A B C D E F G H I K L M N O P R S T U V W Z
All Classes All Packages

P

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

Parameter to know whether LDA should be applied

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

Parameter for the clustering algorithm

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

Parameter for the clustering algorithm

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

Parameter for the number of centroids to be used in the clustering algorithm

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

Parameter for the stop criterion to be used in the clustering algorithm

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

Default parameter base for the problem

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

Parameter for the normalization factor for compactness index

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

Parameter for the compactness index

P_DEBUG - Static variable in class ristretto.problem.fs.subset.FSSubsetProblem

Parameter to activate the debugging logs

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

Parameter to set the evaluation mode

P_FS - Static variable in class ristretto.problem.fs.FSDefaults

Base parameter

P_FS_DATASET - Static variable in class ristretto.problem.fs.subset.FSSubsetProblem

Parameter to load the dataset

P_FS_DATASET_CLASS_INDEX - Static variable in class ristretto.problem.fs.subset.FSSubsetProblem

Parameter to load the class index for labeled datasets

P_FS_PROBLEM - Static variable in class ristretto.problem.fs.subset.FSSubsetProblem

Default parameter base for the problem

P_FS_SUBSET_CROSSOVER - Static variable in class ristretto.problem.fs.subset.breed.FSSubsetCrossoverPipeline

Base parameter for defaults

P_FS_SUBSET_INDIVIDUAL - Static variable in class ristretto.problem.fs.subset.FSSubsetIndividual

Base parameter

P_FS_SUBSET_MUTATION - Static variable in class ristretto.problem.fs.subset.breed.FSSubsetMutationPipeline

Base parameter for defaults

P_FS_SUBSET_SPECIES - Static variable in class ristretto.problem.fs.subset.FSSubsetSpecies

Parameter base

P_LEXICOGRAPHIC - Static variable in class ristretto.ecjtools.LexicographicFitness

Base parameter for defaults

P_MAX_FEATURE - Static variable in class ristretto.problem.fs.subset.FSSubsetSpecies

Parameter for the maximum feature index of individuals

P_MAX_SIZE - Static variable in class ristretto.problem.fs.subset.FSSubsetSpecies

Parameter for the maximum size of individuals

P_MAXIMIZE - Static variable in class ristretto.ecjtools.LexicographicFitness

Is higher better?

P_MAXOBJECTIVES - Static variable in class ristretto.ecjtools.LexicographicFitness

Parameter for the maximum fitness values

P_MIN_FEATURE - Static variable in class ristretto.problem.fs.subset.FSSubsetSpecies

Parameter for the minimum feature index of individuals

P_MIN_SIZE - Static variable in class ristretto.problem.fs.subset.FSSubsetSpecies

Parameter for the minimum size of individuals

P_MINOBJECTIVES - Static variable in class ristretto.ecjtools.LexicographicFitness

Parameter for the minimum fitness values

P_MUTATIONPROB - Static variable in class ristretto.problem.fs.subset.FSSubsetSpecies

Parameter for the mutation probability of individuals

P_N_FEATURES - Static variable in class ristretto.problem.fs.subset.FSSubsetSpecies

Parameter for the number of features in the problem

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

Parameter to set the number of folds (if cross-validation is set)

P_NUMOBJECTIVES - Static variable in class ristretto.ecjtools.LexicographicFitness

Parameter for the number of objectives

P_PARETO_FRONT_FILE - Static variable in class ristretto.ecjtools.MultiObjectiveStatistics

Parameter for the front file

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

Parameter for the normalization factor for the separation index

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

Parameter for the separation index

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

Parameter to decide whether to include the context or not

P_SHOULD_SET_CONTEXT - Static variable in class tests.testCoevolution.CoevolutionaryMaxOnes

Parameter to decide whether to include the context or not

P_SILENT_FRONT_FILE - Static variable in class ristretto.ecjtools.MultiObjectiveStatistics

Parameter for the silent front file

P_SUBSET - Static variable in class ristretto.problem.fs.subset.FSSubsetDefaults

Base parameter

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

Default parameter base for the problem

P_THRESHOLD - Static variable in class ristretto.ecjtools.LexicographicFitness

Similarity threshold for fitness comparisons

P_TOSS - Static variable in class ristretto.problem.fs.subset.breed.FSSubsetCrossoverPipeline

Parameter to configure if the second parent should be tossed

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

Default parameter base for the problem

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

Parameter for the proportion of samples used for validation

P_VALIDATION_PROP - Static variable in class tests.testLibSVM.TestLibSVM

Parameter for the proportion of samples used for validation

parseGenotype(EvolutionState, LineNumberReader) - Method in class ristretto.problem.fs.subset.FSSubsetIndividual

Parse a genome from a numbered reader.

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

Partitions the data according to the centroids.

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

Partition the data according to the centroids.

PerformanceIndexes - Class in ristretto.jmltools.classification.evaluation

This class implements some performance metrics based on the confusion matrix of a classifier

PerformanceIndexes() - Constructor for class ristretto.jmltools.classification.evaluation.PerformanceIndexes

postprocessPopulation(EvolutionState, Population, boolean[], boolean) - Method in class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedCoevolutionaryProblem

Called after evaluation of a Population to form final Fitness scores for the individuals based on the various performance scores they accumulated during trials

postprocessPopulation(EvolutionState, Population, boolean[], boolean) - Method in class tests.testCoevolution.CoevolutionaryMaxOnes

Called after evaluation of a Population to form final Fitness scores for the individuals based on the various performance scores they accumulated during trials

preprocessPopulation(EvolutionState, Population, boolean[], boolean) - Method in class ristretto.problem.fs.subset.supervised.FSSubsetSupervisedCoevolutionaryProblem

Prepare the fitness of individuals belonging to a population (clear trials) before their evaluation

preprocessPopulation(EvolutionState, Population, boolean[], boolean) - Method in class tests.testCoevolution.CoevolutionaryMaxOnes

Prepare the fitness of individuals belonging to a population (clear trials) before their evaluation

produce(int, int, int, int, Individual[], EvolutionState, int) - Method in class ristretto.problem.fs.subset.breed.FSSubsetCrossoverPipeline

Produce n individuals from the given subpopulation and put them into inds[start...start+n-1], where n = Min(Max(q,min),max), where q is the "typical" number of individuals the pipeline produces in one shot, and returns n. max must be >= min, and min must be >= 1.

produce(int, int, int, int, Individual[], EvolutionState, int) - Method in class ristretto.problem.fs.subset.breed.FSSubsetMutationPipeline

Produce n individuals from the given subpopulation and put them into inds[start...start+n-1], where n = Min(Max(q,min),max), where q is the "typical" number of individuals the pipeline produces in one shot, and returns n. max must be >= min, and min must be >= 1.

project(Dataset, boolean[]) - Static method in class ristretto.jmltools.MoreDatasetTools

Generates a new dataset from the input data as a projection of some selected features according to mask.

project(Dataset, TreeSet<Integer>) - Static method in class ristretto.jmltools.MoreDatasetTools

Generates a new dataset from the input data as a projection of some selected features according to some selected features.

project(Dataset, RealMatrix) - Static method in class ristretto.jmltools.MoreDatasetTools

Generates a new dataset from the input data as a projection according to the given projection matrix.

A B C D E F G H I K L M N O P R S T U V W Z
All Classes All Packages