Package be.ac.ulb.mlg.utils.measure

Interface Summary

Kernel

It is a generic Kernel which can be setup by a measure with the data vector

Class Summary

BrayCurtis	BrayCurtis(X,Y) = 1-2*W/(sum(X)+sum(Y)), with W = sum_i[ min(x_i,y_i)]
BrownCorrelation	Jump up to: a b c SzŽkely, Rizzo and Bakirov (2007) Jump up to: a b c d SzŽkely & Rizzo (2009) http://en.wikipedia.org/wiki/Distance_correlation#Distance_correlation The used strategy to handle missing value is to evaluate values with all available value (estimate means) and try to infer the covariance
Entropy	Abstract class of entropy that need to use an estimator.
Euclidean
Hellinger
HilbertSchmidt	Compute the Hilbert-Schmidt independence criterion according to the estimate in the paper "On Kernel Parameter Selection in Hilbert-Schmidt Independence Criterion" p.3
JensenShannon
Kendall	Using Tau-b (adjustments for ties) Knight's Algorithm
KullbackLeibler	Symmetric case ( [ KLD(P|Q) + KLD(Q|P) ] /2 )
MutualInformation	Mutual information that use the entropy formula: I(X,Y) = H(X) - H(X|Y) = H(Y) - H(Y|X) = H(X) + H(Y) - H(X,Y)
Pearson
Spearman
Steinhaus	Steinhaus(X,Y) = 2*W/(sum(X)+sum(Y)), with W = sum_i[ min(x_i,y_i)]
VarianceOfLogRatios	Variance of log ratios scaled to [0;1]: 1-exp(-sqrt(D(x,y))) according to Aitchison where D(X,Y) is the Variance of log ratios.