Index (Smile Core 1.0.3 API)

A B C D E F G H I K L M N O P Q R S T U V W X Y

A

AbsoluteDeviation - Class in smile.validation: Absolute deviation error.
AbsoluteDeviation() - Constructor for class smile.validation.AbsoluteDeviation
Accuracy - Class in smile.validation: The accuracy is the proportion of true results (both true positives and true negatives) in the population.
Accuracy() - Constructor for class smile.validation.Accuracy
AdaBoost - Class in smile.classification: AdaBoost (Adaptive Boosting) classifier with decision trees.
AdaBoost(double[][], int[], int) - Constructor for class smile.classification.AdaBoost: Constructor.
AdaBoost(double[][], int[], int, int) - Constructor for class smile.classification.AdaBoost: Constructor.
AdaBoost(Attribute[], double[][], int[], int) - Constructor for class smile.classification.AdaBoost: Constructor.
AdaBoost(Attribute[], double[][], int[], int, int) - Constructor for class smile.classification.AdaBoost: Constructor.
AdaBoost.Trainer - Class in smile.classification: Trainer for AdaBoost classifiers.
AdaBoost.Trainer(int) - Constructor for class smile.classification.AdaBoost.Trainer: Constructor.
AdaBoost.Trainer(Attribute[], int) - Constructor for class smile.classification.AdaBoost.Trainer: Constructor.
add(int[]) - Method in class smile.association.ARM: Add an item set to the database.
add(int[]) - Method in class smile.association.FPGrowth: Add an item set into the object.
add(double[]) - Method in class smile.clustering.BIRCH: Add a data point into CF tree.
add(Feature<T>) - Method in class smile.feature.FeatureSet: Adds a feature generator.
add(E[]) - Method in class smile.neighbor.BKTree: Add a dataset into BK-tree.
add(Collection<E>) - Method in class smile.neighbor.BKTree: Add a dataset into BK-tree.
add(E) - Method in class smile.neighbor.BKTree: Add a datum into the BK-tree.
addChild(String) - Method in class smile.taxonomy.Concept: Add a child to this node
addChild(Concept) - Method in class smile.taxonomy.Concept: Add a child to this node
addKeyword(String) - Method in class smile.taxonomy.Concept: Add a keyword to the concept synset.
addKeywords(String[]) - Method in class smile.taxonomy.Concept: Add a list of synomym to the concept synset.
addKeywords(List<String>) - Method in class smile.taxonomy.Concept: Add a list of synomym to the concept synset.
AdjustedRandIndex - Class in smile.validation: Adjusted Rand Index.
AdjustedRandIndex() - Constructor for class smile.validation.AdjustedRandIndex
adjustedRSquared() - Method in class smile.regression.OLS: Returns adjusted R² statistic.
antecedent - Variable in class smile.association.AssociationRule: Antecedent itemset.
ARM - Class in smile.association: Association Rule Mining.
ARM(int[], int) - Constructor for class smile.association.ARM: Constructor.
ARM(int[][], double) - Constructor for class smile.association.ARM: Constructor.
ARM(int[][], int) - Constructor for class smile.association.ARM: Constructor.
AssociationRule - Class in smile.association: Association rule object.
AssociationRule(int[], int[], double, double) - Constructor for class smile.association.AssociationRule: Constructor.
attributes() - Method in class smile.feature.DateFeature
attributes() - Method in interface smile.feature.Feature: Returns the variable attributes of generated features.
attributes() - Method in class smile.feature.FeatureSet: Returns the variable attributes of generated features.
attributes() - Method in class smile.feature.Nominal2Binary
attributes() - Method in class smile.feature.NumericAttributeFeature
attributes() - Method in interface smile.feature.SequenceFeature: Returns the variable attributes of generated features.
AverageImputation - Class in smile.imputation: Impute missing values with the average of other attributes in the instance.
AverageImputation() - Constructor for class smile.imputation.AverageImputation: Constructor.

B

Bag<T> - Class in smile.feature: The bag-of-words feature of text used in natural language processing and information retrieval.
Bag(T[]) - Constructor for class smile.feature.Bag: Constructor.
Bag(T[], boolean) - Constructor for class smile.feature.Bag: Constructor.
BestLocalizedWavelet - Class in smile.wavelet: Best localized wavelets.
BestLocalizedWavelet(int) - Constructor for class smile.wavelet.BestLocalizedWavelet: Constructor.
BIRCH - Class in smile.clustering: Balanced Iterative Reducing and Clustering using Hierarchies.
BIRCH(int, int, double) - Constructor for class smile.clustering.BIRCH: Constructor.
bits() - Method in class smile.gap.BitString: Returns the bit string of chromosome.
BitString - Class in smile.gap: The standard bit string representation of the solution domain.
BitString(int, FitnessMeasure<BitString>) - Constructor for class smile.gap.BitString: Constructor.
BitString(int, FitnessMeasure<BitString>, BitString.Crossover, double, double) - Constructor for class smile.gap.BitString: Constructor.
BitString(int[], FitnessMeasure<BitString>) - Constructor for class smile.gap.BitString: Constructor.
BitString(int[], FitnessMeasure<BitString>, BitString.Crossover, double, double) - Constructor for class smile.gap.BitString: Constructor.
BitString.Crossover - Enum in smile.gap: The types of crossover operation.
BKTree<E> - Class in smile.neighbor: A BK-tree is a metric tree specifically adapted to discrete metric spaces.
BKTree(Metric<E>) - Constructor for class smile.neighbor.BKTree: Constructor.
bmu() - Method in class smile.clustering.SOM: Returns the best matched unit for each sample.
Bootstrap - Class in smile.validation: The bootstrap is a general tool for assessing statistical accuracy.
Bootstrap(int, int) - Constructor for class smile.validation.Bootstrap: Constructor.
bootstrap(int, ClassifierTrainer<T>, T[], int[]) - Static method in class smile.validation.Validation: Bootstrap accuracy estimation of a classification model.
bootstrap(int, RegressionTrainer<T>, T[], double[]) - Static method in class smile.validation.Validation: Bootstrap RMSE estimation of a regression model.
bootstrap(int, ClassifierTrainer<T>, T[], int[], ClassificationMeasure) - Static method in class smile.validation.Validation: Bootstrap performance estimation of a classification model.
bootstrap(int, ClassifierTrainer<T>, T[], int[], ClassificationMeasure[]) - Static method in class smile.validation.Validation: Bootstrap performance estimation of a classification model.
bootstrap(int, RegressionTrainer<T>, T[], double[], RegressionMeasure) - Static method in class smile.validation.Validation: Bootstrap performance estimation of a regression model.
bootstrap(int, RegressionTrainer<T>, T[], double[], RegressionMeasure[]) - Static method in class smile.validation.Validation: Bootstrap performance estimation of a regression model.

C

calculate(int[]) - Method in interface smile.regression.RegressionTree.NodeOutput: Calculate the node output.
centroids() - Method in class smile.clustering.BIRCH: Returns the representatives of clusters.
centroids() - Method in class smile.clustering.KMeans: Returns the centroids.
centroids() - Method in class smile.clustering.SIB: Returns the centroids.
Chromosome - Interface in smile.gap: Artificial chromosomes in genetic algorithm/programming encoding candidate solutions to an optimization problem.
CLARANS<T> - Class in smile.clustering: Clustering Large Applications based upon RANdomized Search.
CLARANS(T[], Distance<T>, int) - Constructor for class smile.clustering.CLARANS: Constructor.
CLARANS(T[], Distance<T>, int, int) - Constructor for class smile.clustering.CLARANS: Constructor.
CLARANS(T[], Distance<T>, int, int, int) - Constructor for class smile.clustering.CLARANS: Constructor.
ClassificationMeasure - Interface in smile.validation: An abstract interface to measure the classification performance.
Classifier<T> - Interface in smile.classification: A classifier assigns an input object into one of a given number of categories.
ClassifierTrainer<T> - Class in smile.classification: Abstract classifier trainer.
ClassifierTrainer() - Constructor for class smile.classification.ClassifierTrainer: Constructor.
ClassifierTrainer(Attribute[]) - Constructor for class smile.classification.ClassifierTrainer: Constructor.
clone() - Method in class smile.classification.NeuralNetwork
cluster - Variable in class smile.clustering.SOM.Neuron: Cluster id of this neuron.
Clustering<T> - Interface in smile.clustering: Clustering interface.
ClusterMeasure - Interface in smile.validation: An abstract interface to measure the clustering performance.
coefficients() - Method in class smile.regression.GaussianProcessRegression: Returns the coefficients.
coefficients() - Method in class smile.regression.LASSO: Returns the linear coefficients.
coefficients() - Method in class smile.regression.OLS: Returns the linear coefficients (without intercept).
coefficients() - Method in class smile.regression.RidgeRegression: Returns the (scaled) linear coefficients.
CoifletWavelet - Class in smile.wavelet: Coiflet wavelets.
CoifletWavelet(int) - Constructor for class smile.wavelet.CoifletWavelet: Constructor.
compareTo(Chromosome) - Method in class smile.gap.BitString
compareTo(Neighbor<K, V>) - Method in class smile.neighbor.Neighbor
CompleteLinkage - Class in smile.clustering.linkage: Complete linkage.
CompleteLinkage(double[][]) - Constructor for class smile.clustering.linkage.CompleteLinkage: Constructor.
Concept - Class in smile.taxonomy: Concept is a set of synonyms, i.e.
Concept(Concept) - Constructor for class smile.taxonomy.Concept: Constructor.
Concept(Concept, String) - Constructor for class smile.taxonomy.Concept: Constructor.
Concept(Concept, String[]) - Constructor for class smile.taxonomy.Concept: Constructor.
Concept(Concept, List<String>) - Constructor for class smile.taxonomy.Concept: Constructor.
confidence - Variable in class smile.association.AssociationRule: The confidence value.
ConfusionMatrix - Class in smile.validation: Generates the confusion matrix based on truth and prediction vectors
ConfusionMatrix(int[], int[]) - Constructor for class smile.validation.ConfusionMatrix
consequent - Variable in class smile.association.AssociationRule: Consequent itemset.
CoverTree<E> - Class in smile.neighbor: Cover tree is a data structure for generic nearest neighbor search, which is especially efficient in spaces with small intrinsic dimension.
CoverTree(E[], Metric<E>) - Constructor for class smile.neighbor.CoverTree: Constructor.
CoverTree(E[], Metric<E>, double) - Constructor for class smile.neighbor.CoverTree: Constructor.
CRF - Class in smile.sequence: First-order linear conditional random field.
CRF.Trainer - Class in smile.sequence: Trainer for CRF.
CRF.Trainer(Attribute[], int) - Constructor for class smile.sequence.CRF.Trainer: Constructor.
CRF.Trainer(int, int) - Constructor for class smile.sequence.CRF.Trainer: Constructor.
crossover(Chromosome) - Method in class smile.gap.BitString
crossover(Chromosome) - Method in interface smile.gap.Chromosome: Returns a pair of offsprings by crossovering this one with another one according to the crossover rate, which determines how often will be crossover performed.
CrossValidation - Class in smile.validation: Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set.
CrossValidation(int, int) - Constructor for class smile.validation.CrossValidation: Constructor.
cv(int, ClassifierTrainer<T>, T[], int[]) - Static method in class smile.validation.Validation: Cross validation of a classification model.
cv(int, RegressionTrainer<T>, T[], double[]) - Static method in class smile.validation.Validation: Cross validation of a regression model.
cv(int, ClassifierTrainer<T>, T[], int[], ClassificationMeasure) - Static method in class smile.validation.Validation: Cross validation of a classification model.
cv(int, ClassifierTrainer<T>, T[], int[], ClassificationMeasure[]) - Static method in class smile.validation.Validation: Cross validation of a classification model.
cv(int, RegressionTrainer<T>, T[], double[], RegressionMeasure) - Static method in class smile.validation.Validation: Cross validation of a regression model.
cv(int, RegressionTrainer<T>, T[], double[], RegressionMeasure[]) - Static method in class smile.validation.Validation: Cross validation of a regression model.

D

d(String, String) - Method in class smile.taxonomy.TaxonomicDistance: Compute the distance between two concepts in a taxonomy.
d(Concept, Concept) - Method in class smile.taxonomy.TaxonomicDistance: Compute the distance between two concepts in a taxonomy.
D4Wavelet - Class in smile.wavelet: The simplest and most localized wavelet, Daubechies wavelet of 4 coefficients.
D4Wavelet() - Constructor for class smile.wavelet.D4Wavelet: Constructor.
DateFeature - Class in smile.feature: Date/time feature generator.
DateFeature(Attribute[], DateFeature.Type[]) - Constructor for class smile.feature.DateFeature: Constructor.
DateFeature.Type - Enum in smile.feature: The types of date/time features.
DaubechiesWavelet - Class in smile.wavelet: Daubechies wavelets.
DaubechiesWavelet(int) - Constructor for class smile.wavelet.DaubechiesWavelet: Constructor.
DBScan<T> - Class in smile.clustering: Density-Based Spatial Clustering of Applications with Noise.
DBScan(T[], Distance<T>, int, double) - Constructor for class smile.clustering.DBScan: Constructor.
DBScan(T[], Metric<T>, int, double) - Constructor for class smile.clustering.DBScan: Constructor.
DBScan(T[], RNNSearch<T, T>, int, double) - Constructor for class smile.clustering.DBScan: Clustering the data.
DecisionTree - Class in smile.classification: Decision tree for classification.
DecisionTree(double[][], int[], int) - Constructor for class smile.classification.DecisionTree: Constructor.
DecisionTree(double[][], int[], int, DecisionTree.SplitRule) - Constructor for class smile.classification.DecisionTree: Constructor.
DecisionTree(Attribute[], double[][], int[], int) - Constructor for class smile.classification.DecisionTree: Constructor.
DecisionTree(Attribute[], double[][], int[], int, DecisionTree.SplitRule) - Constructor for class smile.classification.DecisionTree: Constructor.
DecisionTree.SplitRule - Enum in smile.classification: The criterion to choose variable to split instances.
DecisionTree.Trainer - Class in smile.classification: Trainer for decision tree classifiers.
DecisionTree.Trainer(int) - Constructor for class smile.classification.DecisionTree.Trainer: Constructor.
DecisionTree.Trainer(Attribute[], int) - Constructor for class smile.classification.DecisionTree.Trainer: Constructor.
DENCLUE - Class in smile.clustering: DENsity CLUstering.
DENCLUE(double[][], double, int) - Constructor for class smile.clustering.DENCLUE: Constructor.
denoise(double[], Wavelet) - Static method in class smile.wavelet.WaveletShrinkage: Adaptive hard-thresholding denoising a time series with given wavelet.
denoise(double[], Wavelet, boolean) - Static method in class smile.wavelet.WaveletShrinkage: Adaptive denoising a time series with given wavelet.
DeterministicAnnealing - Class in smile.clustering: Deterministic annealing clustering.
DeterministicAnnealing(double[][], int) - Constructor for class smile.clustering.DeterministicAnnealing: Constructor.
DeterministicAnnealing(double[][], int, double) - Constructor for class smile.clustering.DeterministicAnnealing: Constructor.
df() - Method in class smile.regression.OLS: Returns the degree-of-freedom of residual standard error.
dimension() - Method in class smile.clustering.BIRCH: Returns the dimensionality of data.
distance - Variable in class smile.clustering.SOM.Neuron: The distance to neighbors.
distance - Variable in class smile.neighbor.Neighbor: The distance between the query and the neighbor.
distortion() - Method in class smile.clustering.CLARANS: Returns the distortion.
distortion() - Method in class smile.clustering.KMeans: Returns the distortion.
distortion() - Method in class smile.clustering.SIB: Returns the distortion.
distortion() - Method in class smile.clustering.SpectralClustering: Returns the distortion in feature space.

