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

A

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

ActivationFunction - Interface in smile.base.mlp

The activation function in hidden layers.

AdaBoost - Class in smile.classification

AdaBoost (Adaptive Boosting) classifier with decision trees.

AdaBoost(Formula, int, DecisionTree[], double[], double[], double[]) - Constructor for class smile.classification.AdaBoost

Constructor.

AdaBoost(Formula, int, DecisionTree[], double[], double[], double[], IntSet) - Constructor for class smile.classification.AdaBoost

Constructor.

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.

add(int) - Method in class smile.neighbor.lsh.Bucket

Adds a point to bucket.

add(int, double[]) - Method in class smile.neighbor.lsh.Hash

Insert an item into the hash table.

add(int, double[]) - Method in class smile.neighbor.lsh.MultiProbeHash

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

addEdge(Neuron) - Method in class smile.vq.hebb.Neuron

Adds an edge.

addEdge(Neuron, int) - Method in class smile.vq.hebb.Neuron

Adds an edge.

addKeywords(String...) - Method in class smile.taxonomy.Concept

Add a list of synomym to the concept synset.

AdjustedMutualInformation - Class in smile.validation

Adjusted Mutual Information (AMI) for comparing clustering.

AdjustedMutualInformation(AdjustedMutualInformation.Method) - Constructor for class smile.validation.AdjustedMutualInformation

Constructor.

AdjustedMutualInformation.Method - Enum in smile.validation

The normalization method.

AdjustedRandIndex - Class in smile.validation

Adjusted Rand Index.

AdjustedRandIndex() - Constructor for class smile.validation.AdjustedRandIndex

adjustedRSquared() - Method in class smile.regression.LinearModel

Returns adjusted R² statistic.

age - Variable in class smile.vq.hebb.Edge

The age of this edges.

age() - Method in class smile.vq.hebb.Neuron

Increments the age of all edges emanating from the neuron.

alpha - Variable in class smile.base.mlp.MultilayerPerceptron

momentum factor

antecedent - Variable in class smile.association.AssociationRule

Antecedent itemset.

apply(double, FPTree) - Static method in class smile.association.ARM

Mines the association rules.

apply(FPTree) - Static method in class smile.association.FPGrowth

Mines the frequent item sets.

apply(String[]) - Method in class smile.feature.Bag

Returns the bag-of-words features of a document.

apply(int, int, int, FitnessMeasure<BitString>) - Method in class smile.feature.GAFE

Genetic algorithm based feature selection for classification.

apply(Tuple) - Method in class smile.feature.SparseOneHotEncoder

Generates the compact representation of sparse binary features for given object.

apply(DataFrame) - Method in class smile.feature.SparseOneHotEncoder

Generates the compact representation of sparse binary features for a data frame.

apply(BitString, BitString) - Method in enum smile.gap.Crossover

Returns a pair of offsprings by crossovering parent chromosomes.

apply(T[]) - Method in interface smile.gap.Selection

Select a chromosome with replacement from the population based on their fitness.

applyAsDouble(T) - Method in interface smile.classification.Classifier

applyAsDouble(T) - Method in interface smile.regression.Regression

applyAsInt(T) - Method in interface smile.classification.Classifier

ARM - Class in smile.association

Association Rule Mining.

AssociationRule - Class in smile.association

Association rule object.

AssociationRule(int[], int[], double, double, double, double) - Constructor for class smile.association.AssociationRule

Constructor.

attractors - Variable in class smile.clustering.DENCLUE

The density attractor of each observation.

AUC - Class in smile.validation

The area under the curve (AUC).

AUC() - Constructor for class smile.validation.AUC

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

B - Variable in class smile.vq.BIRCH

The branching factor of non-leaf nodes.

backpropagate(double[]) - Method in class smile.base.mlp.HiddenLayer

backpropagate(double[]) - Method in class smile.base.mlp.Layer

Propagates the errors back to a lower layer.

backpropagate(double[]) - Method in class smile.base.mlp.MultilayerPerceptron

Propagates the errors back through the network.

backpropagate(double[]) - Method in class smile.base.mlp.OutputLayer

Bag - Class in smile.feature

The bag-of-words feature of text used in natural language processing and information retrieval.

Bag(String[]) - Constructor for class smile.feature.Bag

Constructor.

Bag(String[], boolean) - Constructor for class smile.feature.Bag

Constructor.

Bagging - Class in smile.sampling

Bagging (Bootstrap aggregating) is a way to improve the classification by combining classifications of randomly generated training sets.

Bagging(int[]) - Constructor for class smile.sampling.Bagging

Constructor.

BBDTree - Class in smile.clustering

Balanced Box-Decomposition Tree.

BBDTree(double[][]) - Constructor for class smile.clustering.BBDTree

Constructs a tree out of the given n data data living in R^d.

BestLocalizedWavelet - Class in smile.wavelet

Best localized wavelets.

BestLocalizedWavelet(int) - Constructor for class smile.wavelet.BestLocalizedWavelet

Constructor.

bias - Variable in class smile.base.mlp.Layer

The bias.

binary(int, KernelMachine<int[]>) - Static method in class smile.base.svm.LinearKernelMachine

Creates a linear kernel machine.

BIRCH - Class in smile.vq

Balanced Iterative Reducing and Clustering using Hierarchies.

BIRCH(int, int, int, double) - Constructor for class smile.vq.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>, Crossover, double, double) - Constructor for class smile.gap.BitString

Constructor.

BitString(byte[], FitnessMeasure<BitString>) - Constructor for class smile.gap.BitString

Constructor.

BitString(byte[], FitnessMeasure<BitString>, Crossover, double, double) - Constructor for class smile.gap.BitString

Constructor.

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.

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.

branch(Tuple) - Method in class smile.base.cart.InternalNode

Returns true if the instance goes to the true branch.

branch(Tuple) - Method in class smile.base.cart.NominalNode

branch(Tuple) - Method in class smile.base.cart.OrdinalNode

bubble(int) - Static method in interface smile.vq.Neighborhood

Returns the bubble neighborhood function.

Bucket - Class in smile.neighbor.lsh

A bucket is a container for points that all have the same value for hash function g (function g is a vector of k LSH functions).

Bucket(int) - Constructor for class smile.neighbor.lsh.Bucket

Constructor.

bucket - Variable in class smile.neighbor.lsh.Bucket

The bucket id is given by the universal bucket hashing.

build(int) - Method in class smile.base.mlp.LayerBuilder

Creates a hidden layer.

C

CART - Class in smile.base.cart

Classification and regression tree.

CART(Formula, StructType, StructField, Node, double[]) - Constructor for class smile.base.cart.CART

Constructor.

CART(DataFrame, StructField, int, int, int, int, int[], int[][]) - Constructor for class smile.base.cart.CART

Constructor.

CentroidClustering<T,U> - Class in smile.clustering

In centroid-based clustering, clusters are represented by a central vector, which may not necessarily be a member of the data set.

CentroidClustering(double, T[], int[]) - Constructor for class smile.clustering.CentroidClustering

Constructor.

centroids - Variable in class smile.clustering.CentroidClustering

The centroids of each cluster.

centroids() - Method in class smile.vq.BIRCH

Returns the cluster centroids of leaf nodes.

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(double, T[], int[], Distance<T>) - Constructor for class smile.clustering.CLARANS

Constructor.

classification(T[], int[], BiFunction<T[], int[], Classifier<T>>) - Method in class smile.validation.Bootstrap

Runs cross validation tests.

classification(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Method in class smile.validation.Bootstrap

Runs cross validation tests.

classification(int, T[], int[], BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.validation.Bootstrap

Runs cross validation tests.

classification(int, Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.validation.Bootstrap

Runs cross validation tests.

classification(T[], int[], BiFunction<T[], int[], Classifier<T>>) - Method in class smile.validation.CrossValidation

Runs cross validation tests.

classification(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Method in class smile.validation.CrossValidation

Runs cross validation tests.

classification(int, T[], int[], BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.validation.CrossValidation

Runs cross validation tests.

classification(int, Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.validation.CrossValidation

Runs cross validation tests.

classification(T[], int[], BiFunction<T[], int[], Classifier<T>>) - Method in class smile.validation.GroupKFold

Runs cross validation tests.

classification(DataFrame, Function<DataFrame, DataFrameClassifier>) - Method in class smile.validation.GroupKFold

Runs cross validation tests.

classification(T[], int[], BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.validation.LOOCV

Runs leave-one-out cross validation tests.

classification(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.validation.LOOCV

Runs leave-one-out cross validation tests.

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.

ClassLabels - Class in smile.classification

To support arbitrary class labels.

ClassLabels(int, int[], IntSet) - Constructor for class smile.classification.ClassLabels

Constructor.

ClassLabels(int, int[], IntSet, StructField) - Constructor for class smile.classification.ClassLabels

Constructor.

clear() - Method in class smile.base.cart.CART

Clear the workspace of building tree.

clear(double) - Method in class smile.vq.NeuralMap

Removes staled neurons and the edges beyond lifetime.

clone() - Method in class smile.neighbor.lsh.Probe

clustering(double[][], double[][], int[], int[]) - Method in class smile.clustering.BBDTree

Given k cluster centroids, this method assigns data to nearest centroids.

ClusterMeasure - Interface in smile.validation

An abstract interface to measure the clustering performance.

coefficients() - Method in class smile.regression.LinearModel

Returns the linear coefficients (without intercept).

CoifletWavelet - Class in smile.wavelet

Coiflet wavelets.

CoifletWavelet(int) - Constructor for class smile.wavelet.CoifletWavelet

Constructor.

comparator - Static variable in class smile.base.cart.Split

compareTo(CentroidClustering<T, U>) - Method in class smile.clustering.CentroidClustering

compareTo(MEC<T>) - Method in class smile.clustering.MEC

compareTo(Chromosome) - Method in class smile.gap.BitString

compareTo(PrH) - Method in class smile.neighbor.lsh.PrH

compareTo(Probe) - Method in class smile.neighbor.lsh.Probe

compareTo(PrZ) - Method in class smile.neighbor.lsh.PrZ

compareTo(Neighbor<K, V>) - Method in class smile.neighbor.Neighbor

compareTo(Neuron) - Method in class smile.vq.hebb.Neuron

CompleteLinkage - Class in smile.clustering.linkage

Complete linkage.

CompleteLinkage(double[][]) - Constructor for class smile.clustering.linkage.CompleteLinkage

Constructor.

CompleteLinkage(int, float[]) - Constructor for class smile.clustering.linkage.CompleteLinkage

Constructor.

components - Variable in class smile.projection.ICA

The independent components (row-wise).

computeError(double[], double) - Method in class smile.base.mlp.OutputLayer

Compute the network output error.

computeUpdate(double, double, double[]) - Method in class smile.base.mlp.Layer

Computes the updates of weight.

Concept - Class in smile.taxonomy

Concept is a set of synonyms, i.e.

Concept(Concept, String...) - Constructor for class smile.taxonomy.Concept

Constructor.

confidence - Variable in class smile.association.AssociationRule

The confidence value.

ConfusionMatrix - Class in smile.validation

The confusion matrix of truth and predictions.

ConfusionMatrix(int[][]) - Constructor for class smile.validation.ConfusionMatrix

Constructor.

consequent - Variable in class smile.association.AssociationRule

Consequent itemset.

coordinates - Variable in class smile.manifold.IsoMap

The coordinates.

coordinates - Variable in class smile.manifold.LaplacianEigenmap

Coordinate matrix.

coordinates - Variable in class smile.manifold.LLE

Coordinate matrix.

coordinates - Variable in class smile.manifold.TSNE

Coordinate matrix.

coordinates - Variable in class smile.mds.IsotonicMDS

The coordinates.

coordinates - Variable in class smile.mds.MDS

The principal coordinates.

coordinates - Variable in class smile.mds.SammonMapping

The coordinates.

cor(double[][]) - Static method in class smile.projection.PCA

Fits principal component analysis with correlation matrix.

Cost - Enum in smile.base.mlp

Neural network cost function.

cost() - Method in class smile.base.mlp.OutputLayer

Returns the cost function of neural network.

count() - Method in class smile.base.cart.DecisionNode

Returns the number of node samples in each class.

counter - Variable in class smile.vq.hebb.Neuron

The local counter variable (e.g.

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(StructType, RegressionTree[][], double) - Constructor for class smile.sequence.CRF

Constructor.

CRFLabeler<T> - Class in smile.sequence

First-order CRF sequence labeler.

CRFLabeler(CRF, Function<T, Tuple>) - Constructor for class smile.sequence.CRFLabeler

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.

Crossover - Enum in smile.gap

The types of crossover operation.

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.

CrossValidation(int, int, boolean) - Constructor for class smile.validation.CrossValidation

Constructor.

D

d(int, int) - Method in class smile.clustering.linkage.Linkage

Returns the distance/dissimilarity between two clusters/objects, which are indexed by integers.

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.

d - Variable in class smile.vq.BIRCH

The dimensionality of data.

D4Wavelet - Class in smile.wavelet

The simplest and most localized wavelet, Daubechies wavelet of 4 coefficients.

D4Wavelet() - Constructor for class smile.wavelet.D4Wavelet

Constructor.

data - Variable in class smile.neighbor.LSH

The data objects.

DataFrameClassifier - Interface in smile.classification

Classification trait on DataFrame.

DataFrameRegression - Interface in smile.regression

Regression trait on DataFrame.

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(int, double, RNNSearch<T, T>, int, int[]) - Constructor for class smile.clustering.DBSCAN

Constructor.

DecisionNode - Class in smile.base.cart

A leaf node in decision tree.

DecisionNode(int[]) - Constructor for class smile.base.cart.DecisionNode

Constructor.

DecisionTree - Class in smile.classification

Decision tree for classification.

DecisionTree(DataFrame, int[], StructField, int, SplitRule, int, int, int, int, int[], int[][]) - Constructor for class smile.classification.DecisionTree

Constructor.

DENCLUE - Class in smile.clustering

DENsity CLUstering.

DENCLUE(int, double[][], double[], double[][], double, int[], double) - Constructor for class smile.clustering.DENCLUE

Constructor.

denoise(double[], Wavelet) - Static method in interface smile.wavelet.WaveletShrinkage

Adaptive hard-thresholding denoising a time series with given wavelet.

denoise(double[], Wavelet, boolean) - Static method in interface smile.wavelet.WaveletShrinkage

Adaptive denoising a time series with given wavelet.

depth() - Method in class smile.base.cart.InternalNode

depth() - Method in class smile.base.cart.LeafNode

depth() - Method in interface smile.base.cart.Node

Returns the maximum depth of the tree -- the number of nodes along the longest path from this node down to the farthest leaf node.

DeterministicAnnealing - Class in smile.clustering

Deterministic annealing clustering.

DeterministicAnnealing(double, double[][], int[]) - Constructor for class smile.clustering.DeterministicAnnealing

Constructor.

deviance() - Method in class smile.base.cart.DecisionNode

deviance(int[], double[]) - Static method in class smile.base.cart.DecisionNode

Returns the deviance of node.

deviance() - Method in class smile.base.cart.InternalNode

deviance() - Method in interface smile.base.cart.Node

Returns the deviance of node.

deviance() - Method in class smile.base.cart.RegressionNode

df() - Method in class smile.regression.LinearModel

Returns the degree-of-freedom of residual standard error.

dimension() - Method in class smile.classification.Maxent

Returns the dimension of input space.

DiscreteNaiveBayes - Class in smile.classification

Naive Bayes classifier for document classification in NLP.

