| Package | Description |
|---|---|
| be.ac.ulb.mlg.utils | |
| be.ac.ulb.mlg.utils.measure | |
| be.ac.ulb.mlg.utils.measure.entropy |
| Modifier and Type | Method and Description |
|---|---|
java.util.Map<Measure,double[][]> |
Measurer.measure(double[][] input,
Measure[] measures)
Compute the given measures on data without ignored pairs
|
java.util.Map<Measure,double[][]> |
Measurer.measure(double[][] input,
Measure[] measures,
boolean[][] measurable)
Compute the given measures on data
|
java.util.Map<Measure,double[][]> |
Measurer.measure(cern.colt.matrix.DoubleMatrix2D input,
Measure[] measures)
Compute the given measures on data without ignored pairs
|
java.util.Map<Measure,double[][]> |
Measurer.measure(cern.colt.matrix.DoubleMatrix2D input,
Measure[] measures,
boolean[][] measurable)
Compute the given measures on data
|
| Modifier and Type | Method and Description |
|---|---|
java.util.Map<Measure,double[][]> |
Measurer.measure(double[][] input,
Measure[] measures)
Compute the given measures on data without ignored pairs
|
java.util.Map<Measure,double[][]> |
Measurer.measure(double[][] input,
Measure[] measures,
boolean[][] measurable)
Compute the given measures on data
|
java.util.Map<Measure,double[][]> |
Measurer.measure(cern.colt.matrix.DoubleMatrix2D input,
Measure[] measures)
Compute the given measures on data without ignored pairs
|
java.util.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) |
| Modifier and Type | Class and Description |
|---|---|
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 Székely, Rizzo and Bakirov (2007)
Jump up to: a b c d Székely & 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.
|
| Modifier and Type | Class and Description |
|---|---|
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.
|