E

entropy() - Method in class smile.clustering.MEC: Returns the cluster conditional entropy.
equals(Object) - Method in class smile.association.AssociationRule
equals(Object) - Method in class smile.association.ItemSet
error() - Method in class smile.classification.RandomForest: Returns the out-of-bag estimation of error rate.
error() - Method in class smile.regression.OLS: Returns the residual standard error.
error() - Method in class smile.regression.RandomForest: Returns the out-of-bag estimation of RMSE.
evolve(int) - Method in class smile.gap.GeneticAlgorithm: Performs genetic algorithm for a given number of generations.
evolve(int, double) - Method in class smile.gap.GeneticAlgorithm: Performs genetic algorithm until the given number of generations is reached or the best fitness is larger than the given threshold.
evolve() - Method in interface smile.gap.LamarckianChromosome: Performs a step of (hill-climbing) local search to evolve this chromosome.

F

f(double[], int) - Method in class smile.feature.DateFeature
f(T, int) - Method in interface smile.feature.Feature: Generates a feature for given object.
f(T) - Method in class smile.feature.FeatureSet: Returns generated feature values.
f(T[]) - Method in class smile.feature.FeatureSet: Returns a dataset with generated features.
f(Dataset<T>) - Method in class smile.feature.FeatureSet: Returns an attribute dataset with generated features.
f(double[], int) - Method in class smile.feature.Nominal2Binary
f(double[]) - Method in class smile.feature.Nominal2SparseBinary: Generates the compact representation of sparse binary features for given object.
f(double[], int) - Method in class smile.feature.NumericAttributeFeature
f(T[], int) - Method in interface smile.feature.SequenceFeature: Generates the feature set of sequence at given index.
Fallout - Class in smile.validation: Fall-out, false alarm rate, or false positive rate (FPR)
Fallout() - Constructor for class smile.validation.Fallout
FDR - Class in smile.validation: The false discovery rate (FDR) is ratio of false positives to combined true and false positives, which is actually 1 - precision.
FDR() - Constructor for class smile.validation.FDR
feature(T[]) - Method in class smile.feature.Bag: Returns the bag-of-words features of a document.
Feature<T> - Interface in smile.feature: Feature generator.
FeatureRanking - Interface in smile.feature: Univariate feature ranking metric.
FeatureSet<T> - Class in smile.feature: A set of feature generators.
FeatureSet() - Constructor for class smile.feature.FeatureSet: Constructor.
featureset(double[], int) - Method in class smile.sequence.CRF: Returns a feature set with the class label of previous position.
featureset(int[], int) - Method in class smile.sequence.CRF: Returns a feature set with the class label of previous position.
finish() - Method in class smile.classification.SVM: Process support vectors until converge.
fit(T) - Method in interface smile.gap.FitnessMeasure: Returns the non-negative fitness value of a chromosome.
fitness() - Method in class smile.gap.BitString
fitness() - Method in interface smile.gap.Chromosome: Returns the fitness of chromosome.
FitnessMeasure<T extends Chromosome> - Interface in smile.gap: A measure to evaluate the fitness of chromosomes.
FLD - Class in smile.classification: Fisher's linear discriminant.
FLD(double[][], int[]) - Constructor for class smile.classification.FLD: Constructor.
FLD(double[][], int[], int) - Constructor for class smile.classification.FLD: Constructor.
FLD(double[][], int[], int, double) - Constructor for class smile.classification.FLD: Constructor.
FLD.Trainer - Class in smile.classification: Trainer for Fisher's linear discriminant.
FLD.Trainer() - Constructor for class smile.classification.FLD.Trainer: Constructor.
FMeasure - Class in smile.validation: The F-measure (also F1 score or F-score) considers both the precision p and the recall r of the test to compute the score.
FMeasure() - Constructor for class smile.validation.FMeasure
FPGrowth - Class in smile.association: Frequent item set mining based on the FP-growth (frequent pattern growth) algorithm, which employs an extended prefix-tree (FP-tree) structure to store the database in a compressed form.
FPGrowth(int[], int) - Constructor for class smile.association.FPGrowth: Constructor.
FPGrowth(int[][], double) - Constructor for class smile.association.FPGrowth: Constructor.
FPGrowth(int[][], int) - Constructor for class smile.association.FPGrowth: Constructor.
ftest() - Method in class smile.regression.OLS: Returns the F-statistic of goodness-of-fit.

G

GAFeatureSelection - Class in smile.feature: Genetic algorithm based feature selection.
GAFeatureSelection() - Constructor for class smile.feature.GAFeatureSelection: Constructor.
GAFeatureSelection(GeneticAlgorithm.Selection, BitString.Crossover, double, double) - Constructor for class smile.feature.GAFeatureSelection: Constructor.
GaussianProcessRegression<T> - Class in smile.regression: Gaussian Process for Regression.
GaussianProcessRegression(T[], double[], MercerKernel<T>, double) - Constructor for class smile.regression.GaussianProcessRegression: Constructor.
GaussianProcessRegression(T[], double[], T[], MercerKernel<T>, double) - Constructor for class smile.regression.GaussianProcessRegression: Constructor.
GaussianProcessRegression.Trainer<T> - Class in smile.regression: Trainer for Gaussian Process for Regression.
GaussianProcessRegression.Trainer(MercerKernel<T>, double) - Constructor for class smile.regression.GaussianProcessRegression.Trainer: Constructor.
GeneticAlgorithm<T extends Chromosome> - Class in smile.gap: A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution.
GeneticAlgorithm(T[]) - Constructor for class smile.gap.GeneticAlgorithm: Constructor.
GeneticAlgorithm(T[], GeneticAlgorithm.Selection) - Constructor for class smile.gap.GeneticAlgorithm: Constructor.
GeneticAlgorithm.Selection - Enum in smile.gap: The way to select chromosomes from the population as parents to crossover.
getAlpha() - Method in class smile.clustering.DeterministicAnnealing: Returns the annealing parameter.
getBrachingFactor() - Method in class smile.clustering.BIRCH: Returns the branching factor, which is the maximum number of children nodes.
getC() - Method in class smile.regression.SVR: Returns the soft margin penalty parameter.
getCenter() - Method in class smile.projection.PCA: Returns the center of data.
getCenter() - Method in class smile.projection.PPCA: Returns the center of data.
getChildren() - Method in class smile.taxonomy.Concept: Get all children concepts.
getClusterLabel() - Method in class smile.clustering.PartitionClustering: Returns the cluster labels of data.
getClusterLabel() - Method in class smile.clustering.SpectralClustering: Returns the cluster labels of data.
getClusterSize() - Method in class smile.clustering.PartitionClustering: Returns the size of clusters.
getClusterSize() - Method in class smile.clustering.SpectralClustering: Returns the size of clusters.
getConcept(String) - Method in class smile.taxonomy.Taxonomy: Returns a concept node which synset contains the keyword.
getConcepts() - Method in class smile.taxonomy.Taxonomy: Returns all named concepts from this taxonomy
getCoordinates() - Method in class smile.manifold.IsoMap: Returns the coordinates of projected data.
getCoordinates() - Method in class smile.manifold.LaplacianEigenmap: Returns the coordinates of projected data.
getCoordinates() - Method in class smile.manifold.LLE: Returns the coordinates of projected data.
getCoordinates() - Method in class smile.mds.IsotonicMDS: Returns the coordinates of projected data.
getCoordinates() - Method in class smile.mds.MDS: Returns the principal coordinates of projected data.
getCoordinates() - Method in class smile.mds.SammonMapping: Returns the coordinates of projected data.
getCoordinates() - Method in class smile.projection.KPCA: Returns the nonlinear principal component scores, i.e., the representation of learning data in the nonlinear principal component space.
getCumulativeVarianceProportion() - Method in class smile.projection.PCA: Returns the cumulative proportion of variance contained in principal components, ordered from largest to smallest.
getDensityAttractors() - Method in class smile.clustering.DENCLUE: Returns the density attractors of cluster.
getDimension() - Method in class smile.classification.Maxent: Returns the dimension of input space.
getEigenValues() - Method in class smile.mds.MDS: Returns the component scores, ordered from largest to smallest.
getElitism() - Method in class smile.gap.GeneticAlgorithm: Returns the number of best chromosomes to copy to new population.
getEpsilon() - Method in class smile.regression.SVR: Returns the loss function error threshold.
getGaussianKernelWidth() - Method in class smile.clustering.SpectralClustering: Returns the width of Gaussian kernel.
getHeatKernelWidth() - Method in class smile.manifold.LaplacianEigenmap: Returns the width of heat kernel.
getHeight() - Method in class smile.clustering.HierarchicalClustering: Returns a set of n-1 non-decreasing real values, which are the clustering height, i.e., the value of the criterion associated with the clustering method for the particular agglomeration.
getIndex() - Method in class smile.manifold.IsoMap: Returns the original sample index.
getIndex() - Method in class smile.manifold.LaplacianEigenmap: Returns the original sample index.
getIndex() - Method in class smile.manifold.LLE: Returns the original sample index.
getInitialStateProbabilities() - Method in class smile.sequence.HMM: Returns the initial state probabilities.
getKeywords() - Method in class smile.taxonomy.Concept: Returns the concept synonym set.
getLearningRate() - Method in class smile.classification.NeuralNetwork: Returns the learning rate.
getLearningRate() - Method in class smile.projection.GHA: Returns the learning rate.
getLoadings() - Method in class smile.projection.PCA: Returns the variable loading matrix, ordered from largest to smallest by corresponding eigenvalues.
getLoadings() - Method in class smile.projection.PPCA: Returns the variable loading matrix, ordered from largest to smallest by corresponding eigenvalues.
getLocalSearchSteps() - Method in class smile.gap.GeneticAlgorithm: Gets the number of iterations of local search for Lamarckian algorithm.
getLossFunction() - Method in class smile.regression.GradientTreeBoost: Returns the loss function.
getMatrix() - Method in class smile.validation.ConfusionMatrix
getMaxNeighbor() - Method in class smile.clustering.CLARANS: Returns the maximum number of neighbors examined during a search of local minima.
getMaxRadius() - Method in class smile.clustering.BIRCH: Returns the maximum radius of a sub-cluster.
getMinPts() - Method in class smile.clustering.DBScan: Returns the parameter of minimum number of neighbors.
getMomentum() - Method in class smile.classification.NeuralNetwork: Returns the momentum factor.
getNearestNeighborGraph() - Method in class smile.manifold.IsoMap: Returns the nearest neighbor graph.
getNearestNeighborGraph() - Method in class smile.manifold.LaplacianEigenmap: Returns the nearest neighbor graph.
getNearestNeighborGraph() - Method in class smile.manifold.LLE: Returns the nearest neighbor graph.
getNoiseVariance() - Method in class smile.projection.PPCA: Returns the variance of noise.
getNumClusters() - Method in class smile.clustering.PartitionClustering: Returns the number of clusters.
getNumClusters() - Method in class smile.clustering.SpectralClustering: Returns the number of clusters.
getNumLeaves() - Method in class smile.regression.GradientTreeBoost: Returns the (maximum) number of leaves in decision tree.
getNumLocalMinima() - Method in class smile.clustering.CLARANS: Returns the number of local minima to search for.
getPathFromRoot() - Method in class smile.taxonomy.Concept: Returns the path from root to the given node.
getPathToRoot() - Method in class smile.taxonomy.Concept: Returns the path from the given node to the root.
getPriori() - Method in class smile.classification.LDA: Returns a priori probabilities.
getPriori() - Method in class smile.classification.NaiveBayes: Returns a priori probabilities.
getPriori() - Method in class smile.classification.QDA: Returns a priori probabilities.
getPriori() - Method in class smile.classification.RDA: Returns a priori probabilities.
getProjection() - Method in class smile.classification.FLD: Returns the projection matrix W.
getProjection() - Method in class smile.projection.GHA: Returns the projection matrix.
getProjection() - Method in class smile.projection.KPCA: Returns the projection matrix.
getProjection() - Method in class smile.projection.PCA: Returns the projection matrix W.
getProjection() - Method in class smile.projection.PPCA: Returns the projection matrix.
getProjection() - Method in class smile.projection.RandomProjection: Returns the projection matrix.
getProportion() - Method in class smile.mds.MDS: Returns the proportion of variance contained in each eigenvectors, ordered from largest to smallest.
getProximity() - Method in class smile.clustering.linkage.Linkage: Returns the proximity matrix.
getRadius() - Method in class smile.clustering.DBScan: Returns the radius of neighborhood.
getRadius() - Method in class smile.clustering.MEC: Returns the radius of neighborhood.
getRoot() - Method in class smile.taxonomy.Taxonomy: Returns the root node of taxonomy tree.
getSamplingRate() - Method in class smile.regression.GradientTreeBoost: Returns the sampling rate for stochastic gradient tree boosting.
getSigma() - Method in class smile.clustering.DENCLUE: Returns the smooth (standard deviation) parameter in the Gaussian kernel.
getStateTransitionProbabilities() - Method in class smile.sequence.HMM: Returns the state transition probabilities.
getStress() - Method in class smile.mds.IsotonicMDS: Returns the final stress achieved.
getStress() - Method in class smile.mds.SammonMapping: Returns the final stress achieved.
getSymbolEmissionProbabilities() - Method in class smile.sequence.HMM: Returns the symbol emission probabilities.
getThreadPoolSize() - Static method in class smile.util.MulticoreExecutor: Returns the number of threads in the thread pool.
getTolerance() - Method in class smile.regression.SVR: Returns the tolerance of convergence test.
getTournamentProbability() - Method in class smile.gap.GeneticAlgorithm: Returns the best-player-wins probability in tournament selection.
getTournamentSize() - Method in class smile.gap.GeneticAlgorithm: Returns the tournament size in tournament selection.
getTree() - Method in class smile.clustering.HierarchicalClustering: Returns an n-1 by 2 matrix of which row i describes the merging of clusters at step i of the clustering.
getVariance() - Method in class smile.projection.PCA: Returns the principal component variances, ordered from largest to smallest, which are the eigenvalues of the covariance or correlation matrix of learning data.
getVarianceProportion() - Method in class smile.projection.PCA: Returns the proportion of variance contained in each principal component, ordered from largest to smallest.
getVariances() - Method in class smile.projection.KPCA: Returns the eigenvalues of kernel principal components, ordered from largest to smallest.
getWeightDecay() - Method in class smile.classification.NeuralNetwork: Returns the weight decay factor.
GHA - Class in smile.projection: Generalized Hebbian Algorithm.
GHA(int, int, double) - Constructor for class smile.projection.GHA: Constructor.
GHA(double[][], double) - Constructor for class smile.projection.GHA: Constructor.
GMeans - Class in smile.clustering: G-Means clustering algorithm, an extended K-Means which tries to automatically determine the number of clusters by normality test.
GMeans(double[][], int) - Constructor for class smile.clustering.GMeans: Constructor.
GradientTreeBoost - Class in smile.classification: Gradient boosting for classification.
GradientTreeBoost(double[][], int[], int) - Constructor for class smile.classification.GradientTreeBoost: Constructor.
GradientTreeBoost(double[][], int[], int, int, double, double) - Constructor for class smile.classification.GradientTreeBoost: Constructor.
GradientTreeBoost(Attribute[], double[][], int[], int) - Constructor for class smile.classification.GradientTreeBoost: Constructor.
GradientTreeBoost(Attribute[], double[][], int[], int, int, double, double) - Constructor for class smile.classification.GradientTreeBoost: Constructor.
GradientTreeBoost - Class in smile.regression: Gradient boosting for regression.
GradientTreeBoost(double[][], double[], int) - Constructor for class smile.regression.GradientTreeBoost: Constructor.
GradientTreeBoost(double[][], double[], GradientTreeBoost.Loss, int, int, double, double) - Constructor for class smile.regression.GradientTreeBoost: Constructor.
GradientTreeBoost(Attribute[], double[][], double[], int) - Constructor for class smile.regression.GradientTreeBoost: Constructor.
GradientTreeBoost(Attribute[], double[][], double[], GradientTreeBoost.Loss, int, int, double, double) - Constructor for class smile.regression.GradientTreeBoost: Constructor.
GradientTreeBoost.Loss - Enum in smile.regression: Regression loss function.
GradientTreeBoost.Trainer - Class in smile.classification: Trainer for GradientTreeBoost classifiers.
GradientTreeBoost.Trainer(int) - Constructor for class smile.classification.GradientTreeBoost.Trainer: Constructor.
GradientTreeBoost.Trainer(Attribute[], int) - Constructor for class smile.classification.GradientTreeBoost.Trainer: Constructor.
GradientTreeBoost.Trainer - Class in smile.regression: Trainer for GradientTreeBoost regression.
GradientTreeBoost.Trainer(int) - Constructor for class smile.regression.GradientTreeBoost.Trainer: Constructor.
GradientTreeBoost.Trainer(Attribute[], int) - Constructor for class smile.regression.GradientTreeBoost.Trainer: Constructor.
GrowingNeuralGas - Class in smile.clustering: Growing Neural Gas.
GrowingNeuralGas(int) - Constructor for class smile.clustering.GrowingNeuralGas: Constructor.
GrowingNeuralGas(int, double, double, int, int, double, double) - Constructor for class smile.clustering.GrowingNeuralGas: Constructor.
GrowingNeuralGas.Neuron - Class in smile.clustering: The neuron vertex in the growing neural gas network.
GrowingNeuralGas.Neuron(double[], GrowingNeuralGas.Neuron[]) - Constructor for class smile.clustering.GrowingNeuralGas.Neuron: Constructor.

