o y“©ig ã @sœddlmZddlmZddlmZddlmZmZddl m Z  ddedeed ee d efd d „Z   ddedeed edd ee d ef dd„Z dS)é)ÚOptional)ÚTensor)ÚLiteral)Ú _check_inputÚ_reduce_distance_matrix)Ú _safe_matmulNÚxÚyÚ zero_diagonalÚreturncCs.t|||ƒ\}}}t||ƒ}|r| d¡|S)zêCalculates the pairwise linear similarity matrix. Args: x: tensor of shape ``[N,d]`` y: tensor of shape ``[M,d]`` zero_diagonal: determines if the diagonal of the distance matrix should be set to zero r)rrÚfill_diagonal_)rr r Údistance©rú[/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/pairwise/linear.pyÚ"_pairwise_linear_similarity_updates   rÚ reduction)ÚmeanÚsumÚnoneNcCst|||ƒ}t||ƒS)a¶Calculates pairwise linear similarity: .. math:: s_{lin}(x,y) = = \sum_{d=1}^D x_d \cdot 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 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.]]) )rr)rr rr r rrrÚpairwise_linear_similarity)s ( r)NN)NNN)ÚtypingrÚtorchrÚtyping_extensionsrÚ(torchmetrics.functional.pairwise.helpersrrÚtorchmetrics.utilities.computerÚboolrrrrrrÚs:   ÿÿÿÿ þüÿþýüû