Index

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 Z
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form

A

a - Variable in class smile.validation.metric.ContingencyTable

The row sum of contingency table.

AbstractClassifier<T> - Class in smile.classification

Abstract base class of classifiers.

AbstractClassifier(int[]) - Constructor for class smile.classification.AbstractClassifier

Constructor.

AbstractClassifier(BaseVector<?, ?, ?>) - Constructor for class smile.classification.AbstractClassifier

Constructor.

AbstractClassifier(IntSet) - Constructor for class smile.classification.AbstractClassifier

Constructor.

accuracy - Variable in class smile.validation.ClassificationMetrics

The accuracy on validation data.

Accuracy - Class in smile.validation.metric

The accuracy is the proportion of true results (both true positives and true negatives) in the population.

Accuracy() - Constructor for class smile.validation.metric.Accuracy

acf(double[], int) - Static method in interface smile.timeseries.TimeSeries

Autocorrelation function.

ActivationFunction - Interface in smile.base.mlp

The activation function in hidden layers.

ActivationFunction - Interface in smile.deep.activation

The activation function.

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.

Adam - Class in smile.deep.optimizer

Adaptive Moment optimizer.

Adam() - Constructor for class smile.deep.optimizer.Adam

Constructor.

Adam(TimeFunction) - Constructor for class smile.deep.optimizer.Adam

Constructor.

Adam(TimeFunction, double, double) - Constructor for class smile.deep.optimizer.Adam

Constructor.

Adam(TimeFunction, double, double, double) - Constructor for class smile.deep.optimizer.Adam

Constructor.

add(String, double) - Method in class smile.hpo.Hyperparameters

Adds a parameter.

add(String, double[]) - Method in class smile.hpo.Hyperparameters

Adds a parameter.

add(String, double, double) - Method in class smile.hpo.Hyperparameters

Adds a parameter.

add(String, double, double, double) - Method in class smile.hpo.Hyperparameters

Adds a parameter.

add(String, int) - Method in class smile.hpo.Hyperparameters

Adds a parameter.

add(String, int[]) - Method in class smile.hpo.Hyperparameters

Adds a parameter.

add(String, int, int) - Method in class smile.hpo.Hyperparameters

Adds a parameter.

add(String, int, int, int) - Method in class smile.hpo.Hyperparameters

Adds a parameter.

add(String, String) - Method in class smile.hpo.Hyperparameters

Adds a parameter.

add(String, String[]) - Method in class smile.hpo.Hyperparameters

Adds a parameter.

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.

AdjustedMutualInformation - Class in smile.validation.metric

Adjusted Mutual Information (AMI) for comparing clustering.

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

Constructor.

AdjustedMutualInformation.Method - Enum Class in smile.validation.metric

The normalization method.

adjustedR2() - Method in class smile.timeseries.AR

Returns adjusted R² statistic.

adjustedR2() - Method in class smile.timeseries.ARMA

Returns adjusted R² statistic.

AdjustedRandIndex - Class in smile.validation.metric

Adjusted Rand Index.

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

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

Returns adjusted R² statistic.

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

The age of the edges.

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

Increments the age of all edges emanating from the neuron.

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

Returns the AIC score.

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

Returns the AIC score.

AIC() - Method in class smile.classification.SparseLogisticRegression

Returns the AIC score.

AIC() - Method in class smile.glm.GLM

Returns the AIC score.

AIC(double, int) - Static method in interface smile.validation.ModelSelection

Akaike information criterion.

antecedent - Variable in class smile.association.AssociationRule

Antecedent itemset.

apply(double[]) - Method in class smile.feature.extraction.KernelPCA

apply(double[]) - Method in class smile.feature.extraction.Projection

Project a data point to the feature space.

apply(double[][]) - Method in class smile.feature.extraction.Projection

Project a set of data to the feature space.

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

Mines the association rules.

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

Genetic algorithm based feature selection for classification.

apply(String) - Method in class smile.feature.extraction.BagOfWords

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

apply(String) - Method in class smile.feature.extraction.HashEncoder

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

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

Mines the frequent item sets.

apply(DataFrame) - Method in class smile.feature.extraction.BinaryEncoder

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

apply(DataFrame) - Method in class smile.feature.extraction.Projection

apply(DataFrame) - Method in class smile.feature.extraction.SparseEncoder

Generates the sparse representation of a data frame.

apply(DataFrame) - Method in class smile.feature.imputation.SimpleImputer

apply(Tuple) - Method in class smile.feature.extraction.BagOfWords

apply(Tuple) - Method in class smile.feature.extraction.BinaryEncoder

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

apply(Tuple) - Method in class smile.feature.extraction.Projection

apply(Tuple) - Method in class smile.feature.extraction.SparseEncoder

Generates the sparse representation of given object.

apply(Tuple) - Method in class smile.feature.imputation.KMedoidsImputer

apply(Tuple) - Method in class smile.feature.imputation.KNNImputer

apply(Tuple) - Method in class smile.feature.imputation.SimpleImputer

apply(Tuple) - Method in class smile.feature.transform.Normalizer

apply(T) - Method in class smile.manifold.KPCA

apply(T[]) - Method in class smile.manifold.KPCA

Project a set of data to the feature space.

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

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

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

ar() - Method in class smile.timeseries.AR

Returns the linear coefficients of AR (without intercept).

ar() - Method in class smile.timeseries.ARMA

Returns the linear coefficients of AR(p).

AR - Class in smile.timeseries

Autoregressive model.

AR(double[], double[], double, AR.Method) - Constructor for class smile.timeseries.AR

Constructor.

AR.Method - Enum Class in smile.timeseries

The fitting method.

ARM - Class in smile.association

Association Rule Mining.

ARMA - Class in smile.timeseries

Autoregressive moving-average model.

ARMA(double[], double[], double[], double, double[], double[]) - Constructor for class smile.timeseries.ARMA

Constructor.

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 - Variable in class smile.validation.ClassificationMetrics

The AUC on validation data.

AUC - Class in smile.validation.metric

The area under the curve (AUC).

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

avg - Variable in class smile.validation.ClassificationValidations

The average of metrics.

avg - Variable in class smile.validation.RegressionValidations

The average of metrics.

B

b - Variable in class smile.validation.metric.ContingencyTable

The column sum of contingency table.

B - Variable in class smile.vq.BIRCH

The branching factor of non-leaf nodes.

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

Propagates the errors back through the (implicit) dropout layer.

backpropagate(boolean) - Method in class smile.base.mlp.MultilayerPerceptron

Propagates the errors back through the network.

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

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

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.OutputLayer

Bag - Class in smile.validation

A bag of random selected samples.

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

Constructor.

BagOfWords - Class in smile.feature.extraction

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

BagOfWords(String[], Function<String, String[]>, String[], boolean) - Constructor for class smile.feature.extraction.BagOfWords

Constructor.

BagOfWords(Function<String, String[]>, String[]) - Constructor for class smile.feature.extraction.BagOfWords

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 points living in R^d.

Bernoulli - Interface in smile.glm.model

The response variable is of Bernoulli distribution.

BERNOULLI - Enum constant in enum class smile.classification.DiscreteNaiveBayes.Model

The document Bernoulli model generates an indicator for each term of the vocabulary, either indicating presence of the term in the document or indicating absence.

beta - Variable in class smile.glm.GLM

The linear weights.

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

The bias.

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

The bias gradient.

biasGradientMoment1 - Variable in class smile.base.mlp.Layer

The first moment of bias gradient.

biasGradientMoment2 - Variable in class smile.base.mlp.Layer

The second moment of bias gradient.

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

The bias update.

BIC() - Method in class smile.glm.GLM

Returns the BIC score.

BIC(double, int, int) - Static method in interface smile.validation.ModelSelection

Bayesian information criterion.

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

Creates a linear kernel machine.

BinaryEncoder - Class in smile.feature.extraction

Encodes categorical features using sparse one-hot scheme.

BinaryEncoder(StructType, String...) - Constructor for class smile.feature.extraction.BinaryEncoder

Constructor.

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

Fits binomial logistic regression.

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

Fits binomial logistic regression.

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

Fits binomial logistic regression.

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

Fits maximum entropy classifier.

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

Fits maximum entropy classifier.

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

Fits maximum entropy classifier.

binomial(SparseDataset, int[]) - Static method in class smile.classification.SparseLogisticRegression

Fits binomial logistic regression.

binomial(SparseDataset, int[], double, double, int) - Static method in class smile.classification.SparseLogisticRegression

Fits binomial logistic regression.

binomial(SparseDataset, int[], Properties) - Static method in class smile.classification.SparseLogisticRegression

Fits binomial logistic regression.

Binomial - Interface in smile.glm.model

The response variable is of Binomial distribution.

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

Constructor.

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

Constructor.

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

Constructor.

BIRCH - Class in smile.vq

Balanced Iterative Reducing and Clustering using Hierarchies.

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

Constructor.

Bootstrap - Interface in smile.validation

The bootstrap is a general tool for assessing statistical accuracy.

Box_Pierce - Enum constant in enum class smile.timeseries.BoxTest.Type

Box-Pierce test.

BoxTest - Class in smile.timeseries

Portmanteau test jointly that several autocorrelations of time series are zero.

BoxTest.Type - Enum Class in smile.timeseries

The type of test.

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

breaks - Variable in class smile.feature.selection.InformationValue

Breakpoints of intervals for numerical variables.

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

Returns the bubble neighborhood function.

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

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

Builds a layer.

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

builder(String, int, double, double) - Static method in class smile.base.mlp.Layer

Returns a hidden layer.

C

CART - Class in smile.base.cart

Classification and regression tree.

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

Constructor.

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

Constructor.

center() - Method in class smile.feature.extraction.PCA

Returns the center of data.

center() - Method in class smile.feature.extraction.ProbabilisticPCA

Returns the center of data.

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.

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.

classes - Variable in class smile.classification.AbstractClassifier

The class labels.

classes - Variable in class smile.classification.ClassLabels

The class labels.

classes() - Method in class smile.classification.AbstractClassifier

classes() - Method in interface smile.classification.Classifier

Returns the class labels.

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

classes() - Method in class smile.classification.MLP

classes() - Method in class smile.classification.SVM

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

Repeated cross validation of classification.

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

Repeated cross validation of classification.

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

Runs classification bootstrap validation.

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

Cross validation of classification.

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

Runs classification bootstrap validation.

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

Cross validation of classification.

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

Runs leave-one-out cross validation tests.

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

Runs leave-one-out cross validation tests.

CLASSIFICATION_ERROR - Enum constant in enum class smile.base.cart.SplitRule

Classification error.

ClassificationMetric - Interface in smile.validation.metric

An abstract interface to measure the classification performance.

ClassificationMetrics - Class in smile.validation

The classification validation metrics.

ClassificationMetrics(double, double, int, int, double) - Constructor for class smile.validation.ClassificationMetrics

Constructor.

ClassificationMetrics(double, double, int, int, double, double) - Constructor for class smile.validation.ClassificationMetrics

Constructor of multiclass soft classifier validation.

ClassificationMetrics(double, double, int, int, double, double, double, double, double, double) - Constructor for class smile.validation.ClassificationMetrics

Constructor of binary classifier validation.

ClassificationMetrics(double, double, int, int, double, double, double, double, double, double, double, double) - Constructor for class smile.validation.ClassificationMetrics

Constructor of binary soft classifier validation.

ClassificationMetrics(double, double, int, int, double, double, double, double, double, double, double, double, double) - Constructor for class smile.validation.ClassificationMetrics

Constructor.

ClassificationValidation<M> - Class in smile.validation

Classification model validation results.

ClassificationValidation(M, double, double, int[], int[]) - Constructor for class smile.validation.ClassificationValidation

Constructor.

ClassificationValidation(M, double, double, int[], int[], double[][]) - Constructor for class smile.validation.ClassificationValidation

Constructor of soft classifier validation.

ClassificationValidations<M> - Class in smile.validation

Classification model validation results.

ClassificationValidations(List<ClassificationValidation<M>>) - Constructor for class smile.validation.ClassificationValidations

Constructor.

Classifier<T> - Interface in smile.classification

A classifier assigns an input object into one of a given number of categories.

Classifier.Trainer<T,M extends Classifier<T>> - Interface in smile.classification

The classifier trainer.

ClassLabels - Class in smile.classification

Map arbitrary class labels to [0, k), where k is the number of classes.

ClassLabels(int, int[], IntSet) - 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.

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

The gradient clipping norm.

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

The gradient clipping value.

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

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

ClusteringMetric - Interface in smile.validation.metric

An abstract interface to measure the clustering performance.