DiscreteNaiveBayes(DiscreteNaiveBayes.Model, int, int) - Constructor for class smile.classification.DiscreteNaiveBayes

Constructor of naive Bayes classifier for document classification.

DiscreteNaiveBayes(DiscreteNaiveBayes.Model, int, int, double, IntSet) - Constructor for class smile.classification.DiscreteNaiveBayes

Constructor of naive Bayes classifier for document classification.

DiscreteNaiveBayes(DiscreteNaiveBayes.Model, double[], int) - Constructor for class smile.classification.DiscreteNaiveBayes

Constructor of naive Bayes classifier for document classification.

DiscreteNaiveBayes(DiscreteNaiveBayes.Model, double[], int, double, IntSet) - Constructor for class smile.classification.DiscreteNaiveBayes

Constructor of naive Bayes classifier for document classification.

DiscreteNaiveBayes.Model - Enum in smile.classification

The generation models of naive Bayes classifier.

distance(T, U) - Method in class smile.clustering.CentroidClustering

The distance function.

distance(T, T) - Method in class smile.clustering.CLARANS

distance(double[], double[]) - Method in class smile.clustering.DeterministicAnnealing

distance(double[], double[]) - Method in class smile.clustering.GMeans

distance(double[], double[]) - Method in class smile.clustering.KMeans

distance(int[], int[]) - Method in class smile.clustering.KModes

distance(double[], SparseArray) - Method in class smile.clustering.SIB

distance(double[], double[]) - Method in class smile.clustering.XMeans

distance - Variable in class smile.neighbor.Neighbor

The distance between the query and the neighbor.

distance - Variable in class smile.vq.hebb.Neuron

The distance between the neuron and an input signal.

distance(double[]) - Method in class smile.vq.hebb.Neuron

Computes the distance between the neuron and a signal.

distortion - Variable in class smile.clustering.CentroidClustering

The total distortion.

distortion - Variable in class smile.clustering.SpectralClustering

The distortion in feature space.

dot() - Method in class smile.base.cart.CART

Returns the graphic representation in Graphviz dot format.

dot(StructType, StructField, int) - Method in class smile.base.cart.DecisionNode

dot(StructType, StructField, int) - Method in interface smile.base.cart.Node

Returns a dot representation for visualization.

dot(StructType, StructField, int) - Method in class smile.base.cart.NominalNode

dot(StructType, StructField, int) - Method in class smile.base.cart.OrdinalNode

dot(StructType, StructField, int) - Method in class smile.base.cart.RegressionNode

E

Edge - Class in smile.vq.hebb

The connection between neurons.

Edge(Neuron) - Constructor for class smile.vq.hebb.Edge

Constructor.

Edge(Neuron, int) - Constructor for class smile.vq.hebb.Edge

Constructor.

edges - Variable in class smile.vq.hebb.Neuron

The direct connected neighbors.

ElasticNet - Class in smile.regression

Elastic Net regularization.

ElasticNet() - Constructor for class smile.regression.ElasticNet

entropy - Variable in class smile.clustering.MEC

The conditional entropy as the objective function.

entry - Variable in class smile.neighbor.lsh.Bucket

The indices of points that all have the same value for hash function g.

equals(Object) - Method in class smile.association.AssociationRule

equals(Object) - Method in class smile.association.ItemSet

equals(Object) - Method in class smile.base.cart.DecisionNode

equals(Object) - Method in class smile.base.cart.RegressionNode

error() - Method in class smile.classification.RandomForest

Returns the out-of-bag estimation of error rate.

error() - Method in class smile.regression.LinearModel

Returns the residual standard error.

error() - Method in class smile.regression.RandomForest

Returns the out-of-bag estimation of RMSE.

Error - Class in smile.validation

The number of errors in the population.

Error() - Constructor for class smile.validation.Error

estimate(int, double) - Method in class smile.neighbor.lsh.HashValueParzenModel

Given a hash value h, estimate the Gaussian model (mean and variance) of neighbors existing in the corresponding bucket.

eta - Variable in class smile.base.mlp.MultilayerPerceptron

learning rate

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.

expand() - Method in class smile.neighbor.lsh.Probe

This operation sets to one the component following the last nonzero component if it is not the last one.

extend() - Method in class smile.neighbor.lsh.Probe

This operation adds one to the last nonzero component.

F

f(double[]) - Method in interface smile.base.mlp.ActivationFunction

The output function.

f(double[]) - Method in class smile.base.mlp.HiddenLayer

f(double[]) - Method in class smile.base.mlp.Layer

The activation or output function.

f(double[]) - Method in enum smile.base.mlp.OutputFunction

The output function.

f(double[]) - Method in class smile.base.mlp.OutputLayer

f(T) - Method in class smile.base.rbf.RBF

The activation function.

f(T) - Method in class smile.base.svm.KernelMachine

Returns the decision function value.

f(double[]) - Method in class smile.base.svm.LinearKernelMachine

Returns the value of decision function.

f(int[]) - Method in class smile.base.svm.LinearKernelMachine

Returns the value of decision function.

f(SparseArray) - Method in class smile.base.svm.LinearKernelMachine

Returns the value of decision function.

f(T) - Method in interface smile.classification.Classifier

Returns the real-valued decision function value.

f(double) - Method in class smile.projection.ica.Gaussian

f(double) - Method in class smile.projection.ica.Kurtosis

f(double) - Method in class smile.projection.ica.LogCosh

Fallout - Class in smile.validation

Fall-out, false alarm rate, or false positive rate (FPR)

Fallout() - Constructor for class smile.validation.Fallout

falseChild() - Method in class smile.base.cart.InternalNode

Returns the false branch child.

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() - Method in class smile.base.cart.InternalNode

Returns the split feature.

FeatureRanking - Interface in smile.feature

Univariate feature ranking metric.

features - Variable in class smile.sequence.CRFLabeler

The feature function.

FeatureTransform - Interface in smile.feature

Feature transformation.

field - Variable in class smile.classification.ClassLabels

The optional meta data of response variable.

findBestSplit(LeafNode, int, int, boolean[]) - Method in class smile.base.cart.CART

Finds the best attribute to split on a set of samples.

findBestSplit(LeafNode, int, double, int, int) - Method in class smile.base.cart.CART

Finds the best split for given column.

findBestSplit(LeafNode, int, double, int, int) - Method in class smile.classification.DecisionTree

findBestSplit(LeafNode, int, double, int, int) - Method in class smile.regression.RegressionTree

fit(double[][], int) - Static method in class smile.base.rbf.RBF

Learns Gaussian RBF function and centers from data.

fit(double[][], int, int) - Static method in class smile.base.rbf.RBF

Learns Gaussian RBF function and centers from data.

fit(double[][], int, double) - Static method in class smile.base.rbf.RBF

Learns Gaussian RBF function and centers from data.

fit(T[], Metric<T>, int) - Static method in class smile.base.rbf.RBF

Learns Gaussian RBF function and centers from data.

fit(T[], Metric<T>, int, int) - Static method in class smile.base.rbf.RBF

Learns Gaussian RBF function and centers from data.

fit(T[], Metric<T>, int, double) - Static method in class smile.base.rbf.RBF

Learns Gaussian RBF function and centers from data.

fit(T[], int[]) - Method in class smile.base.svm.LASVM

Trains the model.

fit(T[], int[], int) - Method in class smile.base.svm.LASVM

Trains the model.

fit(T[], double[]) - Method in class smile.base.svm.SVR

Fits a epsilon support vector regression model.

fit(Formula, DataFrame) - Static method in class smile.classification.AdaBoost

Fits a AdaBoost model.

fit(Formula, DataFrame, Properties) - Static method in class smile.classification.AdaBoost

Fits a AdaBoost model.

fit(Formula, DataFrame, int, int, int, int) - Static method in class smile.classification.AdaBoost

Fits a AdaBoost model.

fit(int[]) - Static method in class smile.classification.ClassLabels

Learns the class label mapping from samples.

fit(int[], StructField) - Static method in class smile.classification.ClassLabels

Learns the class label mapping from samples.

fit(BaseVector) - Static method in class smile.classification.ClassLabels

Learns the class label mapping from samples.

fit(Formula, DataFrame) - Static method in class smile.classification.DecisionTree

Learns a classification tree.

fit(Formula, DataFrame, Properties) - Static method in class smile.classification.DecisionTree

Learns a classification tree.

fit(Formula, DataFrame, SplitRule, int, int, int) - Static method in class smile.classification.DecisionTree

Learns a classification tree.

fit(Formula, DataFrame) - Static method in class smile.classification.FLD

Learn Fisher's linear discriminant.

fit(Formula, DataFrame, Properties) - Static method in class smile.classification.FLD

Learn Fisher's linear discriminant.

fit(double[][], int[]) - Static method in class smile.classification.FLD

Learn Fisher's linear discriminant.

fit(double[][], int[], int, double) - Static method in class smile.classification.FLD

Learn Fisher's linear discriminant.

fit(Formula, DataFrame) - Static method in class smile.classification.GradientTreeBoost

Fits a gradient tree boosting for classification.

fit(Formula, DataFrame, Properties) - Static method in class smile.classification.GradientTreeBoost

Fits a gradient tree boosting for classification.

fit(Formula, DataFrame, int, int, int, int, double, double) - Static method in class smile.classification.GradientTreeBoost

Fits a gradient tree boosting for classification.

fit(double[], int[]) - Static method in class smile.classification.IsotonicRegressionScaling

Trains the Isotonic Regression scaling.

fit(T[], int[], Distance<T>) - Static method in class smile.classification.KNN

Learn the 1-NN classifier.

fit(T[], int[], Distance<T>, int) - Static method in class smile.classification.KNN

Learn the K-NN classifier.

fit(double[][], int[]) - Static method in class smile.classification.KNN

Learn the 1-NN classifier.

fit(double[][], int[], int) - Static method in class smile.classification.KNN

Learn the K-NN classifier.

fit(Formula, DataFrame) - Static method in class smile.classification.LDA

Learns linear discriminant analysis.

fit(Formula, DataFrame, Properties) - Static method in class smile.classification.LDA

Learns linear discriminant analysis.

fit(double[][], int[]) - Static method in class smile.classification.LDA

Learns linear discriminant analysis.

fit(double[][], int[], Properties) - Static method in class smile.classification.LDA

Learns linear discriminant analysis.

fit(double[][], int[], double[], double) - Static method in class smile.classification.LDA

Learns linear discriminant analysis.

fit(Formula, DataFrame) - Static method in class smile.classification.LogisticRegression

Learn logistic regression.

fit(Formula, DataFrame, Properties) - Static method in class smile.classification.LogisticRegression

Learn logistic regression.

fit(double[][], int[]) - Static method in class smile.classification.LogisticRegression

Learn logistic regression.

fit(double[][], int[], Properties) - Static method in class smile.classification.LogisticRegression

Learn logistic regression.

fit(double[][], int[], double, double, int) - Static method in class smile.classification.LogisticRegression

Learn logistic regression.

fit(int, int[][], int[]) - Static method in class smile.classification.Maxent

Learn maximum entropy classifier.

fit(int, int[][], int[], Properties) - Static method in class smile.classification.Maxent

Learn maximum entropy classifier.

fit(int, int[][], int[], double, double, int) - Static method in class smile.classification.Maxent

Learn maximum entropy classifier.

fit(T[], int[], BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusOne

Fits a multi-class model with binary classifiers.

fit(T[], int[], int, int, BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusOne

Fits a multi-class model with binary classifiers.

fit(T[], int[], BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusRest

Fits a multi-class model with binary classifiers.

fit(T[], int[], int, int, BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusRest

Fits a multi-class model with binary classifiers.

fit(double[], int[]) - Static method in class smile.classification.PlattScaling

Trains the Platt scaling.

fit(double[], int[], int) - Static method in class smile.classification.PlattScaling

Trains the Platt scaling.

fit(Classifier<T>, T[], int[]) - Static method in class smile.classification.PlattScaling

Fits Platt Scaling to estimate posteriori probabilities.

fit(Formula, DataFrame) - Static method in class smile.classification.QDA

Learns quadratic discriminant analysis.

fit(Formula, DataFrame, Properties) - Static method in class smile.classification.QDA

Learns quadratic discriminant analysis.

fit(double[][], int[]) - Static method in class smile.classification.QDA

Learn quadratic discriminant analysis.

fit(double[][], int[], Properties) - Static method in class smile.classification.QDA

Learns quadratic discriminant analysis.

fit(double[][], int[], double[], double) - Static method in class smile.classification.QDA

Learn quadratic discriminant analysis.

fit(Formula, DataFrame) - Static method in class smile.classification.RandomForest

Fits a random forest for classification.

fit(Formula, DataFrame, Properties) - Static method in class smile.classification.RandomForest

Fits a random forest for classification.

fit(Formula, DataFrame, int, int, SplitRule, int, int, int, double) - Static method in class smile.classification.RandomForest

Fits a random forest for classification.

fit(Formula, DataFrame, int, int, SplitRule, int, int, int, double, int[]) - Static method in class smile.classification.RandomForest

Fits a random forest for regression.

fit(Formula, DataFrame, int, int, SplitRule, int, int, int, double, int[], LongStream) - Static method in class smile.classification.RandomForest

Fits a random forest for classification.

fit(T[], int[], RBF<T>[]) - Static method in class smile.classification.RBFNetwork

Fits a RBF network.

fit(T[], int[], RBF<T>[], boolean) - Static method in class smile.classification.RBFNetwork

Fits a RBF network.

fit(Formula, DataFrame) - Static method in class smile.classification.RDA

Learns regularized discriminant analysis.

fit(Formula, DataFrame, Properties) - Static method in class smile.classification.RDA

Learns regularized discriminant analysis.

fit(double[][], int[], Properties) - Static method in class smile.classification.RDA

Learns regularized discriminant analysis.

fit(double[][], int[], double) - Static method in class smile.classification.RDA

Learn regularized discriminant analysis.

fit(double[][], int[], double, double[], double) - Static method in class smile.classification.RDA

Learn regularized discriminant analysis.

fit(double[][], int[], double, double) - Static method in class smile.classification.SVM

Fits a binary-class linear SVM.

fit(int[][], int[], int, double, double) - Static method in class smile.classification.SVM

Fits a binary-class linear SVM of binary sparse data.

fit(SparseArray[], int[], int, double, double) - Static method in class smile.classification.SVM

Fits a binary-class linear SVM.

fit(T[], int[], MercerKernel<T>, double, double) - Static method in class smile.classification.SVM

Fits a binary-class SVM.

fit(T[], Distance<T>, int) - Static method in class smile.clustering.CLARANS

Clustering data into k clusters.

fit(T[], Distance<T>, int, int) - Static method in class smile.clustering.CLARANS

Constructor.

fit(double[][], int, double) - Static method in class smile.clustering.DBSCAN

Clustering the data with KD-tree.

fit(T[], Distance<T>, int, double) - Static method in class smile.clustering.DBSCAN

Clustering the data.

fit(T[], RNNSearch<T, T>, int, double) - Static method in class smile.clustering.DBSCAN

Clustering the data.

fit(double[][], double, int) - Static method in class smile.clustering.DENCLUE

Clustering data.

fit(double[][], double, int, double, int) - Static method in class smile.clustering.DENCLUE

Clustering data.

fit(double[][], int) - Static method in class smile.clustering.DeterministicAnnealing

Clustering data into k clusters.

fit(double[][], int, double, int, double, double) - Static method in class smile.clustering.DeterministicAnnealing

Clustering data into k clusters.

fit(double[][], int) - Static method in class smile.clustering.GMeans

Clustering data with the number of clusters determined by G-Means algorithm automatically.