H

HaarWavelet - Class in smile.wavelet: Haar wavelet.
HaarWavelet() - Constructor for class smile.wavelet.HaarWavelet: Constructor.
hashCode() - Method in class smile.association.AssociationRule
hashCode() - Method in class smile.association.ItemSet
HierarchicalClustering - Class in smile.clustering: Agglomerative Hierarchical Clustering.
HierarchicalClustering(Linkage) - Constructor for class smile.clustering.HierarchicalClustering: Constructor.
HMM<O> - Class in smile.sequence: First-order Hidden Markov Model.
HMM(double[], double[][], double[][]) - Constructor for class smile.sequence.HMM: Constructor.
HMM(double[], double[][], double[][], O[]) - Constructor for class smile.sequence.HMM: Constructor.
HMM(int[][], int[][]) - Constructor for class smile.sequence.HMM: Learn an HMM from labeled observation sequences by maximum likelihood estimation.
HMM(O[][], int[][]) - Constructor for class smile.sequence.HMM: Learn an HMM from labeled observation sequences by maximum likelihood estimation.

I

importance() - Method in class smile.classification.AdaBoost: Returns the variable importance.
importance() - Method in class smile.classification.DecisionTree: Returns the variable importance.
importance() - Method in class smile.classification.GradientTreeBoost: Returns the variable importance.
importance() - Method in class smile.classification.RandomForest: Returns the variable importance.
importance() - Method in class smile.regression.GradientTreeBoost: Returns the variable importance.
importance() - Method in class smile.regression.RandomForest: Returns the variable importance.
importance() - Method in class smile.regression.RegressionTree: Returns the variable importance.
impute(double[][]) - Method in class smile.imputation.AverageImputation
impute(double[][]) - Method in class smile.imputation.KMeansImputation
impute(double[][]) - Method in class smile.imputation.KNNImputation
impute(double[][]) - Method in class smile.imputation.LLSImputation
impute(double[][]) - Method in interface smile.imputation.MissingValueImputation: Impute missing values in the dataset.
impute(double[][]) - Method in class smile.imputation.SVDImputation
impute(double[][], int) - Method in class smile.imputation.SVDImputation: Impute missing values in the dataset.
index - Variable in class smile.neighbor.Neighbor: The index of neighbor object in the dataset.
intercept() - Method in class smile.regression.LASSO: Returns the intercept.
intercept() - Method in class smile.regression.OLS: Returns the intercept.
intercept() - Method in class smile.regression.RidgeRegression: Returns the (centered) intercept.
inverse(double[]) - Method in class smile.wavelet.Wavelet: Inverse discrete wavelet transform.
isAncestorOf(Concept) - Method in class smile.taxonomy.Concept: Returns true if this concept is an ancestor of the given concept.
isIdenticalExcluded() - Method in class smile.neighbor.BKTree: Get whether if query object self be excluded from the neighborhood.
isIdenticalExcluded() - Method in class smile.neighbor.CoverTree: Get whether if query object self be excluded from the neighborhood.
isIdenticalExcluded() - Method in class smile.neighbor.KDTree: Get whether if query object self be excluded from the neighborhood.
isIdenticalExcluded() - Method in class smile.neighbor.LinearSearch: Get whether if query object self be excluded from the neighborhood.
isIdenticalExcluded() - Method in class smile.neighbor.LSH: Get whether if query object self be excluded from the neighborhood.
isIdenticalExcluded() - Method in class smile.neighbor.MPLSH: Get whether if query object self be excluded from the neighborhood.
isLeaf() - Method in class smile.taxonomy.Concept: Check if a node is a leaf in the taxonomy tree.
IsoMap - Class in smile.manifold: Isometric feature mapping.
IsoMap(double[][], int, int) - Constructor for class smile.manifold.IsoMap: Constructor.
IsoMap(double[][], int, int, boolean) - Constructor for class smile.manifold.IsoMap: Constructor.
IsotonicMDS - Class in smile.mds: Kruskal's nonmetric MDS.
IsotonicMDS(double[][]) - Constructor for class smile.mds.IsotonicMDS: Constructor.
IsotonicMDS(double[][], int) - Constructor for class smile.mds.IsotonicMDS: Constructor.
IsotonicMDS(double[][], double[][]) - Constructor for class smile.mds.IsotonicMDS: Constructor.
IsotonicMDS(double[][], int, double, int) - Constructor for class smile.mds.IsotonicMDS: Constructor.
IsotonicMDS(double[][], double[][], double, int) - Constructor for class smile.mds.IsotonicMDS: Constructor.
isViterbi() - Method in class smile.sequence.CRF: Returns true if using Viterbi algorithm for sequence labeling.
items - Variable in class smile.association.ItemSet: The set of items.
ItemSet - Class in smile.association: A set of items.
ItemSet(int[], int) - Constructor for class smile.association.ItemSet: Constructor.

K

k - Variable in class smile.validation.Bootstrap: The number of rounds of cross validation.
k - Variable in class smile.validation.CrossValidation: The number of rounds of cross validation.
KDTree<E> - Class in smile.neighbor: A KD-tree (short for k-dimensional tree) is a space-partitioning dataset structure for organizing points in a k-dimensional space.
KDTree(double[][], E[]) - Constructor for class smile.neighbor.KDTree: Constructor.
key - Variable in class smile.neighbor.Neighbor: The key of neighbor.
KMeans - Class in smile.clustering: K-Means learn aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.
KMeans(double[][], int) - Constructor for class smile.clustering.KMeans: Constructor.
KMeans(double[][], int, int) - Constructor for class smile.clustering.KMeans: Constructor.
KMeans(double[][], int, int, int) - Constructor for class smile.clustering.KMeans: Clustering data into k clusters.
KMeansImputation - Class in smile.imputation: Missing value imputation by K-Means clustering.
KMeansImputation(int) - Constructor for class smile.imputation.KMeansImputation: Constructor.
KMeansImputation(int, int) - Constructor for class smile.imputation.KMeansImputation: Constructor.
KNN<T> - Class in smile.classification: K-nearest neighbor classifier.
KNN(KNNSearch<T, T>, int[], int) - Constructor for class smile.classification.KNN: Constructor.
KNN(T[], int[], Distance<T>) - Constructor for class smile.classification.KNN: Constructor.
KNN(T[], int[], Distance<T>, int) - Constructor for class smile.classification.KNN: Learn the K-NN classifier from data of any generalized type with a given distance definition.
knn(E, int) - Method in class smile.neighbor.CoverTree
knn(double[], int) - Method in class smile.neighbor.KDTree
knn(K, int) - Method in interface smile.neighbor.KNNSearch: Search the k nearest neighbors to the query.
knn(T, int) - Method in class smile.neighbor.LinearSearch
knn(double[], int) - Method in class smile.neighbor.LSH
knn(double[], int) - Method in class smile.neighbor.MPLSH
knn(double[], int, double, int) - Method in class smile.neighbor.MPLSH: Returns the approximate k-nearest neighbors.
knn(SNLSH.AbstractSentence, int) - Method in class smile.neighbor.SNLSH
KNN.Trainer<T> - Class in smile.classification: Trainer for KNN classifier.
KNN.Trainer(Distance<T>, int) - Constructor for class smile.classification.KNN.Trainer: Constructor.
KNNImputation - Class in smile.imputation: Missing value imputation by k-nearest neighbors.
KNNImputation(int) - Constructor for class smile.imputation.KNNImputation: Constructor.
KNNSearch<K,V> - Interface in smile.neighbor: K-nearest neighbor search identifies the top k nearest neighbors to the query.
KPCA<T> - Class in smile.projection: Kernel principal component analysis.
KPCA(T[], MercerKernel<T>, double) - Constructor for class smile.projection.KPCA: Constructor.
KPCA(T[], MercerKernel<T>, int) - Constructor for class smile.projection.KPCA: Constructor.
KPCA(T[], MercerKernel<T>, int, double) - Constructor for class smile.projection.KPCA: Constructor.