CNB - Enum constant in enum class smile.classification.DiscreteNaiveBayes.Model

Complement Naive Bayes.

coefficients() - Method in class smile.classification.LogisticRegression.Binomial

Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors.

coefficients() - Method in class smile.classification.LogisticRegression.Multinomial

Returns a 2d-array of size (k-1) x (p+1), containing the linear weights of multi-class logistic regression, where k is the number of classes and p is the dimension of feature vectors.

coefficients() - Method in class smile.classification.Maxent.Binomial

Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors.

coefficients() - Method in class smile.classification.Maxent.Multinomial

Returns a 2d-array of size (k-1) x (p+1), containing the linear weights of multi-class logistic regression, where k is the number of classes and p is the dimension of feature vectors.

coefficients() - Method in class smile.classification.SparseLogisticRegression.Binomial

Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors.

coefficients() - Method in class smile.classification.SparseLogisticRegression.Multinomial

Returns a 2d-array of size (k-1) x (p+1), containing the linear weights of multi-class logistic regression, where k is the number of classes and p is the dimension of feature vectors.

coefficients() - Method in class smile.glm.GLM

Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors.

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

Returns the linear coefficients without intercept.

columns - Variable in class smile.feature.extraction.Projection

The fields of input space.

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

The comparator on the split score.

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

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

compareTo(InformationValue) - Method in class smile.feature.selection.InformationValue

compareTo(SignalNoiseRatio) - Method in class smile.feature.selection.SignalNoiseRatio

compareTo(SumSquaresRatio) - Method in class smile.feature.selection.SumSquaresRatio

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.

computeGradient(double[]) - Method in class smile.base.mlp.InputLayer

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

Computes the parameter gradient for a sample of (mini-)batch.

computeGradientUpdate(double[], double, double, double) - Method in class smile.base.mlp.InputLayer

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

Computes the parameter gradient and update the weights.

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

Compute the network output gradient.

confidence - Variable in class smile.association.AssociationRule

The confidence value.

confusion - Variable in class smile.validation.ClassificationValidation

The confusion matrix.

ConfusionMatrix - Class in smile.validation.metric

The confusion matrix of truth and predictions.

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

Constructor.

consequent - Variable in class smile.association.AssociationRule

Consequent itemset.

ContingencyTable - Class in smile.validation.metric

The contingency table.

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

Constructor.

coordinates - Variable in class smile.manifold.IsoMap

The coordinate matrix in embedding space.

coordinates - Variable in class smile.manifold.IsotonicMDS

The coordinates.

coordinates - Variable in class smile.manifold.LaplacianEigenmap

The coordinate matrix in embedding space.

coordinates - Variable in class smile.manifold.LLE

The coordinate matrix in embedding space.

coordinates - Variable in class smile.manifold.MDS

The principal coordinates.

coordinates - Variable in class smile.manifold.SammonMapping

The coordinates.

coordinates - Variable in class smile.manifold.TSNE

The coordinate matrix in embedding space.

coordinates - Variable in class smile.manifold.UMAP

The coordinate matrix in embedding space.

coordinates() - Method in class smile.manifold.KPCA

Returns the nonlinear principal component scores, i.e., the representation of learning data in the nonlinear principal component space.

cor(double[][], String...) - Static method in class smile.feature.extraction.PCA

Fits principal component analysis with correlation matrix.

cor(DataFrame, String...) - Static method in class smile.feature.extraction.PCA

Fits principal component analysis with correlation matrix.

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

Returns the cost function of neural network.

cost() - Method in class smile.manifold.TSNE

Returns the cost function value.

Cost - Enum Class in smile.base.mlp

Neural network cost function.

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.

cov - Variable in class smile.regression.GaussianProcessRegression.JointPrediction

The covariance matrix of joint predictive distribution at query points.

cov(double[], int) - Static method in interface smile.timeseries.TimeSeries

Autocovariance function.

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.

crossentropy - Variable in class smile.validation.ClassificationMetrics

The cross entropy on validation data.

CrossEntropy - Interface in smile.validation.metric

Cross entropy generalizes the log loss metric to multiclass problems.

CrossValidation - Interface in smile.validation

Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set.

cumulativeVarianceProportion() - Method in class smile.feature.extraction.PCA

Returns the cumulative proportion of variance contained in principal components, ordered from largest to smallest.

D

d - Variable in class smile.vq.BIRCH

The dimensionality of data.

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

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

DataFrameClassifier - Interface in smile.classification

Classification trait on DataFrame.

DataFrameClassifier.Trainer<M extends DataFrameClassifier> - Interface in smile.classification

The classifier trainer.

DataFrameRegression - Interface in smile.regression

Regression trait on DataFrame.

DataFrameRegression.Trainer<M extends DataFrameRegression> - Interface in smile.regression

The regression trainer.

DBSCAN<T> - Class in smile.clustering

Density-Based Spatial Clustering of Applications with Noise.

DBSCAN(int, double, RNNSearch<T, T>, int, int[], boolean[]) - 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.

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.

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 - Variable in class smile.glm.GLM

The deviance = 2 * (LogLikelihood(Saturated Model) - LogLikelihood(Proposed Model)).

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

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

deviance() - Method in class smile.glm.GLM

Returns the deviance of model.

deviance(double[], double[], double[]) - Method in interface smile.glm.model.Model

The deviance function.

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

Returns the deviance of node.

devianceResiduals - Variable in class smile.glm.GLM

The deviance residuals.

devianceResiduals() - Method in class smile.glm.GLM

Returns the deviance residuals.

df - Variable in class smile.glm.GLM

The degrees of freedom of the residual deviance.

df - Variable in class smile.timeseries.BoxTest

The degree of freedom.

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

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

df() - Method in class smile.timeseries.AR

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

df() - Method in class smile.timeseries.ARMA

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

diff(double[], int) - Static method in interface smile.timeseries.TimeSeries

Returns the first-differencing of time series.

diff(double[], int, int) - Static method in interface smile.timeseries.TimeSeries

Returns the differencing of time series.

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, 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(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.Model - Enum Class in smile.classification

The generation models of naive Bayes classifier.

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.

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

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

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

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

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

The distance function.

distortion - Variable in class smile.clustering.CentroidClustering

The total distortion.

distortion - Variable in class smile.clustering.SpectralClustering

The distortion in feature space.

dlink(double) - Method in interface smile.glm.model.Model

The derivative of link function.

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 the dot representation of node.

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

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

The dropout rate.

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

The dropout rate.

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

ensemble(Classifier<T>...) - Static method in interface smile.classification.Classifier

Return an ensemble of multiple base models to obtain better predictive performance.

ensemble(DataFrameClassifier...) - Static method in interface smile.classification.DataFrameClassifier

Return an ensemble of multiple base models to obtain better predictive performance.

ensemble(DataFrameRegression...) - Static method in interface smile.regression.DataFrameRegression

Return an ensemble of multiple base models to obtain better predictive performance.

ensemble(Regression<T>...) - Static method in interface smile.regression.Regression

Return an ensemble of multiple base models to obtain better predictive performance.

entropy - Variable in class smile.clustering.MEC

The conditional entropy as the objective function.

ENTROPY - Enum constant in enum class smile.base.cart.SplitRule

Used by the ID3, C4.5 and C5.0 tree generation algorithms.

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

A small constant for numerical stability in RMSProp.

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 - Variable in class smile.validation.ClassificationMetrics

The number of errors.

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

Returns the residual standard error.

Error - Class in smile.validation.metric

The number of errors in the population.

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

F

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

The output function.

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

The output function.

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

Returns the value of decision function.

f(double[]) - Method in interface smile.deep.activation.ActivationFunction

The output function.

f(double[]) - Method in class smile.deep.activation.LeakyReLU

f(double[]) - Method in class smile.deep.activation.ReLU

f(double[]) - Method in class smile.deep.activation.Sigmoid

f(double[]) - Method in class smile.deep.activation.Softmax

f(double[]) - Method in class smile.deep.activation.Tanh

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 class smile.base.rbf.RBF

The activation function.

f1 - Variable in class smile.validation.ClassificationMetrics

The F-1 score on validation data.

F1 - Static variable in class smile.validation.metric.FScore

The F_1 score, the harmonic mean of precision and recall.

F2 - Static variable in class smile.validation.metric.FScore

The F_2 score, which weighs recall higher than precision.

Fallout - Class in smile.validation.metric

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

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

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

Returns the false branch child.

FDR - Class in smile.validation.metric

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.metric.FDR

feature - Variable in class smile.feature.selection.InformationValue

The feature name.

feature - Variable in class smile.feature.selection.SignalNoiseRatio

The feature name.

feature - Variable in class smile.feature.selection.SumSquaresRatio

The feature name.

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

Returns the split feature.

features - Variable in class smile.sequence.CRFLabeler

The feature function.

features() - Method in class smile.feature.extraction.BagOfWords

Returns the feature words.

FHalf - Static variable in class smile.validation.metric.FScore

The F_0.5 score, which weighs recall lower than precision.

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

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

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

fit(double[][]) - Static method in class smile.anomaly.IsolationForest

Fits an isolation forest.

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

Fits a linear epsilon-SVR.

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

Fits a regular Gaussian process model.

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

Fits a MLP model.

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

Fits a RBF network.

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

Fits an epsilon-SVR.

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.base.rbf.RBF

Fits Gaussian RBF function and centers on data.

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

Partitions data into k clusters up to 100 iterations.

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[]) - Static method in class smile.classification.FLD

Fits Fisher's linear discriminant.

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

Fits the 1-NN classifier.

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

Fits linear discriminant analysis.

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

Fits logistic regression.

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

Fits quadratic discriminant analysis.

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

Fits regularized discriminant analysis.

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

Fits linear discriminant analysis.

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

Fits quadratic discriminant analysis.

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

Fits a binary linear SVM.

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

Fits regularized discriminant analysis.

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

Fits logistic regression.

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

Fits a binary linear SVM.

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

Fits the K-NN classifier.

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

Fits Fisher's linear discriminant.

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

Fits Fisher's linear discriminant.

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

Fits linear discriminant analysis.

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

Fits logistic regression.

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

Fits a MLP model.

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

Fits quadratic discriminant analysis.

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

Fits a RBF network.

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

Fits regularized discriminant analysis.

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

Fits a binary or multiclass SVM.

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

Fits Gaussian RBF function and centers on data.

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

Clustering the data with KD-tree.

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, double, int, double, double) - Static method in class smile.clustering.DeterministicAnnealing

Clustering data into k clusters.

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

Fits Gaussian RBF function and centers on data.

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(double[][], int, int, double) - 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.SpectralClustering

Spectral clustering with Nystrom approximation.

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(double[][], int, int, double, int) - Static method in class smile.anomaly.IsolationForest

Fits a random forest for classification.

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

Spectral clustering with Nystrom approximation.

fit(double[][], int, String...) - Static method in class smile.feature.extraction.ProbabilisticPCA

Fits probabilistic principal component analysis.

fit(double[][], String...) - Static method in class smile.feature.extraction.PCA

Fits principal component analysis with covariance matrix.

fit(double[][], Properties) - Static method in class smile.anomaly.IsolationForest

Fits a random forest for classification.

fit(double[], int) - Static method in class smile.timeseries.AR

Fits an autoregressive model with Yule-Walker procedure.

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

Trains the Isotonic Regression scaling.

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(double[], int, int) - Static method in class smile.timeseries.ARMA

Fits an ARMA model with Hannan-Rissanen algorithm.

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

Fits the class label mapping.

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

Fits a linear epsilon-SVR of binary sparse data.

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

Fits k-modes clustering.

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

Fits an HMM by maximum likelihood estimation.

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

Fits a binary linear SVM of binary sparse data.

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

Fits a binary linear SVM of binary sparse data.

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

Fits k-modes clustering.

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

Fits maximum entropy classifier.

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

Fits maximum entropy classifier.

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

Fits maximum entropy classifier.

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

Fits Platt Scaling to estimate posteriori probabilities.

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

Partitions data into k clusters.

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

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

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

Fits the data transformation with 5% lower limit and 95% upper limit.

fit(DataFrame, double, double, String...) - Static method in class smile.feature.imputation.SimpleImputer

Fits the missing value imputation values.

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

Fits the data transformation.

fit(DataFrame, int, String...) - Static method in class smile.feature.extraction.ProbabilisticPCA

Fits probabilistic principal component analysis.

fit(DataFrame, String) - Static method in class smile.feature.selection.InformationValue

Calculates the information value.

fit(DataFrame, String) - Static method in class smile.feature.selection.SignalNoiseRatio

Calculates the signal noise ratio of numeric variables.

fit(DataFrame, String) - Static method in class smile.feature.selection.SumSquaresRatio

Calculates the sum squares ratio of numeric variables.

fit(DataFrame, String...) - Static method in class smile.feature.extraction.PCA

Fits principal component analysis with covariance matrix.

fit(DataFrame, String...) - Static method in class smile.feature.imputation.SimpleImputer

Fits the missing value imputation values.

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

Fits the data transformation.

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

Fits the data transformation.

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

Fits the data transformation.

