Distance matrix computation from a collection of raw observation vectors stored in a rectangular array.
Pairwise distances between observations in n-dimensional space.
cdist (XA, XB[, metric, out])
Compute distance between each pair of the two collections of inputs.
Convert a vector-form distance vector to a square-form distance matrix, and vice-versa.
Compute the directed Hausdorff distance between two 2-D arrays.
Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix.
is_valid_dm (D[, tol, throw, name, warning])
Return True if input array is a valid distance matrix.
Return True if the input array is a valid condensed distance matrix.
Return the number of original observations that correspond to a square, redundant distance matrix.
Return the number of original observations that correspond to a condensed distance matrix.
Distance functions between two numeric vectors u and v . Computing distances over a large collection of vectors is inefficient for these functions. Use pdist for this purpose.
Compute the Bray-Curtis distance between two 1-D arrays.
Compute the Canberra distance between two 1-D arrays.
Compute the Chebyshev distance.
Compute the City Block (Manhattan) distance.
Compute the correlation distance between two 1-D arrays.
Compute the Cosine distance between 1-D arrays.
Computes the Euclidean distance between two 1-D arrays.
Compute the Jensen-Shannon distance (metric) between two probability arrays.
Compute the Mahalanobis distance between two 1-D arrays.
Compute the Minkowski distance between two 1-D arrays.
Return the standardized Euclidean distance between two 1-D arrays.
Compute the squared Euclidean distance between two 1-D arrays.
Distance functions between two boolean vectors (representing sets) u and v . As in the case of numerical vectors, pdist is more efficient for computing the distances between all pairs.
Compute the Dice dissimilarity between two boolean 1-D arrays.
Compute the Hamming distance between two 1-D arrays.
Compute the Jaccard-Needham dissimilarity between two boolean 1-D arrays.
Compute the Kulczynski 1 dissimilarity between two boolean 1-D arrays.
Compute the Rogers-Tanimoto dissimilarity between two boolean 1-D arrays.
Compute the Russell-Rao dissimilarity between two boolean 1-D arrays.
Compute the Sokal-Michener dissimilarity between two boolean 1-D arrays.
Compute the Sokal-Sneath dissimilarity between two boolean 1-D arrays.
Compute the Yule dissimilarity between two boolean 1-D arrays.
hamming also operates over discrete numerical vectors.
Spatial algorithms and data structures ( scipy.spatial )