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

sklearn.metrics.pairwise.paired_manhattan_distances(X, Y)

  Compute the paired L1 distances between X and Y.

  Distances are calculated between (X[0], Y[0]), (X[1], Y[1]), …, (X[n_samples], Y[n_samples]).

  Read more in the User Guide.

  Parameters:
    X{array-like, sparse matrix} of shape (n_samples, n_features)

      An array-like where each row is a sample and each column is a feature.

    Y{array-like, sparse matrix} of shape (n_samples, n_features)

      An array-like where each row is a sample and each column is a feature.

  Returns:
    distances ndarray of shape (n_samples,)

      L1 paired distances between the row vectors of `X` and the row vectors of `Y`.

  Examples

      >>> from sklearn.metrics.pairwise import paired_manhattan_distances
      >>> import numpy as np
      >>> X = np.array([[1, 1, 0], [0, 1, 0], [0, 0, 1]])
      >>> Y = np.array([[0, 1, 0], [0, 0, 1], [0, 0, 0]])
      >>> paired_manhattan_distances(X, Y)
      array([1., 2., 1.])