scipy.spatial.distance

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

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[Mti07]“Hierarchical clustering.” API Reference Documentation. The Wolfram Research, Inc. http://reference.wolfram.com/mathematica/HierarchicalClustering/tutorial/HierarchicalClustering.html. Accessed October 1, 2007.
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[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.
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[Jai88]Jain, A., and Dubes, R., “Algorithms for Clustering Data.” Prentice-Hall. Englewood Cliffs, NJ. 1988.
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