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, p, w, V, VI]) Computes the pairwise distances between m original observations in n-dimensional space.
cdist(XA, XB[, metric, p, V, VI, w]) Computes distance between each pair of observation vectors in the
squareform(X[, force, checks]) Converts a vector-form distance vector to a square-form distance matrix, and vice-versa.

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]) Returns True if the variable D passed is a valid distance matrix.
is_valid_y(y[, warning, throw, name]) Returns True if the variable y passed is a valid condensed
num_obs_dm(d) Returns the number of original observations that correspond to a
num_obs_y(Y) Returns the number of original observations that correspond to a

Distance functions between two 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) Computes the Bray-Curtis distance between two n-vectors u and
canberra(u, v) Computes the Canberra distance between two n-vectors u and v,
chebyshev(u, v) Computes the Chebyshev distance between two n-vectors u and v,
cityblock(u, v) Computes the Manhattan distance between two n-vectors u and v,
correlation(u, v) Computes the correlation distance between two n-vectors u and v, which is defined as ..
cosine(u, v) Computes the Cosine distance between two n-vectors u and v, which
dice(u, v) Computes the Dice dissimilarity between two boolean n-vectors
euclidean(u, v) Computes the Euclidean distance between two n-vectors u and v,
hamming(u, v) Computes the Hamming distance between two n-vectors u and
jaccard(u, v) Computes the Jaccard-Needham dissimilarity between two boolean
kulsinski(u, v) Computes the Kulsinski dissimilarity between two boolean n-vectors
mahalanobis(u, v, VI) Computes the Mahalanobis distance between two n-vectors u and v,
matching(u, v) Computes the Matching dissimilarity between two boolean n-vectors
minkowski(u, v, p) Computes the Minkowski distance between two vectors u and v,
rogerstanimoto(u, v) Computes the Rogers-Tanimoto dissimilarity between two boolean
russellrao(u, v) Computes the Russell-Rao dissimilarity between two boolean n-vectors
seuclidean(u, v, V) Returns the standardized Euclidean distance between two n-vectors
sokalmichener(u, v) Computes the Sokal-Michener dissimilarity between two boolean vectors
sokalsneath(u, v) Computes the Sokal-Sneath dissimilarity between two boolean vectors
sqeuclidean(u, v) Computes the squared Euclidean distance between two n-vectors u and v,
yule(u, v) Computes the Yule dissimilarity between two boolean n-vectors u and v,

References

[Sta07]“Statistics toolbox.” API Reference Documentation. The MathWorks. http://www.mathworks.com/access/helpdesk/help/toolbox/stats/. Accessed October 1, 2007.
[Mti07]“Hierarchical clustering.” API Reference Documentation. The Wolfram Research, Inc. http://reference.wolfram.com/mathematica/HierarchicalClustering/tutorial/HierarchicalClustering.html. Accessed October 1, 2007.
[Gow69]Gower, JC and Ross, GJS. “Minimum Spanning Trees and Single Linkage Cluster Analysis.” Applied Statistics. 18(1): pp. 54–64. 1969.
[War63]Ward Jr, JH. “Hierarchical grouping to optimize an objective function.” Journal of the American Statistical Association. 58(301): pp. 236–44. 1963.
[Joh66]Johnson, SC. “Hierarchical clustering schemes.” Psychometrika. 32(2): pp. 241–54. 1966.
[Sne62]Sneath, PH and Sokal, RR. “Numerical taxonomy.” Nature. 193: pp. 855–60. 1962.
[Bat95]Batagelj, V. “Comparing resemblance measures.” Journal of Classification. 12: pp. 73–90. 1995.
[Sok58]Sokal, RR and Michener, CD. “A statistical method for evaluating systematic relationships.” Scientific Bulletins. 38(22): pp. 1409–38. 1958.
[Ede79]Edelbrock, C. “Mixture model tests of hierarchical clustering algorithms: the problem of classifying everybody.” Multivariate Behavioral Research. 14: pp. 367–84. 1979.
[Jai88]Jain, A., and Dubes, R., “Algorithms for Clustering Data.” Prentice-Hall. Englewood Cliffs, NJ. 1988.
[Fis36]Fisher, RA “The use of multiple measurements in taxonomic problems.” Annals of Eugenics, 7(2): 179-188. 1936

Table Of Contents

Previous topic

scipy.spatial.tsearch

Next topic

scipy.spatial.distance.pdist

This Page