L

LamarckianChromosome - Interface in smile.gap: Artificial chromosomes used in Lamarckian algorithm that is a hybrid of of evolutionary computation and a local improver such as hill-climbing.
LaplacianEigenmap - Class in smile.manifold: Laplacian Eigenmap.
LaplacianEigenmap(double[][], int, int) - Constructor for class smile.manifold.LaplacianEigenmap: Constructor.
LaplacianEigenmap(double[][], int, int, double) - Constructor for class smile.manifold.LaplacianEigenmap: Constructor.
LASSO - Class in smile.regression: Least absolute shrinkage and selection operator.
LASSO(double[][], double[], double) - Constructor for class smile.regression.LASSO: Constructor.
LASSO(double[][], double[], double, double, int) - Constructor for class smile.regression.LASSO: Constructor.
LASSO.Trainer - Class in smile.regression: Trainer for LASSO regression.
LASSO.Trainer(double) - Constructor for class smile.regression.LASSO.Trainer: Constructor.
LDA - Class in smile.classification: Linear discriminant analysis.
LDA(double[][], int[]) - Constructor for class smile.classification.LDA: Constructor.
LDA(double[][], int[], double[]) - Constructor for class smile.classification.LDA: Constructor.
LDA(double[][], int[], double) - Constructor for class smile.classification.LDA: Constructor.
LDA(double[][], int[], double[], double) - Constructor for class smile.classification.LDA: Constructor.
LDA.Trainer - Class in smile.classification: Trainer for linear discriminant analysis.
LDA.Trainer() - Constructor for class smile.classification.LDA.Trainer: Constructor.
learn(double, PrintStream) - Method in class smile.association.ARM: Mines the association rules.
learn(double) - Method in class smile.association.ARM: Mines the association rules.
learn() - Method in class smile.association.FPGrowth: Mines the frequent item sets.
learn(PrintStream) - Method in class smile.association.FPGrowth: Mines the frequent item sets.
learn(double[][], int[]) - Static method in class smile.classification.KNN: Learn the 1-NN classifier from data of type double[].
learn(double[][], int[], int) - Static method in class smile.classification.KNN: Learn the K-NN classifier from data of type double[].
learn(double[], int) - Method in class smile.classification.NaiveBayes: Online learning of naive Bayes classifier on a sequence, which is modeled as a bag of words.
learn(double[][], int[]) - Method in class smile.classification.NaiveBayes: Online learning of naive Bayes classifier on sequences, which are modeled as a bag of words.
learn(double[], double[], double) - Method in class smile.classification.NeuralNetwork: Update the neural network with given instance and associated target value.
learn(double[], int) - Method in class smile.classification.NeuralNetwork
learn(double[], int, double) - Method in class smile.classification.NeuralNetwork: Online update the neural network with a new training instance.
learn(double[][], int[]) - Method in class smile.classification.NeuralNetwork: Trains the neural network with the given dataset for one epoch by stochastic gradient descent.
learn(T, int) - Method in interface smile.classification.OnlineClassifier: Online update the classifier with a new training instance.
learn(T, int) - Method in class smile.classification.SVM
learn(T, int, double) - Method in class smile.classification.SVM: Online update the classifier with a new training instance.
learn(T[], int[]) - Method in class smile.classification.SVM: Trains the SVM with the given dataset for one epoch.
learn(T[], int[], double[]) - Method in class smile.classification.SVM: Trains the SVM with the given dataset for one epoch.
learn(int, int, ClassifierTrainer<double[]>, ClassificationMeasure, double[][], int[], int) - Method in class smile.feature.GAFeatureSelection: Genetic algorithm based feature selection for classification.
learn(int, int, ClassifierTrainer<double[]>, ClassificationMeasure, double[][], int[], double[][], int[]) - Method in class smile.feature.GAFeatureSelection: Genetic algorithm based feature selection for classification.
learn(int, int, RegressionTrainer<double[]>, RegressionMeasure, double[][], double[], int) - Method in class smile.feature.GAFeatureSelection: Genetic algorithm based feature selection for regression.
learn(int, int, RegressionTrainer<double[]>, RegressionMeasure, double[][], double[], double[][], double[]) - Method in class smile.feature.GAFeatureSelection: Genetic algorithm based feature selection for regression.
learn(RNNSearch<double[], double[]>, double[][], double) - Method in class smile.neighbor.MPLSH: Train the posteriori multiple probe algorithm.
learn(RNNSearch<double[], double[]>, double[][], double, int) - Method in class smile.neighbor.MPLSH: Train the posteriori multiple probe algorithm.
learn(RNNSearch<double[], double[]>, double[][], double, int, double) - Method in class smile.neighbor.MPLSH: Train the posteriori multiple probe algorithm.
learn(double[]) - Method in class smile.projection.GHA: Update the model with a new sample.
learn(T, double) - Method in interface smile.regression.OnlineRegression: Online update the regression model with a new training instance.
learn(O[][], int) - Method in class smile.sequence.HMM: With this HMM as the initial model, learn an HMM by the Baum-Welch algorithm.
learn(int[][], int) - Method in class smile.sequence.HMM: With this HMM as the initial model, learn an HMM by the Baum-Welch algorithm.
learnGaussianRadialBasis(double[][], double[][]) - Static method in class smile.util.SmileUtils: Learns Gaussian RBF function and centers from data.
learnGaussianRadialBasis(double[][], double[][], int) - Static method in class smile.util.SmileUtils: Learns Gaussian RBF function and centers from data.
learnGaussianRadialBasis(double[][], double[][], double) - Static method in class smile.util.SmileUtils: Learns Gaussian RBF function and centers from data.
learnGaussianRadialBasis(T[], T[], Metric<T>) - Static method in class smile.util.SmileUtils: Learns Gaussian RBF function and centers from data.
learnGaussianRadialBasis(T[], T[], Metric<T>, int) - Static method in class smile.util.SmileUtils: Learns Gaussian RBF function and centers from data.
learnGaussianRadialBasis(T[], T[], Metric<T>, double) - Static method in class smile.util.SmileUtils: Learns Gaussian RBF function and centers from data.
length - Variable in class smile.gap.BitString: The length of chromosome.
line - Variable in class smile.neighbor.SNLSH.AbstractSentence
LinearSearch<T> - Class in smile.neighbor: Brute force linear nearest neighbor search.
LinearSearch(T[], Distance<T>) - Constructor for class smile.neighbor.LinearSearch: Constructor.
Linkage - Class in smile.clustering.linkage: A measure of dissimilarity between clusters (i.e.
Linkage() - Constructor for class smile.clustering.linkage.Linkage
LLE - Class in smile.manifold: Locally Linear Embedding.
LLE(double[][], int, int) - Constructor for class smile.manifold.LLE: Constructor.
lloyd(double[][], int) - Static method in class smile.clustering.KMeans: The implementation of Lloyd algorithm as a benchmark.
lloyd(double[][], int, int) - Static method in class smile.clustering.KMeans: The implementation of Lloyd algorithm as a benchmark.
lloyd(double[][], int, int, int) - Static method in class smile.clustering.KMeans: The implementation of Lloyd algorithm as a benchmark.
LLSImputation - Class in smile.imputation: Local least squares missing value imputation.
LLSImputation(int) - Constructor for class smile.imputation.LLSImputation: Constructor.
LogisticRegression - Class in smile.classification: Logistic regression.
LogisticRegression(double[][], int[]) - Constructor for class smile.classification.LogisticRegression: Constructor.
LogisticRegression(double[][], int[], double) - Constructor for class smile.classification.LogisticRegression: Constructor.
LogisticRegression(double[][], int[], double, double, int) - Constructor for class smile.classification.LogisticRegression: Constructor.
LogisticRegression.Trainer - Class in smile.classification: Trainer for logistic regression.
LogisticRegression.Trainer() - Constructor for class smile.classification.LogisticRegression.Trainer: Constructor.
loglikelihood() - Method in class smile.classification.LogisticRegression: Returns the log-likelihood of model.
loglikelihood() - Method in class smile.classification.Maxent: Returns the log-likelihood of model.
logp(int[], int[]) - Method in class smile.sequence.HMM: Returns the log joint probability of an observation sequence along a state sequence given this HMM.
logp(int[]) - Method in class smile.sequence.HMM: Returns the logarithm probability of an observation sequence given this HMM.
logp(O[], int[]) - Method in class smile.sequence.HMM: Returns the log joint probability of an observation sequence along a state sequence given this HMM.
logp(O[]) - Method in class smile.sequence.HMM: Returns the logarithm probability of an observation sequence given this HMM.
LOOCV - Class in smile.validation: Leave-one-out cross validation.
LOOCV(int) - Constructor for class smile.validation.LOOCV: Constructor.
loocv(ClassifierTrainer<T>, T[], int[]) - Static method in class smile.validation.Validation: Leave-one-out cross validation of a classification model.
loocv(RegressionTrainer<T>, T[], double[]) - Static method in class smile.validation.Validation: Leave-one-out cross validation of a regression model.
loocv(ClassifierTrainer<T>, T[], int[], ClassificationMeasure) - Static method in class smile.validation.Validation: Leave-one-out cross validation of a classification model.
loocv(ClassifierTrainer<T>, T[], int[], ClassificationMeasure[]) - Static method in class smile.validation.Validation: Leave-one-out cross validation of a classification model.
loocv(RegressionTrainer<T>, T[], double[], RegressionMeasure) - Static method in class smile.validation.Validation: Leave-one-out cross validation of a regression model.
loocv(RegressionTrainer<T>, T[], double[], RegressionMeasure[]) - Static method in class smile.validation.Validation: Leave-one-out cross validation of a regression model.
lowestCommonAncestor(String, String) - Method in class smile.taxonomy.Taxonomy: Returns the lowest common ancestor (LCA) of concepts v and w.
lowestCommonAncestor(Concept, Concept) - Method in class smile.taxonomy.Taxonomy: Returns the lowest common ancestor (LCA) of concepts v and w.
LSH<E> - Class in smile.neighbor: Locality-Sensitive Hashing.
LSH(double[][], E[]) - Constructor for class smile.neighbor.LSH: Constructor.
LSH(double[][], E[], double) - Constructor for class smile.neighbor.LSH: Constructor.
LSH(double[][], E[], double, int) - Constructor for class smile.neighbor.LSH: Constructor.
LSH(int, int, int) - Constructor for class smile.neighbor.LSH: Constructor.
LSH(int, int, int, double) - Constructor for class smile.neighbor.LSH: Constructor.
LSH(int, int, int, double, int) - Constructor for class smile.neighbor.LSH: Constructor.

M

map() - Method in class smile.clustering.SOM: Returns the SOM map grid.
Maxent - Class in smile.classification: Maximum Entropy Classifier.
Maxent(int, int[][], int[]) - Constructor for class smile.classification.Maxent: Learn maximum entropy classifier from samples of binary sparse features.
Maxent(int, int[][], int[], double) - Constructor for class smile.classification.Maxent: Learn maximum entropy classifier from samples of binary sparse features.
Maxent(int, int[][], int[], double, double, int) - Constructor for class smile.classification.Maxent: Learn maximum entropy classifier from samples of binary sparse features.
Maxent.Trainer - Class in smile.classification: Trainer for maximum entropy classifier.
Maxent.Trainer(int) - Constructor for class smile.classification.Maxent.Trainer: Constructor.
MDS - Class in smile.mds: Classical multidimensional scaling, also known as principal coordinates analysis.
MDS(double[][]) - Constructor for class smile.mds.MDS: Constructor.
MDS(double[][], int) - Constructor for class smile.mds.MDS: Constructor.
MDS(double[][], int, boolean) - Constructor for class smile.mds.MDS: Constructor.
measure(double[], double[]) - Method in class smile.validation.AbsoluteDeviation
measure(int[], int[]) - Method in class smile.validation.Accuracy
measure(int[], int[]) - Method in class smile.validation.AdjustedRandIndex
measure(int[], int[]) - Method in interface smile.validation.ClassificationMeasure: Returns an index to measure the quality of classification.
measure(int[], int[]) - Method in interface smile.validation.ClusterMeasure: Returns an index to measure the quality of clustering.
measure(int[], int[]) - Method in class smile.validation.Fallout
measure(int[], int[]) - Method in class smile.validation.FDR
measure(int[], int[]) - Method in class smile.validation.FMeasure
measure(double[], double[]) - Method in class smile.validation.MSE
measure(int[], int[]) - Method in class smile.validation.Precision
measure(int[], int[]) - Method in class smile.validation.RandIndex
measure(int[], int[]) - Method in class smile.validation.Recall
measure(double[], double[]) - Method in interface smile.validation.RegressionMeasure: Returns an index to measure the quality of regression.
measure(double[], double[]) - Method in class smile.validation.RMSE
measure(double[], double[]) - Method in class smile.validation.RSS
measure(int[], int[]) - Method in class smile.validation.Sensitivity
measure(int[], int[]) - Method in class smile.validation.Specificity
MEC<T> - Class in smile.clustering: Nonparametric Minimum Conditional Entropy Clustering.
MEC(T[], Distance<T>, int, double) - Constructor for class smile.clustering.MEC: Constructor.
MEC(T[], Metric<T>, int, double) - Constructor for class smile.clustering.MEC: Constructor.
MEC(T[], RNNSearch<T, T>, int, double, int[]) - Constructor for class smile.clustering.MEC: Constructor.
medoids() - Method in class smile.clustering.CLARANS: Returns the medoids.
merge(int, int) - Method in class smile.clustering.linkage.CompleteLinkage
merge(int, int) - Method in class smile.clustering.linkage.Linkage: Merge two clusters into one and update the proximity matrix.
merge(int, int) - Method in class smile.clustering.linkage.SingleLinkage
merge(int, int) - Method in class smile.clustering.linkage.UPGMALinkage
merge(int, int) - Method in class smile.clustering.linkage.UPGMCLinkage
merge(int, int) - Method in class smile.clustering.linkage.WardLinkage
merge(int, int) - Method in class smile.clustering.linkage.WPGMALinkage
merge(int, int) - Method in class smile.clustering.linkage.WPGMCLinkage
MissingValueImputation - Interface in smile.imputation: Interface to impute missing values in the dataset.
MissingValueImputationException - Exception in smile.imputation: Exception of missing value imputation.
MissingValueImputationException() - Constructor for exception smile.imputation.MissingValueImputationException: Constructor.
MissingValueImputationException(String) - Constructor for exception smile.imputation.MissingValueImputationException: Constructor.
MPLSH<E> - Class in smile.neighbor: Multi-Probe Locality-Sensitive Hashing.
MPLSH(int, int, int, double) - Constructor for class smile.neighbor.MPLSH: Constructor.
MPLSH(int, int, int, double, int) - Constructor for class smile.neighbor.MPLSH: Constructor.
MSE - Class in smile.validation: Mean squared error.
MSE() - Constructor for class smile.validation.MSE
MulticoreExecutor - Class in smile.util: Utility class to run tasks in a thread pool on multi-core systems.
MulticoreExecutor() - Constructor for class smile.util.MulticoreExecutor
mutate() - Method in class smile.gap.BitString
mutate() - Method in interface smile.gap.Chromosome: For genetic algorithms, this method mutates the chromosome randomly.

