sklearn.metrics.pairwise
.paired_distances¶
- 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.
- Parameters:
- 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.- **kwdsdict
Unused parameters.
- Returns:
- distancesndarray of shape (n_samples,)
Returns the distances between the row vectors of
X
and the row vectors ofY
.
See also
sklearn.metrics.pairwise_distances
Computes the distance between every pair of 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.])