File size: 3,196 Bytes
9c6594c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.pairwise.helpers import _check_input, _reduce_distance_matrix
def _pairwise_manhattan_distance_update(
x: Tensor, y: Optional[Tensor] = None, zero_diagonal: Optional[bool] = None
) -> Tensor:
"""Calculate the pairwise manhattan similarity matrix.
Args:
x: tensor of shape ``[N,d]``
y: if provided, a tensor of shape ``[M,d]``
zero_diagonal: determines if the diagonal of the distance matrix should be set to zero
"""
x, y, zero_diagonal = _check_input(x, y, zero_diagonal)
distance = (x.unsqueeze(1) - y.unsqueeze(0).repeat(x.shape[0], 1, 1)).abs().sum(dim=-1)
if zero_diagonal:
distance.fill_diagonal_(0)
return distance
def pairwise_manhattan_distance(
x: Tensor,
y: Optional[Tensor] = None,
reduction: Literal["mean", "sum", "none", None] = None,
zero_diagonal: Optional[bool] = None,
) -> Tensor:
r"""Calculate pairwise manhattan distance.
.. math::
d_{man}(x,y) = ||x-y||_1 = \sum_{d=1}^D |x_d - y_d|
If both :math:`x` and :math:`y` are passed in, the calculation will be performed pairwise between
the rows of :math:`x` and :math:`y`.
If only :math:`x` is passed in, the calculation will be performed between the rows of :math:`x`.
Args:
x: Tensor with shape ``[N, d]``
y: Tensor with shape ``[M, d]``, optional
reduction: reduction to apply along the last dimension. Choose between `'mean'`, `'sum'`
(applied along column dimension) or `'none'`, `None` for no reduction
zero_diagonal: if the diagonal of the distance matrix should be set to 0. If only `x` is given
this defaults to `True` else if `y` is also given it defaults to `False`
Returns:
A ``[N,N]`` matrix of distances if only ``x`` is given, else a ``[N,M]`` matrix
Example:
>>> import torch
>>> from torchmetrics.functional.pairwise import pairwise_manhattan_distance
>>> x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32)
>>> y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32)
>>> pairwise_manhattan_distance(x, y)
tensor([[ 4., 2.],
[ 7., 5.],
[12., 10.]])
>>> pairwise_manhattan_distance(x)
tensor([[0., 3., 8.],
[3., 0., 5.],
[8., 5., 0.]])
"""
distance = _pairwise_manhattan_distance_update(x, y, zero_diagonal)
return _reduce_distance_matrix(distance, reduction)
|