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.