N

n - Variable in class smile.clustering.NeuralMap.Neuron: The number of samples associated with this neuron.
NaiveBayes - Class in smile.classification: Naive Bayes classifier.
NaiveBayes(double[], Distribution[][]) - Constructor for class smile.classification.NaiveBayes: Constructor of general naive Bayes classifier.
NaiveBayes(NaiveBayes.Model, int, int) - Constructor for class smile.classification.NaiveBayes: Constructor of naive Bayes classifier for document classification.
NaiveBayes(NaiveBayes.Model, int, int, double) - Constructor for class smile.classification.NaiveBayes: Constructor of naive Bayes classifier for document classification.
NaiveBayes(NaiveBayes.Model, double[], int) - Constructor for class smile.classification.NaiveBayes: Constructor of naive Bayes classifier for document classification.
NaiveBayes(NaiveBayes.Model, double[], int, double) - Constructor for class smile.classification.NaiveBayes: Constructor of naive Bayes classifier for document classification.
NaiveBayes.Model - Enum in smile.classification: The generation models of naive Bayes classifier.
NaiveBayes.Trainer - Class in smile.classification: Trainer for naive Bayes classifier for document classification.
NaiveBayes.Trainer(NaiveBayes.Model, int, int) - Constructor for class smile.classification.NaiveBayes.Trainer: Constructor.
NaiveBayes.Trainer(NaiveBayes.Model, double[], int) - Constructor for class smile.classification.NaiveBayes.Trainer: Constructor.
nearest(E) - Method in class smile.neighbor.CoverTree
nearest(double[]) - Method in class smile.neighbor.KDTree
nearest(T) - Method in class smile.neighbor.LinearSearch
nearest(double[]) - Method in class smile.neighbor.LSH
nearest(double[]) - Method in class smile.neighbor.MPLSH
nearest(double[], double, int) - Method in class smile.neighbor.MPLSH: Returns the approximate nearest neighbor.
nearest(K) - Method in interface smile.neighbor.NearestNeighborSearch: Search the nearest neighbor to the given sample.
nearest(SNLSH.AbstractSentence) - Method in class smile.neighbor.SNLSH
NearestNeighborSearch<K,V> - Interface in smile.neighbor: Nearest neighbor search, also known as proximity search, similarity search or closest point search, is an optimization problem for finding closest points in metric spaces.
Neighbor<K,V> - Class in smile.neighbor: The object encapsulates the results of nearest neighbor search.
Neighbor(K, V, int, double) - Constructor for class smile.neighbor.Neighbor: Constructor.
neighbors - Variable in class smile.clustering.GrowingNeuralGas.Neuron: Direct connected neighbors.
neighbors - Variable in class smile.clustering.NeuralMap.Neuron: Connected neighbors.
NeuralGas - Class in smile.clustering: Neural Gas soft competitive learning algorithm.
NeuralGas(double[][], int) - Constructor for class smile.clustering.NeuralGas: Constructor.
NeuralGas(double[][], int, double, double, double, double, int) - Constructor for class smile.clustering.NeuralGas: Constructor.
NeuralMap - Class in smile.clustering: NeuralMap is an efficient competitive learning algorithm inspired by growing neural gas and BIRCH.
NeuralMap(int, double, double, double, int, int) - Constructor for class smile.clustering.NeuralMap: Constructor.
NeuralMap.Neuron - Class in smile.clustering: The neurons in the network.
NeuralMap.Neuron(double[]) - Constructor for class smile.clustering.NeuralMap.Neuron: Constructor.
NeuralNetwork - Class in smile.classification: Multilayer perceptron neural network.
NeuralNetwork(NeuralNetwork.ErrorFunction, int...) - Constructor for class smile.classification.NeuralNetwork: Constructor.
NeuralNetwork(NeuralNetwork.ErrorFunction, NeuralNetwork.ActivationFunction, int...) - Constructor for class smile.classification.NeuralNetwork: Constructor.
NeuralNetwork.ActivationFunction - Enum in smile.classification: The types of activation functions in output layer.
NeuralNetwork.ErrorFunction - Enum in smile.classification: The types of error functions.
NeuralNetwork.Trainer - Class in smile.classification: Trainer for neural networks.
NeuralNetwork.Trainer(NeuralNetwork.ErrorFunction, int...) - Constructor for class smile.classification.NeuralNetwork.Trainer: Constructor.
NeuralNetwork.Trainer(NeuralNetwork.ErrorFunction, NeuralNetwork.ActivationFunction, int...) - Constructor for class smile.classification.NeuralNetwork.Trainer: Constructor.
neurons() - Method in class smile.clustering.GrowingNeuralGas: Returns the neurons in the network.
neurons() - Method in class smile.clustering.NeuralMap: Returns the set of neurons.
newInstance() - Method in class smile.gap.BitString
newInstance() - Method in interface smile.gap.Chromosome: Returns a new random instance.
ni - Variable in class smile.clustering.SOM.Neuron: The count of each class that best matched samples belong to.
Nominal2Binary - Class in smile.feature: Nominal variable to binary dummy variables feature generator.
Nominal2Binary(Attribute[]) - Constructor for class smile.feature.Nominal2Binary: Constructor.
Nominal2SparseBinary - Class in smile.feature: Nominal variables to sparse binary representation convertor.
Nominal2SparseBinary(Attribute[]) - Constructor for class smile.feature.Nominal2SparseBinary: Constructor.
NumericAttributeFeature - Class in smile.feature: Numeric attribute normalization/standardization feature generator.
NumericAttributeFeature(Attribute[], NumericAttributeFeature.Scaling) - Constructor for class smile.feature.NumericAttributeFeature: Constructor.
NumericAttributeFeature(Attribute[], NumericAttributeFeature.Scaling, double[][]) - Constructor for class smile.feature.NumericAttributeFeature: Constructor.
NumericAttributeFeature(Attribute[], double, double, double[][]) - Constructor for class smile.feature.NumericAttributeFeature: Constructor.
NumericAttributeFeature(Attribute[], double[][]) - Constructor for class smile.feature.NumericAttributeFeature: Constructor.
NumericAttributeFeature.Scaling - Enum in smile.feature: The types of data scaling.
numStates() - Method in class smile.sequence.HMM: Returns the number of states.
numSymbols() - Method in class smile.sequence.HMM: Returns the number of emission symbols.

O

OLS - Class in smile.regression: Ordinary least squares.
OLS(double[][], double[]) - Constructor for class smile.regression.OLS: Constructor.
OLS.Trainer - Class in smile.regression: Trainer for linear regression by ordinary least squares.
OLS.Trainer() - Constructor for class smile.regression.OLS.Trainer: Constructor.
OnlineClassifier<T> - Interface in smile.classification: Classifier with online learning capability.
OnlineRegression<T> - Interface in smile.regression: Regression model with online learning capability.
OUTLIER - Static variable in interface smile.clustering.Clustering: Cluster label for outliers or noises.

P

p(int[], int[]) - Method in class smile.sequence.HMM: Returns the joint probability of an observation sequence along a state sequence given this HMM.
p(int[]) - Method in class smile.sequence.HMM: Returns the probability of an observation sequence given this HMM.
p(O[], int[]) - Method in class smile.sequence.HMM: Returns the joint probability of an observation sequence along a state sequence given this HMM.
p(O[]) - Method in class smile.sequence.HMM: Returns the probability of an observation sequence given this HMM.
partition(int) - Method in class smile.clustering.BIRCH: Clustering leaves of CF tree into k clusters.
partition(int, int) - Method in class smile.clustering.BIRCH: Clustering leaves of CF tree into k clusters.
partition(int) - Method in class smile.clustering.GrowingNeuralGas: Clustering neurons into k clusters.
partition(int) - Method in class smile.clustering.HierarchicalClustering: Cuts a tree into several groups by specifying the desired number.
partition(double) - Method in class smile.clustering.HierarchicalClustering: Cuts a tree into several groups by specifying the cut height.
partition(int) - Method in class smile.clustering.NeuralMap: Clustering neurons into k clusters.
partition(int, int) - Method in class smile.clustering.NeuralMap: Clustering neurons into k clusters.
partition(int) - Method in class smile.clustering.SOM: Clustering the neurons into k groups.
PartitionClustering<T> - Class in smile.clustering: Abstract class of partition clustering.
PCA - Class in smile.projection: Principal component analysis.
PCA(double[][]) - Constructor for class smile.projection.PCA: Constructor.
PCA(double[][], boolean) - Constructor for class smile.projection.PCA: Constructor.
population() - Method in class smile.gap.GeneticAlgorithm: Returns the population of current generation.
PPCA - Class in smile.projection: Probabilistic principal component analysis.
PPCA(double[][], int) - Constructor for class smile.projection.PPCA: Constructor.
Precision - Class in smile.validation: The precision or positive predictive value (PPV) is ratio of true positives to combined true and false positives, which is different from sensitivity.
Precision() - Constructor for class smile.validation.Precision
predict(double[]) - Method in class smile.classification.AdaBoost
predict(double[], double[]) - Method in class smile.classification.AdaBoost: Predicts the class label of an instance and also calculate a posteriori probabilities.
predict(T) - Method in interface smile.classification.Classifier: Predicts the class label of an instance.
predict(T, double[]) - Method in interface smile.classification.Classifier: Predicts the class label of an instance and also calculate a posteriori probabilities.
predict(double[]) - Method in class smile.classification.DecisionTree
predict(double[], double[]) - Method in class smile.classification.DecisionTree: Predicts the class label of an instance and also calculate a posteriori probabilities.
predict(double[]) - Method in class smile.classification.FLD
predict(double[], double[]) - Method in class smile.classification.FLD: Predicts the class label of an instance and also calculate a posteriori probabilities.
predict(double[]) - Method in class smile.classification.GradientTreeBoost
predict(double[], double[]) - Method in class smile.classification.GradientTreeBoost
predict(T) - Method in class smile.classification.KNN
predict(T, double[]) - Method in class smile.classification.KNN
predict(double[]) - Method in class smile.classification.LDA
predict(double[], double[]) - Method in class smile.classification.LDA
predict(double[]) - Method in class smile.classification.LogisticRegression
predict(double[], double[]) - Method in class smile.classification.LogisticRegression
predict(int[]) - Method in class smile.classification.Maxent
predict(int[], double[]) - Method in class smile.classification.Maxent
predict(double[]) - Method in class smile.classification.NaiveBayes: Predict the class of an instance.
predict(double[], double[]) - Method in class smile.classification.NaiveBayes: Predict the class of an instance.
predict(double[], double[]) - Method in class smile.classification.NeuralNetwork: Predict the target value of a given instance.
predict(double[]) - Method in class smile.classification.NeuralNetwork: Predict the class of a given instance.
predict(double[]) - Method in class smile.classification.QDA
predict(double[], double[]) - Method in class smile.classification.QDA
predict(double[]) - Method in class smile.classification.RandomForest
predict(double[], double[]) - Method in class smile.classification.RandomForest
predict(T) - Method in class smile.classification.RBFNetwork
predict(T, double[]) - Method in class smile.classification.RBFNetwork: Predicts the class label of an instance and also calculate a posteriori probabilities.
predict(double[]) - Method in class smile.classification.RDA
predict(double[], double[]) - Method in class smile.classification.RDA
predict(T) - Method in class smile.classification.SVM
predict(T, double[]) - Method in class smile.classification.SVM: Predicts the class label of an instance and also calculate a posteriori probabilities.
predict(double[]) - Method in class smile.clustering.BIRCH: Cluster a new instance to the nearest CF leaf.
predict(T) - Method in class smile.clustering.CLARANS: Cluster a new instance.
predict(T) - Method in interface smile.clustering.Clustering: Cluster a new instance.
predict(T) - Method in class smile.clustering.DBScan: Cluster a new instance.
predict(double[]) - Method in class smile.clustering.DENCLUE
predict(double[]) - Method in class smile.clustering.GrowingNeuralGas: Cluster a new instance to the nearest neuron.
predict(double[]) - Method in class smile.clustering.KMeans: Cluster a new instance.
predict(T) - Method in class smile.clustering.MEC: Cluster a new instance.
predict(double[]) - Method in class smile.clustering.NeuralMap: Cluster a new instance to the nearest neuron.
predict(double[]) - Method in class smile.clustering.SIB: Cluster a new instance.
predict(SparseArray) - Method in class smile.clustering.SIB: Cluster a new instance.
predict(double[]) - Method in class smile.clustering.SOM: Cluster a new instance to the nearest neuron.
predict(T) - Method in class smile.regression.GaussianProcessRegression
predict(double[]) - Method in class smile.regression.GradientTreeBoost
predict(double[]) - Method in class smile.regression.LASSO
predict(double[]) - Method in class smile.regression.OLS
predict(double[]) - Method in class smile.regression.RandomForest
predict(T) - Method in class smile.regression.RBFNetwork
predict(T) - Method in interface smile.regression.Regression: Predicts the dependent variable of an instance.
predict(double[]) - Method in class smile.regression.RegressionTree
predict(int[]) - Method in class smile.regression.RegressionTree: Predicts the dependent variable of an instance with sparse binary features.
predict(double[]) - Method in class smile.regression.RidgeRegression
predict(T) - Method in class smile.regression.SVR
predict(double[][]) - Method in class smile.sequence.CRF
predict(int[][]) - Method in class smile.sequence.CRF
predict(int[]) - Method in class smile.sequence.HMM: Returns the most likely state sequence given the observation sequence by the Viterbi algorithm, which maximizes the probability of P(I | O, HMM).
predict(O[]) - Method in class smile.sequence.HMM: Returns the most likely state sequence given the observation sequence by the Viterbi algorithm, which maximizes the probability of P(I | O, HMM).
predict(T[]) - Method in interface smile.sequence.SequenceLabeler: Predicts the sequence labels.
project(double[]) - Method in class smile.classification.FLD
project(double[][]) - Method in class smile.classification.FLD
project(double[]) - Method in class smile.projection.GHA
project(double[][]) - Method in class smile.projection.GHA
project(T) - Method in class smile.projection.KPCA
project(T[]) - Method in class smile.projection.KPCA
project(double[]) - Method in class smile.projection.PCA
project(double[][]) - Method in class smile.projection.PCA
project(double[]) - Method in class smile.projection.PPCA
project(double[][]) - Method in class smile.projection.PPCA
project(T) - Method in interface smile.projection.Projection: Project a data point to the feature space.
project(T[]) - Method in interface smile.projection.Projection: Project a set of data toe the feature space.
project(double[]) - Method in class smile.projection.RandomProjection
project(double[][]) - Method in class smile.projection.RandomProjection
Projection<T> - Interface in smile.projection: A projection is a kind of feature extraction technique that transforms data from the input space to a feature space, linearly or nonlinearly.
purge(int) - Method in class smile.clustering.NeuralMap: Removes neurons with the number of samples less than a given threshold.
put(double[], E) - Method in class smile.neighbor.LSH: Insert an item into the hash table.
put(double[], E) - Method in class smile.neighbor.MPLSH: Insert an item into the hash table.
put(SNLSH.AbstractSentence, E) - Method in class smile.neighbor.SNLSH
pvalue() - Method in class smile.regression.OLS: Returns the p-value of goodness-of-fit test.

