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

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

  Compute the paired cosine distances between X and Y.

  Read more in the User Guide.

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

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

  Returns:
    distances ndarray of shape (n_samples,)

      Returns the distances between the row vectors of `X` and the row vectors of `Y`, where `distances[i]` is the distance between `X[i]` and `Y[i]`.

  Notes

  The cosine distance is equivalent to the half the squared euclidean distance if each sample is normalized to unit norm.