cosine_distances

sklearn.metrics.pairwise.cosine_distances(X, Y=None)[source]

Compute cosine distance between samples in X and Y.

Cosine distance is defined as 1.0 minus the cosine similarity.

Read more in the User Guide.

Parameters:

X{array-like, sparse matrix} of shape (n_samples_X, n_features)

Matrix X.

Y{array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None

Matrix Y.

Returns:

distancesndarray of shape (n_samples_X, n_samples_Y)

Returns the cosine distance between samples in X and Y.

See also

cosine_similarity

Compute cosine similarity between samples in X and Y.

scipy.spatial.distance.cosine

Dense matrices only.

Examples

>>> from sklearn.metrics.pairwise import cosine_distances
>>> X = [[0, 0, 0], [1, 1, 1]]
>>> Y = [[1, 0, 0], [1, 1, 0]]
>>> cosine_distances(X, Y)
array([[1.     , 1.     ],
       [0.42..., 0.18...]])