fit(double[][], int, int, double) - Static method in class smile.clustering.GMeans

Clustering data with the number of clusters determined by G-Means algorithm automatically.

fit(Linkage) - Static method in class smile.clustering.HierarchicalClustering

Fits the Agglomerative Hierarchical Clustering with given linkage method, which includes proximity matrix.

fit(double[][], int) - Static method in class smile.clustering.KMeans

Partitions data into k clusters up to 100 iterations.

fit(double[][], int, int, double) - Static method in class smile.clustering.KMeans

Partitions data into k clusters up to 100 iterations.

fit(BBDTree, double[][], int, int, double) - Static method in class smile.clustering.KMeans

Partitions data into k clusters.

fit(int[][], int) - Static method in class smile.clustering.KModes

Fits k-modes clustering.

fit(int[][], int, int) - Static method in class smile.clustering.KModes

Fits k-modes clustering.

fit(T[], Distance<T>, int, double) - Static method in class smile.clustering.MEC

Clustering the data.

fit(T[], RNNSearch<T, T>, int, double, int[], double) - Static method in class smile.clustering.MEC

Clustering the data.

fit(SparseArray[], int) - Static method in class smile.clustering.SIB

Clustering data into k clusters up to 100 iterations.

fit(SparseArray[], int, int) - Static method in class smile.clustering.SIB

Clustering data into k clusters.

fit(DenseMatrix, int) - Static method in class smile.clustering.SpectralClustering

Spectral graph clustering.

fit(DenseMatrix, int, int, double) - Static method in class smile.clustering.SpectralClustering

Spectral graph clustering.

fit(double[][], int, double) - Static method in class smile.clustering.SpectralClustering

Spectral clustering the data.

fit(double[][], int, double, int, double) - Static method in class smile.clustering.SpectralClustering

Spectral clustering the data.

fit(double[][], int, int, double) - Static method in class smile.clustering.SpectralClustering

Spectral clustering with Nystrom approximation.

fit(double[][], int, int, double, int, double) - Static method in class smile.clustering.SpectralClustering

Spectral clustering with Nystrom approximation.

fit(double[][], int) - Static method in class smile.clustering.XMeans

Clustering data with the number of clusters determined by X-Means algorithm automatically.

fit(double[][], int, int, double) - Static method in class smile.clustering.XMeans

Clustering data with the number of clusters determined by X-Means algorithm automatically.

fit(DataFrame) - Static method in class smile.feature.MaxAbsScaler

Learns transformation parameters from a dataset.

fit(double[][]) - Static method in class smile.feature.MaxAbsScaler

Learns transformation parameters from a dataset.

fit(DataFrame) - Static method in class smile.feature.RobustStandardizer

Learns transformation parameters from a dataset.

fit(double[][]) - Static method in class smile.feature.RobustStandardizer

Learns transformation parameters from a dataset.

fit(DataFrame) - Static method in class smile.feature.Scaler

Learns transformation parameters from a dataset.

fit(double[][]) - Static method in class smile.feature.Scaler

Learns transformation parameters from a dataset.

fit(DataFrame) - Static method in class smile.feature.Standardizer

Learns transformation parameters from a dataset.

fit(double[][]) - Static method in class smile.feature.Standardizer

Learns transformation parameters from a dataset.

fit(DataFrame) - Static method in class smile.feature.WinsorScaler

Learns transformation parameters from a dataset with 5% lower limit and 95% upper limit.

fit(DataFrame, double, double) - Static method in class smile.feature.WinsorScaler

Learns transformation parameters from a dataset.

fit(double[][]) - Static method in class smile.feature.WinsorScaler

Learns transformation parameters from a dataset.

fit(double[][], double, double) - Static method in class smile.feature.WinsorScaler

Learns transformation parameters from a dataset.

fit(T) - Method in interface smile.gap.FitnessMeasure

Returns the non-negative fitness value of a chromosome.

fit(RNNSearch<double[], double[]>, double[][], double) - Method in class smile.neighbor.MPLSH

Fits the posteriori multiple probe algorithm.

fit(RNNSearch<double[], double[]>, double[][], double, int) - Method in class smile.neighbor.MPLSH

Fits the posteriori multiple probe algorithm.

fit(RNNSearch<double[], double[]>, double[][], double, int, double) - Method in class smile.neighbor.MPLSH

Train the posteriori multiple probe algorithm.

fit(double[][], int) - Static method in class smile.projection.ICA

Fits independent component analysis.

fit(double[][], int, Properties) - Static method in class smile.projection.ICA

Fits independent component analysis.

fit(double[][], int, DifferentiableFunction, double, int) - Static method in class smile.projection.ICA

Fits independent component analysis.

fit(T[], MercerKernel<T>, int) - Static method in class smile.projection.KPCA

Fits kernel principal component analysis.

fit(T[], MercerKernel<T>, int, double) - Static method in class smile.projection.KPCA

Fits kernel principal component analysis.

fit(double[][]) - Static method in class smile.projection.PCA

Fits principal component analysis with covariance matrix.

fit(double[][], int) - Static method in class smile.projection.PPCA

Fits probabilistic principal component analysis.

fit(Formula, DataFrame, Properties) - Static method in class smile.regression.ElasticNet

Fit an Elastic Net model.

fit(Formula, DataFrame, double, double) - Static method in class smile.regression.ElasticNet

Fit an Elastic Net model.

fit(Formula, DataFrame, double, double, double, int) - Static method in class smile.regression.ElasticNet

Fit an Elastic Net model.

fit(T[], double[], MercerKernel<T>, double) - Static method in class smile.regression.GaussianProcessRegression

Fits a regular Gaussian process model.

fit(T[], double[], T[], MercerKernel<T>, double) - Static method in class smile.regression.GaussianProcessRegression

Fits an approximate Gaussian process model by the method of subset of regressors.

fit(Formula, DataFrame) - Static method in class smile.regression.GradientTreeBoost

Fits a gradient tree boosting for regression.

fit(Formula, DataFrame, Properties) - Static method in class smile.regression.GradientTreeBoost

Fits a gradient tree boosting for regression.

fit(Formula, DataFrame, Loss, int, int, int, int, double, double) - Static method in class smile.regression.GradientTreeBoost

Fits a gradient tree boosting for regression.

fit(Formula, DataFrame) - Static method in class smile.regression.LASSO

Fits a L1-regularized least squares model.

fit(Formula, DataFrame, Properties) - Static method in class smile.regression.LASSO

Fits a L1-regularized least squares model.

fit(Formula, DataFrame, double) - Static method in class smile.regression.LASSO

Fits a L1-regularized least squares model.

fit(Formula, DataFrame, double, double, int) - Static method in class smile.regression.LASSO

Fits a L1-regularized least squares model.

fit(Formula, DataFrame) - Static method in class smile.regression.OLS

Fits an ordinary least squares model.

fit(Formula, DataFrame, Properties) - Static method in class smile.regression.OLS

Fits an ordinary least squares model.

fit(Formula, DataFrame, String, boolean, boolean) - Static method in class smile.regression.OLS

Fits an ordinary least squares model.

fit(Formula, DataFrame) - Static method in class smile.regression.RandomForest

Learns a random forest for regression.

fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RandomForest

Learns a random forest for regression.

fit(Formula, DataFrame, int, int, int, int, int, double) - Static method in class smile.regression.RandomForest

Learns a random forest for regression.

fit(Formula, DataFrame, int, int, int, int, int, double, LongStream) - Static method in class smile.regression.RandomForest

Learns a random forest for regression.

fit(T[], double[], RBF<T>[]) - Static method in class smile.regression.RBFNetwork

Fits a RBF network.

fit(T[], double[], RBF<T>[], boolean) - Static method in class smile.regression.RBFNetwork

Fits a RBF network.

fit(Formula, DataFrame) - Static method in class smile.regression.RegressionTree

Learns a regression tree.

fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RegressionTree

Learns a regression tree.

fit(Formula, DataFrame, int, int, int) - Static method in class smile.regression.RegressionTree

Learns a regression tree.

fit(Formula, DataFrame) - Static method in class smile.regression.RidgeRegression

Fits a ridge regression model.

fit(Formula, DataFrame, Properties) - Static method in class smile.regression.RidgeRegression

Fits a ridge regression model.

fit(Formula, DataFrame, double) - Static method in class smile.regression.RidgeRegression

Fits a ridge regression model.

fit(double[][], double[], double, double, double) - Static method in class smile.regression.SVR

Fits a linear epsilon-SVR.

fit(int[][], double[], int, double, double, double) - Static method in class smile.regression.SVR

Fits a linear epsilon-SVR of binary sparse data.

fit(SparseArray[], double[], int, double, double, double) - Static method in class smile.regression.SVR

Fits a linear epsilon-SVR of sparse data.

fit(T[], double[], MercerKernel<T>, double, double, double) - Static method in class smile.regression.SVR

Fits a epsilon-SVR.

fit(Tuple[][], int[][]) - Static method in class smile.sequence.CRF

Fits a CRF model.

fit(Tuple[][], int[][], Properties) - Static method in class smile.sequence.CRF

Fits a CRF model.

fit(Tuple[][], int[][], int, int, int, int, double) - Static method in class smile.sequence.CRF

Fits a CRF model.

fit(T[][], int[][], Function<T, Tuple>) - Static method in class smile.sequence.CRFLabeler

Fits a CRF model.

fit(T[][], int[][], Function<T, Tuple>, Properties) - Static method in class smile.sequence.CRFLabeler

Fits a CRF model.

fit(T[][], int[][], Function<T, Tuple>, int, int, int, int, double) - Static method in class smile.sequence.CRFLabeler

Fits a CRF.

fit(int[][], int[][]) - Static method in class smile.sequence.HMM

Fits an HMM by maximum likelihood estimation.

fit(T[][], int[][], ToIntFunction<T>) - Static method in class smile.sequence.HMM

Fits an HMM by maximum likelihood estimation.

fit(T[][], int[][], ToIntFunction<T>) - Static method in class smile.sequence.HMMLabeler

Fits an HMM by maximum likelihood estimation.

fitness(double[][], int[], double[][], int[], ClassificationMeasure, BiFunction<double[][], int[], Classifier<double[]>>) - Static method in class smile.feature.GAFE

Returns a classification fitness measure.

fitness(double[][], double[], double[][], double[], RegressionMeasure, BiFunction<double[][], double[], Regression<double[]>>) - Static method in class smile.feature.GAFE

Returns a regression fitness measure.

fitness(String, DataFrame, DataFrame, ClassificationMeasure, BiFunction<Formula, DataFrame, DataFrameClassifier>) - Static method in class smile.feature.GAFE

Returns a classification fitness measure.

fitness(String, DataFrame, DataFrame, RegressionMeasure, BiFunction<Formula, DataFrame, DataFrameRegression>) - Static method in class smile.feature.GAFE

Returns a regression fitness measure.

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.

fittedValues() - Method in class smile.regression.LinearModel

Returns the fitted values.

FLD - Class in smile.classification

Fisher's linear discriminant.

FLD(double[], double[][], DenseMatrix) - Constructor for class smile.classification.FLD

Constructor.

FLD(double[], double[][], DenseMatrix, IntSet) - Constructor for class smile.classification.FLD

Constructor.

FMeasure - Class in smile.validation

The F-score (or F-measure) considers both the precision and the recall of the test to compute the score.

FMeasure() - Constructor for class smile.validation.FMeasure

Constructor of F1 score.

FMeasure(double) - Constructor for class smile.validation.FMeasure

Constructor of general F-score.

formula - Variable in class smile.base.cart.CART

Design matrix formula

formula() - Method in class smile.classification.AdaBoost

formula() - Method in interface smile.classification.DataFrameClassifier

Returns the formula associated with the model.

formula() - Method in class smile.classification.DecisionTree

Returns null if the tree is part of ensemble algorithm.

formula() - Method in class smile.classification.GradientTreeBoost

formula() - Method in class smile.classification.RandomForest

formula() - Method in interface smile.regression.DataFrameRegression

Returns the formula associated with the model.

formula() - Method in class smile.regression.GradientTreeBoost

formula() - Method in class smile.regression.LinearModel

formula() - Method in class smile.regression.RandomForest

formula() - Method in class smile.regression.RegressionTree

Returns null if the tree is part of ensemble algorithm.

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.

FPTree - Class in smile.association

FP-tree data structure used in FP-growth (frequent pattern growth) algorithm for frequent item set mining.

ftest() - Method in class smile.regression.LinearModel

Returns the F-statistic of goodness-of-fit.

G

g(double[], double[]) - Method in interface smile.base.mlp.ActivationFunction

The gradient function.

g(Cost, double[], double[]) - Method in enum smile.base.mlp.OutputFunction

The gradient function.

g(double) - Method in class smile.projection.ica.Gaussian

g(double) - Method in class smile.projection.ica.Kurtosis

g(double) - Method in class smile.projection.ica.LogCosh

g2(double) - Method in class smile.projection.ica.Gaussian

g2(double) - Method in class smile.projection.ica.Kurtosis

g2(double) - Method in class smile.projection.ica.LogCosh

GAFE - Class in smile.feature

Genetic algorithm based feature selection.

GAFE() - Constructor for class smile.feature.GAFE

Constructor.

GAFE(Selection, int, Crossover, double, double) - Constructor for class smile.feature.GAFE

Constructor.

Gaussian - Class in smile.projection.ica

This function may be better than LogCosh when the independent components are highly super-Gaussian, or when robustness is very important.

Gaussian() - Constructor for class smile.projection.ica.Gaussian

Gaussian(double, double) - Static method in interface smile.vq.Neighborhood

Returns Gaussian neighborhood function.

GaussianProcessRegression - Class in smile.regression

Gaussian Process for Regression.

GaussianProcessRegression() - Constructor for class smile.regression.GaussianProcessRegression

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[], Selection, int) - Constructor for class smile.gap.GeneticAlgorithm

Constructor.

get(int) - Method in class smile.neighbor.lsh.Hash

Returns the bucket entry for the given hash value.

get(double[]) - Method in class smile.neighbor.lsh.Hash

Returns the bucket entry for the given point.

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.

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.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.

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.

getInitialStateProbabilities() - Method in class smile.sequence.HMM

Returns the initial state probabilities.

getInputSize() - Method in class smile.base.mlp.Layer

Returns the dimension of input vector (not including bias value).

getKeywords() - Method in class smile.taxonomy.Concept

Returns the concept synonym set.

getLearningRate() - Method in class smile.base.mlp.MultilayerPerceptron

Returns the learning rate.

getLearningRate() - Method in class smile.classification.LogisticRegression

Returns the learning rate of stochastic gradient descent.

getLearningRate() - Method in class smile.classification.Maxent

Returns the learning rate of stochastic gradient descent.

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.

getMomentum() - Method in class smile.base.mlp.MultilayerPerceptron

Returns the momentum factor.

getNoiseVariance() - Method in class smile.projection.PPCA

Returns the variance of noise.

getOutputSize() - Method in class smile.base.mlp.Layer

Returns the dimension of output vector.

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.

getProbeSequence(double[], double, int) - Method in class smile.neighbor.lsh.PosterioriModel

Generate query-directed probes.

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 interface smile.projection.LinearProjection

Returns the projection matrix.

getProjection() - Method in class smile.projection.PCA

getProjection() - Method in class smile.projection.PPCA

Returns the projection matrix.

getProjection() - Method in class smile.projection.RandomProjection

getRoot() - Method in class smile.taxonomy.Taxonomy

Returns the root node of taxonomy tree.

getStateTransitionProbabilities() - Method in class smile.sequence.HMM

Returns the state transition probabilities.

getSymbolEmissionProbabilities() - Method in class smile.sequence.HMM

Returns the symbol emission probabilities.