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

Fits the data transformation.

fit(DataFrame, String, int) - Static method in class smile.feature.selection.InformationValue

Calculates the information value.

fit(DataFrame, Function<String, String[]>, int, String...) - Static method in class smile.feature.extraction.BagOfWords

Learns a vocabulary dictionary of top-k frequent tokens in the raw documents.

fit(DataFrame, Distance<Tuple>, int) - Static method in class smile.feature.imputation.KMedoidsImputer

Fits the missing value imputation values.

fit(DataFrame, MercerKernel<double[]>, int, double, String...) - Static method in class smile.feature.extraction.KernelPCA

Fits kernel principal component analysis.

fit(DataFrame, MercerKernel<double[]>, int, String...) - Static method in class smile.feature.extraction.KernelPCA

Fits kernel principal component analysis.

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

Fits a AdaBoost model.

fit(Formula, DataFrame) - Method in interface smile.classification.DataFrameClassifier.Trainer

Fits a classification model with the default hyper-parameters.

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

Fits a classification tree.

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

Fits a gradient tree boosting for classification.

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

Fits a random forest for classification.

fit(Formula, DataFrame) - Method in interface smile.regression.DataFrameRegression.Trainer

Fits a regression model with the default hyper-parameters.

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

Fits an ordinary least squares model.

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

Fits a random forest for regression.

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

Fits a regression tree.

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

Fits a ridge regression model.

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

Fits a L1-regularized least squares model.

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

Fits a ridge regression model.

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

Fits a generalized ridge regression model that minimizes a weighted least squares criterion augmented with a generalized ridge penalty:

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

Fits an Elastic Net model.

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

Fits an Elastic Net model.

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

Fits a L1-regularized least squares model.

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

Fits a regression tree.

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

Fits a AdaBoost model.

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

Fits a gradient tree boosting for classification.

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

Fits a random forest for regression.

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

Fits a random forest for regression.

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

Fits an ordinary least squares model.

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

Fits a multi-class model with binary data frame classifiers.

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

Fits a multi-class model with binary data frame classifiers.

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

Fits a AdaBoost model.

fit(Formula, DataFrame, Properties) - Method in interface smile.classification.DataFrameClassifier.Trainer

Fits a classification model.

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

Fits a classification tree.

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

Fits a gradient tree boosting for classification.

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

Fits a random forest for classification.

fit(Formula, DataFrame, Properties) - Method in interface smile.regression.DataFrameRegression.Trainer

Fits a regression model.

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

Fits an Elastic Net model.

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

Fits a gradient tree boosting for regression.

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

Fits a L1-regularized least squares model.

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

Fits an ordinary least squares model.

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

Fits a random forest for regression.

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

Fits a regression tree.

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

Fits a ridge regression model.

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, SplitRule, int, int, int) - Static method in class smile.classification.DecisionTree

Fits a classification tree.

fit(Formula, DataFrame, Model) - Static method in class smile.glm.GLM

Fits the generalized linear model with IWLS (iteratively reweighted least squares).

fit(Formula, DataFrame, Model, double, int) - Static method in class smile.glm.GLM

Fits the generalized linear model with IWLS (iteratively reweighted least squares).

fit(Formula, DataFrame, Model, Properties) - Static method in class smile.glm.GLM

Fits the generalized linear model with IWLS (iteratively reweighted least squares).

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

Fits logistic regression.

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

Fits logistic regression.

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

Fits logistic regression.

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

Fits a CRF model.

fit(BaseVector<?, ?, ?>) - Static method in class smile.classification.ClassLabels

Fits the class label mapping.

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

Spectral graph clustering.

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

Spectral graph clustering.

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

Fits a linear epsilon-SVR of sparse data.

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

Clustering data into k clusters up to 100 iterations.

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

Fits a binary linear SVM.

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

Fits a binary linear SVM.

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

Clustering data into k clusters.

fit(T[]) - Method in class smile.base.svm.OCSVM

Fits an one-class support vector machine.

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

Fits a CRF model.

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.

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

Fits an epsilon support vector regression model.

fit(T[], double[]) - Method in interface smile.regression.Regression.Trainer

Fits a regression model with the default hyper-parameters.

fit(T[], double[], Properties) - Method in interface smile.regression.Regression.Trainer

Fits a regression model.

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

Fits a regular Gaussian process model by the method of subset of regressors.

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

Fits a regular Gaussian process model.

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

Fits an epsilon-SVR.

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

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

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

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

fit(T[], int[]) - Method in interface smile.classification.Classifier.Trainer

Fits a classification model with the default hyper-parameters.

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

Trains the model.

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

Fits the K-NN 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[], BiFunction<T[], int[], Classifier<T>>) - Static method in class smile.classification.OneVersusRest

Fits a multi-class model with binary classifiers.

fit(T[], int[], Properties) - Method in interface smile.classification.Classifier.Trainer

Fits a classification model.

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

Fits the 1-NN classifier.

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

Fits a binary SVM.

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

Fits a binary SVM.

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

Clustering data into k clusters.

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

Clustering the data.

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

Clustering the data.

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

Constructor.

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

Fits Gaussian RBF function and centers on data.

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

Fits Gaussian RBF function and centers on data.

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

Fits Gaussian RBF function and centers on data.

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

Fits an one-class SVM.

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

Fits an one-class SVM.

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

Fits kernel principal component analysis.

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

Fits kernel principal component analysis.

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

Clustering the data.

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

Clustering the data.

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

Returns the fitness of the regression model.

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

Returns the fitness of the classification model.

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

Returns the fitness of the classification model.

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

Returns the fitness of the regression model.

fittedValues() - Method in class smile.glm.GLM

Returns the fitted mean values.

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

Returns the fitted values.

fittedValues() - Method in class smile.timeseries.AR

Returns the fitted values.

fittedValues() - Method in class smile.timeseries.ARMA

Returns the fitted values.

fitTime - Variable in class smile.validation.ClassificationMetrics

The time in milliseconds of fitting the model.

fitTime - Variable in class smile.validation.RegressionMetrics

The time in milliseconds of fitting the model.

FLD - Class in smile.classification

Fisher's linear discriminant.

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

Constructor.

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

Constructor.

forecast() - Method in class smile.timeseries.AR

Returns 1-step ahead forecast.

forecast() - Method in class smile.timeseries.ARMA

Returns 1-step ahead forecast.

forecast(int) - Method in class smile.timeseries.AR

Returns l-step ahead forecast.

forecast(int) - Method in class smile.timeseries.ARMA

Returns l-step ahead forecast.

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

The model formula.

formula - Variable in class smile.glm.GLM

The symbolic description of the model to be fitted.

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.feature.importance.TreeSHAP

Returns the formula associated with the model.

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

Returns the model formula.

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.

FScore - Class in smile.validation.metric

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

FScore() - Constructor for class smile.validation.metric.FScore

Constructor of F1 score.

FScore(double) - Constructor for class smile.validation.metric.FScore

Constructor of general F-score.

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(double[], double[]) - Method in interface smile.deep.activation.ActivationFunction

The gradient function.

g(double[], double[]) - Method in class smile.deep.activation.LeakyReLU

g(double[], double[]) - Method in class smile.deep.activation.ReLU

g(double[], double[]) - Method in class smile.deep.activation.Sigmoid

g(double[], double[]) - Method in class smile.deep.activation.Softmax

g(double[], double[]) - Method in class smile.deep.activation.Tanh

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

The gradient function.

GAFE - Class in smile.feature.selection

Genetic algorithm based feature selection.

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

Constructor.

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

Constructor.

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

Returns Gaussian neighborhood function.

GaussianProcessRegression<T> - Class in smile.regression

Gaussian Process for Regression.

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

Constructor.

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

Constructor.

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

Constructor.

GaussianProcessRegression.JointPrediction - Class in smile.regression

The joint prediction of multiple data points.

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

Returns the gradient clipping norm.

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

Returns the gradient clipping value.

getExtensionLevel() - Method in class smile.anomaly.IsolationForest

Returns the extension level.

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).

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.classification.SparseLogisticRegression

Returns the learning rate of stochastic gradient descent.

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

Returns the momentum factor.

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

Returns the dimension of output vector.

getProjection() - Method in class smile.classification.FLD

Returns the projection matrix W.

getProjection(double) - Method in class smile.feature.extraction.PCA

Returns the projection with top principal components that contain (more than) the given percentage of variance.

getProjection(int) - Method in class smile.feature.extraction.PCA

Returns the projection with given number of principal components.

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

Returns the state transition probabilities.

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

Returns the symbol emission probabilities.

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

Returns the weight decay factor.

GHA - Class in smile.feature.extraction

Generalized Hebbian Algorithm.

GHA(double[][], TimeFunction, String...) - Constructor for class smile.feature.extraction.GHA

Constructor.

GHA(int, int, TimeFunction, String...) - Constructor for class smile.feature.extraction.GHA

Constructor.

GINI - Enum constant in enum class smile.base.cart.SplitRule

Used by the CART algorithm, Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it were randomly labeled according to the distribution of labels in the subset.

GLM - Class in smile.glm

Generalized linear models.

GLM(Formula, String[], Model, double[], double, double, double, double[], double[], double[][]) - Constructor for class smile.glm.GLM

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

Returns the output gradient vector.

GradientTreeBoost - Class in smile.classification

Gradient boosting for classification.

GradientTreeBoost - Class in smile.regression

Gradient boosting for regression.

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

Constructor of binary class.

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

Constructor.

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

Constructor of binary class.

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.

graph - Variable in class smile.manifold.UMAP

The nearest neighbor graph.

grid() - Method in class smile.hpo.Hyperparameters

Generates a stream of hyperparameters for grid search.

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

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

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

HashEncoder - Class in smile.feature.extraction

Feature hashing, also known as the hashing trick, is a fast and space-efficient way of vectorizing features, i.e.

HashEncoder(Function<String, String[]>, int) - Constructor for class smile.feature.extraction.HashEncoder

Constructor.

HashEncoder(Function<String, String[]>, int, boolean) - Constructor for class smile.feature.extraction.HashEncoder

Constructor.

hasMissing(Tuple) - Static method in class smile.feature.imputation.SimpleImputer

Return true if the tuple x has missing values.

height() - 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.

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.

HiddenLayerBuilder - Class in smile.base.mlp

The builder of hidden layers.

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

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[], Matrix, Matrix) - 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.

Huber - Enum constant in enum class smile.base.cart.Loss.Type

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

Hyperparameters - Class in smile.hpo

Hyperparameter configuration.

Hyperparameters() - Constructor for class smile.hpo.Hyperparameters

Constructor.

I

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

Returns the residual sum of squares.

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

Returns the impurity of node.

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

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

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.

impute(double[][]) - Static method in class smile.feature.imputation.SimpleImputer

Impute the missing values with column averages.

impute(double[][], int, int) - Static method in interface smile.feature.imputation.SVDImputer

Impute missing values in the dataset.

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.manifold.UMAP

The original sample index.

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

Maps the class labels to index.

InformationValue - Class in smile.feature.selection

Information Value (IV) measures the predictive strength of a feature for a binary dependent variable.

InformationValue(String, double, double[], double[]) - Constructor for class smile.feature.selection.InformationValue

Constructor.

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

Returns an input layer.

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

Returns an input layer.

InputLayer - Class in smile.base.mlp

An input layer in the neural network.

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

Constructor.

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

Constructor.

instance - Static variable in class smile.validation.metric.Accuracy

Default instance.

instance - Static variable in class smile.validation.metric.AdjustedRandIndex

Default instance.

instance - Static variable in class smile.validation.metric.AUC

Default instance.

instance - Static variable in class smile.validation.metric.Error

Default instance.

instance - Static variable in class smile.validation.metric.Fallout

Default instance.

instance - Static variable in class smile.validation.metric.FDR

Default instance.

instance - Static variable in class smile.validation.metric.LogLoss

Default instance.

instance - Static variable in class smile.validation.metric.MAD

Default instance.

instance - Static variable in class smile.validation.metric.MatthewsCorrelation

Default instance.

instance - Static variable in class smile.validation.metric.MSE

Default instance.

instance - Static variable in class smile.validation.metric.MutualInformation

Default instance.

instance - Static variable in class smile.validation.metric.Precision

Default instance.

instance - Static variable in class smile.validation.metric.R2

Default instance.

instance - Static variable in class smile.validation.metric.RandIndex

Default instance.

instance - Static variable in class smile.validation.metric.Recall

Default instance.

instance - Static variable in class smile.validation.metric.RMSE

Default instance.

instance - Static variable in class smile.validation.metric.RSS

Default instance.

instance - Static variable in class smile.validation.metric.Sensitivity

Default instance.

instance - Static variable in class smile.validation.metric.Specificity

Default instance.

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

Returns the intercept.

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

Returns the intercept.

intercept() - Method in class smile.timeseries.AR

Returns the intercept.

intercept() - Method in class smile.timeseries.ARMA

Returns the intercept.

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

Returns the intercept of model.

InternalNode - Class in smile.base.cart

An internal node in CART.

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

Constructor.

invlink(double) - Method in interface smile.glm.model.Model

The inverse of link function (aka the mean function).

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.

IsolationForest - Class in smile.anomaly

Isolation forest is an unsupervised learning algorithm for anomaly detection that works on the principle of isolating anomalies.

