Login Register

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

with shape (n_samples_X, n_features).

Y : array_like, sparse matrix (optional)

with shape (n_samples_Y, n_features).

Returns:

distance matrix : array

An array with shape (n_samples_X, n_samples_Y).

See also

sklearn.metrics.pairwise.cosine_similarity, scipy.spatial.distance.cosine

Links:

http://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_distances.html

doc_scikit_learn

2017-01-15 04:26:30

Comments

Leave a Comment

Please login to continue.

Popular Articles

sklearn.metrics.pairwise.cosine_similarity() sklearn.metrics.pairwise.cosine_similarity(X, Y=None, dense_output=True) [source] Compute cosine similarity between samples in X and Y. Cosine si

sklearn.metrics.pairwise.paired_cosine_distances() sklearn.metrics.pairwise.paired_cosine_distances(X, Y) [source] Computes the paired cosine distances between X and Y Read more in the User Guide.

sklearn.metrics.pairwise_distances() sklearn.metrics.pairwise_distances(X, Y=None, metric='euclidean', n_jobs=1, **kwds) [source] Compute the distance matrix from a vector array X an

sklearn.metrics.pairwise.pairwise_distances() sklearn.metrics.pairwise.pairwise_distances(X, Y=None, metric='euclidean', n_jobs=1, **kwds) [source] Compute the distance matrix from a vector a

sklearn.metrics.pairwise.distance_metrics() sklearn.metrics.pairwise.distance_metrics() [source] Valid metrics for pairwise_distances. This function simply returns the valid pairwise distan