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.base.mlp.MultilayerPerceptron

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, double[][], int[]) - Constructor for class smile.clustering.GMeans

Constructor.

gradient - Variable in class smile.base.mlp.Layer

The gradient vector.

gradient() - Method in class smile.base.mlp.Layer

Returns the error/gradient vector.

GradientTreeBoost - Class in smile.classification

Gradient boosting for classification.

GradientTreeBoost(Formula, RegressionTree[], double, double, double[]) - Constructor for class smile.classification.GradientTreeBoost

Constructor of binary class.

GradientTreeBoost(Formula, RegressionTree[], double, double, double[], IntSet) - Constructor for class smile.classification.GradientTreeBoost

Constructor of binary class.

GradientTreeBoost(Formula, RegressionTree[][], double, double[]) - Constructor for class smile.classification.GradientTreeBoost

Constructor of multi-class.

GradientTreeBoost(Formula, RegressionTree[][], double, double[], IntSet) - Constructor for class smile.classification.GradientTreeBoost

Constructor of multi-class.

GradientTreeBoost - Class in smile.regression

Gradient boosting for regression.

GradientTreeBoost(Formula, RegressionTree[], double, double, double[]) - Constructor for class smile.regression.GradientTreeBoost

Constructor.

graph - Variable in class smile.manifold.IsoMap

The nearest neighbor graph.

graph - Variable in class smile.manifold.LaplacianEigenmap

Nearest neighbor graph.

graph - Variable in class smile.manifold.LLE

Nearest neighbor graph.

GroupKFold - Class in smile.validation

GroupKfold is a cross validation technique that splits the data by respecting additional information about groups.

GroupKFold(int, int, int[]) - Constructor for class smile.validation.GroupKFold

Constructor.

GrowingNeuralGas - Class in smile.vq

Growing Neural Gas.

GrowingNeuralGas(int) - Constructor for class smile.vq.GrowingNeuralGas

Constructor.

GrowingNeuralGas(int, double, double, int, int, double, double) - Constructor for class smile.vq.GrowingNeuralGas

Constructor.

H

H - Variable in class smile.neighbor.LSH

The size of hash table.

H - Variable in class smile.neighbor.lsh.NeighborHashValueModel

Hash values of query object.

HaarWavelet - Class in smile.wavelet

Haar wavelet.

HaarWavelet() - Constructor for class smile.wavelet.HaarWavelet

Constructor.

Hash - Class in smile.neighbor.lsh

The hash function for Euclidean spaces.

hash - Variable in class smile.neighbor.LSH

Hash functions.

Hash(int, int, double, int) - Constructor for class smile.neighbor.lsh.Hash

Constructor.

hash(double[]) - Method in class smile.neighbor.lsh.Hash

Apply hash functions on given vector x.

hash(Hash, PrZ[]) - Method in class smile.neighbor.lsh.Probe

Returns the bucket number of the probe.

hash(T) - Method in interface smile.neighbor.lsh.SimHash

hashCode() - Method in class smile.association.AssociationRule

hashCode() - Method in class smile.association.ItemSet

HashValueParzenModel - Class in smile.neighbor.lsh

HashValueParzenModel(MultiProbeHash, MultiProbeSample[], double) - Constructor for class smile.neighbor.lsh.HashValueParzenModel

Constructor.

HiddenLayer - Class in smile.base.mlp

A hidden layer in the neural network.

HiddenLayer(int, int, ActivationFunction) - Constructor for class smile.base.mlp.HiddenLayer

Constructor.

HierarchicalClustering - Class in smile.clustering

Agglomerative Hierarchical Clustering.

HierarchicalClustering(int[][], double[]) - Constructor for class smile.clustering.HierarchicalClustering

Constructor.

HMM - Class in smile.sequence

First-order Hidden Markov Model.

HMM(double[], DenseMatrix, DenseMatrix) - Constructor for class smile.sequence.HMM

Constructor.

HMMLabeler<T> - Class in smile.sequence

First-order Hidden Markov Model sequence labeler.

HMMLabeler(HMM, ToIntFunction<T>) - Constructor for class smile.sequence.HMMLabeler

Constructor.

huber(double) - Static method in interface smile.base.cart.Loss

Huber loss function for M-regression, which attempts resistance to long-tailed error distributions and outliers while maintaining high efficiency for normally distributed errors.

I

ICA - Class in smile.projection

Independent Component Analysis (ICA) is a computational method for separating a multivariate signal into additive components.

ICA(double[][]) - Constructor for class smile.projection.ICA

Constructor.

importance - Variable in class smile.base.cart.CART

Variable importance.

importance() - Method in class smile.base.cart.CART

Returns the variable importance.

importance() - Method in class smile.classification.AdaBoost

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.

impurity(LeafNode) - Method in class smile.base.cart.CART

Returns the impurity of node.

impurity(SplitRule) - Method in class smile.base.cart.DecisionNode

Returns the impurity of node.

impurity(SplitRule, int, int[]) - Static method in class smile.base.cart.DecisionNode

Returns the impurity of samples.

impurity() - Method in class smile.base.cart.RegressionNode

Returns the residual sum of squares.

impurity(LeafNode) - Method in class smile.classification.DecisionTree

impurity(LeafNode) - Method in class smile.regression.RegressionTree

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 data.

impute(double[][]) - Method in class smile.imputation.SVDImputation

impute(double[][], int) - Method in class smile.imputation.SVDImputation

Impute missing values in the dataset.

imputeWithColumnAverage(double[][]) - Static method in interface smile.imputation.MissingValueImputation

Impute the missing values with column averages.

index - Variable in class smile.base.cart.CART

An index of samples to their original locations in training dataset.

index - Variable in class smile.manifold.IsoMap

The original sample index.

index - Variable in class smile.manifold.LaplacianEigenmap

The original sample index.

index - Variable in class smile.manifold.LLE

The original sample index.

index - Variable in class smile.neighbor.Neighbor

The index of neighbor object in the dataset.

indexOf(int[]) - Method in class smile.classification.ClassLabels

Maps the class labels to index.

initHashTable(int, int, int, double, int) - Method in class smile.neighbor.LSH

Initialize the hash tables.

initHashTable(int, int, int, double, int) - Method in class smile.neighbor.MPLSH

instance - Static variable in class smile.feature.SignalNoiseRatio

instance - Static variable in class smile.feature.SumSquaresRatio

instance - Static variable in class smile.validation.Accuracy

instance - Static variable in class smile.validation.AdjustedRandIndex

instance - Static variable in class smile.validation.Error

instance - Static variable in class smile.validation.Fallout

instance - Static variable in class smile.validation.FDR

instance - Static variable in class smile.validation.FMeasure

instance - Static variable in class smile.validation.MCC

instance - Static variable in class smile.validation.MeanAbsoluteDeviation

instance - Static variable in class smile.validation.MSE

instance - Static variable in class smile.validation.MutualInformation

instance - Static variable in class smile.validation.Precision

instance - Static variable in class smile.validation.RandIndex

instance - Static variable in class smile.validation.Recall

instance - Static variable in class smile.validation.RMSE

instance - Static variable in class smile.validation.RSS

instance - Static variable in class smile.validation.Sensitivity

instance - Static variable in class smile.validation.Specificity

instances() - Method in class smile.base.svm.KernelMachine

Returns the instances of kernel machines.

intercept(double[]) - Method in interface smile.base.cart.Loss

Returns the intercept of model.

intercept() - Method in class smile.base.svm.KernelMachine

Returns the intercept.

intercept() - Method in class smile.regression.LinearModel

Returns the intercept.

InternalNode - Class in smile.base.cart

An internal node in CART.

InternalNode(int, double, double, Node, Node) - Constructor for class smile.base.cart.InternalNode

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.

isExpandable() - Method in class smile.neighbor.lsh.Probe

isExtendable() - Method in class smile.neighbor.lsh.Probe

isLeaf() - Method in class smile.taxonomy.Concept

Check if a node is a leaf in the taxonomy tree.

isNormalized() - Method in class smile.classification.RBFNetwork

Returns true if the model is normalized.

isNormalized() - Method in class smile.regression.RBFNetwork

Returns true if the model is normalized.

IsoMap - Class in smile.manifold

Isometric feature mapping.

IsoMap(int[], double[][], Graph) - Constructor for class smile.manifold.IsoMap

Constructor.

IsotonicMDS - Class in smile.mds

Kruskal's nonmetric MDS.

IsotonicMDS(double, double[][]) - Constructor for class smile.mds.IsotonicMDS

Constructor.

IsotonicRegressionScaling - Class in smile.classification

A method to calibrate decision function value to probability.

IsotonicRegressionScaling(double[], double[]) - Constructor for class smile.classification.IsotonicRegressionScaling

Constructor.

isShiftable() - Method in class smile.neighbor.lsh.Probe

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.

iterator() - Method in class smile.association.ARM

iterator() - Method in class smile.association.FPGrowth

J

joint(int[], int[]) - Static method in class smile.validation.NormalizedMutualInformation

Calculates the normalized mutual information of I(y1, y2) / H(y1, y2).

K

k - Variable in class smile.classification.ClassLabels

The number of classes.

k - Variable in class smile.clustering.PartitionClustering

The number of clusters.

k - Variable in class smile.neighbor.LSH

The number of random projections per hash value.

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.

k - Variable in class smile.validation.GroupKFold

The number of folds.

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.

kernel() - Method in class smile.base.svm.KernelMachine

Returns the kernel function.

KernelMachine<T> - Class in smile.base.svm

Kernel machines.

KernelMachine(MercerKernel<T>, T[], double[]) - Constructor for class smile.base.svm.KernelMachine

Constructor.

KernelMachine(MercerKernel<T>, T[], double[], double) - Constructor for class smile.base.svm.KernelMachine

Constructor.

KernelMachine<T> - Class in smile.regression

The learning methods building on kernels.

KernelMachine(MercerKernel<T>, T[], double[]) - Constructor for class smile.regression.KernelMachine

Constructor.

KernelMachine(MercerKernel<T>, T[], double[], double) - Constructor for class smile.regression.KernelMachine

Constructor.

key - Variable in class smile.neighbor.Neighbor

The key of neighbor.

keys - Variable in class smile.neighbor.LSH

The keys of data objects.

keys() - Method in class smile.neighbor.MutableLSH

Returns the keys.

KMeans - Class in smile.clustering

K-Means clustering.

KMeans(double, double[][], int[]) - Constructor for class smile.clustering.KMeans

Constructor.

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.

KModes - Class in smile.clustering

K-Modes clustering.

KModes(double, int[][], int[]) - Constructor for class smile.clustering.KModes

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(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.

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[], double, double[][], double[], DenseMatrix) - Constructor for class smile.projection.KPCA

Constructor.

Kurtosis - Class in smile.projection.ica

This function is justified on statistical grounds only for estimating sub-Gaussian independent components when there are no outliers.

Kurtosis() - Constructor for class smile.projection.ica.Kurtosis

L

L - Variable in class smile.vq.BIRCH

The number of CF entries in the leaf nodes.

labels - Variable in class smile.classification.ClassLabels

The class labels.

lad() - Static method in interface smile.base.cart.Loss

Least absolute deviation regression.

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.

lambda - Variable in class smile.base.mlp.MultilayerPerceptron

weight decay factor, which is also a regularization term.

LaplacianEigenmap - Class in smile.manifold

Laplacian Eigenmap.

LaplacianEigenmap(int[], double[][], Graph) - Constructor for class smile.manifold.LaplacianEigenmap

Constructor with discrete weights.

LaplacianEigenmap(double, int[], double[][], Graph) - Constructor for class smile.manifold.LaplacianEigenmap

Constructor with Gaussian kernel.

LASSO - Class in smile.regression

Lasso (least absolute shrinkage and selection operator) regression.

LASSO() - Constructor for class smile.regression.LASSO

LASVM<T> - Class in smile.base.svm

LASVM is an approximate SVM solver that uses online approximation.

LASVM(MercerKernel<T>, double, double) - Constructor for class smile.base.svm.LASVM

Constructor.

LASVM(MercerKernel<T>, double, double, double) - Constructor for class smile.base.svm.LASVM

Constructor.

lattice(int, int, double[][]) - Static method in class smile.vq.SOM

Creates a lattice of which the weight vectors are randomly selected from samples.

Layer - Class in smile.base.mlp

A layer in the neural network.

Layer(int, int) - Constructor for class smile.base.mlp.Layer

Constructor.

LayerBuilder - Class in smile.base.mlp

The builder of layers.

LayerBuilder(int) - Constructor for class smile.base.mlp.LayerBuilder

Constructor.

LDA - Class in smile.classification

Linear discriminant analysis.

LDA(double[], double[][], double[], DenseMatrix) - Constructor for class smile.classification.LDA

Constructor.

LDA(double[], double[][], double[], DenseMatrix, IntSet) - Constructor for class smile.classification.LDA

Constructor.

LeafNode - Class in smile.base.cart

A leaf node in decision tree.

LeafNode(int) - Constructor for class smile.base.cart.LeafNode

Constructor.

leafs() - Method in class smile.base.cart.InternalNode

leafs() - Method in class smile.base.cart.LeafNode

leafs() - Method in interface smile.base.cart.Node

Returns the number of leaf nodes in the subtree.

length - Variable in class smile.gap.BitString

The length of chromosome.

length() - Method in class smile.gap.BitString

Returns the length of bit string.

leverage - Variable in class smile.association.AssociationRule

The difference between the probability of the rule and the expected probability if the items were statistically independent.

lift - Variable in class smile.association.AssociationRule

How many times more often antecedent and consequent occur together than expected if they were statistically independent.

linear() - Static method in interface smile.base.mlp.ActivationFunction

Linear/Identity function.

linear(int) - Static method in class smile.base.mlp.Layer

Returns a hidden layer with linear activation function.

LinearKernelMachine - Class in smile.base.svm

Linear kernel machine.

LinearKernelMachine(double[], double) - Constructor for class smile.base.svm.LinearKernelMachine

Constructor.

LinearModel - Class in smile.regression

Linear model.

LinearProjection - Interface in smile.projection

Linear projection.

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(double[][]) - Constructor for class smile.clustering.linkage.Linkage

Initialize the linkage with the lower triangular proximity matrix.

Linkage(int, float[]) - Constructor for class smile.clustering.linkage.Linkage

Initialize the linkage with the lower triangular proximity matrix.

LLE - Class in smile.manifold

Locally Linear Embedding.

LLE(int[], double[][], Graph) - 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, double) - 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.

LogCosh - Class in smile.projection.ica

A good general-purpose function for ICA.

LogCosh() - Constructor for class smile.projection.ica.LogCosh

logistic(int[]) - Static method in interface smile.base.cart.Loss

Logistic regression loss for binary classification.

logistic(int, int, int[], double[][]) - Static method in interface smile.base.cart.Loss

Logistic regression loss for multi-class classification.

LogisticRegression - Class in smile.classification

Logistic regression.

LogisticRegression(double[], double, double) - Constructor for class smile.classification.LogisticRegression

Constructor of binary logistic regression.

LogisticRegression(double, double[], double, IntSet) - Constructor for class smile.classification.LogisticRegression

Constructor of binary logistic regression.

LogisticRegression(double, double[][], double) - Constructor for class smile.classification.LogisticRegression

Constructor of multi-class logistic regression.

LogisticRegression(double, double[][], double, IntSet) - Constructor for class smile.classification.LogisticRegression

Constructor of multi-class logistic regression.

