sklearn.metrics.pairwise
.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...]])