Uses of Interface
be.ac.ulb.mlg.utils.Measure

Packages that use Measure
be.ac.ulb.mlg.utils   
be.ac.ulb.mlg.utils.measure   
be.ac.ulb.mlg.utils.measure.entropy   
be.ac.ulb.mlg.utils.renormalizer   
 

Uses of Measure in be.ac.ulb.mlg.utils
 

Methods in be.ac.ulb.mlg.utils that return types with arguments of type Measure
 Map<Measure,double[][]> Measurer.measure(double[][] input, Measure[] measures)
          Compute the given measures on data without ignored pairs
 Map<Measure,double[][]> Measurer.measure(double[][] input, Measure[] measures, boolean[][] measurable)
          Compute the given measures on data
 Map<Measure,double[][]> Measurer.measure(cern.colt.matrix.DoubleMatrix2D input, Measure[] measures)
          Compute the given measures on data without ignored pairs
 Map<Measure,double[][]> Measurer.measure(cern.colt.matrix.DoubleMatrix2D input, Measure[] measures, boolean[][] measurable)
          Compute the given measures on data
 

Methods in be.ac.ulb.mlg.utils with parameters of type Measure
 Map<Measure,double[][]> Measurer.measure(double[][] input, Measure[] measures)
          Compute the given measures on data without ignored pairs
 Map<Measure,double[][]> Measurer.measure(double[][] input, Measure[] measures, boolean[][] measurable)
          Compute the given measures on data
 Map<Measure,double[][]> Measurer.measure(cern.colt.matrix.DoubleMatrix2D input, Measure[] measures)
          Compute the given measures on data without ignored pairs
 Map<Measure,double[][]> Measurer.measure(cern.colt.matrix.DoubleMatrix2D input, Measure[] measures, boolean[][] measurable)
          Compute the given measures on data
 double[][] Renormalizer.normalizeOutput(double[][] input, double[][] output, Measure measure)
          Apply the normalization process on the given vector of data vectors and result measures
 double[][] DefaultRenormalizer.normalizeOutput(double[][] input, double[][] output, Measure measure)
           
 

Uses of Measure in be.ac.ulb.mlg.utils.measure
 

Classes in be.ac.ulb.mlg.utils.measure that implement Measure
 class BrayCurtis
          BrayCurtis(X,Y) = 1-2*W/(sum(X)+sum(Y)), with W = sum_i[ min(x_i,y_i)]
 class 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
 class Entropy
          Abstract class of entropy that need to use an estimator.
 class Euclidean
           
 class Hellinger
           
 class 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
 class JensenShannon
           
 class Kendall
          Using Tau-b (adjustments for ties) Knight's Algorithm
 class KullbackLeibler
          Symmetric case ( [ KLD(P|Q) + KLD(Q|P) ] /2 )
 class 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)
 class Pearson
           
 class Spearman
           
 class Steinhaus
          Steinhaus(X,Y) = 2*W/(sum(X)+sum(Y)), with W = sum_i[ min(x_i,y_i)]
 class 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.
 

Uses of Measure in be.ac.ulb.mlg.utils.measure.entropy
 

Classes in be.ac.ulb.mlg.utils.measure.entropy that implement Measure
 class DirichletEntropy
          Abstract Dirichlet probability distribution for entropy estimator.
 class EmpiricalEntropy
          The classic empirical entropy estimate of Uniform probability distribution.
 class SchurmannGrassbergerEntropy
          Schurmann-Grassberger entropy estimate of Dirichlet probability distribution.
 class ShannonEntropy
          Shannon entropy estimate (empirical) of Uniform probability distribution.
 

Uses of Measure in be.ac.ulb.mlg.utils.renormalizer
 

Methods in be.ac.ulb.mlg.utils.renormalizer with parameters of type Measure
 double[][] TaxonRenormalizer.normalizeOutput(double[][] input, double[][] output, Measure measure)