paired_cosine_distances#

sklearn.metrics.pairwise.paired_cosine_distances(X, Y)[source]#

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:

distancesndarray 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.

Examples

>>> from sklearn.metrics.pairwise import paired_cosine_distances
>>> X = [[0, 0, 0], [1, 1, 1]]
>>> Y = [[1, 0, 0], [1, 1, 0]]
>>> paired_cosine_distances(X, Y)
array([0.5       , 0.18...])