IsolationForest(int, int, IsolationTree...) - Constructor for class smile.anomaly.IsolationForest

Constructor.

IsolationTree - Class in smile.anomaly

Isolation tree.

IsolationTree(List<double[]>, int, int) - Constructor for class smile.anomaly.IsolationTree

Constructor.

IsoMap - Class in smile.manifold

Isometric feature mapping.

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

Constructor.

IsotonicMDS - Class in smile.manifold

Kruskal's non-metric MDS.

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

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

iv - Variable in class smile.feature.selection.InformationValue

Information value.

J

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

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

JOINT - Enum constant in enum class smile.validation.metric.NormalizedMutualInformation.Method

I(y1, y2) / H(y1, y2)

JOINT - Static variable in class smile.validation.metric.NormalizedMutualInformation

Default instance with max normalization.

JointPrediction(T[], double[], double[], Matrix) - Constructor for class smile.regression.GaussianProcessRegression.JointPrediction

Constructor.

K

k - Variable in class smile.classification.ClassLabels

The number of classes.

k - Variable in class smile.clustering.PartitionClustering

The number of clusters.

kernel - Variable in class smile.regression.GaussianProcessRegression

The covariance/kernel function.

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

Returns the kernel function.

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

Kernel machines.

KernelMachine<T> - Class in smile.regression

The learning methods building on kernels.

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

Constructor.

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

Constructor.

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

Constructor.

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

Constructor.

KernelPCA - Class in smile.feature.extraction

Kernel PCA transform.

KernelPCA(KPCA<double[]>, String...) - Constructor for class smile.feature.extraction.KernelPCA

Constructor.

KMeans - Class in smile.clustering

K-Means clustering.

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

Constructor.

KMedoidsImputer - Class in smile.feature.imputation

Missing value imputation by K-Medoids clustering.

KMedoidsImputer(CLARANS<Tuple>) - Constructor for class smile.feature.imputation.KMedoidsImputer

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.

KNNImputer - Class in smile.feature.imputation

Missing value imputation with k-nearest neighbors.

KNNImputer(DataFrame, int, String...) - Constructor for class smile.feature.imputation.KNNImputer

Constructor with Euclidean distance on selected columns.

KNNImputer(DataFrame, int, Distance<Tuple>) - Constructor for class smile.feature.imputation.KNNImputer

Constructor.

kpca - Variable in class smile.feature.extraction.KernelPCA

Kernel PCA.

KPCA<T> - Class in smile.manifold

Kernel principal component analysis.

KPCA(T[], MercerKernel<T>, double[], double, double[][], double[], Matrix) - Constructor for class smile.manifold.KPCA

Constructor.

L

L - Variable in class smile.regression.GaussianProcessRegression

The log marginal likelihood, which may be not available (NaN) when the model is fit with approximate methods.

L - Variable in class smile.vq.BIRCH

The number of CF entries in the leaf nodes.

L_INF - Enum constant in enum class smile.feature.transform.Normalizer.Norm

Normalize L-infinity vector norm.

L1 - Enum constant in enum class smile.feature.transform.Normalizer.Norm

Normalize L1 vector norm.

L2 - Enum constant in enum class smile.feature.transform.Normalizer.Norm

Normalize L2 vector norm.

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

Least absolute deviation regression loss.

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

The L2 regularization factor, which is also the weight decay factor.

LaplacianEigenmap - Class in smile.manifold

Laplacian Eigenmap.

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

Constructor with Gaussian kernel.

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

Constructor with discrete weights.

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.

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

Constructor.

Layer(Matrix, double[]) - Constructor for class smile.base.mlp.Layer

Constructor.

Layer(Matrix, double[], double) - Constructor for class smile.base.mlp.Layer

Constructor.

LayerBuilder - Class in smile.base.mlp

The builder of layers.

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

Constructor.

LDA - Class in smile.classification

Linear discriminant analysis.

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

Constructor.

LDA(double[], double[][], double[], Matrix, 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.

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

The leaky rectifier activation function max(x, 0.01x).

leaky() - Static method in interface smile.deep.activation.ActivationFunction

Returns the leaky rectifier activation function max(x, 0.01x).

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

The leaky rectifier activation function max(x, ax) where 0 <= a < 1.

leaky(double) - Static method in interface smile.deep.activation.ActivationFunction

Returns the leaky rectifier activation function max(x, ax) where 0 <= a < 1.

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

Returns a hidden layer with leaky rectified linear activation function.

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

Returns a hidden layer with leaky rectified linear activation function.

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

Returns a hidden layer with leaky rectified linear activation function.

LeakyReLU - Class in smile.deep.activation

The leaky rectifier activation function max(x, ax) where 0 <= a < 1.

LeakyReLU(double) - Constructor for class smile.deep.activation.LeakyReLU

Constructor.

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

The learning rate.

LeastAbsoluteDeviation - Enum constant in enum class smile.base.cart.Loss.Type

Least absolute deviation regression.

LeastSquares - Enum constant in enum class smile.base.cart.Loss.Type

Least squares regression.

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

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

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

Returns the number of leaf nodes in the subtree.

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.

LIKELIHOOD - Enum constant in enum class smile.base.mlp.Cost

Negative likelihood (or log-likelihood) cost.

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

Linear/Identity activation function.

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

Returns a hidden layer with linear activation function.

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

Returns a hidden layer with linear activation function.

LINEAR - Enum constant in enum class smile.base.mlp.OutputFunction

Linear/Identity 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.

LinearModel(Formula, StructType, Matrix, double[], double[], double) - Constructor for class smile.regression.LinearModel

Constructor.

link(double) - Method in interface smile.glm.model.Model

The link function.

Linkage - Class in smile.clustering.linkage

A measure of dissimilarity between clusters (i.e.

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

Constructor.

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

Constructor.

ljung(double[], int) - Static method in class smile.timeseries.BoxTest

Box-Pierce test.

Ljung_Box - Enum constant in enum class smile.timeseries.BoxTest.Type

Ljung-Box test.

LLE - Class in smile.manifold

Locally Linear Embedding.

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

loadings() - Method in class smile.feature.extraction.PCA

Returns the variable loading matrix, ordered from largest to smallest by corresponding eigenvalues.

loadings() - Method in class smile.feature.extraction.ProbabilisticPCA

Returns the variable loading matrix, ordered from largest to smallest by corresponding eigenvalues.

log() - Static method in interface smile.glm.model.Poisson

log link function.

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

Constructor.

LogisticRegression.Binomial - Class in smile.classification

Binomial logistic regression.

LogisticRegression.Multinomial - Class in smile.classification

Multinomial logistic regression.

logit() - Static method in interface smile.glm.model.Bernoulli

logit link function.

logit(int[]) - Static method in interface smile.glm.model.Binomial

logit link function.

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.

loglikelihood() - Method in class smile.classification.SparseLogisticRegression

Returns the log-likelihood of model.

logLikelihood - Variable in class smile.glm.GLM

Log-likelihood.

logLikelihood() - Method in class smile.glm.GLM

Returns the log-likelihood of model.

logLikelihood(double[], double[]) - Method in interface smile.glm.model.Model

The log-likelihood function.

logloss - Variable in class smile.validation.ClassificationMetrics

The log loss on validation data.

LogLoss - Class in smile.validation.metric

Log loss is a evaluation metric for binary classifiers and it is sometimes the optimization objective as well in case of logistic regression and neural networks.

LogLoss() - Constructor for class smile.validation.metric.LogLoss

logp(int[]) - Method in class smile.sequence.HMM

Returns the logarithm probability of an observation sequence given this HMM.

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

Returns the logarithm probability of an observation sequence.

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

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

LOOCV - Interface in smile.validation

Leave-one-out cross validation.

Loss - Interface in smile.base.cart

Regression loss function.

Loss.Type - Enum Class in smile.base.cart

The type of loss.

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

Least squares regression loss.

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

Least squares regression loss.

M

ma() - Method in class smile.timeseries.ARMA

Returns the linear coefficients of MA(q).

mad - Variable in class smile.validation.RegressionMetrics

The mean absolute deviation on validation data.

MAD - Class in smile.validation.metric

Mean absolute deviation error.

MAD() - Constructor for class smile.validation.metric.MAD

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

The dropout mask.

matrix - Variable in class smile.validation.metric.ConfusionMatrix

Confusion matrix.

MatthewsCorrelation - Class in smile.validation.metric

Matthews correlation coefficient.

MatthewsCorrelation() - Constructor for class smile.validation.metric.MatthewsCorrelation

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

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

MAX - Enum constant in enum class smile.validation.metric.AdjustedMutualInformation.Method

I(y1, y2) / max(H(y1), H(y2))

MAX - Enum constant in enum class smile.validation.metric.NormalizedMutualInformation.Method

I(y1, y2) / max(H(y1), H(y2))

MAX - Static variable in class smile.validation.metric.AdjustedMutualInformation

Default instance with max normalization.

MAX - Static variable in class smile.validation.metric.NormalizedMutualInformation

Default instance with max normalization.

MaxAbsScaler - Class in smile.feature.transform

Scales each feature by its maximum absolute value.

MaxAbsScaler() - Constructor for class smile.feature.transform.MaxAbsScaler

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

The maximum depth of the tree.

Maxent - Class in smile.classification

Maximum Entropy Classifier.

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

Constructor.

Maxent.Binomial - Class in smile.classification

Binomial maximum entropy classifier.

Maxent.Multinomial - Class in smile.classification

Multinomial maximum entropy classifier.

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

The maximum number of leaf nodes in the tree.

mcc - Variable in class smile.validation.ClassificationMetrics

The Matthews correlation coefficient on validation data.

MDS - Class in smile.manifold

Classical multidimensional scaling, also known as principal coordinates analysis.

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

Constructor.

mean - Variable in class smile.regression.GaussianProcessRegression

The mean of responsible variable.

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

Returns the mean of response variable.

mean() - Method in class smile.timeseries.AR

Returns the mean of time series.

mean() - Method in class smile.timeseries.ARMA

Returns the mean of time series.

MEAN_SQUARED_ERROR - Enum constant in enum class smile.base.mlp.Cost

Mean squares error cost.

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

Merges 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.classification.RandomForest

Merges two random forests.

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

Merges two random forests.

metrics - Variable in class smile.classification.RandomForest.Model

The performance metrics on out-of-bag samples.

metrics - Variable in class smile.regression.RandomForest.Model

The performance metrics on out-of-bag samples.

metrics - Variable in class smile.validation.ClassificationValidation

The classification metrics.

metrics - Variable in class smile.validation.RegressionValidation

The regression metrics.

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

Returns the overall out-of-bag metric estimations.

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

Returns the overall out-of-bag metric estimations.

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

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

MIN - Enum constant in enum class smile.validation.metric.AdjustedMutualInformation.Method

I(y1, y2) / min(H(y1), H(y2))

MIN - Enum constant in enum class smile.validation.metric.NormalizedMutualInformation.Method

I(y1, y2) / min(H(y1), H(y2))

MIN - Static variable in class smile.validation.metric.AdjustedMutualInformation

Default instance with min normalization.

MIN - Static variable in class smile.validation.metric.NormalizedMutualInformation

Default instance with min normalization.

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.

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 - Class in smile.regression

Fully connected multilayer perceptron neural network for regression.

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

Constructor.

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

Constructor.

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

Constructor.

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

Constructor.

model - Variable in class smile.glm.GLM

The model specifications (link function, deviance, etc.).

model - Variable in class smile.sequence.CRFLabeler

The CRF model.

model - Variable in class smile.sequence.HMMLabeler

The HMM model.

model - Variable in class smile.validation.ClassificationValidation

The model.

model - Variable in class smile.validation.RegressionValidation

The model.

Model - Interface in smile.glm.model

The GLM model specification.

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

Returns the base models.

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

Returns the base models.

ModelSelection - Interface in smile.validation

Model selection criteria.

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

The momentum factor.

mse - Variable in class smile.validation.RegressionMetrics

The mean squared error on validation data.

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.metric

Mean squared error.

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

mu - Variable in class smile.glm.GLM

The fitted mean values.

mu - Variable in class smile.regression.GaussianProcessRegression.JointPrediction

The mean of predictive distribution at query points.

MultilayerPerceptron - Class in smile.base.mlp

Fully connected multilayer perceptron neural network.

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

Constructor.

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

Fits multinomial logistic regression.

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

Fits multinomial logistic regression.

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

Fits multinomial logistic regression.

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

Fits maximum entropy classifier.

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

Fits maximum entropy classifier.

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

Fits maximum entropy classifier.

multinomial(SparseDataset, int[]) - Static method in class smile.classification.SparseLogisticRegression

Fits multinomial logistic regression.

multinomial(SparseDataset, int[], double, double, int) - Static method in class smile.classification.SparseLogisticRegression

Fits multinomial logistic regression.

multinomial(SparseDataset, int[], Properties) - Static method in class smile.classification.SparseLogisticRegression

Fits multinomial logistic regression.

