tj, pddlmcmZddlmZddlmZddgZGddeZ GddeZ dS) N)Tensor)ModulePairwiseDistanceCosineSimilarityc teZdZUdZgdZeed<eed<eed< dd ededed d ffd Zd e de d e fdZ xZ S)raL Computes the pairwise distance between input vectors, or between columns of input matrices. Distances are computed using ``p``-norm, with constant ``eps`` added to avoid division by zero if ``p`` is negative, i.e.: .. math :: \mathrm{dist}\left(x, y\right) = \left\Vert x-y + \epsilon e \right\Vert_p, where :math:`e` is the vector of ones and the ``p``-norm is given by. .. math :: \Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}. Args: p (real, optional): the norm degree. Can be negative. Default: 2 eps (float, optional): Small value to avoid division by zero. Default: 1e-6 keepdim (bool, optional): Determines whether or not to keep the vector dimension. Default: False Shape: - Input1: :math:`(N, D)` or :math:`(D)` where `N = batch dimension` and `D = vector dimension` - Input2: :math:`(N, D)` or :math:`(D)`, same shape as the Input1 - Output: :math:`(N)` or :math:`()` based on input dimension. If :attr:`keepdim` is ``True``, then :math:`(N, 1)` or :math:`(1)` based on input dimension. Examples: >>> pdist = nn.PairwiseDistance(p=2) >>> input1 = torch.randn(100, 128) >>> input2 = torch.randn(100, 128) >>> output = pdist(input1, input2) )normepskeepdimr r r @ư>FpreturnNcrt||_||_||_dSN)super__init__r r r )selfrr r __class__s W/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/torch/nn/modules/distance.pyrzPairwiseDistance.__init__1s4   x1x2cPtj|||j|j|jSz( Runs the forward pass. )Fpairwise_distancer r r rrrs rforwardzPairwiseDistance.forward9s#"2r49dh MMMr)r r F) __name__ __module__ __qualname____doc__ __constants__float__annotations__boolrrr __classcell__rs@rrr sB/..M KKK JJJ MMMBG#(:> N&NfNNNNNNNNNrcdeZdZUdZddgZeed<eed<d dededdffd Zd e d e de fd Z xZ S) raReturns cosine similarity between :math:`x_1` and :math:`x_2`, computed along `dim`. .. math :: \text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}. Args: dim (int, optional): Dimension where cosine similarity is computed. Default: 1 eps (float, optional): Small value to avoid division by zero. Default: 1e-8 Shape: - Input1: :math:`(\ast_1, D, \ast_2)` where D is at position `dim` - Input2: :math:`(\ast_1, D, \ast_2)`, same number of dimensions as x1, matching x1 size at dimension `dim`, and broadcastable with x1 at other dimensions. - Output: :math:`(\ast_1, \ast_2)` Examples: >>> input1 = torch.randn(100, 128) >>> input2 = torch.randn(100, 128) >>> cos = nn.CosineSimilarity(dim=1, eps=1e-6) >>> output = cos(input1, input2) dimr r:0yE>rNcdt||_||_dSr)rrr+r )rr+r rs rrzCosineSimilarity.__init__[s+ rrrcDtj|||j|jSr)rcosine_similarityr+r rs rrzCosineSimilarity.forward`s"2r48TX>>>r)rr,) r r!r"r#r$intr&r%rrrr(r)s@rrr@s,ENM HHH JJJC%4 ?&?f?????????r) torch.nn.functionalnn functionalrtorchrmoduler__all__rrrrr8s 1 23N3N3N3N3Nv3N3N3Nl$?$?$?$?$?v$?$?$?$?$?r