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# PairwiseDistance

class torch.nn.PairwiseDistance(p=2.0, eps=1e-06, keepdim=False)

  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.:

  d i s t(x,y)=∥x−y+ϵ e∥p,\mathrm{dist}\left(x, y\right) = \left\Vert x-y + \epsilon e \right\Vert_p, dist(x,y)=∥x−y+ϵe∥p​,

  where e e e is the vector of ones and the `p`-norm is given by.

  ∥x∥p=(∑i=1 n∣x i∣p)1/p.\Vert x \Vert _p = \left( \sum_{i=1}^n \vert x_i \vert ^ p \right) ^ {1/p}. ∥x∥p​=(i=1∑n​∣x i​∣p)1/p.
  Parameters:

    - 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: (N,D)(N, D)(N,D) or (D)(D)(D) where N = batch dimension and D = vector dimension

    - Input2: (N,D)(N, D)(N,D) or (D)(D)(D), same shape as the Input1

    - Output: (N)(N)(N) or ()()() based on input dimension. If `keepdim` is `True`, then (N,1)(N, 1)(N,1) or (1)(1)(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)

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