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

Constructor.

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

Constructor.

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

Constructor.

MULTINOMIAL - Enum constant in enum class smile.classification.DiscreteNaiveBayes.Model

The document multinomial model generates one term from the vocabulary in each position of the document.

mustart(double) - Method in interface smile.glm.model.Model

The function to estimates the starting value of mean given y.

MutualInformation - Class in smile.validation.metric

Mutual Information for comparing clustering.

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

N

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

The number of neurons in this layer

n - Variable in class smile.validation.metric.ContingencyTable

The number of observations.

n1 - Variable in class smile.validation.metric.ContingencyTable

The number of clusters of first clustering.

n2 - Variable in class smile.validation.metric.ContingencyTable

The number of clusters of second clustering.

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.

name() - Method in interface smile.deep.activation.ActivationFunction

Returns the name of activation function.

name() - Method in class smile.deep.activation.LeakyReLU

name() - Method in class smile.deep.activation.ReLU

name() - Method in class smile.deep.activation.Sigmoid

name() - Method in class smile.deep.activation.Softmax

name() - Method in class smile.deep.activation.Tanh

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

The neighbor neuron.

Neighborhood - Interface in smile.vq

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

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

The input and 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, TimeFunction) - 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 - Variable in class smile.base.mlp.LayerBuilder

The number of neurons.

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 neurons.

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

Returns the lattice of neurons.

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.

noise - Variable in class smile.regression.GaussianProcessRegression

The variance of noise.

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.

nonoverlap(int[], int) - Static method in interface smile.validation.CrossValidation

Cross validation with non-overlapping groups.

NormalizedMutualInformation - Class in smile.validation.metric

Normalized Mutual Information (NMI) for comparing clustering.

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

Constructor.

NormalizedMutualInformation.Method - Enum Class in smile.validation.metric

The normalization method.

Normalizer - Class in smile.feature.transform

Normalize samples individually to unit norm.

Normalizer(Normalizer.Norm, String...) - Constructor for class smile.feature.transform.Normalizer

Constructor.

Normalizer.Norm - Enum Class in smile.feature.transform

Vector norm.

nullDeviance - Variable in class smile.glm.GLM

The null deviance = 2 * (LogLikelihood(Saturated Model) - LogLikelihood(Null Model)).

nullDeviance(double[], double) - Method in interface smile.glm.model.Model

The NULL deviance function.

numClasses() - Method in class smile.classification.AbstractClassifier

numClasses() - Method in interface smile.classification.Classifier

Returns the number of classes.

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

numClasses() - Method in class smile.classification.MLP

numClasses() - Method in class smile.classification.SVM

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.

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

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

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

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

O

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

One-class support vector machine.

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

Constructor.

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

Computes the proximity and the linkage.

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

Computes the proximity and the linkage.

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

Computes the proximity and the linkage.

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

Computes the proximity and the linkage.

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

Computes the proximity and the linkage.

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

Computes the proximity and the linkage.

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

Computes the proximity and the linkage.

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

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

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

Fits the classical multidimensional scaling.

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

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

of(double[][]) - Static method in class smile.manifold.UMAP

Runs the UMAP algorithm.

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

Fits Sammon's mapping.

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

Fits Kruskal's non-metric MDS.

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

Runs the C-Isomap algorithm with Euclidean distance.

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

Fits Kruskal's non-metric MDS.

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

Laplacian Eigenmaps with discrete weights.

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

Runs the LLE algorithm.

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

Fits the classical multidimensional scaling.

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

Fits Sammon's mapping.

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

Runs the UMAP algorithm.

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

Fits the classical multidimensional scaling.

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

Fits Sammon's mapping.

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

Fits Kruskal's non-metric MDS.

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

Runs the LLE algorithm.

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

Runs the Isomap algorithm.

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

Laplacian Eigenmap with Gaussian kernel.

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

Runs the UMAP algorithm.

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

Fits Kruskal's non-metric MDS.

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

Fits the classical multidimensional scaling.

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

Fits Sammon's mapping.

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

Calculates the mean absolute deviation error.

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

Calculates the mean squared error.

of(double[], double[]) - Static method in class smile.validation.metric.R2

Calculates the R squared coefficient.

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

Calculates the root mean squared error.

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

Calculates the residual sum of squares.

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

Computes the regression metrics.

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

Computes the classification metrics.

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

Computes the soft classification metrics.

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(double, int[], int[]) - Static method in class smile.validation.metric.FScore

Calculates the F1 score.

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(double, M, Formula, DataFrame) - Static method in class smile.validation.ClassificationMetrics

Validates a model on a test data.

of(double, M, Formula, DataFrame) - Static method in class smile.validation.RegressionMetrics

Trains and validates a model on a train/validation split.

of(double, M, T[], double[]) - Static method in class smile.validation.RegressionMetrics

Validates a model on a test data.

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

Validates a model on a test data.

of(int) - Static method in interface smile.validation.LOOCV

Returns the training sample index for each round.

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

Calculates AUC for binary classifier.

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

Calculates the Log Loss for binary classifier.

of(int[], double[][]) - Static method in interface smile.validation.metric.CrossEntropy

Calculates the cross entropy for multiclass classifier.

of(int[], int) - Static method in interface smile.validation.Bootstrap

Stratified bootstrap sampling.

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

Calculates the classification accuracy.

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

Calculates the adjusted rand index.

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

Creates the confusion matrix.

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

Calculates the number of errors.

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

Calculates the false alarm rate.

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

Calculates the false discovery rate.

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

Calculates Matthews correlation coefficient.

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

Calculates the mutual information.

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

Calculates the precision.

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

Calculates the rand index.

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

Calculates the recall/sensitivity.

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

Calculates the sensitivity.

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

Calculates the specificity.

of(int, int) - Static method in interface smile.validation.Bootstrap

Bootstrap sampling.

of(int, int) - Static method in interface smile.validation.CrossValidation

Creates a k-fold cross validation.

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

Returns the changing rate of neighborhood at a given iteration.

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

Returns the layer builders given a string representation such as "Input(10, 0.2)|ReLU(50, 0.5)|Sigmoid(30, 0.5)|...".

of(int, int, String...) - Static method in class smile.feature.extraction.RandomProjection

Generates a non-sparse random projection.

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(M, Formula, DataFrame) - Static method in class smile.validation.ClassificationMetrics

Validates a model on a test data.

of(M, Formula, DataFrame) - Static method in class smile.validation.RegressionMetrics

Trains and validates a model on a train/validation split.

of(M, T[], double[]) - Static method in class smile.validation.RegressionMetrics

Validates a model on a test data.

of(M, T[], int[]) - Static method in class smile.validation.ClassificationMetrics

Validates a model on a test data.

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

Creates a linear kernel machine.

of(Formula, DataFrame, Properties, Classifier.Trainer<double[], ?>) - Static method in interface smile.classification.DataFrameClassifier

Fits a vector classifier on data frame.

of(Formula, DataFrame, Properties, Regression.Trainer<double[], ?>) - Static method in interface smile.regression.DataFrameRegression

Fits a vector regression model on data frame.

of(Formula, DataFrame, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in class smile.validation.ClassificationValidation

Trains and validates a model on a train/validation split.

of(Formula, DataFrame, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in class smile.validation.RegressionValidation

Trains and validates a model on a train/validation split.

of(Bag[], Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in class smile.validation.ClassificationValidation

Trains and validates a model on multiple train/validation split.

of(Bag[], Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in class smile.validation.RegressionValidation

Trains and validates a model on multiple train/validation split.

of(Bag[], T[], double[], BiFunction<T[], double[], M>) - Static method in class smile.validation.RegressionValidation

Trains and validates a model on multiple train/validation split.

of(Bag[], T[], int[], BiFunction<T[], int[], M>) - Static method in class smile.validation.ClassificationValidation

Trains and validates a model on multiple train/validation split.

of(T[], double[], T[], double[], BiFunction<T[], double[], M>) - Static method in class smile.validation.RegressionValidation

Trains and validates a model on a train/validation split.

of(T[], int[], T[], int[], BiFunction<T[], int[], M>) - Static method in class smile.validation.ClassificationValidation

Trains and validates a model on a train/validation split.

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

Computes the proximity and the linkage.

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

Computes the proximity and the linkage.

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

Computes the proximity and the linkage.

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

Computes the proximity and the linkage.

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

Computes the proximity and the linkage.

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

Computes the proximity and the linkage.

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

Computes the proximity and the linkage.

of(T[], Distance<T>) - Static method in class smile.manifold.UMAP

Runs the UMAP algorithm.

of(T[], Distance<T>, int) - Static method in class smile.manifold.IsoMap

Runs the C-Isomap algorithm.

of(T[], Distance<T>, int) - Static method in class smile.manifold.LaplacianEigenmap

Laplacian Eigenmaps with discrete weights.

of(T[], Distance<T>, int) - Static method in class smile.manifold.UMAP

Runs the UMAP algorithm.

of(T[], Distance<T>, int, int, boolean) - Static method in class smile.manifold.IsoMap

Runs the Isomap algorithm.

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

Laplacian Eigenmap with Gaussian kernel.

of(T[], Distance<T>, int, int, int, double, double, double, int, double) - Static method in class smile.manifold.UMAP

Runs the UMAP algorithm.

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.

ols(double[], int) - Static method in class smile.timeseries.AR

Fits an autoregressive model with least squares method.

ols(double[], int, boolean) - Static method in class smile.timeseries.AR

Fits an autoregressive model with least squares method.

OLS - Class in smile.regression

Ordinary least squares.

OLS - Enum constant in enum class smile.timeseries.AR.Method

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.

online() - Method in interface smile.classification.Classifier

Returns true if this is an online learner.

online() - Method in class smile.classification.DiscreteNaiveBayes

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

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

online() - Method in class smile.classification.MLP

online() - Method in class smile.classification.SparseLogisticRegression

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

online() - Method in class smile.regression.MLP

online() - Method in interface smile.regression.Regression

Returns true if this is an online learner.

oob - Variable in class smile.validation.Bag

The index of testing instances.

Optimizer - Interface in smile.deep.optimizer

The neural network optimizer.

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 - Variable in class smile.base.mlp.Layer

The output vector.

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

The output layer.

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

Returns the predicted value.

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

Returns the predicted value.

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

Returns the output vector.

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

Calculate the node output.

OutputFunction - Enum Class in smile.base.mlp

The output function of neural networks.

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

The output gradient.

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.

OutputLayerBuilder - Class in smile.base.mlp

The builder of output layers.

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

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() - Method in class smile.timeseries.AR

Returns the order of AR.

p() - Method in class smile.timeseries.ARMA

Returns the order of AR.

p(int[]) - Method in class smile.sequence.HMM

Returns the probability of an observation sequence given this HMM.

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

Returns the probability of an observation sequence.

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

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

pacf(double[], int) - Static method in interface smile.timeseries.TimeSeries

Partial autocorrelation function.

partition(double) - Method in class smile.clustering.HierarchicalClustering

Cuts a tree into several groups by specifying the cut height.

partition(int) - Method in class smile.clustering.HierarchicalClustering

Cuts a tree into several groups by specifying the desired number.

PartitionClustering - Class in smile.clustering

Partition clustering.

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

Constructor.

path(double[]) - Method in class smile.anomaly.IsolationTree

Returns the path length from the root to the leaf node.

PCA - Class in smile.feature.extraction

Principal component analysis.

PCA(double[], double[], Matrix, Matrix, String...) - Constructor for class smile.feature.extraction.PCA

Constructor.

pierce(double[], int) - Static method in class smile.timeseries.BoxTest

Box-Pierce test.

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.

Poisson - Interface in smile.glm.model

The response variable is of Poisson distribution.

POLYAURN - Enum constant in enum class smile.classification.DiscreteNaiveBayes.Model

The document Polya Urn model is similar to MULTINOMIAL but different in the conditional probability update during learning.

posteriori - Variable in class smile.validation.ClassificationValidation

The posteriori probability of prediction if the model is a soft classifier.

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.

postprocess(double[]) - Method in class smile.feature.extraction.PCA

postprocess(double[]) - Method in class smile.feature.extraction.ProbabilisticPCA

postprocess(double[]) - Method in class smile.feature.extraction.Projection

Postprocess the output vector after projection.

precision - Variable in class smile.validation.ClassificationMetrics

The precision on validation data.

Precision - Class in smile.validation.metric

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

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

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

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

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

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

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

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

Predict the class of an instance.

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

predict(double[]) - Method in class smile.clustering.DENCLUE

Classifies a new observation.

predict(double[]) - Method in class smile.regression.LinearModel

Predicts the dependent variable of an instance.

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

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

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

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

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

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

Predict the class of an instance.

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

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

Predict the class of an instance.

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

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

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

Predict the class of an instance.

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

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

predict(List<T>) - Method in interface smile.classification.Classifier

Predicts the class labels of a list of instances.

predict(List<T>) - Method in interface smile.regression.Regression

Predicts the dependent variable of a list of instances.

predict(List<T>, List<double[]>) - Method in interface smile.classification.Classifier

Predicts the class labels of a list of instances.