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(T[], int[]) - Method in class smile.sequence.HMMLabeler

Returns the log joint probability of an observation sequence along a state sequence.

logp(T[]) - Method in class smile.sequence.HMMLabeler

Returns the logarithm probability of an observation sequence.

LOOCV - Class in smile.validation

Leave-one-out cross validation.

LOOCV(int) - Constructor for class smile.validation.LOOCV

Constructor.

Loss - Interface in smile.base.cart

Regression loss function.

Loss.Type - Enum in smile.base.cart

The type of loss.

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.

ls() - Static method in interface smile.base.cart.Loss

Least squares regression.

ls(double[]) - Static method in interface smile.base.cart.Loss

Least squares regression.

LSH<E> - Class in smile.neighbor

Locality-Sensitive Hashing.

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, double) - Constructor for class smile.neighbor.LSH

Constructor.

LSH(int, int, int, double, int) - Constructor for class smile.neighbor.LSH

Constructor.

M

m - Variable in class smile.neighbor.lsh.PrZ

The index of hash function.

matrix - Variable in class smile.validation.ConfusionMatrix

Confusion matrix.

max(int[], int[]) - Static method in class smile.validation.AdjustedMutualInformation

Calculates the adjusted mutual information of (I(y1, y2) - E(MI)) / (max(H(y1), H(y2)) - E(MI)).

max(int[], int[]) - Static method in class smile.validation.NormalizedMutualInformation

Calculates the normalized mutual information of I(y1, y2) / max(H(y1), H(y2)).

MaxAbsScaler - Class in smile.feature

Scales each feature by its maximum absolute value.

MaxAbsScaler(StructType, double[]) - Constructor for class smile.feature.MaxAbsScaler

Constructor.

maxDepth - Variable in class smile.base.cart.CART

The maximum depth of the tree.

Maxent - Class in smile.classification

Maximum Entropy Classifier.

Maxent(double, double[]) - Constructor for class smile.classification.Maxent

Constructor of binary maximum entropy classifier.

Maxent(double, double[], IntSet) - Constructor for class smile.classification.Maxent

Constructor of binary maximum entropy classifier.

Maxent(double, double[][]) - Constructor for class smile.classification.Maxent

Constructor of multi-class maximum entropy classifier.

Maxent(double, double[][], IntSet) - Constructor for class smile.classification.Maxent

Constructor of multi-class maximum entropy classifier.

maxNodes - Variable in class smile.base.cart.CART

The maximum number of leaf nodes in the tree.

MCC - Class in smile.validation

Matthews correlation coefficient.The MCC is in essence a correlation coefficient between the observed and predicted binary classifications It is considered as a balanced measure for binary classification, even in unbalanced data sets.

MCC() - Constructor for class smile.validation.MCC

MDS - Class in smile.mds

Classical multidimensional scaling, also known as principal coordinates analysis.

MDS(double[], double[], double[][]) - Constructor for class smile.mds.MDS

Constructor.

mean() - Method in class smile.base.cart.RegressionNode

Returns the mean of response variable.

mean() - Method in class smile.neighbor.lsh.HashValueParzenModel

Returns the mean.

mean - Variable in class smile.neighbor.lsh.NeighborHashValueModel

Mean of hash values of neighbors.

MeanAbsoluteDeviation - Class in smile.validation

Mean absolute deviation error.

MeanAbsoluteDeviation() - Constructor for class smile.validation.MeanAbsoluteDeviation

measure(int[], int[]) - Method in class smile.validation.Accuracy

measure(int[], int[]) - Method in class smile.validation.AdjustedMutualInformation

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.Error

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(int[], int[]) - Method in class smile.validation.MCC

measure(double[], double[]) - Method in class smile.validation.MeanAbsoluteDeviation

measure(double[], double[]) - Method in class smile.validation.MSE

measure(int[], int[]) - Method in class smile.validation.MutualInformation

measure(int[], int[]) - Method in class smile.validation.NormalizedMutualInformation

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

Non-parametric Minimum Conditional Entropy Clustering.

MEC(double, double, RNNSearch<T, T>, int, int[]) - Constructor for class smile.clustering.MEC

Constructor.

merge() - Method in class smile.base.cart.InternalNode

merge() - Method in class smile.base.cart.LeafNode

merge() - Method in interface smile.base.cart.Node

Try to merge the children nodes and return a leaf node.

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

merge(RandomForest) - Method in class smile.regression.RandomForest

Merges together two random forests and returns a new forest consisting of trees from both input forests.

min(int[], int[]) - Static method in class smile.validation.AdjustedMutualInformation

Calculates the adjusted mutual information of (I(y1, y2) - E(MI)) / (min(H(y1), H(y2)) - E(MI)).

min(int[], int[]) - Static method in class smile.validation.NormalizedMutualInformation

Calculates the normalized mutual information of I(y1, y2) / min(H(y1), H(y2)).

minPts - Variable in class smile.clustering.DBSCAN

The minimum number of points required to form a cluster

minSupport() - Method in class smile.association.FPTree

Returns the required minimum support of item sets in terms of frequency.

MissingValueImputation - Interface in smile.imputation

Interface to impute missing values in the data.

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.

mle(int, OutputFunction) - Static method in class smile.base.mlp.Layer

Returns an output layer with (log-)likelihood cost function.

MLP - Class in smile.classification

Fully connected multilayer perceptron neural network for classification.

MLP(int, LayerBuilder...) - Constructor for class smile.classification.MLP

Constructor.

MLP(IntSet, int, LayerBuilder...) - Constructor for class smile.classification.MLP

Constructor.

MLP - Class in smile.regression

Fully connected multilayer perceptron neural network for regression.

MLP(int, LayerBuilder...) - Constructor for class smile.regression.MLP

Constructor.

model - Variable in class smile.sequence.CRFLabeler

The CRF model.

model - Variable in class smile.sequence.HMMLabeler

The HMM model.

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(int, OutputFunction) - Static method in class smile.base.mlp.Layer

Returns an output layer with mean squared error cost function.

MSE - Class in smile.validation

Mean squared error.

MSE() - Constructor for class smile.validation.MSE

mtry - Variable in class smile.base.cart.CART

The number of input variables to be used to determine the decision at a node of the tree.

MultilayerPerceptron - Class in smile.base.mlp

Fully connected multilayer perceptron neural network.

MultilayerPerceptron(Layer...) - Constructor for class smile.base.mlp.MultilayerPerceptron

Constructor.

MultiProbeHash - Class in smile.neighbor.lsh

The hash function for data in Euclidean spaces.

MultiProbeHash(int, int, double, int) - Constructor for class smile.neighbor.lsh.MultiProbeHash

Constructor.

MultiProbeSample - Class in smile.neighbor.lsh

Training sample for MPLSH.

MultiProbeSample(double[], List<double[]>) - Constructor for class smile.neighbor.lsh.MultiProbeSample

Constructor.

MutableLSH<E> - Class in smile.neighbor

Mutable LSH.

MutableLSH(int, int, int, double) - Constructor for class smile.neighbor.MutableLSH

Constructor.

mutate() - Method in class smile.gap.BitString

mutate() - Method in interface smile.gap.Chromosome

For genetic algorithms, this method mutates the chromosome randomly.

MutualInformation - Class in smile.validation

Mutual Information for comparing clustering.

MutualInformation() - Constructor for class smile.validation.MutualInformation

N

n - Variable in class smile.base.mlp.Layer

The number of neurons in this layer

n - Variable in class smile.base.mlp.LayerBuilder

The number of neurons.

NaiveBayes - Class in smile.classification

Naive Bayes classifier.

NaiveBayes(double[], Distribution[][]) - Constructor for class smile.classification.NaiveBayes

Constructor of general naive Bayes classifier.

NaiveBayes(double[], Distribution[][], IntSet) - Constructor for class smile.classification.NaiveBayes

Constructor of general naive Bayes classifier.

name() - Method in interface smile.base.mlp.ActivationFunction

Returns the name of activation function.

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.

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 immutable object encapsulates the results of nearest neighbor search.

Neighbor(K, V, int, double) - Constructor for class smile.neighbor.Neighbor

Constructor.

neighbor - Variable in class smile.vq.hebb.Edge

The neighbor neuron.

NeighborHashValueModel - Class in smile.neighbor.lsh

Gaussian model of hash values of nearest neighbor.

NeighborHashValueModel(double[], double[], double[]) - Constructor for class smile.neighbor.lsh.NeighborHashValueModel

Constructor.

Neighborhood - Interface in smile.vq

The neighborhood function for 2-dimensional lattice topology (e.g.

neighbors - Variable in class smile.neighbor.lsh.MultiProbeSample

Neighbors of query object in terms of kNN or range search.

net - Variable in class smile.base.mlp.MultilayerPerceptron

The hidden layers.

network() - Method in class smile.vq.NeuralGas

Returns the network of neurons.

NeuralGas - Class in smile.vq

Neural Gas soft competitive learning algorithm.

NeuralGas(double[][], TimeFunction, TimeFunction, int) - Constructor for class smile.vq.NeuralGas

Constructor.

NeuralMap - Class in smile.vq

NeuralMap is an efficient competitive learning algorithm inspired by growing neural gas and BIRCH.

NeuralMap(double, double, double, int, double) - Constructor for class smile.vq.NeuralMap

Constructor.

Neuron - Class in smile.vq.hebb

The neuron vertex in the growing neural gas network.

Neuron(double[]) - Constructor for class smile.vq.hebb.Neuron

Constructor.

Neuron(double[], double) - Constructor for class smile.vq.hebb.Neuron

Constructor.

neurons() - Method in class smile.base.mlp.LayerBuilder

Returns the number of neurons.

neurons() - Method in class smile.vq.GrowingNeuralGas

Returns the neurons in the network.

neurons() - Method in class smile.vq.NeuralGas

Returns the neurons.

neurons() - Method in class smile.vq.NeuralMap

Returns the set of neurons.

neurons() - Method in class smile.vq.SOM

Returns the lattice of neurons.

newInstance() - Method in class smile.gap.BitString

newInstance(byte[]) - Method in class smile.gap.BitString

Creates a new instance with given bits.

newInstance() - Method in interface smile.gap.Chromosome

Returns a new random instance.

newNode(int[]) - Method in class smile.base.cart.CART

Creates a new leaf node.

newNode(int[]) - Method in class smile.classification.DecisionTree

newNode(int[]) - Method in class smile.regression.RegressionTree

ni - Variable in class smile.classification.ClassLabels

The number of samples per classes.

Node - Interface in smile.base.cart

CART tree node.

nodeSize - Variable in class smile.base.cart.CART

The number of instances in a node below which the tree will not split, setting nodeSize = 5 generally gives good results.

NominalNode - Class in smile.base.cart

A node with a nominal split variable.

NominalNode(int, int, double, double, Node, Node) - Constructor for class smile.base.cart.NominalNode

Constructor.

NominalSplit - Class in smile.base.cart

The data about of a potential split for a leaf node.

NominalSplit(LeafNode, int, int, double, int, int, int, int, IntPredicate) - Constructor for class smile.base.cart.NominalSplit

Constructor.

NormalizedMutualInformation - Class in smile.validation

Normalized Mutual Information (NMI) for comparing clustering.

NormalizedMutualInformation(NormalizedMutualInformation.Method) - Constructor for class smile.validation.NormalizedMutualInformation

Constructor.

NormalizedMutualInformation.Method - Enum in smile.validation

The normalization method.

Normalizer - Class in smile.feature

Normalize samples individually to unit norm.

Normalizer() - Constructor for class smile.feature.Normalizer

Default constructor with L2 norm.

Normalizer(Normalizer.Norm) - Constructor for class smile.feature.Normalizer

Constructor.

Normalizer.Norm - Enum in smile.feature

The types of data scaling.

nystrom(T[], double[], T[], MercerKernel<T>, double) - Static method in class smile.regression.GaussianProcessRegression

Fits an approximate Gaussian process model with Nystrom approximation of kernel matrix.

O

of(int, Supplier<Stream<int[]>>) - Static method in class smile.association.FPTree

One-step construction of FP-tree if the database is available as stream.

of(double, Supplier<Stream<int[]>>) - Static method in class smile.association.FPTree

One-step construction of FP-tree if the database is available as stream.

of(int, int[][]) - Static method in class smile.association.FPTree

One-step construction of FP-tree if the database is available in main memory.

of(double, int[][]) - Static method in class smile.association.FPTree

One-step construction of FP-tree if the database is available in main memory.

of(T[], RadialBasisFunction, Metric<T>) - Static method in class smile.base.rbf.RBF

Makes a set of RBF neurons.

of(T[], RadialBasisFunction[], Metric<T>) - Static method in class smile.base.rbf.RBF

Makes a set of RBF neurons.

of(KernelMachine<double[]>) - Static method in class smile.base.svm.LinearKernelMachine

Creates a linear kernel machine.

of(double[][]) - Static method in class smile.clustering.linkage.CompleteLinkage

Given a set of data, computes the proximity and then the linkage.

of(T[], Distance<T>) - Static method in class smile.clustering.linkage.CompleteLinkage

Given a set of data, computes the proximity and then the linkage.

of(double[][]) - Static method in class smile.clustering.linkage.SingleLinkage

Given a set of data, computes the proximity and then the linkage.

of(T[], Distance<T>) - Static method in class smile.clustering.linkage.SingleLinkage

Given a set of data, computes the proximity and then the linkage.

of(double[][]) - Static method in class smile.clustering.linkage.UPGMALinkage

Given a set of data, computes the proximity and then the linkage.

of(T[], Distance<T>) - Static method in class smile.clustering.linkage.UPGMALinkage

Given a set of data, computes the proximity and then the linkage.

of(double[][]) - Static method in class smile.clustering.linkage.UPGMCLinkage

Given a set of data, computes the proximity and then the linkage.

of(T[], Distance<T>) - Static method in class smile.clustering.linkage.UPGMCLinkage

Given a set of data, computes the proximity and then the linkage.

of(double[][]) - Static method in class smile.clustering.linkage.WardLinkage

Given a set of data, computes the proximity and then the linkage.

of(T[], Distance<T>) - Static method in class smile.clustering.linkage.WardLinkage

Given a set of data, computes the proximity and then the linkage.

of(double[][]) - Static method in class smile.clustering.linkage.WPGMALinkage

Given a set of data, computes the proximity and then the linkage.

of(T[], Distance<T>) - Static method in class smile.clustering.linkage.WPGMALinkage

Given a set of data, computes the proximity and then the linkage.

of(double[][]) - Static method in class smile.clustering.linkage.WPGMCLinkage

Given a set of data, computes the proximity and then the linkage.

of(T[], Distance<T>) - Static method in class smile.clustering.linkage.WPGMCLinkage

Given a set of data, computes the proximity and then the linkage.

of(double[][], int[]) - Static method in class smile.feature.SignalNoiseRatio

Univariate feature ranking.

of(double[][], int[]) - Static method in class smile.feature.SumSquaresRatio

Univariate feature ranking.

of(double[][], int) - Static method in class smile.manifold.IsoMap

Runs the C-Isomap algorithm.

of(double[][], int, int, boolean) - Static method in class smile.manifold.IsoMap

Runs the Isomap algorithm.

of(double[][], int) - Static method in class smile.manifold.LaplacianEigenmap

Laplacian Eigenmaps with discrete weights.

of(double[][], int, int, double) - Static method in class smile.manifold.LaplacianEigenmap

Laplacian Eigenmap with Gaussian kernel.

of(double[][], int) - Static method in class smile.manifold.LLE

Runs the LLE algorithm.

