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cosine_distances — scikit-learn 1.7.0 documentation

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.

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.   ],
       [0.422, 0.183]])

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