0].inputs[0].op.seed)
with self.assertRaises(np.AxisError):
self.assertRaises(permutation('abc'))
def test_permutation():
x = permutation(10)
assert x.shape == (10, )
assert isinstance(x.op, TensorPermutation)
x = tile(x)
assert len(x.chunks) == 1
assert isinstance(x.chunks[0].op, TensorPermutation)
arr = from_ndarray([1, 4, 9, 12, 15], chunk_size=2)
x = permutation(arr)
assert x.shape == (5, )
assert isinstance(x.op, TensorPermutation)
x = tile(x)
arr = tile(arr)
assert len(x.chunks) == 3
assert np.isnan(x.chunks[0].shape[0])
assert x.chunks[0].inputs[0].inputs[0].inputs[0].key == arr.chunks[
0].data.key
arr = rand(3, 3, chunk_size=2)
x = permutation(arr)
assert x.shape == (3, 3)
assert isinstance(x.op, TensorPermutation)
x = tile(x)
arr = tile(arr)
assert len(x.chunks) == 4
assert np.isnan(x.chunks[0].shape[0])
assert x.chunks[0].shape[1] == 2
assert x.cix[0, 0].op.seed == x.cix[0, 1].op.seed
assert x.cix[0, 0].inputs[0].inputs[0].inputs[0].op.seed == \
x.cix[1, 0].inputs[0].inputs[0].inputs[0].op.seed
with pytest.raises(np.AxisError):
pytest.raises(permutation('abc'))