of(double[][], int, int) - Static method in class smile.manifold.LLE

Runs the LLE algorithm.

of(double[][]) - Static method in class smile.mds.IsotonicMDS

Fits Kruskal's non-metric MDS with default k = 2, tolerance = 1E-4 and maxIter = 200.

of(double[][], int) - Static method in class smile.mds.IsotonicMDS

Fits Kruskal's non-metric MDS.

of(double[][], Properties) - Static method in class smile.mds.IsotonicMDS

Fits Kruskal's non-metric MDS.

of(double[][], int, double, int) - Static method in class smile.mds.IsotonicMDS

Fits Kruskal's non-metric MDS.

of(double[][], double[][], double, int) - Static method in class smile.mds.IsotonicMDS

Fits Kruskal's non-metric MDS.

of(double[][]) - Static method in class smile.mds.MDS

Fits the classical multidimensional scaling.

of(double[][], int) - Static method in class smile.mds.MDS

Fits the classical multidimensional scaling.

of(double[][], Properties) - Static method in class smile.mds.MDS

Fits the classical multidimensional scaling.

of(double[][], int, boolean) - Static method in class smile.mds.MDS

Fits the classical multidimensional scaling.

of(double[][]) - Static method in class smile.mds.SammonMapping

Fits Sammon's mapping with default k = 2, lambda = 0.2, tolerance = 1E-4 and maxIter = 100.

of(double[][], int) - Static method in class smile.mds.SammonMapping

Fits Sammon's mapping.

of(double[][], Properties) - Static method in class smile.mds.SammonMapping

Fits Sammon's mapping.

of(double[][], int, double, double, double, int) - Static method in class smile.mds.SammonMapping

Fits Sammon's mapping.

of(double[][], double[][], double, double, double, int) - Static method in class smile.mds.SammonMapping

Fits Sammon's mapping.

of(byte[][]) - Static method in interface smile.neighbor.lsh.SimHash

Returns the simhash for a set of generic features (represented as byte[]).

of(T, int, double) - Static method in class smile.neighbor.Neighbor

Creates a neighbor object, of which key and object are the same.

of(int, int) - Static method in class smile.projection.RandomProjection

Generates a non-sparse random projection.

of(int[], int[]) - Static method in class smile.validation.Accuracy

Calculates the classification accuracy.

of(int[], int[]) - Static method in class smile.validation.AdjustedRandIndex

Calculates the adjusted rand index.

of(int[], double[]) - Static method in class smile.validation.AUC

Caulculate AUC for binary classifier.

of(int[], int[]) - Static method in class smile.validation.ConfusionMatrix

Creates the confusion matrix.

of(int[], int[]) - Static method in class smile.validation.Error

Calculates the number of errors.

of(int[], int[]) - Static method in class smile.validation.Fallout

Calculates the false alarm rate.

of(int[], int[]) - Static method in class smile.validation.FDR

Calculates the false discovery rate.

of(int[], int[]) - Static method in class smile.validation.FMeasure

Calculates the F1 score.

of(int[], int[]) - Static method in class smile.validation.MCC

Calculates Matthews correlation coefficient.

of(double[], double[]) - Static method in class smile.validation.MeanAbsoluteDeviation

Calculates the mean absolute deviation error.

of(double[], double[]) - Static method in class smile.validation.MSE

Calculates the mean squared error.

of(int[], int[]) - Static method in class smile.validation.MutualInformation

Calculates the mutual information.

of(int[], int[]) - Static method in class smile.validation.Precision

Calculates the precision.

of(int[], int[]) - Static method in class smile.validation.RandIndex

Calculates the rand index.

of(int[], int[]) - Static method in class smile.validation.Recall

Calculates the recall/sensitivity.

of(double[], double[]) - Static method in class smile.validation.RMSE

Calculates the root mean squared error.

of(double[], double[]) - Static method in class smile.validation.RSS

Calculates the residual sum of squares.

of(int[], int[]) - Static method in class smile.validation.Sensitivity

Calculates the sensitivity.

of(int[], int[]) - Static method in class smile.validation.Specificity

Calculates the specificity.

of(int, int, int) - Method in interface smile.vq.Neighborhood

Returns the changing rate of neighborhood at a given iteration.

OLS - Class in smile.regression

Ordinary least squares.

OLS() - Constructor for class smile.regression.OLS

OneVersusOne<T> - Class in smile.classification

One-vs-one strategy for reducing the problem of multiclass classification to multiple binary classification problems.

OneVersusOne(Classifier<T>[][], PlattScaling[][]) - Constructor for class smile.classification.OneVersusOne

Constructor.

OneVersusOne(Classifier<T>[][], PlattScaling[][], IntSet) - Constructor for class smile.classification.OneVersusOne

Constructor.

OneVersusRest<T> - Class in smile.classification

One-vs-rest (or one-vs-all) strategy for reducing the problem of multiclass classification to multiple binary classification problems.

OneVersusRest(Classifier<T>[], PlattScaling[]) - Constructor for class smile.classification.OneVersusRest

Constructor.

OneVersusRest(Classifier<T>[], PlattScaling[], IntSet) - Constructor for class smile.classification.OneVersusRest

Constructor.

OnlineClassifier<T> - Interface in smile.classification

Classifier with online learning capability.

OnlineRegression<T> - Interface in smile.regression

Regression model with online learning capability.

order - Variable in class smile.base.cart.CART

An index of training values.

order(DataFrame) - Static method in class smile.base.cart.CART

Returns the index of ordered samples for each ordinal column.

ordinal - Variable in class smile.sequence.HMMLabeler

The lambda returns the ordinal numbers of symbols.

OrdinalNode - Class in smile.base.cart

A node with a ordinal split variable (real-valued or ordinal categorical value).

OrdinalNode(int, double, double, double, Node, Node) - Constructor for class smile.base.cart.OrdinalNode

Constructor.

OrdinalSplit - Class in smile.base.cart

The data about of a potential split for a leaf node.

OrdinalSplit(LeafNode, int, double, double, int, int, int, int, IntPredicate) - Constructor for class smile.base.cart.OrdinalSplit

Constructor.

OUTLIER - Static variable in class smile.clustering.PartitionClustering

Cluster label for outliers or noises.

OUTLIER - Static variable in interface smile.vq.VectorQuantizer

The label for outliers or noises.

output() - Method in class smile.base.cart.DecisionNode

Returns the predicted value.

output(int[], int[]) - Method in interface smile.base.cart.Loss

Calculate the node output.

output() - Method in class smile.base.cart.RegressionNode

Returns the predicted value.

output - Variable in class smile.base.mlp.Layer

The output vector.

output() - Method in class smile.base.mlp.Layer

Returns the output vector.

output - Variable in class smile.base.mlp.MultilayerPerceptron

The output layer.

OutputFunction - Enum in smile.base.mlp

The output function of neural networks.

OutputLayer - Class in smile.base.mlp

The output layer in the neural network.

OutputLayer(int, int, OutputFunction, Cost) - Constructor for class smile.base.mlp.OutputLayer

Constructor.

P

p - Variable in class smile.base.mlp.Layer

The number of input variables.

p - Variable in class smile.base.mlp.MultilayerPerceptron

The dimensionality of input data.

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(T[], int[]) - Method in class smile.sequence.HMMLabeler

Returns the joint probability of an observation sequence along a state sequence.

p(T[]) - Method in class smile.sequence.HMMLabeler

Returns the probability of an observation sequence.

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.

PartitionClustering - Class in smile.clustering

Partition clustering.

PartitionClustering(int, int[]) - Constructor for class smile.clustering.PartitionClustering

Constructor.

PCA - Class in smile.projection

Principal component analysis.

PCA(double[], double[], DenseMatrix) - Constructor for class smile.projection.PCA

Constructor.

PlattScaling - Class in smile.classification

Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes.

PlattScaling(double, double) - Constructor for class smile.classification.PlattScaling

Constructor.

points() - Method in class smile.neighbor.lsh.Bucket

Returns the points in the bucket.

population() - Method in class smile.gap.GeneticAlgorithm

Returns the population of current generation.

posteriori(double[]) - Method in class smile.base.cart.DecisionNode

Returns the class probability.

posteriori(int[], double[]) - Static method in class smile.base.cart.DecisionNode

Returns the class probability.

PosterioriModel - Class in smile.neighbor.lsh

Pre-computed posteriori probabilities for generating multiple probes.

PosterioriModel(MultiProbeHash, MultiProbeSample[], int, double) - Constructor for class smile.neighbor.lsh.PosterioriModel

Constructor.

PPCA - Class in smile.projection

Probabilistic principal component analysis.

PPCA(double, double[], DenseMatrix, DenseMatrix) - Constructor for class smile.projection.PPCA

Constructor.

pr - Variable in class smile.neighbor.lsh.PrH

The probability

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

predicate() - Method in class smile.base.cart.NominalSplit

predicate() - Method in class smile.base.cart.OrdinalSplit

predicate() - Method in class smile.base.cart.Split

Returns the lambda that tests on the split feature.

predict(Tuple) - Method in class smile.base.cart.InternalNode

Evaluates the tree over an instance.

predict(Tuple) - Method in class smile.base.cart.LeafNode

predict(Tuple) - Method in interface smile.base.cart.Node

Evaluate the tree over an instance.

predict(Tuple) - Method in class smile.base.cart.NominalNode

predict(Tuple) - Method in class smile.base.cart.OrdinalNode

predict(Tuple) - Method in class smile.classification.AdaBoost

predict(Tuple, 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[]) - Method in interface smile.classification.Classifier

Predicts the class labels of an array of instances.

predict(Tuple) - Method in interface smile.classification.DataFrameClassifier

Predicts the class label of an instance.

predict(DataFrame) - Method in interface smile.classification.DataFrameClassifier

Predicts the class labels of a data frame.

predict(Tuple) - Method in class smile.classification.DecisionTree

predict(Tuple, double[]) - Method in class smile.classification.DecisionTree

Predicts the class label of an instance and also calculate a posteriori probabilities.

predict(int[]) - Method in class smile.classification.DiscreteNaiveBayes

Predict the class of an instance.

predict(int[], double[]) - Method in class smile.classification.DiscreteNaiveBayes

Predict the class of an instance.

predict(SparseArray) - Method in class smile.classification.DiscreteNaiveBayes

Predict the class of an instance.

predict(SparseArray, double[]) - Method in class smile.classification.DiscreteNaiveBayes

Predict the class of an instance.

predict(double[]) - Method in class smile.classification.FLD

predict(Tuple) - Method in class smile.classification.GradientTreeBoost

predict(Tuple, double[]) - Method in class smile.classification.GradientTreeBoost

predict(double) - Method in class smile.classification.IsotonicRegressionScaling

Returns the posterior probability estimate P(y = 1 | x).

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[], double[]) - Method in class smile.classification.MLP

predict(double[]) - Method in class smile.classification.MLP

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(T) - Method in class smile.classification.OneVersusOne

Prediction is based on voting.

predict(T, double[]) - Method in class smile.classification.OneVersusOne

Prediction is based posteriori probability estimation.

predict(T) - Method in class smile.classification.OneVersusRest

predict(T, double[]) - Method in class smile.classification.OneVersusRest

predict(double[]) - Method in class smile.classification.QDA

predict(double[], double[]) - Method in class smile.classification.QDA

predict(Tuple) - Method in class smile.classification.RandomForest

predict(Tuple, double[]) - Method in class smile.classification.RandomForest

predict(T) - Method in class smile.classification.RBFNetwork

predict(T, double[]) - Method in interface smile.classification.SoftClassifier

Predicts the class label of an instance and also calculate a posteriori probabilities.

predict(T) - Method in class smile.classification.SVM

predict(U) - Method in class smile.clustering.CentroidClustering

Classifies a new observation.

predict(T) - Method in class smile.clustering.DBSCAN

Classifies a new observation.

predict(double[]) - Method in class smile.clustering.DENCLUE

Classifies a new observation.

predict(T) - Method in class smile.clustering.MEC

Cluster a new instance.

predict(Tuple) - Method in interface smile.regression.DataFrameRegression

Predicts the dependent variable of a tuple instance.

predict(DataFrame) - Method in interface smile.regression.DataFrameRegression

Predicts the dependent variables of a data frame.

predict(Tuple) - Method in class smile.regression.GradientTreeBoost

predict(T) - Method in class smile.regression.KernelMachine

predict(double[]) - Method in class smile.regression.LinearModel

predict(Tuple) - Method in class smile.regression.LinearModel

predict(DataFrame) - Method in class smile.regression.LinearModel

predict(double[]) - Method in class smile.regression.MLP

predict(Tuple) - 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(T[]) - Method in interface smile.regression.Regression

Predicts the dependent variables of an array of instances.

predict(Tuple) - Method in class smile.regression.RegressionTree

predict(Tuple[]) - Method in class smile.sequence.CRF

Returns the most likely label sequence given the feature sequence by the forward-backward algorithm.

predict(T[]) - Method in class smile.sequence.CRFLabeler

Returns the most likely label sequence given the feature sequence by the forward-backward algorithm.

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(T[]) - Method in class smile.sequence.HMMLabeler

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.

predictors(Tuple) - Method in class smile.base.cart.CART

Returns the predictors by the model formula if it is not null.

PrH - Class in smile.neighbor.lsh

Probability for given query object and hash function.

PrH(int, double) - Constructor for class smile.neighbor.lsh.PrH

Constructor.

prh - Variable in class smile.neighbor.lsh.PrZ

The n_i probabilities for h_m hash function, where n_i = u_i_max - u_i_min + 1.

priori - Variable in class smile.classification.ClassLabels

The estimated priori probabilities.

priori() - Method in class smile.classification.DiscreteNaiveBayes

Returns a priori probabilities.

priori() - Method in class smile.classification.LDA

Returns a priori probabilities.

priori() - Method in class smile.classification.NaiveBayes

Returns a priori probabilities.

priori() - Method in class smile.classification.QDA

Returns a priori probabilities.

Probe - Class in smile.neighbor.lsh

Probe to check for nearest neighbors.

Probe(int[]) - Constructor for class smile.neighbor.lsh.Probe

Constructor.

project(double[]) - Method in class smile.classification.FLD

project(double[][]) - Method in class smile.classification.FLD

project(T) - Method in class smile.projection.KPCA

project(T[]) - Method in class smile.projection.KPCA

project(double[]) - Method in interface smile.projection.LinearProjection

project(double[][]) - Method in interface smile.projection.LinearProjection

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 to the feature space.

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.

propagate(double[]) - Method in class smile.base.mlp.Layer

Propagates signals from a lower layer to this layer.

propagate(double[]) - Method in class smile.base.mlp.MultilayerPerceptron

Propagates the signals through the neural network.

proportion - Variable in class smile.mds.MDS

The proportion of variance contained in each principal component.

proximity(double[][]) - Static method in class smile.clustering.linkage.Linkage

Calculate the proximity matrix (linearized in column major) with Euclidean distance.

proximity(T[], Distance<T>) - Static method in class smile.clustering.linkage.Linkage

Calculate the proximity matrix (linearized in column major).

prune(DataFrame) - Method in class smile.classification.DecisionTree

Returns a new decision tree by reduced error pruning.

prune(DataFrame) - Method in class smile.classification.RandomForest

Returns a new random forest by reduced error pruning.

PrZ - Class in smile.neighbor.lsh

Probability list of all buckets for given query object.