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

Predicts the class labels of a data frame.

predict(DataFrame) - Method in class smile.glm.GLM

Predicts the mean response.

predict(DataFrame) - Method in interface smile.regression.DataFrameRegression

Predicts the dependent variables of a data frame.

predict(DataFrame) - Method in class smile.regression.LinearModel

predict(DataFrame, List<double[]>) - Method in interface smile.classification.DataFrameClassifier

Predicts the class labels of a dataset.

predict(Dataset<T>) - Method in interface smile.classification.Classifier

Predicts the class labels of a dataset.

predict(Dataset<T>) - Method in interface smile.regression.Regression

Predicts the dependent variable of a dataset.

predict(Dataset<T>, List<double[]>) - Method in interface smile.classification.Classifier

Predicts the class labels of a dataset.

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

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) - Method in class smile.classification.DecisionTree

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

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

predict(Tuple) - Method in class smile.glm.GLM

Predicts the mean response.

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

predict(Tuple) - Method in class smile.regression.LinearModel

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

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

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

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

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

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

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

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

Predict the class of an instance.

predict(SparseArray) - Method in class smile.classification.SparseLogisticRegression.Binomial

predict(SparseArray) - Method in class smile.classification.SparseLogisticRegression.Multinomial

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

Predict the class of an instance.

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

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

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

Predicts the class label of an instance.

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

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

Prediction is based on voting.

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

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

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

predict(T) - Method in class smile.clustering.DBSCAN

Classifies a new observation.

predict(T) - Method in class smile.clustering.MEC

Cluster a new instance.

predict(T) - Method in class smile.regression.GaussianProcessRegression

predict(T) - Method in class smile.regression.KernelMachine

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.classification.Classifier

Predicts the class labels of an array of instances.

predict(T[]) - Method in interface smile.regression.Regression

Predicts the dependent variable of an array of instances.

predict(T[]) - Method in class smile.sequence.CRFLabeler

Returns the most likely label sequence given the feature sequence by the forward-backward algorithm.

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.

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

Predicts the class labels of an array of instances.

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

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

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

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

Prediction is based posteriori probability estimation.

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

predict(T, double[]) - Method in class smile.regression.GaussianProcessRegression

Predicts the mean and standard deviation of an instance.

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

Classifies a new observation.

prediction - Variable in class smile.validation.ClassificationValidation

The model prediction.

prediction - Variable in class smile.validation.RegressionValidation

The model prediction.

predictors(Tuple) - Method in class smile.base.cart.CART

Returns the predictors by the model formula if it is not null.

preprocess(double[]) - Method in class smile.feature.extraction.Projection

Preprocess the input vector before projection.

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.

ProbabilisticClassificationMetric - Interface in smile.validation.metric

An abstract interface to measure the probabilistic classification performance.

ProbabilisticPCA - Class in smile.feature.extraction

Probabilistic principal component analysis.

ProbabilisticPCA(double, double[], Matrix, Matrix, String...) - Constructor for class smile.feature.extraction.ProbabilisticPCA

Constructor.

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

Projects a sample to the feature space.

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

Projects samples to the feature space.

projection - Variable in class smile.feature.extraction.Projection

The projection matrix.

projection() - Method in class smile.manifold.KPCA

Returns the projection matrix.

Projection - Class in smile.feature.extraction

A projection is a kind of feature extraction technique that transforms data from the input space to a feature space, linearly or non-linearly.

Projection(Matrix, String, String...) - Constructor for class smile.feature.extraction.Projection

Constructor.

propagate(double[]) - Method in class smile.base.mlp.InputLayer

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

Propagates the signals from a lower layer to this layer.

propagate(double[], boolean) - Method in class smile.base.mlp.MultilayerPerceptron

Propagates the signals through the neural network.

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

Propagates the output signals through the implicit dropout layer.

proportion - Variable in class smile.manifold.MDS

The proportion of variance contained in each principal component.

proximity(double[][]) - Static method in class smile.clustering.linkage.Linkage

Computes the proximity matrix (linearized in column major) based on Euclidean distance.

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

Computes 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.

pvalue - Variable in class smile.timeseries.BoxTest

p-value

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

Returns the p-value of goodness-of-fit test.

Q

q - Variable in class smile.timeseries.BoxTest

Box-Pierce or Ljung-Box statistic.

q() - Method in class smile.timeseries.ARMA

Returns the order of MA.

QDA - Class in smile.classification

Quadratic discriminant analysis.

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

Constructor.

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

Constructor.

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

Quantile regression loss.

Quantile - Enum constant in enum class smile.base.cart.Loss.Type

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(T[]) - Method in class smile.regression.GaussianProcessRegression

Evaluates the Gaussian Process at some query points.

R

r2 - Variable in class smile.validation.RegressionMetrics

The R-squared score on validation data.

R2 - Class in smile.validation.metric

R².

R2() - Constructor for class smile.validation.metric.R2

R2() - Method in class smile.timeseries.AR

Returns R² statistic.

R2() - Method in class smile.timeseries.ARMA

Returns R² statistic.

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.metric

Rand Index.

RandIndex() - Constructor for class smile.validation.metric.RandIndex

random() - Method in class smile.hpo.Hyperparameters

Generates a stream of hyperparameters for random search.

RandomForest - Class in smile.classification

Random forest for classification.

RandomForest - Class in smile.regression

Random forest for regression.

RandomForest(Formula, int, RandomForest.Model[], ClassificationMetrics, double[]) - Constructor for class smile.classification.RandomForest

Constructor.

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

Constructor.

RandomForest(Formula, RandomForest.Model[], RegressionMetrics, double[]) - Constructor for class smile.regression.RandomForest

Constructor.

RandomForest.Model - Class in smile.classification

The base model.

RandomForest.Model - Class in smile.regression

The base model.

RandomProjection - Class in smile.feature.extraction

Random projection is a promising dimensionality reduction technique for learning mixtures of Gaussians.

RandomProjection(Matrix, String...) - Constructor for class smile.feature.extraction.RandomProjection

Constructor.

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<T> - Class in smile.regression

Radial basis function network.

RBFNetwork(int, RBF<T>[], Matrix, boolean) - Constructor for class smile.classification.RBFNetwork

Constructor.

RBFNetwork(int, RBF<T>[], Matrix, boolean, IntSet) - Constructor for class smile.classification.RBFNetwork

Constructor.

RBFNetwork(RBF<T>[], double[], boolean) - Constructor for class smile.regression.RBFNetwork

Constructor.

RDA - Class in smile.classification

Regularized discriminant analysis.

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

Constructor.

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

Constructor.

Recall - Class in smile.validation.metric

In information retrieval area, sensitivity is called recall.

Recall() - Constructor for class smile.validation.metric.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.

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

Returns a hidden layer with rectified linear activation function.

regression(int, int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.CrossValidation

Repeated cross validation of regression.

regression(int, int, T[], double[], BiFunction<T[], double[], M>) - Static method in interface smile.validation.CrossValidation

Repeated cross validation of regression.

regression(int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.Bootstrap

Runs regression bootstrap validation.

regression(int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.CrossValidation

Cross validation of regression.

regression(int, T[], double[], BiFunction<T[], double[], M>) - Static method in interface smile.validation.Bootstrap

Runs regression bootstrap validation.

regression(int, T[], double[], BiFunction<T[], double[], M>) - Static method in interface smile.validation.CrossValidation

Cross validation of regression.

regression(Formula, DataFrame, BiFunction<Formula, DataFrame, DataFrameRegression>) - Static method in interface smile.validation.LOOCV

Runs leave-one-out cross validation tests.

regression(T[], double[], BiFunction<T[], double[], M>) - Static method in interface smile.validation.LOOCV

Runs leave-one-out cross validation tests.

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.Trainer<T,M extends Regression<T>> - Interface in smile.regression

The regression trainer.

RegressionMetric - Interface in smile.validation.metric

An abstract interface to measure the regression performance.

RegressionMetrics - Class in smile.validation

The regression validation metrics.

RegressionMetrics(double, double, int, double, double, double, double, double) - Constructor for class smile.validation.RegressionMetrics

Constructor.

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

Regression tree.

RegressionTree(DataFrame, Loss, StructField, int, int, int, int, int[], int[][]) - Constructor for class smile.regression.RegressionTree

Constructor.

RegressionValidation<M> - Class in smile.validation

Regression model validation results.

RegressionValidation(M, double[], double[], RegressionMetrics) - Constructor for class smile.validation.RegressionValidation

Constructor.

RegressionValidations<M> - Class in smile.validation

Regression model validation results.

RegressionValidations(List<RegressionValidation<M>>) - Constructor for class smile.validation.RegressionValidations

Constructor.

regressors - Variable in class smile.regression.GaussianProcessRegression

The regressors.

relu() - Static method in interface smile.deep.activation.ActivationFunction

Returns the rectifier activation function max(0, x).

ReLU - Class in smile.deep.activation

The rectifier activation function max(0, x).

ReLU() - Constructor for class smile.deep.activation.ReLU

Constructor.

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

Removes an edge.

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, which is response minus fitted values.

residuals() - Method in class smile.timeseries.AR

Returns the residuals, that is response minus fitted values.

residuals() - Method in class smile.timeseries.ARMA

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.

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

The discounting factor for the history/coming gradient in RMSProp.

RidgeRegression - Class in smile.regression

Ridge Regression.

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

rmse - Variable in class smile.validation.RegressionMetrics

The root mean squared error on validation data.

RMSE - Class in smile.validation.metric

Root mean squared error.

RMSE() - Constructor for class smile.validation.metric.RMSE

RMSProp - Class in smile.deep.optimizer

RMSProp optimizer with adaptive learning rate.

RMSProp() - Constructor for class smile.deep.optimizer.RMSProp

Constructor.

RMSProp(TimeFunction) - Constructor for class smile.deep.optimizer.RMSProp

Constructor.

RMSProp(TimeFunction, double, double) - Constructor for class smile.deep.optimizer.RMSProp

Constructor.

RobustStandardizer - Class in smile.feature.transform

Robustly standardizes numeric feature by subtracting the median and dividing by the IQR.

RobustStandardizer() - Constructor for class smile.feature.transform.RobustStandardizer

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.

rounds - Variable in class smile.validation.ClassificationValidations

The multiple round validations.

rounds - Variable in class smile.validation.RegressionValidations

The multiple round validations.

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

Returns R² statistic.

rss - Variable in class smile.validation.RegressionMetrics

The residual sum of squares on validation data.

RSS - Class in smile.validation.metric

Residual sum of squares.

RSS() - Constructor for class smile.validation.metric.RSS

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

Returns the residual sum of squares.

RSS() - Method in class smile.timeseries.AR

Returns the residual sum of squares.

RSS() - Method in class smile.timeseries.ARMA

Returns the residual sum of squares.

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

s2n - Variable in class smile.feature.selection.SignalNoiseRatio

Signal noise ratio.

SammonMapping - Class in smile.manifold

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.manifold.SammonMapping

Constructor.

sample(int) - Method in class smile.regression.GaussianProcessRegression.JointPrediction

Draw samples from Gaussian process.

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

The samples for training this node.

samples - Variable in class smile.validation.Bag

The random samples.

scale() - Method in class smile.classification.ClassLabels

Returns the nominal scale of the class labels.

scale(double) - Method in class smile.classification.PlattScaling

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

Scaler - Class in smile.feature.transform

Scales the numeric variables into the range [0, 1].

Scaler() - Constructor for class smile.feature.transform.Scaler

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

The schema of predictors.

schema - Variable in class smile.feature.extraction.Projection

The schema of output space.

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

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

Returns the predictor schema.

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

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

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

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

Returns the schema of predictors.

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).

score(double[]) - Method in class smile.anomaly.IsolationForest

Returns the anomaly score.

score(double[]) - Method in class smile.classification.LogisticRegression.Binomial

score(double[][]) - Method in class smile.anomaly.IsolationForest

Returns the anomaly scores.

score(double[], double[]) - Method in class smile.validation.metric.MAD

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

score(double[], double[]) - Method in class smile.validation.metric.R2

score(double[], double[]) - Method in interface smile.validation.metric.RegressionMetric

Returns a score to measure the quality of regression.

score(double[], double[]) - Method in class smile.validation.metric.RMSE

score(double[], double[]) - Method in class smile.validation.metric.RSS

score(int[]) - Method in class smile.classification.Maxent.Binomial

score(int[], double[]) - Method in class smile.validation.metric.AUC

score(int[], double[]) - Method in class smile.validation.metric.LogLoss

score(int[], double[]) - Method in interface smile.validation.metric.ProbabilisticClassificationMetric

Returns a score to measure the quality of classification.

