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be.ac.ulb.mlg.utils - package be.ac.ulb.mlg.utils
 
be.ac.ulb.mlg.utils.discretizer - package be.ac.ulb.mlg.utils.discretizer
 
be.ac.ulb.mlg.utils.measure - package be.ac.ulb.mlg.utils.measure
 
be.ac.ulb.mlg.utils.measure.entropy - package be.ac.ulb.mlg.utils.measure.entropy
 
be.ac.ulb.mlg.utils.measure.kernel - package be.ac.ulb.mlg.utils.measure.kernel
 
be.ac.ulb.mlg.utils.renormalizer - package be.ac.ulb.mlg.utils.renormalizer
 
BrayCurtis - Class in be.ac.ulb.mlg.utils.measure
BrayCurtis(X,Y) = 1-2*W/(sum(X)+sum(Y)), with W = sum_i[ min(x_i,y_i)]
BrayCurtis() - Constructor for class be.ac.ulb.mlg.utils.measure.BrayCurtis
 
BrownCorrelation - Class in be.ac.ulb.mlg.utils.measure
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
BrownCorrelation() - Constructor for class be.ac.ulb.mlg.utils.measure.BrownCorrelation
 
bubbleSort(double[], int[]) - Static method in class be.ac.ulb.mlg.utils.MeasureUtils
Apply bubble sort to sort indices (toSort) array based on the values (comparable) array which is also sorted

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