Q

QDA - Class in smile.classification: Quadratic discriminant analysis.
QDA(double[][], int[]) - Constructor for class smile.classification.QDA: Learn quadratic discriminant analysis.
QDA(double[][], int[], double[]) - Constructor for class smile.classification.QDA: Learn quadratic discriminant analysis.
QDA(double[][], int[], double) - Constructor for class smile.classification.QDA: Learn quadratic discriminant analysis.
QDA(double[][], int[], double[], double) - Constructor for class smile.classification.QDA: Learn quadratic discriminant analysis.
QDA.Trainer - Class in smile.classification: Trainer for quadratic discriminant analysis.
QDA.Trainer() - Constructor for class smile.classification.QDA.Trainer: Constructor.

R

RandIndex - Class in smile.validation: Rand Index.
RandIndex() - Constructor for class smile.validation.RandIndex
RandomForest - Class in smile.classification: Random forest for classification.
RandomForest(double[][], int[], int) - Constructor for class smile.classification.RandomForest: Constructor.
RandomForest(double[][], int[], int, int) - Constructor for class smile.classification.RandomForest: Constructor.
RandomForest(Attribute[], double[][], int[], int) - Constructor for class smile.classification.RandomForest: Constructor.
RandomForest(Attribute[], double[][], int[], int, int) - Constructor for class smile.classification.RandomForest: Constructor.
RandomForest - Class in smile.regression: Random forest for regression.
RandomForest(double[][], double[], int) - Constructor for class smile.regression.RandomForest: Constructor.
RandomForest(double[][], double[], int, int, int) - Constructor for class smile.regression.RandomForest: Constructor.
RandomForest(Attribute[], double[][], double[], int) - Constructor for class smile.regression.RandomForest: Constructor.
RandomForest(Attribute[], double[][], double[], int, int, int) - Constructor for class smile.regression.RandomForest: Constructor.
RandomForest.Trainer - Class in smile.classification: Trainer for random forest classifiers.
RandomForest.Trainer(int) - Constructor for class smile.classification.RandomForest.Trainer: Constructor.
RandomForest.Trainer(Attribute[], int) - Constructor for class smile.classification.RandomForest.Trainer: Constructor.
RandomForest.Trainer - Class in smile.regression: Trainer for random forest.
RandomForest.Trainer(int) - Constructor for class smile.regression.RandomForest.Trainer: Constructor.
RandomForest.Trainer(Attribute[], int) - Constructor for class smile.regression.RandomForest.Trainer: Constructor.
RandomProjection - Class in smile.projection: Random projection is a promising dimensionality reduction technique for learning mixtures of Gaussians.
RandomProjection(int, int) - Constructor for class smile.projection.RandomProjection: Constructor.
RandomProjection(int, int, boolean) - Constructor for class smile.projection.RandomProjection: Constructor.
range(E, double, List<Neighbor<E, E>>) - Method in class smile.neighbor.BKTree
range(E, int, List<Neighbor<E, E>>) - Method in class smile.neighbor.BKTree: Search the neighbors in the given radius of query object, i.e.
range(E, double, List<Neighbor<E, E>>) - Method in class smile.neighbor.CoverTree
range(double[], double, List<Neighbor<double[], E>>) - Method in class smile.neighbor.KDTree
range(T, double, List<Neighbor<T, T>>) - Method in class smile.neighbor.LinearSearch
range(double[], double, List<Neighbor<double[], E>>) - Method in class smile.neighbor.LSH
range(double[], double, List<Neighbor<double[], E>>) - Method in class smile.neighbor.MPLSH
range(double[], double, List<Neighbor<double[], E>>, double, int) - Method in class smile.neighbor.MPLSH: Search the neighbors in the given radius of query object, i.e.
range(K, double, List<Neighbor<K, V>>) - Method in interface smile.neighbor.RNNSearch: Search the neighbors in the given radius of query object, i.e.
range(SNLSH.AbstractSentence, double, List<Neighbor<SNLSH.AbstractSentence, E>>) - Method in class smile.neighbor.SNLSH
rank(double[][], int[]) - Method in interface smile.feature.FeatureRanking: Univariate feature ranking.
rank(double[][], int[]) - Method in class smile.feature.SignalNoiseRatio
rank(double[][], int[]) - Method in class smile.feature.SumSquaresRatio
RBFNetwork<T> - Class in smile.classification: Radial basis function networks.
RBFNetwork(T[], int[], Metric<T>, RadialBasisFunction, T[]) - Constructor for class smile.classification.RBFNetwork: Constructor.
RBFNetwork(T[], int[], Metric<T>, RadialBasisFunction[], T[]) - Constructor for class smile.classification.RBFNetwork: Constructor.
RBFNetwork(T[], int[], Metric<T>, RadialBasisFunction, T[], boolean) - Constructor for class smile.classification.RBFNetwork: Constructor.
RBFNetwork(T[], int[], Metric<T>, RadialBasisFunction[], T[], boolean) - Constructor for class smile.classification.RBFNetwork: Constructor.
RBFNetwork<T> - Class in smile.regression: Radial basis function network.
RBFNetwork(T[], double[], Metric<T>, RadialBasisFunction, T[]) - Constructor for class smile.regression.RBFNetwork: Constructor.
RBFNetwork(T[], double[], Metric<T>, RadialBasisFunction[], T[]) - Constructor for class smile.regression.RBFNetwork: Constructor.
RBFNetwork(T[], double[], Metric<T>, RadialBasisFunction, T[], boolean) - Constructor for class smile.regression.RBFNetwork: Constructor.
RBFNetwork(T[], double[], Metric<T>, RadialBasisFunction[], T[], boolean) - Constructor for class smile.regression.RBFNetwork: Constructor.
RBFNetwork.Trainer<T> - Class in smile.classification: Trainer for RBF networks.
RBFNetwork.Trainer(Metric<T>) - Constructor for class smile.classification.RBFNetwork.Trainer: Constructor.
RBFNetwork.Trainer<T> - Class in smile.regression: Trainer for RBF networks.
RBFNetwork.Trainer(Metric<T>) - Constructor for class smile.regression.RBFNetwork.Trainer: Constructor.
RDA - Class in smile.classification: Regularized discriminant analysis.
RDA(double[][], int[], double) - Constructor for class smile.classification.RDA: Constructor.
RDA(double[][], int[], double[], double) - Constructor for class smile.classification.RDA: Constructor.
RDA(double[][], int[], double[], double, double) - Constructor for class smile.classification.RDA: Constructor.
RDA.Trainer - Class in smile.classification: Trainer for regularized discriminant analysis.
RDA.Trainer(double) - Constructor for class smile.classification.RDA.Trainer: Constructor.
Recall - Class in smile.validation: In information retrieval area, sensitivity is called recall.
Recall() - Constructor for class smile.validation.Recall
Regression<T> - Interface in smile.regression: Regression analysis includes any techniques for modeling and analyzing the relationship between a dependent variable and one or more independent variables.
RegressionMeasure - Interface in smile.validation: An abstract interface to measure the regression performance.
RegressionTrainer<T> - Class in smile.regression: Abstract regression model trainer.
RegressionTrainer() - Constructor for class smile.regression.RegressionTrainer: Constructor.
RegressionTrainer(Attribute[]) - Constructor for class smile.regression.RegressionTrainer: Constructor.
RegressionTree - Class in smile.regression: Decision tree for regression.
RegressionTree(double[][], double[], int) - Constructor for class smile.regression.RegressionTree: Constructor.
RegressionTree(Attribute[], double[][], double[], int) - Constructor for class smile.regression.RegressionTree: Constructor.
RegressionTree(Attribute[], double[][], double[], int, int[][], int[], RegressionTree.NodeOutput) - Constructor for class smile.regression.RegressionTree: Constructor.
RegressionTree(int, int[][], double[], int) - Constructor for class smile.regression.RegressionTree: Constructor.
RegressionTree(int, int[][], double[], int, int[], RegressionTree.NodeOutput) - Constructor for class smile.regression.RegressionTree: Constructor.
RegressionTree.NodeOutput - Interface in smile.regression: An interface to calculate node output.
RegressionTree.Trainer - Class in smile.regression: Trainer for regression tree.
RegressionTree.Trainer(int) - Constructor for class smile.regression.RegressionTree.Trainer: Constructor.
RegressionTree.Trainer(Attribute[], int) - Constructor for class smile.regression.RegressionTree.Trainer: Constructor.
RegressionTree.Trainer(int, int) - Constructor for class smile.regression.RegressionTree.Trainer: Constructor.
remove(Feature<T>) - Method in class smile.feature.FeatureSet: Removes a feature generator.
removeChild(Concept) - Method in class smile.taxonomy.Concept: Remove a child to this node
removeKeyword(String) - Method in class smile.taxonomy.Concept: Remove a keyword from the concept synset.
residuals() - Method in class smile.regression.OLS: Returns the residuals, that is response minus fitted values.
RidgeRegression - Class in smile.regression: Ridge Regression.
RidgeRegression(double[][], double[], double) - Constructor for class smile.regression.RidgeRegression: Constructor.
RidgeRegression.Trainer - Class in smile.regression: Trainer for ridge regression.
RidgeRegression.Trainer(double) - Constructor for class smile.regression.RidgeRegression.Trainer: Constructor.
RMSE - Class in smile.validation: Root mean squared error.
RMSE() - Constructor for class smile.validation.RMSE
RNNSearch<K,V> - Interface in smile.neighbor: A range nearest neighbor search retrieves the nearest neighbors to a query in a range.
RSquared() - Method in class smile.regression.OLS: Returns R² statistic.
RSS() - Method in class smile.regression.OLS: Returns the residual sum of squares.
RSS - Class in smile.validation: Residual sum of squares.
RSS() - Constructor for class smile.validation.RSS
run(Collection<? extends Callable<T>>) - Static method in class smile.util.MulticoreExecutor: Executes the given tasks serially or parallel depending on the number of cores of the system.

