Shortcuts

Linear Similarity

Functional Interface

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

Calculates pairwise linear similarity:

s_{lin}(x,y) = <x,y> = \sum_{d=1}^D x_d \cdot 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_linear_similarity
>>> x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32)
>>> y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32)
>>> pairwise_linear_similarity(x, y)
tensor([[ 2.,  7.],
        [ 3., 11.],
        [ 5., 18.]])
>>> pairwise_linear_similarity(x)
tensor([[ 0., 21., 34.],
        [21.,  0., 55.],
        [34., 55.,  0.]])
Read the Docs v: stable
Versions
latest
stable
v0.9.1
v0.9.0
v0.8.2
v0.8.1
v0.8.0
v0.7.3
v0.7.2
v0.7.1
v0.7.0
v0.6.2
v0.6.1
v0.6.0
v0.5.1
v0.5.0
v0.4.1
v0.4.0
v0.3.2
v0.3.1
v0.3.0
v0.2.0
v0.1.0
refactor-structure
Downloads
pdf
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.