def test_tree_arithmetic():
raws = [np.random.rand(10, 10) for _ in range(10)]
tensors = [tensor(a, chunk_size=3) for a in raws]
t = tree_add(*tensors, combine_size=4)
assert isinstance(t.op, TensorTreeAdd)
assert t.issparse() is False
assert len(t.inputs) == 3
assert len(t.inputs[0].inputs) == 4
assert len(t.inputs[-1].inputs) == 2
t = tree_multiply(*tensors, combine_size=4)
assert isinstance(t.op, TensorTreeMultiply)
assert t.issparse() is False
assert len(t.inputs) == 3
assert len(t.inputs[0].inputs) == 4
assert len(t.inputs[-1].inputs) == 2
raws = [sps.random(5, 9, density=.1) for _ in range(10)]
tensors = [tensor(a, chunk_size=3) for a in raws]
t = tree_add(*tensors, combine_size=4)
assert isinstance(t.op, TensorTreeAdd)
assert t.issparse() is True
assert len(t.inputs) == 3
assert len(t.inputs[0].inputs) == 4
assert len(t.inputs[-1].inputs) == 2
t = tree_multiply(*tensors, combine_size=4)
assert isinstance(t.op, TensorTreeMultiply)
assert t.issparse() is True
assert len(t.inputs) == 3
assert len(t.inputs[0].inputs) == 4
assert len(t.inputs[-1].inputs) == 2
def testTreeArithmetic(self):
raws = [np.random.rand(10, 10) for _ in range(10)]
tensors = [tensor(a, chunk_size=3) for a in raws]
t = tree_add(*tensors, combine_size=4)
self.assertIsInstance(t.op, TensorTreeAdd)
self.assertFalse(t.issparse())
self.assertEqual(len(t.inputs), 3)
self.assertEqual(len(t.inputs[0].inputs), 4)
self.assertEqual(len(t.inputs[-1].inputs), 2)
t = tree_multiply(*tensors, combine_size=4)
self.assertIsInstance(t.op, TensorTreeMultiply)
self.assertFalse(t.issparse())
self.assertEqual(len(t.inputs), 3)
self.assertEqual(len(t.inputs[0].inputs), 4)
self.assertEqual(len(t.inputs[-1].inputs), 2)
raws = [sps.random(5, 9, density=.1) for _ in range(10)]
tensors = [tensor(a, chunk_size=3) for a in raws]
t = tree_add(*tensors, combine_size=4)
self.assertIsInstance(t.op, TensorTreeAdd)
self.assertTrue(t.issparse())
self.assertEqual(len(t.inputs), 3)
self.assertEqual(len(t.inputs[0].inputs), 4)
self.assertEqual(len(t.inputs[-1].inputs), 2)
t = tree_multiply(*tensors, combine_size=4)
self.assertIsInstance(t.op, TensorTreeMultiply)
self.assertTrue(t.issparse())
self.assertEqual(len(t.inputs), 3)
self.assertEqual(len(t.inputs[0].inputs), 4)
self.assertEqual(len(t.inputs[-1].inputs), 2)
def test_tree_arithmetic_execution(setup):
raws = [np.random.rand(10, 10) for _ in range(10)]
tensors = [tensor(a, chunk_size=3) for a in raws]
res = tree_add(*tensors, 1.0).execute().fetch()
np.testing.assert_array_almost_equal(
res, 1.0 + functools.reduce(operator.add, raws))
res = tree_multiply(*tensors, 2.0).execute().fetch()
np.testing.assert_array_almost_equal(
res, 2.0 * functools.reduce(operator.mul, raws))
raws = [sps.random(5, 9, density=.1) for _ in range(10)]
tensors = [tensor(a, chunk_size=3) for a in raws]
res = tree_add(*tensors).execute().fetch()
np.testing.assert_array_almost_equal(
res.toarray(),
functools.reduce(operator.add, raws).toarray())
def testTreeArithmeticExecution(self):
raws = [np.random.rand(10, 10) for _ in range(10)]
tensors = [tensor(a, chunk_size=3) for a in raws]
res = self.executor.execute_tensor(tree_add(*tensors, 1.0),
concat=True)[0]
np.testing.assert_array_almost_equal(
res, 1.0 + functools.reduce(operator.add, raws))
res = self.executor.execute_tensor(tree_multiply(*tensors, 2.0),
concat=True)[0]
np.testing.assert_array_almost_equal(
res, 2.0 * functools.reduce(operator.mul, raws))
raws = [sps.random(5, 9, density=.1) for _ in range(10)]
tensors = [tensor(a, chunk_size=3) for a in raws]
res = self.executor.execute_tensor(tree_add(*tensors), concat=True)[0]
np.testing.assert_array_almost_equal(
res.toarray(),
functools.reduce(operator.add, raws).toarray())