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¶
| [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 |
Copyright Notice¶
Copyright (C) Damian Eads, 2007-2008. New BSD License.
Functions
| 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, |
| cdist(XA, XB[, metric, p, V, VI, w]) | Computes distance between each pair of observation vectors in the |
| 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 |
| 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 |
| 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, |
| norm(x[, ord]) | Matrix or vector norm. |
| 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 |
| pdist(X[, metric, p, w, V, VI]) | Computes the pairwise distances between m original observations in n-dimensional space. |
| 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, |
| squareform(X[, force, checks]) | Converts a vector-form distance vector to a square-form distance matrix, and vice-versa. |
| wminkowski(u, v, p, w) | Computes the weighted Minkowski distance between two vectors u |
| yule(u, v) | Computes the Yule dissimilarity between two boolean n-vectors u and v, |