S

SammonMapping - Class in smile.mds: The Sammon's mapping is an iterative technique for making interpoint distances in the low-dimensional projection as close as possible to the interpoint distances in the high-dimensional object.
SammonMapping(double[][]) - Constructor for class smile.mds.SammonMapping: Constructor.
SammonMapping(double[][], int) - Constructor for class smile.mds.SammonMapping: Constructor.
SammonMapping(double[][], double[][]) - Constructor for class smile.mds.SammonMapping: Constructor.
SammonMapping(double[][], int, double, double, int) - Constructor for class smile.mds.SammonMapping: Constructor.
SammonMapping(double[][], double[][], double, double, int) - Constructor for class smile.mds.SammonMapping: Constructor.
samples - Variable in class smile.clustering.SOM.Neuron: The samples that are best matched.
Sensitivity - Class in smile.validation: Sensitivity or true positive rate (TPR) (also called hit rate, recall) is a statistical measures of the performance of a binary classification test.
Sensitivity() - Constructor for class smile.validation.Sensitivity
SequenceFeature<T> - Interface in smile.feature: Sequence feature generator.
SequenceLabeler<T> - Interface in smile.sequence: A sequence labeler assigns a class label to each position of the sequence.
setAttributes(Attribute[]) - Method in class smile.classification.ClassifierTrainer: Sets feature attributes.
setAttributes(Attribute[]) - Method in class smile.regression.RegressionTrainer: Sets feature attributes.
setDimension(int) - Method in class smile.classification.FLD.Trainer: Sets the dimensionality of mapped space.
setElitism(int) - Method in class smile.gap.GeneticAlgorithm: Sets the number of best chromosomes to copy to new population.
setIdenticalExcluded(boolean) - Method in class smile.neighbor.BKTree: Set if exclude query object self from the neighborhood.
setIdenticalExcluded(boolean) - Method in class smile.neighbor.CoverTree: Set if exclude query object self from the neighborhood.
setIdenticalExcluded(boolean) - Method in class smile.neighbor.KDTree: Set if exclude query object self from the neighborhood.
setIdenticalExcluded(boolean) - Method in class smile.neighbor.LinearSearch: Set if exclude query object self from the neighborhood.
setIdenticalExcluded(boolean) - Method in class smile.neighbor.LSH: Set if exclude query object self from the neighborhood.
setIdenticalExcluded(boolean) - Method in class smile.neighbor.MPLSH: Set if exclude query object self from the neighborhood.
setLearningRate(double) - Method in class smile.classification.NeuralNetwork: Sets the learning rate.
setLearningRate(double) - Method in class smile.classification.NeuralNetwork.Trainer: Sets the learning rate.
setLearningRate(double) - Method in class smile.projection.GHA: Set the learning rate.
setLearningRate(double) - Method in class smile.sequence.CRF.Trainer
setLocalSearchSteps(int) - Method in class smile.gap.GeneticAlgorithm: Sets the number of iterations of local search for Lamarckian algorithm.
setLoss(GradientTreeBoost.Loss) - Method in class smile.regression.GradientTreeBoost.Trainer: Sets the loss function.
setMaximumLeafNodes(int) - Method in class smile.classification.AdaBoost.Trainer: Sets the maximum number of leaf nodes in the tree.
setMaximumLeafNodes(int) - Method in class smile.classification.DecisionTree.Trainer: Sets the maximum number of leaf nodes in the tree.
setMaximumLeafNodes(int) - Method in class smile.classification.GradientTreeBoost.Trainer: Sets the maximum number of leaf nodes in the tree.
setMaximumLeafNodes(int) - Method in class smile.regression.GradientTreeBoost.Trainer: Sets the maximum number of leaf nodes in the tree.
setMaximumLeafNodes(int) - Method in class smile.regression.RegressionTree.Trainer: Sets the maximum number of leaf nodes in the tree.
setMaximumLeafNodes(int) - Method in class smile.sequence.CRF.Trainer: Sets the maximum number of leaf nodes in the tree.
setMaxNumIteration(int) - Method in class smile.classification.LogisticRegression.Trainer: Sets the maximum number of iterations.
setMaxNumIteration(int) - Method in class smile.classification.Maxent.Trainer: Sets the maximum number of BFGS stopping iterations.
setMaxNumIteration(int) - Method in class smile.regression.LASSO.Trainer: Sets the maximum number of iterations.
setMinimumNodeSize(int) - Method in class smile.regression.RandomForest.Trainer: Sets the minimum size of leaf nodes.
setMomentum(double) - Method in class smile.classification.NeuralNetwork: Sets the momentum factor.
setMomentum(double) - Method in class smile.classification.NeuralNetwork.Trainer: Sets the momentum factor.
setNormalized(boolean) - Method in class smile.classification.RBFNetwork.Trainer: Sets true to learn normalized RBF network.
setNormalized(boolean) - Method in class smile.regression.RBFNetwork.Trainer: Sets true to learn normalized RBF network.
setNumCenters(int) - Method in class smile.regression.RBFNetwork.Trainer: Sets the number of centers.
setNumEpochs(int) - Method in class smile.classification.NeuralNetwork.Trainer: Sets the number of epochs of stochastic learning.
setNumEpochs(int) - Method in class smile.classification.SVM.Trainer: Sets the number of epochs of stochastic learning.
setNumIterations(int) - Method in class smile.sequence.CRF.Trainer
setNumRandomFeatures(int) - Method in class smile.classification.RandomForest.Trainer: Sets the number of random selected features for splitting.
setNumRandomFeatures(int) - Method in class smile.regression.RandomForest.Trainer: Sets the number of random selected features for splitting.
setNumTrees(int) - Method in class smile.classification.AdaBoost.Trainer: Sets the number of trees in the random forest.
setNumTrees(int) - Method in class smile.classification.GradientTreeBoost.Trainer: Sets the number of trees in the random forest.
setNumTrees(int) - Method in class smile.classification.RandomForest.Trainer: Sets the number of trees in the random forest.
setNumTrees(int) - Method in class smile.regression.GradientTreeBoost.Trainer: Sets the number of trees in the random forest.
setNumTrees(int) - Method in class smile.regression.RandomForest.Trainer: Sets the number of trees in the random forest.
setPriori(double[]) - Method in class smile.classification.LDA.Trainer: Sets a priori probabilities of each class.
setPriori(double[]) - Method in class smile.classification.NaiveBayes.Trainer: Sets a priori probabilities of each class.
setPriori(double[]) - Method in class smile.classification.QDA.Trainer: Sets a priori probabilities of each class.
setPriori(double[]) - Method in class smile.classification.RDA.Trainer: Sets a priori probabilities of each class.
setProjection(int) - Method in class smile.projection.PCA: Set the projection matrix with given number of principal components.
setProjection(double) - Method in class smile.projection.PCA: Set the projection matrix with top principal components that contain (more than) the given percentage of variance.
setRBF(RadialBasisFunction, int) - Method in class smile.classification.RBFNetwork.Trainer: Sets the radial basis function.
setRBF(RadialBasisFunction[]) - Method in class smile.classification.RBFNetwork.Trainer: Sets the radial basis functions.
setRBF(RadialBasisFunction, int) - Method in class smile.regression.RBFNetwork.Trainer: Sets the radial basis function.
setRBF(RadialBasisFunction[]) - Method in class smile.regression.RBFNetwork.Trainer: Sets the radial basis functions.
setRegularizationFactor(double) - Method in class smile.classification.LogisticRegression.Trainer: Sets the regularization factor.
setRegularizationFactor(double) - Method in class smile.classification.Maxent.Trainer: Sets the regularization factor.
setSamplingRates(double) - Method in class smile.classification.GradientTreeBoost.Trainer: Sets the sampling rate for stochastic tree boosting.
setSamplingRates(double) - Method in class smile.regression.GradientTreeBoost.Trainer: Sets the sampling rate for stochastic tree boosting.
setShrinkage(double) - Method in class smile.classification.GradientTreeBoost.Trainer: Sets the shrinkage parameter in (0, 1] controls the learning rate of procedure.
setShrinkage(double) - Method in class smile.regression.GradientTreeBoost.Trainer: Sets the shrinkage parameter in (0, 1] controls the learning rate of procedure.
setSmooth(double) - Method in class smile.classification.NaiveBayes.Trainer: Sets add-k prior count of terms for smoothing.
setSplitRule(DecisionTree.SplitRule) - Method in class smile.classification.DecisionTree.Trainer: Sets the splitting rule.
setTolerance(double) - Method in class smile.classification.FLD.Trainer: Sets covariance matrix singular tolerance.
setTolerance(double) - Method in class smile.classification.LDA.Trainer: Sets covariance matrix singularity tolerance.
setTolerance(double) - Method in class smile.classification.LogisticRegression.Trainer: Sets the tolerance for BFGS stopping iterations.
setTolerance(double) - Method in class smile.classification.Maxent.Trainer: Sets the tolerance for BFGS stopping iterations.
setTolerance(double) - Method in class smile.classification.QDA.Trainer: Sets covariance matrix singularity tolerance.
setTolerance(double) - Method in class smile.classification.RDA.Trainer: Sets covariance matrix singular tolerance.
setTolerance(double) - Method in class smile.classification.SVM: Sets the tolerance of convergence test.
setTolerance(double) - Method in class smile.classification.SVM.Trainer: Sets the tolerance of convergence test.
setTolerance(double) - Method in class smile.regression.LASSO.Trainer: Sets the tolerance for stopping iterations (relative target duality gap).
setTolerance(double) - Method in class smile.regression.SVR.Trainer: Sets the tolerance of convergence test.
setTournament(int, double) - Method in class smile.gap.GeneticAlgorithm: Set the tournament size and the best-player-wins probability in tournament selection.
setViterbi(boolean) - Method in class smile.sequence.CRF: Sets if using Viterbi algorithm for sequence labeling.
setWeightDecay(double) - Method in class smile.classification.NeuralNetwork: Sets the weight decay factor.
setWeightDecay(double) - Method in class smile.classification.NeuralNetwork.Trainer: Sets the weight decay factor.
shrinkage() - Method in class smile.regression.GaussianProcessRegression: Returns the shrinkage parameter.
shrinkage() - Method in class smile.regression.LASSO: Returns the shrinkage parameter.
shrinkage() - Method in class smile.regression.RidgeRegression: Returns the shrinkage parameter.
shutdown() - Static method in class smile.util.MulticoreExecutor: Shutdown the thread pool.
SIB - Class in smile.clustering: The Sequential Information Bottleneck algorithm.
SIB(double[][], int) - Constructor for class smile.clustering.SIB: Constructor.
SIB(double[][], int, int) - Constructor for class smile.clustering.SIB: Constructor.
SIB(double[][], int, int, int) - Constructor for class smile.clustering.SIB: Constructor.
SIB(SparseDataset, int) - Constructor for class smile.clustering.SIB: Constructor.
SIB(SparseDataset, int, int) - Constructor for class smile.clustering.SIB: Constructor.
SIB(SparseDataset, int, int, int) - Constructor for class smile.clustering.SIB: Constructor.
SignalNoiseRatio - Class in smile.feature: The signal-to-noise (S2N) metric ratio is a univariate feature ranking metric, which can be used as a feature selection criterion for binary classification problems.
SignalNoiseRatio() - Constructor for class smile.feature.SignalNoiseRatio
simhash64(List<String>) - Static method in class smile.neighbor.SNLSH.SimHash
SimpleNeighbor<T> - Class in smile.neighbor: The simple neighbor object, in which key and object are the same.
SimpleNeighbor(T, int, double) - Constructor for class smile.neighbor.SimpleNeighbor: Constructor.
SingleLinkage - Class in smile.clustering.linkage: Single linkage.
SingleLinkage(double[][]) - Constructor for class smile.clustering.linkage.SingleLinkage: Constructor.
size() - Method in class smile.association.FPGrowth: Returns the number transactions in the database.
size() - Method in class smile.classification.AdaBoost: Returns the number of trees in the model.
size() - Method in class smile.classification.GradientTreeBoost: Returns the number of trees in the model.
size() - Method in class smile.classification.RandomForest: Returns the number of trees in the model.
size() - Method in class smile.clustering.SOM: Returns the number of samples in each unit.
size() - Method in class smile.regression.GradientTreeBoost: Returns the number of trees in the model.
size() - Method in class smile.regression.RandomForest: Returns the number of trees in the model.
smile.association - package smile.association: Frequent item set mining and association rule mining.
smile.classification - package smile.classification: Classification algorithms.
smile.clustering - package smile.clustering: Clustering analysis.
smile.clustering.linkage - package smile.clustering.linkage: Cluster dissimilarity measures.
smile.feature - package smile.feature: Feature generation, normalization and selection.
smile.gap - package smile.gap: Genetic algorithm and programming.
smile.imputation - package smile.imputation: Missing value imputation.
smile.manifold - package smile.manifold: Manifold learning finds a low-dimensional basis for describing high-dimensional data.
smile.mds - package smile.mds: Multidimensional scaling.
smile.neighbor - package smile.neighbor: Nearest neighbor search.
smile.projection - package smile.projection: Feature extraction.
smile.regression - package smile.regression: Regression analysis.
smile.sequence - package smile.sequence: Learning algorithms for sequence data.
smile.taxonomy - package smile.taxonomy: A taxonomy is a tree of terms (concepts) where leaves must be named but intermediary nodes can be anonymous.
smile.util - package smile.util: Utilitiy functions used in many places and multicore executor.
smile.validation - package smile.validation: Model validation.
smile.wavelet - package smile.wavelet: Discrete wavelet transform (DWT).
SmileUtils - Class in smile.util: Some useful functions.
SmileUtils() - Constructor for class smile.util.SmileUtils
SNLSH<E> - Class in smile.neighbor: Locality-Sensitive Hashing for Signatures.
SNLSH(int) - Constructor for class smile.neighbor.SNLSH
SNLSH.AbstractSentence - Class in smile.neighbor
SNLSH.AbstractSentence() - Constructor for class smile.neighbor.SNLSH.AbstractSentence
SNLSH.SimHash - Class in smile.neighbor
SNLSH.SimHash() - Constructor for class smile.neighbor.SNLSH.SimHash
SOM - Class in smile.clustering: Self-Organizing Map.
SOM(double[][], int) - Constructor for class smile.clustering.SOM: Constructor.
SOM(double[][], int, int) - Constructor for class smile.clustering.SOM: Constructor.
SOM.Neuron - Class in smile.clustering: Self-Organizing Map Neuron.
SOM.Neuron() - Constructor for class smile.clustering.SOM.Neuron
sort(Attribute[], double[][]) - Static method in class smile.util.SmileUtils: Sorts each variable and returns the index of values in ascending order.
Specificity - Class in smile.validation: Specificity (SPC) or True Negative Rate is a statistical measures of the performance of a binary classification test.
Specificity() - Constructor for class smile.validation.Specificity
SpectralClustering - Class in smile.clustering: Spectral Clustering.
SpectralClustering(double[][], int) - Constructor for class smile.clustering.SpectralClustering: Constructor.
SpectralClustering(double[][], int, double) - Constructor for class smile.clustering.SpectralClustering: Constructor.
SpectralClustering(double[][], int, int, double) - Constructor for class smile.clustering.SpectralClustering: Constructor.
SumSquaresRatio - Class in smile.feature: The ratio of between-groups to within-groups sum of squares is a univariate feature ranking metric, which can be used as a feature selection criterion for multi-class classification problems.
SumSquaresRatio() - Constructor for class smile.feature.SumSquaresRatio
support - Variable in class smile.association.AssociationRule: The support value.
support - Variable in class smile.association.ItemSet: The associated support of item set.
SVDImputation - Class in smile.imputation: Missing value imputation with singular value decomposition.
SVDImputation(int) - Constructor for class smile.imputation.SVDImputation: Constructor.
SVM<T> - Class in smile.classification: Support vector machines for classification.
SVM(MercerKernel<T>, double) - Constructor for class smile.classification.SVM: Constructor of binary SVM.
SVM(MercerKernel<T>, double, double) - Constructor for class smile.classification.SVM: Constructor of binary SVM.
SVM(MercerKernel<T>, double, int, SVM.Multiclass) - Constructor for class smile.classification.SVM: Constructor of multi-class SVM.
SVM(MercerKernel<T>, double, double[], SVM.Multiclass) - Constructor for class smile.classification.SVM: Constructor of multi-class SVM.
SVM.Multiclass - Enum in smile.classification: The type of multi-class SVMs.
SVM.Trainer<T> - Class in smile.classification: Trainer for support vector machines.
SVM.Trainer(MercerKernel<T>, double) - Constructor for class smile.classification.SVM.Trainer: Constructor of trainer for binary SVMs.
SVM.Trainer(MercerKernel<T>, double, double) - Constructor for class smile.classification.SVM.Trainer: Constructor of trainer for binary SVMs.
SVM.Trainer(MercerKernel<T>, double, int, SVM.Multiclass) - Constructor for class smile.classification.SVM.Trainer: Constructor of trainer for multi-class SVMs.
SVM.Trainer(MercerKernel<T>, double, double[], SVM.Multiclass) - Constructor for class smile.classification.SVM.Trainer: Constructor of trainer for multi-class SVMs.
SVR<T> - Class in smile.regression: Support vector regression.
SVR(T[], double[], MercerKernel<T>, double, double) - Constructor for class smile.regression.SVR: Constructor.
SVR(T[], double[], double[], MercerKernel<T>, double, double) - Constructor for class smile.regression.SVR: Constructor.
SVR(T[], double[], MercerKernel<T>, double, double, double) - Constructor for class smile.regression.SVR: Constructor.
SVR(T[], double[], double[], MercerKernel<T>, double, double, double) - Constructor for class smile.regression.SVR: Constructor.
SVR.Trainer<T> - Class in smile.regression: Trainer for support vector regression.
SVR.Trainer(MercerKernel<T>, double, double) - Constructor for class smile.regression.SVR.Trainer: Constructor of trainer for binary SVMs.
SymmletWavelet - Class in smile.wavelet: Symmlet wavelets.
SymmletWavelet(int) - Constructor for class smile.wavelet.SymmletWavelet: Constructor.

