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C

C - Variable in class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedProblem.ClassifierParameters

C Value for SVM

C_CROSS_VALIDATION - Static variable in class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedCoevolutionaryProblem

Cross-validation code the evaluation mode

C_EVALUATION_DEFAULT - Static variable in class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedCoevolutionaryProblem

Default evaluation mode

C_NFOLDS_DEFAULT - Static variable in class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedCoevolutionaryProblem

Default number of folds for cross-validation

C_VALIDATION_ONLY - Static variable in class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedCoevolutionaryProblem

Validation-only code the evaluation mode

C_VALIDATION_TRAINING - Static variable in class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedCoevolutionaryProblem

Validation-training code the evaluation mode

centroids - Variable in class ristretto.jmltools.clustering.LBG

Centroids of the different clusters.

classDistribution(Instance) - Method in class ristretto.jmltools.classification.NaiveBayes

Generate the membership distribution for this instance using this classifier.

classifierClass - Variable in class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedProblem

Classifier class

ClassifierParameters() - Constructor for class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedProblem.ClassifierParameters

classifierParams - Static variable in class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedProblem

Classifier parameters

clone() - Method in class ristretto.ecjtools.LexicographicFitness

Clone this fitness.

clone() - Method in class ristretto.problem.fs.subset.breed.FSSubsetCrossoverPipeline

Clone this pipeline

clone() - Method in class ristretto.problem.fs.subset.FSSubsetIndividual

Returns a clone of this individual

cluster(Dataset) - Method in class ristretto.jmltools.clustering.ELBG

Execute the ELBG clustering algorithm on the data set that is provided.

cluster(Dataset) - Method in class ristretto.jmltools.clustering.KMedians

Execute the KMedians clustering algorithm on the data set that is provided.

cluster(Dataset) - Method in class ristretto.jmltools.clustering.LBG

Execute the LBG clustering algorithm on the data set that is provided.

clustererClass - Variable in class ristretto.problem.fs.subset.unsupervised.FSSubsetUnsupervisedProblem

Clustering algorithm

clustererMinNumCentroids - Static variable in class ristretto.problem.fs.subset.unsupervised.FSSubsetUnsupervisedProblem

Minimum number of centroids for the clustering algorithm

clustererNumCentroids - Variable in class ristretto.problem.fs.subset.unsupervised.FSSubsetUnsupervisedProblem

Number of centroids to be used in the clustering algorithm

clustererStopCriterion - Variable in class ristretto.problem.fs.subset.unsupervised.FSSubsetUnsupervisedProblem

Stop criterion to be used in the clustering algorithm

CoevolutionaryMaxOnes - Class in tests.testCoevolution

Dummy test for coevolutionary problems.

CoevolutionaryMaxOnes() - Constructor for class tests.testCoevolution.CoevolutionaryMaxOnes

CoevolutionaryStatistics - Class in ristretto.ecjtools

Coevolutionary statistics.

CoevolutionaryStatistics() - Constructor for class ristretto.ecjtools.CoevolutionaryStatistics

combineFeatures(EvolutionState, Individual[]) - Method in class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedCoevolutionaryProblem

Combine the features of the individuals from all subpopulations

combineFeatures(EvolutionState, Individual, Individual[]) - Method in class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedCoevolutionaryProblem

Combine the features of and individual with all the features selected in its context

compactnessIndexClass - Variable in class ristretto.problem.fs.subset.unsupervised.FSSubsetUnsupervisedProblem

Compactness index

compactnessNormMethodName - Variable in class ristretto.problem.fs.subset.unsupervised.FSSubsetUnsupervisedProblem

Normalization factor for compactness index

compareScore(double, double) - Method in class ristretto.jmltools.clustering.evaluation.DaviesBouldin

Compare the two scores according to the criterion in the implementation.

compareScore(double, double) - Method in class ristretto.jmltools.clustering.evaluation.DunnIndex

Compare the two scores according to the criterion in the implementation Some criterions should be maximized, others should be minimized.

compareScore(double, double) - Method in class ristretto.jmltools.clustering.evaluation.FarthestCentroids

Compares the two scores according to the criterion in the implementation Some criterions should be maximized, others should be minimized.

compareScore(double, double) - Method in class ristretto.jmltools.clustering.evaluation.MaxClusterDiameter

Compare the two scores according to the criterion in the implementation Some criterions should be maximized, others should be minimized.

compareScore(double, double) - Method in class ristretto.jmltools.clustering.evaluation.MinFarthestCentroid

Compare the two scores according to the criterion in the implementation Some criterions should be maximized, others should be minimized.

compareScore(double, double) - Method in class ristretto.jmltools.clustering.evaluation.OverallDeviation

Compare the two scores according to the criterion in the implementation Some criterions should be maximized, others should be minimized.

constructClassifier() - Method in class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedProblem

Construct the classifier

contextIsBetterThan(Fitness) - Method in class ristretto.ecjtools.LexicographicFitness

Given another Fitness, returns true if the trial which produced my current context is "better" in fitness than the trial which produced his current context, and thus should be retained in lieu of his.

countNonEmptyClusters(Dataset[]) - Method in class ristretto.jmltools.clustering.evaluation.CVIFSNormalizer

Check if there are empty clusters.

CVIFSNormalizer - Class in ristretto.jmltools.clustering.evaluation

This class allows to normalize CVI values for Feature Selection problems.

CVIFSNormalizer(int, Dataset[]) - Constructor for class ristretto.jmltools.clustering.evaluation.CVIFSNormalizer

Construct the normalizer

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