sokalsneath - the Sokal-Sneath dissimilarity. sokalmichener - the Sokal-Michener dissimilarity. russellrao - the Russell-Rao dissimilarity. rogerstanimoto - the Rogers-Tanimoto dissimilarity. autosummary:: :toctree: generated/ dice - the Dice dissimilarity. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. wminkowski - (deprecated) alias of `minkowski`. sqeuclidean - the squared Euclidean distance. seuclidean - the normalized Euclidean distance. jensenshannon - the Jensen-Shannon distance. autosummary:: :toctree: generated/ braycurtis - the Bray-Curtis distance. Computing distances over a large collection of vectors is inefficient for these functions. autosummary:: :toctree: generated/ is_valid_dm - checks for a valid distance matrix is_valid_y - checks for a valid condensed distance matrix num_obs_dm - # of observations in a distance matrix num_obs_y - # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Also contained in this module are functions for computing the number of observations in a distance matrix. cdist - distances between two collections of observation vectors squareform - convert distance matrix to a condensed one and vice versa directed_hausdorff - directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. autosummary:: :toctree: generated/ pdist - pairwise distances between observation vectors. sectionauthor:: Damian Eads Function reference - Distance matrix computation from a collection of raw observation vectors stored in a rectangular array.

Scipy cdist