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Manhattan Distance

Functional Interface

torchmetrics.functional.pairwise_manhattan_distance(x, y=None, reduction=None, zero_diagonal=None)[source]

Calculates pairwise manhattan distance:

d_{man}(x,y) = ||x-y||_1 = \sum_{d=1}^D |x_d - y_d|

If both x and y are passed in, the calculation will be performed pairwise between the rows of x and y. If only x is passed in, the calculation will be performed between the rows of x.

Parameters
  • x (Tensor) – Tensor with shape [N, d]

  • y (Optional[Tensor]) – Tensor with shape [M, d], optional

  • reduction (Optional[Literal[‘mean’, ‘sum’, ‘none’, None]]) – reduction to apply along the last dimension. Choose between ‘mean’, ‘sum’ (applied along column dimension) or ‘none’, None for no reduction

  • zero_diagonal (Optional[bool]) – 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

Return type

Tensor

Returns

A [N,N] matrix of distances if only x is given, else a [N,M] matrix

Example

>>> import torch
>>> from torchmetrics.functional 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.]])