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Accord.NET (logo) Accord.Math.Distances Namespace
 
Classes
  ClassDescription
Public classArgMax
ArgMax distance (L0) distance.
Public classBhattacharyya
Bhattacharyya distance.
Public classBrayCurtis
Bray-Curtis distance.
Public classCanberra
Canberra distance.
Public classChebyshev
Chebyshev distance.
Public classCosine
Cosine distance.
Public classDice
Dice dissimilarity.
Public classEuclidean
Euclidean distance metric.
Public classHamming
Hamming distance.
Public classHammingT
Hamming distance.
Public classHellinger
Herlinger distance.
Public classJaccard
Jaccard (Index) distance.
Public classJaccardT
Jaccard (Index) distance.
Public classKulczynski
Kulczynski dissimilarity.
Public classLevenshtein
Levenshtein distance.
Public classLevenshteinT
Levenshtein distance.
Public classLogLikelihoodT
Log-likelihood distance between a sample and a statistical distribution.
Public classMahalanobis
Mahalanobis distance.
Public classManhattan
Manhattan (also known as Taxicab or L1) distance.
Public classMatching
Matching dissimilarity.
Public classMinkowski
The Minkowski distance is a metric in a normed vector space which can be considered as a generalization of both the Euclidean distance and the Manhattan distance.
Public classModular
Modular distance (shortest distance between two marks on a circle).
Public classPearsonCorrelation
Pearson Correlation similarity.
Public classRogersTanimoto
Rogers-Tanimoto dissimilarity.
Public classRusselRao
Russel-Rao dissimilarity.
Public classSokalMichener
Sokal-Michener dissimilarity.
Public classSokalSneath
Sokal-Sneath dissimilarity.
Public classSquareEuclidean
Square-Euclidean distance and similarity. Please note that this distance is not a metric as it doesn't obey the triangle inequality.
Public classSquareMahalanobis
Squared Mahalanobis distance.
Public classYule
Yule dissimilarity.
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