- SchurmannGrassbergerEntropy - Class in be.ac.ulb.mlg.utils.measure.entropy
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Schurmann-Grassberger entropy estimate of Dirichlet probability distribution.
- SchurmannGrassbergerEntropy() - Constructor for class be.ac.ulb.mlg.utils.measure.entropy.SchurmannGrassbergerEntropy
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- setGroup(int, String) - Method in class be.ac.ulb.mlg.utils.DefaultRenormalizer
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Define the group for one input data vector
- setGroup(int, int) - Method in class be.ac.ulb.mlg.utils.DefaultRenormalizer
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Define the group for one input data vector
- setGroups(int[], int) - Method in class be.ac.ulb.mlg.utils.DefaultRenormalizer
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Define the group of all input data vectors by their indexes
- setGroupsNames(int, String[]) - Method in class be.ac.ulb.mlg.utils.DefaultRenormalizer
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Define groups and associate them a label.
- setHandelingMissingValues(boolean) - Method in class be.ac.ulb.mlg.utils.Measurer
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Define the checking of missing values
- setMaximumMissingValues(int) - Method in class be.ac.ulb.mlg.utils.Measurer
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Define the number of maximum allowed missing values
- setNormalizer(String, int, double) - Method in class be.ac.ulb.mlg.utils.DefaultRenormalizer
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Define a specific value to use instead of the sum by column for groups
- setNormalizer(int, int, double) - Method in class be.ac.ulb.mlg.utils.DefaultRenormalizer
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Define a specific value to use instead of the sum by column for groups
- setNormalizer(int, double) - Method in class be.ac.ulb.mlg.utils.DefaultRenormalizer
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Define a specific value to use instead of the sum by column
- setNumberOfThreads(int) - Method in class be.ac.ulb.mlg.utils.Measurer
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Define the number of threads to be used
- setRenormalizer(Renormalizer) - Method in class be.ac.ulb.mlg.utils.Measurer
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Define the renormalizer object
- setTaxa(String[]) - Method in class be.ac.ulb.mlg.utils.renormalizer.TaxonRenormalizer
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Set the taxa for all row (null or out of taxon results in considering the row as a feature)
This method (instead of
addTaxa,
addTaxa) is able to handle duplicated values
- setTaxa(String[], String) - Method in class be.ac.ulb.mlg.utils.renormalizer.TaxonRenormalizer
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Set the taxa for all row (null or out of taxon results in considering the row as a feature)
This method (instead of
addTaxa,
addTaxa) is able to handle duplicated values
- setTaxa(int, String, String) - Method in class be.ac.ulb.mlg.utils.renormalizer.TaxonRenormalizer
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Set the taxa for a row (null or out of taxon results in considering the row as a feature)
- setTaxa(int, String) - Method in class be.ac.ulb.mlg.utils.renormalizer.TaxonRenormalizer
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Set the taxa for a row (null or out of taxon results in considering the row as a feature)
- setup(double[], boolean) - Method in class be.ac.ulb.mlg.utils.measure.kernel.GaussianKernel
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- setup(double[], boolean) - Method in interface be.ac.ulb.mlg.utils.measure.Kernel
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Setup the Kernel for a specific vector
- ShannonEntropy - Class in be.ac.ulb.mlg.utils.measure.entropy
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Shannon entropy estimate (empirical) of Uniform probability distribution.
- ShannonEntropy() - Constructor for class be.ac.ulb.mlg.utils.measure.entropy.ShannonEntropy
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- shufflePair() - Method in class be.ac.ulb.mlg.utils.DefaultRenormalizer
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- shufflePair() - Method in interface be.ac.ulb.mlg.utils.Renormalizer
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Know if the measurer use pre-shuffle
- simulateRenormalization(double[][], double[], double[], int, int) - Method in class be.ac.ulb.mlg.utils.DefaultRenormalizer
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- simulateRenormalization(RenormalizerState, double[], double[], int, int) - Method in interface be.ac.ulb.mlg.utils.Renormalizer
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Method called when pairwise renormalization is used in order to obtain a normalized version of the shuffled vector as if it was in the original input matrix
- Spearman - Class in be.ac.ulb.mlg.utils.measure
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- Spearman() - Constructor for class be.ac.ulb.mlg.utils.measure.Spearman
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- sqrt(double) - Static method in class be.ac.ulb.mlg.utils.MeasureUtils
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Compute the square root value by using the formula exp(0.5*log(x)) <=> sqrt(x) in order to avoid underflow.
- Steinhaus - Class in be.ac.ulb.mlg.utils.measure
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Steinhaus(X,Y) = 2*W/(sum(X)+sum(Y)), with W = sum_i[ min(x_i,y_i)]
- Steinhaus() - Constructor for class be.ac.ulb.mlg.utils.measure.Steinhaus
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- SUM_NORMILIZER - Static variable in class be.ac.ulb.mlg.utils.DefaultRenormalizer
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Sentinel value meaning that the renormalizer use the sum function on columns