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scikit-learn 1.3.2
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# `sklearn.metrics.pairwise`.paired_distances

sklearn.metrics.pairwise.paired_distances(X, Y, *, metric='euclidean', **kwds)

  Compute the paired distances between X and Y.

  Compute the distances between (X[0], Y[0]), (X[1], Y[1]), etc…

  Read more in the User Guide.

  Parameters:
    X ndarray of shape (n_samples, n_features)

      Array 1 for distance computation.

    Y ndarray of shape (n_samples, n_features)

      Array 2 for distance computation.

    metric str or callable, default=”euclidean”

      The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options specified in PAIRED_DISTANCES, including “euclidean”, “manhattan”, or “cosine”. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from `X` as input and return a value indicating the distance between them.

    **kwds dict

      Unused parameters.

  Returns:
    distances ndarray of shape (n_samples,)

      Returns the distances between the row vectors of `X` and the row vectors of `Y`.

  See also

  `sklearn.metrics.pairwise_distances`

    Computes the distance between every pair of samples.

  Examples

      >>> from sklearn.metrics.pairwise import paired_distances
      >>> X = [[0, 1], [1, 1]]
      >>> Y = [[0, 1], [2, 1]]
      >>> paired_distances(X, Y)
      array([0., 1.])