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

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

score(int[], int[]) - Method in class smile.validation.metric.AdjustedRandIndex

score(int[], int[]) - Method in interface smile.validation.metric.ClassificationMetric

Returns a score to measure the quality of classification.

score(int[], int[]) - Method in interface smile.validation.metric.ClusteringMetric

Returns a score to measure the quality of clustering.

score(int[], int[]) - Method in class smile.validation.metric.Error

score(int[], int[]) - Method in class smile.validation.metric.Fallout

score(int[], int[]) - Method in class smile.validation.metric.FDR

score(int[], int[]) - Method in class smile.validation.metric.FScore

score(int[], int[]) - Method in class smile.validation.metric.MatthewsCorrelation

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

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

score(int[], int[]) - Method in class smile.validation.metric.Precision

score(int[], int[]) - Method in class smile.validation.metric.RandIndex

score(int[], int[]) - Method in class smile.validation.metric.Recall

score(int[], int[]) - Method in class smile.validation.metric.Sensitivity

score(int[], int[]) - Method in class smile.validation.metric.Specificity

score(SparseArray) - Method in class smile.classification.SparseLogisticRegression.Binomial

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

Returns the decision function value.

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

The raw prediction score.

scores - Variable in class smile.manifold.MDS

The component scores.

scoreTime - Variable in class smile.validation.ClassificationMetrics

The time in milliseconds of scoring the validation data.

scoreTime - Variable in class smile.validation.RegressionMetrics

The time in milliseconds of scoring the validation data.

sd - Variable in class smile.regression.GaussianProcessRegression.JointPrediction

The standard deviation of predictive distribution at query points.

sd - Variable in class smile.regression.GaussianProcessRegression

The standard deviation of responsible variable.

sd - Variable in class smile.validation.ClassificationValidations

The standard deviation of metrics.

sd - Variable in class smile.validation.RegressionValidations

The standard deviation of metrics.

seed(int, double[][]) - Static method in class smile.vq.NeuralGas

Selects random samples as initial neurons of Neural Gas.

seed(T[], T[], int[], ToDoubleBiFunction<T, T>) - Static method in class smile.clustering.PartitionClustering

Initialize cluster membership of input objects with K-Means++ algorithm.

sensitivity - Variable in class smile.validation.ClassificationMetrics

The sensitivity on validation data.

Sensitivity - Class in smile.validation.metric

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.metric.Sensitivity

SequenceLabeler<T> - Interface in smile.sequence

A sequence labeler assigns a class label to each position of the sequence.

setClipNorm(double) - Method in class smile.base.mlp.MultilayerPerceptron

Sets the gradient clipping norm.

setClipValue(double) - Method in class smile.base.mlp.MultilayerPerceptron

Sets the gradient clipping value.

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

Sets the age of edge.

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.classification.SparseLogisticRegression

Sets the learning rate of stochastic gradient descent.

setLearningRate(TimeFunction) - Method in class smile.base.mlp.MultilayerPerceptron

Sets the learning rate.

setMomentum(TimeFunction) - Method in class smile.base.mlp.MultilayerPerceptron

Sets the momentum factor.

setParameters(Properties) - Method in class smile.base.mlp.MultilayerPerceptron

Sets MLP hyper-parameters such as learning rate, weight decay, momentum, RMSProp, etc.

setRMSProp(double, double) - Method in class smile.base.mlp.MultilayerPerceptron

Sets RMSProp parameters.

setWeightDecay(double) - Method in class smile.base.mlp.MultilayerPerceptron

Sets the weight decay factor.

SGD - Class in smile.deep.optimizer

Stochastic gradient descent (with momentum) optimizer.

SGD() - Constructor for class smile.deep.optimizer.SGD

Constructor.

SGD(TimeFunction) - Constructor for class smile.deep.optimizer.SGD

Constructor.

SGD(TimeFunction, TimeFunction) - Constructor for class smile.deep.optimizer.SGD

Constructor.

shap(Stream<T>) - Method in interface smile.feature.importance.SHAP

Returns the average of absolute SHAP values over a data set.

shap(DataFrame) - Method in class smile.base.cart.CART

Returns the average of absolute SHAP values over a data frame.

shap(DataFrame) - Method in class smile.classification.GradientTreeBoost

Returns the average of absolute SHAP values over a data frame.

shap(DataFrame) - Method in interface smile.feature.importance.TreeSHAP

Returns the average of absolute SHAP values over a data frame.

shap(Tuple) - Method in class smile.base.cart.CART

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

shap(Tuple) - Method in interface smile.feature.importance.TreeSHAP

shap(T) - Method in interface smile.feature.importance.SHAP

Returns the SHAP values.

SHAP<T> - Interface in smile.feature.importance

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model.

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() - Static method in interface smile.deep.activation.ActivationFunction

Returns the 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.

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

Returns a hidden layer with sigmoid activation function.

Sigmoid - Class in smile.deep.activation

Logistic sigmoid function: sigmoid(v)=1/(1+exp(-v)).

Sigmoid() - Constructor for class smile.deep.activation.Sigmoid

Constructor.

SIGMOID - Enum constant in enum class smile.base.mlp.OutputFunction

Logistic sigmoid function: sigmoid(v)=1/(1+exp(-v)).

SignalNoiseRatio - Class in smile.feature.selection

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(String, double) - Constructor for class smile.feature.selection.SignalNoiseRatio

Constructor.

SimpleImputer - Class in smile.feature.imputation

Simple algorithm replaces missing values with the constant value along each column.

SimpleImputer(Map<String, Object>) - Constructor for class smile.feature.imputation.SimpleImputer

Constructor.

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 - Variable in class smile.base.cart.LeafNode

The number of samples in the node.

size - Variable in class smile.clustering.PartitionClustering

The number of observations in each cluster.

size - Variable in class smile.validation.ClassificationMetrics

The validation data size.

size - Variable in class smile.validation.RegressionMetrics

The validation data size.

size() - Method in class smile.anomaly.IsolationForest

Returns the number of trees in the model.

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() - 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() - 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.anomaly - package smile.anomaly

Anomaly detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.

smile.association - package smile.association

Frequent item set mining and association rule mining.

smile.base.cart - package smile.base.cart

Classification and regression tree base package.

smile.base.mlp - package smile.base.mlp

Multilayer perceptron neural network base package.

smile.base.rbf - package smile.base.rbf

RBF network base package.

smile.base.svm - package smile.base.svm

Support vector machine base package.

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.deep.activation - package smile.deep.activation

smile.deep.optimizer - package smile.deep.optimizer

smile.feature.extraction - package smile.feature.extraction

Feature extraction.

smile.feature.importance - package smile.feature.importance

Feature importance.

smile.feature.imputation - package smile.feature.imputation

Missing value imputation.

smile.feature.selection - package smile.feature.selection

Feature selection.

smile.feature.transform - package smile.feature.transform

smile.glm - package smile.glm

Generalized linear models.

smile.glm.model - package smile.glm.model

The error distribution models.

smile.hpo - package smile.hpo

Hyperparameter optimization.

smile.manifold - package smile.manifold

Manifold learning finds a low-dimensional basis for describing high-dimensional data.

smile.regression - package smile.regression

Regression analysis.

smile.sequence - package smile.sequence

Learning algorithms for sequence data.

smile.timeseries - package smile.timeseries

Time series analysis.

smile.validation - package smile.validation

Model validation and selection.

smile.validation.metric - package smile.validation.metric

Model validation metrics.

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.

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

soft() - Method in interface smile.classification.Classifier

Returns true if this is a soft classifier that can estimate the posteriori probabilities of classification.

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

soft() - Method in class smile.classification.DiscreteNaiveBayes

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

soft() - Method in class smile.classification.KNN

soft() - Method in class smile.classification.LDA

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

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

soft() - Method in class smile.classification.MLP

soft() - Method in class smile.classification.NaiveBayes

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

soft() - Method in class smile.classification.OneVersusRest

soft() - Method in class smile.classification.QDA

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

soft() - Method in class smile.classification.SparseLogisticRegression

softmax() - Static method in interface smile.deep.activation.ActivationFunction

Returns the softmax activation function for multi-class output layer.

Softmax - Class in smile.deep.activation

Softmax for multi-class cross entropy objection function.

Softmax() - Constructor for class smile.deep.activation.Softmax

Constructor.

SOFTMAX - Enum constant in enum class smile.base.mlp.OutputFunction

Softmax for multi-class cross entropy objection function.

SOM - Class in smile.vq

Self-Organizing Map.

SOM(double[][][], TimeFunction, Neighborhood) - Constructor for class smile.vq.SOM

Constructor.

sparse(int, int, String...) - Static method in class smile.feature.extraction.RandomProjection

Generates a sparse random projection.

sparse(int, KernelMachine<SparseArray>) - Static method in class smile.base.svm.LinearKernelMachine

Creates a linear kernel machine.

SparseEncoder - Class in smile.feature.extraction

Encodes numeric and categorical features into sparse array with on-hot encoding of categorical variables.

SparseEncoder(StructType, String...) - Constructor for class smile.feature.extraction.SparseEncoder

Constructor.

SparseLogisticRegression - Class in smile.classification

Logistic regression on sparse data.

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

Constructor.

SparseLogisticRegression.Binomial - Class in smile.classification

Binomial logistic regression.

SparseLogisticRegression.Multinomial - Class in smile.classification

Multinomial logistic regression.

specificity - Variable in class smile.validation.ClassificationMetrics

The specificity on validation data.

Specificity - Class in smile.validation.metric

Specificity (SPC) or True Negative Rate is a statistical measures of the performance of a binary classification test.

Specificity() - Constructor for class smile.validation.metric.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 Class in smile.base.cart

The criterion to choose variable to split instances.

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

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

SQRT - Enum constant in enum class smile.validation.metric.AdjustedMutualInformation.Method

I(y1, y2) / sqrt(H(y1) * H(y2))

SQRT - Enum constant in enum class smile.validation.metric.NormalizedMutualInformation.Method

I(y1, y2) / sqrt(H(y1) * H(y2))

SQRT - Static variable in class smile.validation.metric.AdjustedMutualInformation

Default instance with sqrt normalization.

SQRT - Static variable in class smile.validation.metric.NormalizedMutualInformation

Default instance with sqrt normalization.

ssr - Variable in class smile.feature.selection.SumSquaresRatio

Sum squares ratio.

Standardizer - Class in smile.feature.transform

Standardizes numeric feature to 0 mean and unit variance.

Standardizer() - Constructor for class smile.feature.transform.Standardizer

stratify(int[], int) - Static method in interface smile.validation.CrossValidation

Cross validation with stratified folds.

stratify(int, int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.CrossValidation

Repeated stratified cross validation of classification.

stratify(int, int, T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.CrossValidation

Repeated stratified cross validation of classification.

stratify(int, Formula, DataFrame, BiFunction<Formula, DataFrame, M>) - Static method in interface smile.validation.CrossValidation

Stratified cross validation of classification.

stratify(int, T[], int[], BiFunction<T[], int[], M>) - Static method in interface smile.validation.CrossValidation

Stratified cross validation of classification.

stress - Variable in class smile.manifold.IsotonicMDS

The final stress achieved.

stress - Variable in class smile.manifold.SammonMapping

The final stress achieved.

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

Calculates the normalized mutual information of 2 * I(y1, y2) / (H(y1) + H(y2)).

SUM - Enum constant in enum class smile.validation.metric.AdjustedMutualInformation.Method

2 * I(y1, y2) / (H(y1) + H(y2))

SUM - Enum constant in enum class smile.validation.metric.NormalizedMutualInformation.Method

2 * I(y1, y2) / (H(y1) + H(y2))

SUM - Static variable in class smile.validation.metric.AdjustedMutualInformation

Default instance with sum normalization.

SUM - Static variable in class smile.validation.metric.NormalizedMutualInformation

Default instance with sum normalization.

SumSquaresRatio - Class in smile.feature.selection

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(String, double) - Constructor for class smile.feature.selection.SumSquaresRatio

Constructor.

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

Constructor.

SVDImputer - Interface in smile.feature.imputation

Missing value imputation with singular value decomposition.

SVM<T> - Class in smile.anomaly

One-class support vector machines for novelty detection.

SVM<T> - Class in smile.classification

Support vector machines for classification.

SVM - Class in smile.regression

Epsilon support vector regression.

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

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

Constructor.

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.

T

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

The training iterations.

t - Variable in class smile.feature.extraction.GHA

The training iterations.

T - Variable in class smile.vq.BIRCH

THe maximum radius of a sub-cluster.

table - Variable in class smile.validation.metric.ContingencyTable

The contingency table.

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

Hyperbolic tangent activation function.

tanh() - Static method in interface smile.deep.activation.ActivationFunction

Returns the hyperbolic tangent activation function.

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

Returns a hidden layer with hyperbolic tangent activation function.

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

Returns a hidden layer with hyperbolic tangent activation function.

Tanh - Class in smile.deep.activation

Hyperbolic tangent activation function.

Tanh() - Constructor for class smile.deep.activation.Tanh

Constructor.

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

The buffer to store desired target value of training instance.

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.

