Login Register

sklearn.metrics.pairwise.paired_manhattan_distances()

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

Compute the L1 distances between the vectors in X and Y.

Read more in the User Guide.

Parameters:

X : array-like, shape (n_samples, n_features)

Y : array-like, shape (n_samples, n_features)

Returns:

distances : ndarray (n_samples, )

Links:

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

doc_scikit_learn

2017-01-15 04:26:35

Comments

Leave a Comment

Please login to continue.

Popular Articles

sklearn.metrics.pairwise.paired_distances() sklearn.metrics.pairwise.paired_distances(X, Y, metric='euclidean', **kwds) [source] Computes the paired distances between X and Y. Computes the

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

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.manhattan_distances() sklearn.metrics.pairwise.manhattan_distances(X, Y=None, sum_over_features=True, size_threshold=500000000.0) [source] Compute the L1 distances bet

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