PrZ(int, PrH[]) - Constructor for class smile.neighbor.lsh.PrZ

Constructor.

put(double[], E) - Method in class smile.neighbor.LSH

Insert an item into the hash table.

put(double[], E) - Method in class smile.neighbor.MutableLSH

put(K, V) - Method in class smile.neighbor.SNLSH

Adds a new item.

pvalue() - Method in class smile.regression.LinearModel

Returns the p-value of goodness-of-fit test.

Q

QDA - Class in smile.classification

Quadratic discriminant analysis.

QDA(double[], double[][], double[][], DenseMatrix[]) - Constructor for class smile.classification.QDA

Constructor.

QDA(double[], double[][], double[][], DenseMatrix[], IntSet) - Constructor for class smile.classification.QDA

Constructor.

quantile(double) - Static method in interface smile.base.cart.Loss

Quantile regression.

quantize(double[]) - Method in class smile.vq.BIRCH

quantize(double[]) - Method in class smile.vq.GrowingNeuralGas

quantize(double[]) - Method in class smile.vq.NeuralGas

quantize(double[]) - Method in class smile.vq.NeuralMap

quantize(double[]) - Method in class smile.vq.SOM

quantize(double[]) - Method in interface smile.vq.VectorQuantizer

Quantize a new observation.

query - Variable in class smile.neighbor.lsh.MultiProbeSample

The query object.

R

radius - Variable in class smile.clustering.DBSCAN

The neighborhood radius.

radius - Variable in class smile.clustering.MEC

The range of neighborhood.

RandIndex - Class in smile.validation

Rand Index.

RandIndex() - Constructor for class smile.validation.RandIndex

random(int, double) - Static method in class smile.sampling.Bagging

Random sampling.

RandomForest - Class in smile.classification

Random forest for classification.

RandomForest(Formula, int, List<RandomForest.Tree>, double, double[]) - Constructor for class smile.classification.RandomForest

Constructor.

RandomForest(Formula, int, List<RandomForest.Tree>, double, double[], IntSet) - Constructor for class smile.classification.RandomForest

Constructor.

RandomForest - Class in smile.regression

Random forest for regression.

RandomForest(Formula, RegressionTree[], double, double[]) - Constructor for class smile.regression.RandomForest

Constructor.

RandomProjection - Class in smile.projection

Random projection is a promising dimensionality reduction technique for learning mixtures of Gaussians.

RandomProjection(DenseMatrix) - 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(K, double, List<Neighbor<K, V>>) - 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

Rank() - Static method in interface smile.gap.Selection

Rank Selection.

RBF<T> - Class in smile.base.rbf

A neuron in radial basis function network.

RBF(T, RadialBasisFunction, Metric<T>) - Constructor for class smile.base.rbf.RBF

Constructor.

RBFNetwork<T> - Class in smile.classification

Radial basis function networks.

RBFNetwork(int, RBF<T>[], DenseMatrix, boolean) - Constructor for class smile.classification.RBFNetwork

Constructor.

RBFNetwork(int, RBF<T>[], DenseMatrix, boolean, IntSet) - Constructor for class smile.classification.RBFNetwork

Constructor.

RBFNetwork<T> - Class in smile.regression

Radial basis function network.

RBFNetwork(RBF<T>[], double[], boolean) - Constructor for class smile.regression.RBFNetwork

Constructor.

RDA - Class in smile.classification

Regularized discriminant analysis.

RDA(double[], double[][], double[][], DenseMatrix[]) - Constructor for class smile.classification.RDA

Constructor.

RDA(double[], double[][], double[][], DenseMatrix[], IntSet) - Constructor for class smile.classification.RDA

Constructor.

Recall - Class in smile.validation

In information retrieval area, sensitivity is called recall.

Recall() - Constructor for class smile.validation.Recall

rectifier() - Static method in interface smile.base.mlp.ActivationFunction

The rectifier activation function max(0, x).

rectifier(int) - Static method in class smile.base.mlp.Layer

Returns a hidden layer with rectified linear activation function.

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.

regression(T[], double[], BiFunction<T[], double[], Regression<T>>) - Method in class smile.validation.Bootstrap

Runs bootstrap tests.

regression(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameRegression>) - Method in class smile.validation.Bootstrap

Runs bootstrap tests.

regression(int, T[], double[], BiFunction<T[], double[], Regression<T>>) - Static method in class smile.validation.Bootstrap

Runs bootstrap tests.

regression(int, Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameRegression>) - Static method in class smile.validation.Bootstrap

Runs bootstrap tests.

regression(T[], double[], BiFunction<T[], double[], Regression<T>>) - Method in class smile.validation.CrossValidation

Runs cross validation tests.

regression(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameRegression>) - Method in class smile.validation.CrossValidation

Runs cross validation tests.

regression(int, T[], double[], BiFunction<T[], double[], Regression<T>>) - Static method in class smile.validation.CrossValidation

Runs cross validation tests.

regression(int, Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameRegression>) - Static method in class smile.validation.CrossValidation

Runs cross validation tests.

regression(T[], double[], BiFunction<T[], double[], Regression<T>>) - Method in class smile.validation.GroupKFold

Runs cross validation tests.

regression(DataFrame, Function<DataFrame, DataFrameRegression>) - Method in class smile.validation.GroupKFold

Runs cross validation tests.

regression(T[], double[], BiFunction<T[], double[], Regression<T>>) - Static method in class smile.validation.LOOCV

Runs leave-one-out cross validation tests.

regression(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameRegression>) - Static method in class smile.validation.LOOCV

Runs leave-one-out cross validation tests.

RegressionMeasure - Interface in smile.validation

An abstract interface to measure the regression performance.

RegressionNode - Class in smile.base.cart

A leaf node in regression tree.

RegressionNode(int, double, double, double) - Constructor for class smile.base.cart.RegressionNode

Constructor.

RegressionTree - Class in smile.regression

Decision tree for regression.

RegressionTree(DataFrame, Loss, StructField, int, int, int, int, int[], int[][]) - Constructor for class smile.regression.RegressionTree

Constructor.

remove(int) - Method in class smile.neighbor.lsh.Bucket

Removes a point from bucket.

remove(double[], E) - Method in class smile.neighbor.MutableLSH

Remove an entry from the hash table.

removeChild(Concept) - Method in class smile.taxonomy.Concept

Remove a child to this node

removeEdge(Neuron) - Method in class smile.vq.hebb.Neuron

Removes an edge.

removeKeyword(String) - Method in class smile.taxonomy.Concept

Remove a keyword from the concept synset.

replace(Node, Node) - Method in class smile.base.cart.InternalNode

Returns a new internal node with children replaced.

replace(Node, Node) - Method in class smile.base.cart.NominalNode

replace(Node, Node) - Method in class smile.base.cart.OrdinalNode

residual() - Method in interface smile.base.cart.Loss

Returns the residual vector.

residuals() - Method in class smile.regression.LinearModel

Returns the residuals, that is response minus fitted values.

response - Variable in class smile.base.cart.CART

The schema of response variable.

response() - Method in interface smile.base.cart.Loss

Returns the response variable for next iteration.

RidgeRegression - Class in smile.regression

Ridge Regression.

RidgeRegression() - Constructor for class smile.regression.RidgeRegression

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.

RobustStandardizer - Class in smile.feature

Robustly standardizes numeric feature by subtracting the median and dividing by the IQR.

RobustStandardizer(StructType, double[], double[]) - Constructor for class smile.feature.RobustStandardizer

Constructor.

root - Variable in class smile.base.cart.CART

The root of decision tree.

root() - Method in class smile.base.cart.CART

Returs the root node.

RouletteWheel() - Static method in interface smile.gap.Selection

Roulette Wheel Selection, also called fitness proportionate selection.

RSquared() - Method in class smile.regression.LinearModel

Returns R² statistic.

RSS() - Method in class smile.regression.LinearModel

Returns the residual sum of squares.

RSS - Class in smile.validation

Residual sum of squares.

RSS() - Constructor for class smile.validation.RSS

run(int, Supplier<T>) - Static method in class smile.clustering.PartitionClustering

Runs a clustering algorithm multiple times and return the best one (e.g.

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, double[][]) - Constructor for class smile.mds.SammonMapping

Constructor.

samples - Variable in class smile.base.cart.CART

The samples for training this node.

samples - Variable in class smile.sampling.Bagging

The samples.

scale() - Method in class smile.classification.ClassLabels

Returns the nominal scale for the class labels.

scale(double) - Method in class smile.classification.PlattScaling

Returns the posterior probability estimate P(y = 1 | x).

ScaledRouletteWheel() - Static method in interface smile.gap.Selection

Scaled Roulette Wheel Selection.

Scaler - Class in smile.feature

Scales all numeric variables into the range [0, 1].

Scaler(StructType, double[], double[]) - Constructor for class smile.feature.Scaler

Constructor.

schema - Variable in class smile.base.cart.CART

The schema of data.

schema() - Method in class smile.classification.AdaBoost

schema() - Method in interface smile.classification.DataFrameClassifier

Returns the design matrix schema.

schema() - Method in class smile.classification.DecisionTree

schema() - Method in class smile.classification.GradientTreeBoost

schema() - Method in class smile.classification.RandomForest

schema - Variable in class smile.feature.MaxAbsScaler

The schema of data.

schema() - Method in interface smile.regression.DataFrameRegression

Returns the design matrix schema.

schema() - Method in class smile.regression.GradientTreeBoost

schema() - Method in class smile.regression.LinearModel

schema() - Method in class smile.regression.RandomForest

schema() - Method in class smile.regression.RegressionTree

score() - Method in class smile.base.cart.InternalNode

Returns the split score (reduction of impurity).

scores - Variable in class smile.mds.MDS

The component scores.

sd() - Method in class smile.neighbor.lsh.HashValueParzenModel

Returns the standard deviation.

seed(T[], T[], int[], ToDoubleBiFunction<T, T>) - Static method in class smile.clustering.PartitionClustering

Initialize cluster membership of input objects with K-Means++ algorithm.

seed(int, double[][]) - Static method in class smile.vq.NeuralGas

Selects random samples as initial neurons of Neural Gas.

Selection - Interface in smile.gap

The way to select chromosomes from the population as parents to crossover.

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

SequenceLabeler<T> - Interface in smile.sequence

A sequence labeler assigns a class label to each position of the sequence.

setEdgeAge(Neuron, int) - Method in class smile.vq.hebb.Neuron

Sets the age of edge.

setLearningRate(double) - Method in class smile.base.mlp.MultilayerPerceptron

Sets the learning rate.

setLearningRate(double) - Method in class smile.classification.LogisticRegression

Sets the learning rate of stochastic gradient descent.

setLearningRate(double) - Method in class smile.classification.Maxent

Sets the learning rate of stochastic gradient descent.

setLearningRate(double) - Method in class smile.projection.GHA

Set the learning rate.

setLocalSearchSteps(int) - Method in class smile.gap.GeneticAlgorithm

Sets the number of iterations of local search for Lamarckian algorithm.

setMomentum(double) - Method in class smile.base.mlp.MultilayerPerceptron

Sets the momentum factor.

setProb(PrZ[]) - Method in class smile.neighbor.lsh.Probe

Calculate the probability of the probe.

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.

setWeightDecay(double) - Method in class smile.base.mlp.MultilayerPerceptron

Sets the weight decay factor.

shift() - Method in class smile.neighbor.lsh.Probe

This operation shifts to the right the last nonzero component if it is equal to one and if it is not the last one.

SIB - Class in smile.clustering

The Sequential Information Bottleneck algorithm.

SIB(double, double[][], int[]) - Constructor for class smile.clustering.SIB

Constructor.

sigmoid() - Static method in interface smile.base.mlp.ActivationFunction

Logistic sigmoid function: sigmoid(v)=1/(1+exp(-v)).

sigmoid(int) - Static method in class smile.base.mlp.Layer

Returns a hidden layer with sigmoid activation function.

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

SimHash<T> - Interface in smile.neighbor.lsh

SimHash is a technique for quickly estimating how similar two sets are.

SingleLinkage - Class in smile.clustering.linkage

Single linkage.

SingleLinkage(double[][]) - Constructor for class smile.clustering.linkage.SingleLinkage

Constructor.

SingleLinkage(int, float[]) - 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.association.FPTree

Returns the number transactions in the database.

size() - Method in class smile.base.cart.CART

Returns the number of nodes in the tree.

size() - Method in class smile.base.cart.InternalNode

size - Variable in class smile.base.cart.LeafNode

The number of samples in the node.

size() - Method in class smile.base.cart.LeafNode

size() - Method in interface smile.base.cart.Node

Returns the number of samples in the node.

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.linkage.Linkage

Returns the proximity matrix size.

size - Variable in class smile.clustering.PartitionClustering

The number of observations in each cluster.

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.base.cart - package smile.base.cart

smile.base.mlp - package smile.base.mlp

smile.base.rbf - package smile.base.rbf

smile.base.svm - package smile.base.svm

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.neighbor.lsh - package smile.neighbor.lsh

smile.projection - package smile.projection

Feature extraction.

smile.projection.ica - package smile.projection.ica

smile.regression - package smile.regression

Regression analysis.

smile.sampling - package smile.sampling

Sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population.

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.validation - package smile.validation

Model validation.

smile.vq - package smile.vq

Vector quantization is a lossy compression technique used in speech and image coding.

smile.vq.hebb - package smile.vq.hebb

Hebbian theory is a neuroscientific theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell.

smile.wavelet - package smile.wavelet

Discrete wavelet transform (DWT).

SNLSH<K,V> - Class in smile.neighbor

Locality-Sensitive Hashing for Signatures.

SNLSH(int, SimHash<K>) - Constructor for class smile.neighbor.SNLSH

Constructor.

SoftClassifier<T> - Interface in smile.classification

Soft classifiers calculate a posteriori probabilities besides the class label of an instance.

SOM - Class in smile.vq

Self-Organizing Map.

SOM(double[][][], TimeFunction, Neighborhood) - Constructor for class smile.vq.SOM

Constructor.

sparse(int, KernelMachine<SparseArray>) - Static method in class smile.base.svm.LinearKernelMachine

Creates a linear kernel machine.

sparse(int, int) - Static method in class smile.projection.RandomProjection

Generates a sparse random projection.

SparseOneHotEncoder - Class in smile.feature

Encode categorical integer features using sparse one-hot scheme.

SparseOneHotEncoder(StructType) - Constructor for class smile.feature.SparseOneHotEncoder

Constructor.

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, int[]) - Constructor for class smile.clustering.SpectralClustering

Constructor.

split(Split, PriorityQueue<Split>) - Method in class smile.base.cart.CART

Split a node into two children nodes.

Split - Class in smile.base.cart

The data about of a potential split for a leaf node.

Split(LeafNode, int, double, int, int, int, int) - Constructor for class smile.base.cart.Split

Constructor.

SplitRule - Enum in smile.base.cart

The criterion to choose variable to split instances.

sqrt(int[], int[]) - Static method in class smile.validation.AdjustedMutualInformation

Calculates the adjusted mutual information of (I(y1, y2) - E(MI)) / (sqrt(H(y1) * H(y2)) - E(MI)).

sqrt(int[], int[]) - Static method in class smile.validation.NormalizedMutualInformation

Calculates the normalized mutual information of I(y1, y2) / sqrt(H(y1) * H(y2)).

Standardizer - Class in smile.feature

Standardizes numeric feature to 0 mean and unit variance.