TimeSeries - Interface in smile.timeseries

Time series utility functions.

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

toString() - Method in class smile.base.mlp.HiddenLayer

toString() - Method in class smile.base.mlp.HiddenLayerBuilder

toString() - Method in class smile.base.mlp.InputLayer

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

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

toString() - Method in class smile.base.mlp.OutputLayerBuilder

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.deep.optimizer.Adam

toString() - Method in class smile.deep.optimizer.RMSProp

toString() - Method in class smile.deep.optimizer.SGD

toString() - Method in class smile.feature.imputation.SimpleImputer

toString() - Method in class smile.feature.selection.InformationValue

toString() - Method in class smile.feature.selection.SignalNoiseRatio

toString() - Method in class smile.feature.selection.SumSquaresRatio

toString() - Method in class smile.feature.transform.Normalizer

toString() - Method in class smile.glm.GLM

toString() - Method in class smile.regression.GaussianProcessRegression.JointPrediction

toString() - Method in class smile.regression.GaussianProcessRegression

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

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.timeseries.AR

toString() - Method in class smile.timeseries.ARMA

toString() - Method in class smile.timeseries.BoxTest

toString() - Method in class smile.validation.ClassificationMetrics

toString() - Method in class smile.validation.ClassificationValidation

toString() - Method in class smile.validation.ClassificationValidations

toString() - Method in class smile.validation.metric.Accuracy

toString() - Method in class smile.validation.metric.AdjustedMutualInformation

toString() - Method in class smile.validation.metric.AdjustedRandIndex

toString() - Method in class smile.validation.metric.AUC

toString() - Method in class smile.validation.metric.ConfusionMatrix

toString() - Method in class smile.validation.metric.Error

toString() - Method in class smile.validation.metric.Fallout

toString() - Method in class smile.validation.metric.FDR

toString() - Method in class smile.validation.metric.FScore

toString() - Method in class smile.validation.metric.LogLoss

toString() - Method in class smile.validation.metric.MAD

toString() - Method in class smile.validation.metric.MatthewsCorrelation

toString() - Method in class smile.validation.metric.MSE

toString() - Method in class smile.validation.metric.MutualInformation

toString() - Method in class smile.validation.metric.NormalizedMutualInformation

toString() - Method in class smile.validation.metric.Precision

toString() - Method in class smile.validation.metric.R2

toString() - Method in class smile.validation.metric.RandIndex

toString() - Method in class smile.validation.metric.Recall

toString() - Method in class smile.validation.metric.RMSE

toString() - Method in class smile.validation.metric.RSS

toString() - Method in class smile.validation.metric.Sensitivity

toString() - Method in class smile.validation.metric.Specificity

toString() - Method in class smile.validation.RegressionMetrics

toString() - Method in class smile.validation.RegressionValidation

toString() - Method in class smile.validation.RegressionValidations

toString(StructType, boolean) - Method in class smile.base.cart.InternalNode

Returns the string representation of branch.

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.DecisionNode

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, StructField, InternalNode, int, BigInteger, List<String>) - Method in class smile.base.cart.RegressionNode

toString(InformationValue[]) - Static method in class smile.feature.selection.InformationValue

Returns a string representation of the array of information values.

toTransform(InformationValue[]) - Static method in class smile.feature.selection.InformationValue

Returns the data transformation that covert feature value to its weight of evidence.

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

transform(double[]) - Method in class smile.base.mlp.InputLayer

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

The activation or output function.

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

tree - Variable in class smile.classification.RandomForest.Model

The decision tree.

tree - Variable in class smile.regression.RandomForest.Model

The decision tree.

tree() - 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.

trees() - Method in class smile.anomaly.IsolationForest

Returns the isolation trees in the model.

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

trees() - Method in interface smile.feature.importance.TreeSHAP

Returns the decision trees.

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

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

TreeSHAP - Interface in smile.feature.importance

SHAP of ensemble tree methods.

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.

truth - Variable in class smile.validation.ClassificationValidation

The true class labels of validation data.

truth - Variable in class smile.validation.RegressionValidation

The true response variable of validation data.

TSNE - Class in smile.manifold

The t-distributed stochastic neighbor embedding.

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).

ttest() - Method in class smile.timeseries.AR

Returns the t-test of the coefficients (including intercept).

ttest() - Method in class smile.timeseries.ARMA

Returns the t-test of the coefficients (including intercept).

TWCNB - Enum constant in enum class smile.classification.DiscreteNaiveBayes.Model

Transformed Weight-normalized Complement Naive Bayes.

type - Variable in class smile.timeseries.BoxTest

The type of test.

U

UMAP - Class in smile.manifold

Uniform Manifold Approximation and Projection.

UMAP(int[], double[][], AdjacencyList) - Constructor for class smile.manifold.UMAP

Constructor.

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

Calculates the unified distance matrix (u-matrix) for visualization.

update(double[]) - Method in class smile.feature.extraction.GHA

Update the model with a new sample.

update(double[]) - Method in class smile.vq.BIRCH

update(double[]) - Method in class smile.vq.GrowingNeuralGas

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.

update(double[][]) - Method in class smile.feature.extraction.GHA

Update the model with a set of samples.

update(double[][], double[]) - Method in class smile.regression.MLP

Updates the model with a mini-batch.

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

Updates the model with a mini-batch.

update(double[], double) - Method in class smile.regression.LinearModel

Growing window recursive least squares with lambda = 1.

update(double[], double) - Method in class smile.regression.MLP

Updates the model with a single sample.

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

Updates the reference vector by w += eps * (x - w).

update(double[], double, double) - Method in class smile.regression.LinearModel

Recursive least squares.

update(double[], int) - Method in class smile.classification.LogisticRegression.Binomial

update(double[], int) - Method in class smile.classification.LogisticRegression.Multinomial

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

Updates the model with a single sample.

update(int) - Method in class smile.base.mlp.MultilayerPerceptron

Updates the weights for mini-batch training.

update(int) - Method in class smile.manifold.TSNE

Performs additional iterations.

update(int[][], int) - Method in class smile.sequence.HMM

Updates the HMM by the Baum-Welch algorithm.

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(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(int[], int) - Method in class smile.classification.Maxent.Binomial

update(int[], int) - Method in class smile.classification.Maxent.Multinomial

update(int, double, double, double, double, double) - Method in class smile.base.mlp.InputLayer

update(int, double, double, double, double, double) - Method in class smile.base.mlp.Layer

Adjust network weights by back-propagation algorithm.

update(Layer, int, int) - Method in class smile.deep.optimizer.Adam

update(Layer, int, int) - Method in interface smile.deep.optimizer.Optimizer

Updates a layer.

update(Layer, int, int) - Method in class smile.deep.optimizer.RMSProp

update(Layer, int, int) - Method in class smile.deep.optimizer.SGD

update(DataFrame) - Method in class smile.feature.extraction.GHA

Update the model with a new data frame.

update(DataFrame) - Method in class smile.regression.LinearModel

Online update the regression model with a new data frame.

update(Dataset<Instance<T>>) - Method in interface smile.classification.Classifier

Updates the model with a mini-batch of new samples.

update(Dataset<Instance<T>>) - Method in interface smile.regression.Regression

Updates the model with a mini-batch of new samples.

update(Tuple) - Method in class smile.feature.extraction.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(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(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(SparseArray, int) - Method in class smile.classification.SparseLogisticRegression.Binomial

update(SparseArray, int) - Method in class smile.classification.SparseLogisticRegression.Multinomial

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

Updates the HMM by the Baum-Welch algorithm.

update(T[][], int, ToIntFunction<T>) - Method in class smile.sequence.HMM

Updates the HMM by the Baum-Welch algorithm.

update(T[], double[]) - Method in interface smile.regression.Regression

Updates the model with a mini-batch of new samples.

update(T[], int[]) - Method in interface smile.classification.Classifier

Updates the model with a mini-batch of new samples.

update(T, double) - Method in interface smile.regression.Regression

Online update the classifier with a new training instance.

update(T, int) - Method in interface smile.classification.Classifier

Online update the classifier with a new training instance.

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

valueOf(String) - Static method in enum class smile.base.cart.Loss.Type

Returns the enum constant of this class with the specified name.

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

Parses the loss.

valueOf(String) - Static method in enum class smile.base.cart.SplitRule

Returns the enum constant of this class with the specified name.

valueOf(String) - Static method in enum class smile.base.mlp.Cost

Returns the enum constant of this class with the specified name.

valueOf(String) - Static method in enum class smile.base.mlp.OutputFunction

Returns the enum constant of this class with the specified name.

valueOf(String) - Static method in enum class smile.classification.DiscreteNaiveBayes.Model

Returns the enum constant of this class with the specified name.

valueOf(String) - Static method in enum class smile.feature.transform.Normalizer.Norm

Returns the enum constant of this class with the specified name.

valueOf(String) - Static method in enum class smile.timeseries.AR.Method

Returns the enum constant of this class with the specified name.

valueOf(String) - Static method in enum class smile.timeseries.BoxTest.Type

Returns the enum constant of this class with the specified name.

valueOf(String) - Static method in enum class smile.validation.metric.AdjustedMutualInformation.Method

Returns the enum constant of this class with the specified name.

valueOf(String) - Static method in enum class smile.validation.metric.NormalizedMutualInformation.Method

Returns the enum constant of this class with the specified name.

values() - Static method in enum class smile.base.cart.Loss.Type

Returns an array containing the constants of this enum class, in the order they are declared.

values() - Static method in enum class smile.base.cart.SplitRule

Returns an array containing the constants of this enum class, in the order they are declared.

values() - Static method in enum class smile.base.mlp.Cost

Returns an array containing the constants of this enum class, in the order they are declared.

values() - Static method in enum class smile.base.mlp.OutputFunction

Returns an array containing the constants of this enum class, in the order they are declared.

values() - Static method in enum class smile.classification.DiscreteNaiveBayes.Model

Returns an array containing the constants of this enum class, in the order they are declared.

values() - Static method in enum class smile.feature.transform.Normalizer.Norm

Returns an array containing the constants of this enum class, in the order they are declared.

values() - Static method in enum class smile.timeseries.AR.Method

Returns an array containing the constants of this enum class, in the order they are declared.

values() - Static method in enum class smile.timeseries.BoxTest.Type

Returns an array containing the constants of this enum class, in the order they are declared.

values() - Static method in enum class smile.validation.metric.AdjustedMutualInformation.Method

Returns an array containing the constants of this enum class, in the order they are declared.

values() - Static method in enum class smile.validation.metric.NormalizedMutualInformation.Method

Returns an array containing the constants of this enum class, in the order they are declared.

variance() - Method in class smile.feature.extraction.PCA

Returns the principal component variances, ordered from largest to smallest, which are the eigenvalues of the covariance or correlation matrix of learning data.

variance() - Method in class smile.feature.extraction.ProbabilisticPCA

Returns the variance of noise.

variance() - Method in class smile.timeseries.AR

Returns the residual variance.

variance() - Method in class smile.timeseries.ARMA

Returns the residual variance.

variance(double) - Method in interface smile.glm.model.Model

The variance function.

varianceProportion() - Method in class smile.feature.extraction.PCA

Returns the proportion of variance contained in each principal component, ordered from largest to smallest.

variances() - Method in class smile.manifold.KPCA

Returns the eigenvalues of kernel principal components, ordered from largest to smallest.

VectorQuantizer - Interface in smile.vq

Vector quantizer with competitive learning.

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

Returns the support vectors of kernel machines.

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.regression.GaussianProcessRegression

The linear weights.

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.

WCNB - Enum constant in enum class smile.classification.DiscreteNaiveBayes.Model

Weight-normalized Complement Naive Bayes.

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

The affine transformation matrix.

weight - Variable in class smile.classification.RandomForest.Model

The weight of tree, which can be used when aggregating tree votes.

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

The weight gradient.

weightGradientMoment1 - Variable in class smile.base.mlp.Layer

The first moment of weight gradient.

weightGradientMoment2 - Variable in class smile.base.mlp.Layer

The second moment of weight gradient.

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

Returns the weights of instances.

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

The weight update.

width - Variable in class smile.manifold.LaplacianEigenmap

The width of heat kernel.

WinsorScaler - Class in smile.feature.transform

Scales all numeric variables into the range [0, 1].

WinsorScaler() - Constructor for class smile.feature.transform.WinsorScaler

woe - Variable in class smile.feature.selection.InformationValue

Weight of evidence.

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.

x - Variable in class smile.regression.GaussianProcessRegression.JointPrediction

The query points where the GP is evaluated.

x() - Method in class smile.timeseries.AR

Returns the time series.

x() - Method in class smile.timeseries.ARMA

Returns the time series.

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.

YuleWalker - Enum constant in enum class smile.timeseries.AR.Method

Yule-Walker method.

Z

ztest - Variable in class smile.glm.GLM

The coefficients, their standard errors, z-scores, and p-values.

ztest() - Method in class smile.glm.GLM

Returns the z-test of the coefficients (including intercept).

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