def concatenate(values, axis=0):
"""Concatenate a sequence of tensors along the specified axis.
.. warning::
Tensors that are incompatible (such as Torch and TensorFlow tensors)
cannot both be present.
Args:
values (Sequence[tensor_like]): Sequence of tensor-like objects to
concatenate. The objects must have the same shape, except in the dimension corresponding
to axis (the first, by default).
axis (int): The axis along which the input tensors are concatenated. If axis is None,
tensors are flattened before use. Default is 0.
Returns:
tensor_like: The concatenated tensor.
**Example**
>>> x = tf.constant([0.6, 0.1, 0.6])
>>> y = tf.Variable([0.1, 0.2, 0.3])
>>> z = np.array([5., 8., 101.])
>>> concatenate([x, y, z])
<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
array([6.00e-01, 1.00e-01, 6.00e-01, 1.00e-01, 2.00e-01, 3.00e-01, 5.00e+00, 8.00e+00, 1.01e+02], dtype=float32)>
"""
interface = _multi_dispatch(values)
if interface == "torch":
import torch
if axis is None:
# flatten and then concatenate zero'th dimension
# to reproduce numpy's behaviour
values = [np.flatten(torch.as_tensor(t)) for t in values]
axis = 0
else:
values = [torch.as_tensor(t) for t in values]
if interface == "tensorflow" and axis is None:
# flatten and then concatenate zero'th dimension
# to reproduce numpy's behaviour
values = [np.flatten(np.array(t)) for t in values]
axis = 0
return np.concatenate(values, axis=axis, like=interface)
def order_matrix(original, sortd, sigma=0.1):
"""Apply a simple RBF kernel to the difference between original and sortd,
with the kernel width set by sigma. Normalise each row to sum to 1.0."""
diff = (np.array(original).reshape(-1, 1) - sortd)**2
rbf = np.exp(-(diff) / (2 * sigma**2))
return (rbf.T / np.sum(rbf, axis=1)).T
import numpy as onp
import pytest
from autoray import numpy as anp
from pennylane import numpy as np
from pennylane import math as fn
tf = pytest.importorskip("tensorflow", minversion="2.1")
torch = pytest.importorskip("torch")
jax = pytest.importorskip("jax")
test_multi_dispatch_stack_data = [
[[1.0, 0.0], [2.0, 3.0]],
([1.0, 0.0], [2.0, 3.0]),
onp.array([[1.0, 0.0], [2.0, 3.0]]),
anp.array([[1.0, 0.0], [2.0, 3.0]]),
np.array([[1.0, 0.0], [2.0, 3.0]]),
jax.numpy.array([[1.0, 0.0], [2.0, 3.0]]),
tf.constant([[1.0, 0.0], [2.0, 3.0]]),
]
@pytest.mark.parametrize("x", test_multi_dispatch_stack_data)
def test_multi_dispatch_stack(x):
"""Test that the decorated autoray function stack can handle all inputs"""
stack = fn.multi_dispatch(argnum=0, tensor_list=0)(autoray.numpy.stack)
res = stack(x)
assert fn.allequal(res, [[1.0, 0.0], [2.0, 3.0]])
@pytest.mark.parametrize("x", test_multi_dispatch_stack_data)