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

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.

Xndarray of shape (n_samples, n_features)

Array 1 for distance computation.

Yndarray of shape (n_samples, n_features)

Array 2 for distance computation.

metricstr 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.


Unused parameters.

distancesndarray of shape (n_samples,)

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

See also


Computes the distance between every pair of samples.


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