sklearn.metrics.pairwise.paired_distances()

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

Computes the paired distances between X and Y.

Computes the distances between (X[0], Y[0]), (X[1], Y[1]), etc...

Parameters:	X : ndarray (n_samples, n_features) Array 1 for distance computation. Y : ndarray (n_samples, n_features) Array 2 for distance computation. metric : string or callable 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.
Returns:	distances : ndarray (n_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.])

Links:

http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.paired_distances.html

doc_scikit_learn

2025-01-10 15:47:30

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