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…

Read more in the User Guide.


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.


distances : ndarray (n_samples, )

See also

pairwise distances.


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