Standardizer(StructType, double[], double[]) - Constructor for class smile.feature.Standardizer

Constructor.

strateify(int[], double) - Static method in class smile.sampling.Bagging

Stratified sampling.

stress - Variable in class smile.mds.IsotonicMDS

The final stress achieved.

stress - Variable in class smile.mds.SammonMapping

The final stress achieved.

sum(int[], int[]) - Static method in class smile.validation.AdjustedMutualInformation

Calculates the adjusted mutual information of (I(y1, y2) - E(MI)) / (0.5 * (H(y1) + H(y2)) - E(MI)).

sum(int[], int[]) - Static method in class smile.validation.NormalizedMutualInformation

Calculates the normalized mutual information of 2 * I(y1, y2) / (H(y1) + H(y2)).

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.

SupportVector<T> - Class in smile.base.svm

Support vector.

SupportVector(int, T, int, double, double, double, double, double) - Constructor for class smile.base.svm.SupportVector

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>, T[], double[], double) - Constructor for class smile.classification.SVM

Constructor.

SVR<T> - Class in smile.base.svm

Epsilon support vector regression.

SVR(MercerKernel<T>, double, double, double) - Constructor for class smile.base.svm.SVR

Constructor.

SVR - Class in smile.regression

Epsilon support vector regression.

SVR() - Constructor for class smile.regression.SVR

SymletWavelet - Class in smile.wavelet

Symlet wavelets.

SymletWavelet(int) - Constructor for class smile.wavelet.SymletWavelet

Constructor.

T

T - Variable in class smile.vq.BIRCH

THe maximum radius of a sub-cluster.

tanh() - Static method in interface smile.base.mlp.ActivationFunction

Hyperbolic tangent activation function.

tanh(int) - Static method in class smile.base.mlp.Layer

Returns a hidden layer with hyperbolic tangent activation function.

target - Variable in class smile.base.mlp.MultilayerPerceptron

The buffer to store desired target value of training instance.

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(String...) - Constructor for class smile.taxonomy.Taxonomy

Constructor.

test(DataFrame) - Method in class smile.classification.AdaBoost

Test the model on a validation dataset.

test(DataFrame) - Method in class smile.classification.GradientTreeBoost

Test the model on a validation dataset.

test(DataFrame) - Method in class smile.classification.RandomForest

Test the model on a validation dataset.

test(DataFrame) - Method in class smile.regression.GradientTreeBoost

Test the model on a validation dataset.

test(DataFrame) - 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.GroupKFold

The index of testing instances.

test - Variable in class smile.validation.LOOCV

The index of testing instances.

test(Classifier<T>, T[]) - Static method in interface smile.validation.Validation

Tests a classifier on a validation set.

test(DataFrameClassifier, DataFrame) - Static method in interface smile.validation.Validation

Tests a regression model on a validation set.

test(Regression<T>, T[]) - Static method in interface smile.validation.Validation

Tests a regression model on a validation set.

test(DataFrameRegression, DataFrame) - Static method in interface smile.validation.Validation

Tests a regression model on a validation set.

text() - Static method in interface smile.neighbor.lsh.SimHash

Returns the simhash for string tokens.

toNode(Node, Node) - Method in class smile.base.cart.NominalSplit

toNode(Node, Node) - Method in class smile.base.cart.OrdinalSplit

toNode(Node, Node) - Method in class smile.base.cart.Split

Returns an internal node with the feature, value, and score of this split.

toString() - Method in class smile.association.AssociationRule

toString() - Method in class smile.association.ItemSet

toString() - Method in class smile.base.cart.CART

Returns a text representation of the tree in R's rpart format.

toString(StructType, StructField, InternalNode, int, BigInteger, List<String>) - Method in class smile.base.cart.DecisionNode

toString(StructType, boolean) - Method in class smile.base.cart.InternalNode

Returns the string representation of branch.

toString(StructType, StructField, InternalNode, int, BigInteger, List<String>) - Method in class smile.base.cart.InternalNode

toString(StructType, StructField, InternalNode, int, BigInteger, List<String>) - Method in interface smile.base.cart.Node

Adds the string representation (R's rpart format) to a collection.

toString(StructType, boolean) - Method in class smile.base.cart.NominalNode

toString(StructType, boolean) - Method in class smile.base.cart.OrdinalNode

toString(StructType, StructField, InternalNode, int, BigInteger, List<String>) - Method in class smile.base.cart.RegressionNode

toString() - Method in class smile.base.cart.Split

toString() - Method in class smile.base.mlp.HiddenLayer

toString() - Method in class smile.base.mlp.MultilayerPerceptron

toString() - Method in class smile.base.mlp.OutputLayer

toString() - Method in class smile.base.svm.KernelMachine

toString() - Method in class smile.classification.IsotonicRegressionScaling

toString() - Method in class smile.clustering.CentroidClustering

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.PartitionClustering

toString() - Method in class smile.feature.MaxAbsScaler

toString() - Method in class smile.feature.Normalizer

toString() - Method in class smile.feature.RobustStandardizer

toString() - Method in class smile.feature.Scaler

toString() - Method in class smile.feature.Standardizer

toString() - Method in class smile.feature.WinsorScaler

toString() - Method in class smile.gap.BitString

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.neighbor.Neighbor

toString() - Method in class smile.regression.LinearModel

toString() - Method in class smile.sampling.Bagging

toString() - Method in class smile.sequence.CRFLabeler

toString() - Method in class smile.sequence.HMM

toString() - Method in class smile.sequence.HMMLabeler

toString() - Method in class smile.taxonomy.Concept

toString() - Method in class smile.taxonomy.TaxonomicDistance

toString() - Method in class smile.validation.Accuracy

toString() - Method in class smile.validation.AdjustedMutualInformation

toString() - Method in class smile.validation.AdjustedRandIndex

toString() - Method in class smile.validation.ConfusionMatrix

toString() - Method in class smile.validation.Error

toString() - Method in class smile.validation.Fallout

toString() - Method in class smile.validation.FDR

toString() - Method in class smile.validation.MCC

toString() - Method in class smile.validation.MeanAbsoluteDeviation

toString() - Method in class smile.validation.MSE

toString() - Method in class smile.validation.MutualInformation

toString() - Method in class smile.validation.NormalizedMutualInformation

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

toSVM() - Method in class smile.base.svm.KernelMachine

Convert the kernel machine to SVM instance.

Tournament(int, double) - Static method in interface smile.gap.Selection

Tournament Selection.

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.GroupKFold

The index of training instances.

train - Variable in class smile.validation.LOOCV

The index of training instances.

transform(double[]) - Method in interface smile.feature.FeatureTransform

Transform a feature vector.

transform(double[][]) - Method in interface smile.feature.FeatureTransform

Transform a data frame.

transform(Tuple) - Method in interface smile.feature.FeatureTransform

Transform a feature vector.

transform(DataFrame) - Method in interface smile.feature.FeatureTransform

Transform a data frame.

transform(double[]) - Method in class smile.feature.MaxAbsScaler

transform(Tuple) - Method in class smile.feature.MaxAbsScaler

transform(DataFrame) - Method in class smile.feature.MaxAbsScaler

transform(double[]) - Method in class smile.feature.Normalizer

transform(Tuple) - Method in class smile.feature.Normalizer

transform(DataFrame) - Method in class smile.feature.Normalizer

transform(double[]) - Method in class smile.feature.Scaler

transform(Tuple) - Method in class smile.feature.Scaler

transform(DataFrame) - Method in class smile.feature.Scaler

transform(double[]) - Method in class smile.feature.Standardizer

transform(Tuple) - Method in class smile.feature.Standardizer

transform(DataFrame) - Method in class smile.feature.Standardizer

transform(double[]) - Method in class smile.wavelet.Wavelet

Discrete wavelet transform.

trees() - Method in class smile.classification.AdaBoost

Returns the decision trees.

trees() - Method in class smile.classification.GradientTreeBoost

Returns the regression trees.

trees() - Method in class smile.classification.RandomForest

Returns the decision trees.

trees() - Method in class smile.regression.GradientTreeBoost

Returns the regression trees.

trees() - Method in class smile.regression.RandomForest

Returns the regression trees.

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.

trueChild() - Method in class smile.base.cart.InternalNode

Returns the true branch child.

TSNE - Class in smile.manifold

The t-distributed stochastic neighbor embedding (t-SNE) is a nonlinear dimensionality reduction technique that is particularly well suited for embedding high-dimensional data into a space of two or three dimensions, which can then be visualized in a scatter plot.

TSNE(double[][], int) - Constructor for class smile.manifold.TSNE

Constructor.

TSNE(double[][], int, double, double, int) - Constructor for class smile.manifold.TSNE

Constructor.

ttest() - Method in class smile.regression.LinearModel

Returns the t-test of the coefficients (including intercept).

U

u - Variable in class smile.neighbor.lsh.PrH

The index of bucket.

umatrix() - Method in class smile.vq.SOM

Calculates the unified distance matrix (u-matrix) for visualization.

update - Variable in class smile.base.mlp.Layer

The weight update of mini batch or momentum.

update(double, double) - Method in class smile.base.mlp.Layer

Adjust network weights by back-propagation algorithm.

update() - Method in class smile.base.mlp.MultilayerPerceptron

Updates the weights.

update(int[], int) - Method in class smile.classification.DiscreteNaiveBayes

Online learning of naive Bayes classifier on a sequence, which is modeled as a bag of words.

update(SparseArray, int) - Method in class smile.classification.DiscreteNaiveBayes

Online learning of naive Bayes classifier on a sequence, which is modeled as a bag of words.

update(int[][], int[]) - Method in class smile.classification.DiscreteNaiveBayes

Batch learning of naive Bayes classifier on sequences, which are modeled as a bag of words.

update(SparseArray[], int[]) - Method in class smile.classification.DiscreteNaiveBayes

Batch learning of naive Bayes classifier on sequences, which are modeled as a bag of words.

update(double[], int) - Method in class smile.classification.LogisticRegression

update(int[], int) - Method in class smile.classification.Maxent

update(double[], int) - Method in class smile.classification.MLP

update(double[][], int[]) - Method in class smile.classification.MLP

Mini-batch.

update(T[], int[]) - Method in interface smile.classification.OnlineClassifier

Updates the model with a (micro-)batch of new samples.

update(T, int) - Method in interface smile.classification.OnlineClassifier

Online update the classifier with a new training instance.

update(int) - Method in class smile.manifold.TSNE

Performs additional iterations.

update(double[], E) - Method in class smile.neighbor.MutableLSH

Update an entry with new key.

update(double[]) - Method in class smile.projection.GHA

Update the model with a new sample.

update(Tuple) - Method in class smile.regression.LinearModel

Online update the regression model with a new training instance.

update(DataFrame) - Method in class smile.regression.LinearModel

Online update the regression model with a new data frame.

update(double[], double) - Method in class smile.regression.LinearModel

update(double[], double, double) - Method in class smile.regression.LinearModel

Recursive least squares.

update(double[], double) - Method in class smile.regression.MLP

update(double[][], double[]) - Method in class smile.regression.MLP

update(T[], double[]) - Method in interface smile.regression.OnlineRegression

Updates the model with a (micro-)batch of new samples.

update(T, double) - Method in interface smile.regression.OnlineRegression

Online update the regression model with a new training instance.

update(T[][], int, ToIntFunction<T>) - Method in class smile.sequence.HMM

Updates the HMM by the Baum-Welch algorithm.

update(int[][], int) - Method in class smile.sequence.HMM

Updates the HMM by the Baum-Welch algorithm.

update(T[][], int) - Method in class smile.sequence.HMMLabeler

Updates the HMM by the Baum-Welch algorithm.

update(double[]) - Method in class smile.vq.BIRCH

update(double[]) - Method in class smile.vq.GrowingNeuralGas

update(double[], double) - Method in class smile.vq.hebb.Neuron

Updates the reference vector by w += eps * (x - w).

update(double[]) - Method in class smile.vq.NeuralGas

update(double[]) - Method in class smile.vq.NeuralMap

update(double[]) - Method in class smile.vq.SOM

update(double[]) - Method in interface smile.vq.VectorQuantizer

Update the codebook with a new observation.

updateBias - Variable in class smile.base.mlp.Layer

The bias update of mini batch or momentum.

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.

UPGMALinkage(int, float[]) - 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.

UPGMCLinkage(int, float[]) - Constructor for class smile.clustering.linkage.UPGMCLinkage

Constructor.

V

Validation - Interface in smile.validation

A utility class for validating predictive models on test data.

value - Variable in class smile.neighbor.Neighbor

The data object of neighbor.

valueOf(String) - Static method in enum smile.base.cart.Loss.Type

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in interface smile.base.cart.Loss

Parses the loss.

valueOf(String) - Static method in enum smile.base.cart.SplitRule

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum smile.base.mlp.Cost

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum smile.base.mlp.OutputFunction

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum smile.classification.DiscreteNaiveBayes.Model

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum smile.feature.Normalizer.Norm

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum smile.gap.Crossover

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum smile.validation.AdjustedMutualInformation.Method

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum smile.validation.NormalizedMutualInformation.Method

Returns the enum constant of this type with the specified name.

values() - Static method in enum smile.base.cart.Loss.Type

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum smile.base.cart.SplitRule

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum smile.base.mlp.Cost

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum smile.base.mlp.OutputFunction

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum smile.classification.DiscreteNaiveBayes.Model

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum smile.feature.Normalizer.Norm

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum smile.gap.Crossover

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Method in class smile.neighbor.MutableLSH

Returns the values.

values() - Static method in enum smile.validation.AdjustedMutualInformation.Method

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum smile.validation.NormalizedMutualInformation.Method

Returns an array containing the constants of this enum type, in the order they are declared.

var - Variable in class smile.neighbor.lsh.NeighborHashValueModel

Variance of hash values of neighbors.

VectorQuantizer - Interface in smile.vq

Vector quantizer with competitive learning.

viterbi(Tuple[]) - Method in class smile.sequence.CRF

Labels sequence with Viterbi algorithm.

viterbi(T[]) - Method in class smile.sequence.CRFLabeler

Labels sequence with Viterbi algorithm.

vote(Tuple, double[]) - Method in class smile.classification.RandomForest

Predict and estimate the probability by voting.

W

w - Variable in class smile.neighbor.LSH

The width of projection.

w - Variable in class smile.vq.hebb.Neuron

The reference vector.

WardLinkage - Class in smile.clustering.linkage

Ward's linkage.

WardLinkage(double[][]) - Constructor for class smile.clustering.linkage.WardLinkage

Constructor.

WardLinkage(int, float[]) - 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 - Interface in smile.wavelet

The wavelet shrinkage is a signal denoising technique based on the idea of thresholding the wavelet coefficients.

weight - Variable in class smile.base.mlp.Layer

The affine transformation matrix.

weights() - Method in class smile.base.svm.KernelMachine

Returns the weights of instances.

width - Variable in class smile.manifold.LaplacianEigenmap

The width of heat kernel.

WinsorScaler - Class in smile.feature

Scales all numeric variables into the range [0, 1].

WinsorScaler(StructType, double[], double[]) - Constructor for class smile.feature.WinsorScaler

Constructor.

WPGMALinkage - Class in smile.clustering.linkage

Weighted Pair Group Method with Arithmetic mean.

WPGMALinkage(double[][]) - Constructor for class smile.clustering.linkage.WPGMALinkage

Constructor.

WPGMALinkage(int, float[]) - 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.

WPGMCLinkage(int, float[]) - Constructor for class smile.clustering.linkage.WPGMCLinkage

Constructor.

X

x - Variable in class smile.base.cart.CART

The training data.

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, double[][], int[]) - Constructor for class smile.clustering.XMeans

Constructor.

Y

y - Variable in class smile.classification.ClassLabels

The sample class id in [0, k).

y - Variable in class smile.clustering.PartitionClustering

The cluster labels of data.

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