Distance computations (scipy.spatial.distance)#
Function reference#
Distance matrix computation from a collection of raw observation vectors stored in a rectangular array.
pdist(X[, metric, out])
Pairwise distances between observations in n-dimensional space.
cdist(XA, XB[, metric, out])
Compute distance between each pair of the two collections of inputs.
squareform(X[, force, checks])
Convert a vector-form distance vector to a square-form distance matrix, and vice-versa.
directed_hausdorff(u, v[, rng, seed])
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 satisfies basic distance matrix properties (symmetry and zero diagonal).
is_valid_y(y[, warning, throw, name])
Return True if the input array is a valid condensed distance matrix.
num_obs_dm(d)
Return the number of original observations that correspond to a square, redundant distance matrix.
num_obs_y(Y)
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.
braycurtis(u, v[, w])
Compute the Bray-Curtis distance between two 1-D arrays.
canberra(u, v[, w])
Compute the Canberra distance between two 1-D arrays.
chebyshev(u, v[, w])
Compute the Chebyshev distance.
cityblock(u, v[, w])
Compute the City Block (Manhattan) distance.
correlation(u, v[, w, centered])
Compute the correlation distance between two 1-D arrays.
cosine(u, v[, w])
Compute the Cosine distance between 1-D arrays.
euclidean(u, v[, w])
Computes the Euclidean distance between two 1-D arrays.
jensenshannon(p, q[, base, axis, keepdims])
Compute the Jensen-Shannon distance (metric) between two probability arrays.
mahalanobis(u, v, VI)
Compute the Mahalanobis distance between two 1-D arrays.
minkowski(u, v[, p, w])
Compute the Minkowski distance between two 1-D arrays.
seuclidean(u, v, V)
Return the standardized Euclidean distance between two 1-D arrays.
sqeuclidean(u, v[, w])
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.
dice(u, v[, w])
Compute the Dice dissimilarity between two boolean 1-D arrays.
hamming(u, v[, w])
Compute the Hamming distance between two 1-D arrays.
jaccard(u, v[, w])
Compute the Jaccard dissimilarity between two boolean vectors.
rogerstanimoto(u, v[, w])
Compute the Rogers-Tanimoto dissimilarity between two boolean 1-D arrays.
russellrao(u, v[, w])
Compute the Russell-Rao dissimilarity between two boolean 1-D arrays.
sokalsneath(u, v[, w])
Compute the Sokal-Sneath dissimilarity between two boolean 1-D arrays.
yule(u, v[, w])
Compute the Yule dissimilarity between two boolean 1-D arrays.
hamming also operates over discrete numerical vectors.