T

TaxonomicDistance - Class in smile.taxonomy: The distance between concepts in a taxonomy.
TaxonomicDistance(Taxonomy) - Constructor for class smile.taxonomy.TaxonomicDistance: Constructor.
Taxonomy - Class in smile.taxonomy: A taxonomy is a tree of terms (aka concept) where leaves must be named but intermediary nodes can be anonymous.
Taxonomy() - Constructor for class smile.taxonomy.Taxonomy: Constructor.
Taxonomy(String) - Constructor for class smile.taxonomy.Taxonomy: Constructor.
test(double[][], int[]) - Method in class smile.classification.AdaBoost: Test the model on a validation dataset.
test(double[][], int[], ClassificationMeasure[]) - Method in class smile.classification.AdaBoost: Test the model on a validation dataset.
test(double[][], int[]) - Method in class smile.classification.GradientTreeBoost: Test the model on a validation dataset.
test(double[][], int[], ClassificationMeasure[]) - Method in class smile.classification.GradientTreeBoost: Test the model on a validation dataset.
test(double[][], int[]) - Method in class smile.classification.RandomForest: Test the model on a validation dataset.
test(double[][], int[], ClassificationMeasure[]) - Method in class smile.classification.RandomForest: Test the model on a validation dataset.
test(double[][], double[]) - Method in class smile.regression.GradientTreeBoost: Test the model on a validation dataset.
test(double[][], double[], RegressionMeasure[]) - Method in class smile.regression.GradientTreeBoost: Test the model on a validation dataset.
test(double[][], double[]) - Method in class smile.regression.RandomForest: Test the model on a validation dataset.
test(double[][], double[], RegressionMeasure[]) - Method in class smile.regression.RandomForest: Test the model on a validation dataset.
test - Variable in class smile.validation.Bootstrap: The index of testing instances.
test - Variable in class smile.validation.CrossValidation: The index of testing instances.
test - Variable in class smile.validation.LOOCV: The index of testing instances.
test(Classifier<T>, T[], int[]) - Static method in class smile.validation.Validation: Tests a classifier on a validation set.
test(Regression<T>, T[], double[]) - Static method in class smile.validation.Validation: Tests a regression model on a validation set.
test(Classifier<T>, T[], int[], ClassificationMeasure) - Static method in class smile.validation.Validation: Tests a classifier on a validation set.
test(Classifier<T>, T[], int[], ClassificationMeasure[]) - Static method in class smile.validation.Validation: Tests a classifier on a validation set.
test(Regression<T>, T[], double[], RegressionMeasure) - Static method in class smile.validation.Validation: Tests a regression model on a validation set.
test(Regression<T>, T[], double[], RegressionMeasure[]) - Static method in class smile.validation.Validation: Tests a regression model on a validation set.
tokens - Variable in class smile.neighbor.SNLSH.AbstractSentence
toString() - Method in class smile.association.AssociationRule
toString() - Method in class smile.association.ItemSet
toString() - Method in class smile.clustering.CLARANS
toString() - Method in class smile.clustering.DBScan
toString() - Method in class smile.clustering.DENCLUE
toString() - Method in class smile.clustering.DeterministicAnnealing
toString() - Method in class smile.clustering.GMeans
toString() - Method in class smile.clustering.KMeans
toString() - Method in class smile.clustering.linkage.CompleteLinkage
toString() - Method in class smile.clustering.linkage.SingleLinkage
toString() - Method in class smile.clustering.linkage.UPGMALinkage
toString() - Method in class smile.clustering.linkage.UPGMCLinkage
toString() - Method in class smile.clustering.linkage.WardLinkage
toString() - Method in class smile.clustering.linkage.WPGMALinkage
toString() - Method in class smile.clustering.linkage.WPGMCLinkage
toString() - Method in class smile.clustering.MEC
toString() - Method in class smile.clustering.NeuralGas
toString() - Method in class smile.clustering.SIB
toString() - Method in class smile.clustering.SpectralClustering
toString() - Method in class smile.clustering.XMeans
toString() - Method in class smile.neighbor.BKTree
toString() - Method in class smile.neighbor.CoverTree
toString() - Method in class smile.neighbor.KDTree
toString() - Method in class smile.neighbor.LinearSearch
toString() - Method in class smile.neighbor.LSH
toString() - Method in class smile.neighbor.MPLSH
toString() - Method in class smile.regression.OLS
toString() - Method in class smile.sequence.HMM
toString() - Method in class smile.taxonomy.Concept
toString() - Method in class smile.taxonomy.TaxonomicDistance
toString() - Method in class smile.validation.AbsoluteDeviation
toString() - Method in class smile.validation.Accuracy
toString() - Method in class smile.validation.AdjustedRandIndex
toString() - Method in class smile.validation.ConfusionMatrix
toString() - Method in class smile.validation.Fallout
toString() - Method in class smile.validation.FDR
toString() - Method in class smile.validation.FMeasure
toString() - Method in class smile.validation.MSE
toString() - Method in class smile.validation.Precision
toString() - Method in class smile.validation.RandIndex
toString() - Method in class smile.validation.Recall
toString() - Method in class smile.validation.RMSE
toString() - Method in class smile.validation.RSS
toString() - Method in class smile.validation.Sensitivity
toString() - Method in class smile.validation.Specificity
train(double[][], int[]) - Method in class smile.classification.AdaBoost.Trainer
train(T[], int[]) - Method in class smile.classification.ClassifierTrainer: Learns a classifier with given training data.
train(double[][], int[]) - Method in class smile.classification.DecisionTree.Trainer
train(double[][], int[]) - Method in class smile.classification.FLD.Trainer
train(double[][], int[]) - Method in class smile.classification.GradientTreeBoost.Trainer
train(T[], int[]) - Method in class smile.classification.KNN.Trainer
train(double[][], int[]) - Method in class smile.classification.LDA.Trainer
train(double[][], int[]) - Method in class smile.classification.LogisticRegression.Trainer
train(int[][], int[]) - Method in class smile.classification.Maxent.Trainer
train(double[][], int[]) - Method in class smile.classification.NaiveBayes.Trainer
train(double[][], int[]) - Method in class smile.classification.NeuralNetwork.Trainer
train(double[][], int[]) - Method in class smile.classification.QDA.Trainer
train(double[][], int[]) - Method in class smile.classification.RandomForest.Trainer
train(T[], int[]) - Method in class smile.classification.RBFNetwork.Trainer
train(T[], int[], T[]) - Method in class smile.classification.RBFNetwork.Trainer: Learns a RBF network with given centers.
train(double[][], int[]) - Method in class smile.classification.RDA.Trainer
train(T[], int[]) - Method in class smile.classification.SVM.Trainer
train(T[], int[], double[]) - Method in class smile.classification.SVM.Trainer: Learns a SVM classifier with given training data.
train(T[], double[]) - Method in class smile.regression.GaussianProcessRegression.Trainer
train(T[], double[], T[]) - Method in class smile.regression.GaussianProcessRegression.Trainer: Learns a Gaussian Process with given subset of regressors.
train(double[][], double[]) - Method in class smile.regression.GradientTreeBoost.Trainer
train(double[][], double[]) - Method in class smile.regression.LASSO.Trainer
train(double[][], double[]) - Method in class smile.regression.OLS.Trainer
train(double[][], double[]) - Method in class smile.regression.RandomForest.Trainer
train(T[], double[]) - Method in class smile.regression.RBFNetwork.Trainer
train(T[], double[], T[]) - Method in class smile.regression.RBFNetwork.Trainer: Learns a RBF network with given centers.
train(T[], double[]) - Method in class smile.regression.RegressionTrainer: Learns a regression model with given training data.
train(double[][], double[]) - Method in class smile.regression.RegressionTree.Trainer
train(int[][], double[]) - Method in class smile.regression.RegressionTree.Trainer
train(double[][], double[]) - Method in class smile.regression.RidgeRegression.Trainer
train(T[], double[]) - Method in class smile.regression.SVR.Trainer
train(double[][][], int[][]) - Method in class smile.sequence.CRF.Trainer
train(int[][][], int[][]) - Method in class smile.sequence.CRF.Trainer
train - Variable in class smile.validation.Bootstrap: The index of training instances.
train - Variable in class smile.validation.CrossValidation: The index of training instances.
train - Variable in class smile.validation.LOOCV: The index of training instances.
transform(double[]) - Method in class smile.wavelet.Wavelet: Discrete wavelet transform.
trim(int) - Method in class smile.classification.AdaBoost: Trims the tree model set to a smaller size in case of over-fitting.
trim(int) - Method in class smile.classification.GradientTreeBoost: Trims the tree model set to a smaller size in case of over-fitting.
trim(int) - Method in class smile.classification.RandomForest: Trims the tree model set to a smaller size in case of over-fitting.
trim(int) - Method in class smile.regression.GradientTreeBoost: Trims the tree model set to a smaller size in case of over-fitting.
trim(int) - Method in class smile.regression.RandomForest: Trims the tree model set to a smaller size in case of over-fitting.
ttest() - Method in class smile.regression.OLS: Returns the t-test of the coefficients (including intercept).

U

umatrix() - Method in class smile.clustering.SOM: Returns the U-Matrix of SOM map for visualization.
update(double[]) - Method in class smile.clustering.GrowingNeuralGas: Update the Neural Gas with a new signal.
update(double[]) - Method in class smile.clustering.NeuralMap: Update the network with a new signal.
UPGMALinkage - Class in smile.clustering.linkage: Unweighted Pair Group Method with Arithmetic mean (also known as average linkage).
UPGMALinkage(double[][]) - Constructor for class smile.clustering.linkage.UPGMALinkage: Constructor.
UPGMCLinkage - Class in smile.clustering.linkage: Unweighted Pair Group Method using Centroids (also known as centroid linkage).
UPGMCLinkage(double[][]) - Constructor for class smile.clustering.linkage.UPGMCLinkage: Constructor.

V

Validation - Class in smile.validation: A utility class for validating predictive models on test data.
Validation() - Constructor for class smile.validation.Validation
value - Variable in class smile.neighbor.Neighbor: The data object of neighbor.
valueOf(String) - Static method in enum smile.classification.DecisionTree.SplitRule: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum smile.classification.NaiveBayes.Model: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum smile.classification.NeuralNetwork.ActivationFunction: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum smile.classification.NeuralNetwork.ErrorFunction: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum smile.classification.SVM.Multiclass: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum smile.feature.DateFeature.Type: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum smile.feature.NumericAttributeFeature.Scaling: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum smile.gap.BitString.Crossover: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum smile.gap.GeneticAlgorithm.Selection: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum smile.regression.GradientTreeBoost.Loss: Returns the enum constant of this type with the specified name.
values() - Static method in enum smile.classification.DecisionTree.SplitRule: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum smile.classification.NaiveBayes.Model: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum smile.classification.NeuralNetwork.ActivationFunction: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum smile.classification.NeuralNetwork.ErrorFunction: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum smile.classification.SVM.Multiclass: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum smile.feature.DateFeature.Type: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum smile.feature.NumericAttributeFeature.Scaling: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum smile.gap.BitString.Crossover: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum smile.gap.GeneticAlgorithm.Selection: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum smile.regression.GradientTreeBoost.Loss: Returns an array containing the constants of this enum type, in the order they are declared.

W

w - Variable in class smile.clustering.GrowingNeuralGas.Neuron: Reference vector.
w - Variable in class smile.clustering.NeuralMap.Neuron: Reference vector.
w - Variable in class smile.clustering.SOM.Neuron: Weight vector.
WardLinkage - Class in smile.clustering.linkage: Ward's linkage.
WardLinkage(double[][]) - Constructor for class smile.clustering.linkage.WardLinkage: Constructor.
Wavelet - Class in smile.wavelet: A wavelet is a wave-like oscillation with an amplitude that starts out at zero, increases, and then decreases back to zero.
Wavelet(double[]) - Constructor for class smile.wavelet.Wavelet: Constructor.
WaveletShrinkage - Class in smile.wavelet: The wavelet shrinkage is a signal denoising technique based on the idea of thresholding the wavelet coefficients.
WaveletShrinkage() - Constructor for class smile.wavelet.WaveletShrinkage
WPGMALinkage - Class in smile.clustering.linkage: Weighted Pair Group Method with Arithmetic mean.
WPGMALinkage(double[][]) - Constructor for class smile.clustering.linkage.WPGMALinkage: Constructor.
WPGMCLinkage - Class in smile.clustering.linkage: Weighted Pair Group Method using Centroids (also known as median linkage).
WPGMCLinkage(double[][]) - Constructor for class smile.clustering.linkage.WPGMCLinkage: Constructor.

X

XMeans - Class in smile.clustering: X-Means clustering algorithm, an extended K-Means which tries to automatically determine the number of clusters based on BIC scores.
XMeans(double[][], int) - Constructor for class smile.clustering.XMeans: Constructor.

Y

y - Variable in class smile.clustering.NeuralMap.Neuron: The cluster label.
y - Variable in class smile.clustering.SOM.Neuron: The class label of majority, y = which.max(ni).

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