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Packages that use Measure | |
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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 |
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Methods in be.ac.ulb.mlg.utils that return types with arguments of type Measure | |
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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 | |
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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)
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Uses of Measure in be.ac.ulb.mlg.utils.measure |
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Classes in be.ac.ulb.mlg.utils.measure that implement Measure | |
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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 Szkely, Rizzo and Bakirov (2007) Jump up to: a b c d Szkely & 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
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class |
Hellinger
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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
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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
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class |
Spearman
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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 |
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Classes in be.ac.ulb.mlg.utils.measure.entropy that implement Measure | |
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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 |
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Methods in be.ac.ulb.mlg.utils.renormalizer with parameters of type Measure | |
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double[][] |
TaxonRenormalizer.normalizeOutput(double[][] input,
double[][] output,
Measure measure)
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