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# Cosine Similarity¶

## Functional Interface¶

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

Calculates pairwise cosine similarity: If both and are passed in, the calculation will be performed pairwise between the rows of and . If only is passed in, the calculation will be performed between the rows of .

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 is given this defaults to True else if 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_cosine_similarity
>>> x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32)
>>> y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32)
>>> pairwise_cosine_similarity(x, y)
tensor([[0.5547, 0.8682],
[0.5145, 0.8437],
[0.5300, 0.8533]])
>>> pairwise_cosine_similarity(x)
tensor([[0.0000, 0.9989, 0.9996],
[0.9989, 0.0000, 0.9998],
[0.9996, 0.9998, 0.0000]])


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