Index

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

A

AccuracyEvaluator<T> - Class in fr.lip6.jkernelmachines.evaluation: Simple evaluation class for computing the accuracy on a testing set.
AccuracyEvaluator() - Constructor for class fr.lip6.jkernelmachines.evaluation.AccuracyEvaluator
ActiveLearner<T> - Class in fr.lip6.jkernelmachines.active: Base abstract class for active learning strategies.
ActiveLearner() - Constructor for class fr.lip6.jkernelmachines.active.ActiveLearner
activeTrain(List<TrainingSample<T>>, int, int, int) - Method in class fr.lip6.jkernelmachines.kernel.extra.NystromKernel
add(double[], double, double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.VectorOperations: Performs a linear combination of 2 vectors and store the result in a newly allocated array C: C = A + lambda * B
addEvaluator(String, Evaluator<T>) - Method in class fr.lip6.jkernelmachines.evaluation.MultipleEvaluator: Adds an evaluator to the list of things being computed
addEvaluator(String, Evaluator<T>) - Method in interface fr.lip6.jkernelmachines.evaluation.MultipleEvaluatorCrossValidation: Register a new evaluator to this corssvalidation
addEvaluator(String, Evaluator<T>) - Method in class fr.lip6.jkernelmachines.evaluation.NFoldCrossValidation
addi(double[], double[], double, double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.VectorOperations: Performs a linear combination of 2 vectors and store the result in an already allocated array C: C = A + lambda * B
addKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.classifier.GradMKL
addKernel(Kernel<T>) - Method in interface fr.lip6.jkernelmachines.classifier.MKL: Adds a kernel to the MKL problem
addKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL: adds a kernel to the MKL problem
addKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.classifier.TSMKL: Adds a kernel to the combination
addKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.density.SimpleMKLDensity: adds a kernel to the MKL problem
addKernel(GaussianKernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.GaussianProductKernel: adds a kernel to the sum with weight 1.0
addKernel(GaussianKernel<T>, double) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.GaussianProductKernel: adds a kernel to the product with weight d
addKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedProductKernel: adds a kernel to the sum with weight 1.0
addKernel(Kernel<T>, double) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedProductKernel: adds a kernel to the sum with weight d
addKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedSumKernel: adds a kernel to the sum with weight 1.0
addKernel(Kernel<T>, double) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedSumKernel: adds a kernel to the sum with weight d
addKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedProductKernel: adds a kernel to to product
addKernel(Kernel<T>, double) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedProductKernel
addKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedSumKernel: adds a kernel to the sum with weight 1.0
addKernel(Kernel<T>, double) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedSumKernel: adds a kernel to the sum with weight d
addXXTrans(double[][], double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixVectorOperations: Adds the tensor product of a vector to a matrix (usefull for covariance matrices)
ApEvaluator<T> - Class in fr.lip6.jkernelmachines.evaluation: Simple evaluation class for computing the mean average precision, VOC style.
ApEvaluator() - Constructor for class fr.lip6.jkernelmachines.evaluation.ApEvaluator: default constructor
ApEvaluator(Classifier<T>, List<TrainingSample<T>>, List<TrainingSample<T>>) - Constructor for class fr.lip6.jkernelmachines.evaluation.ApEvaluator: Constructor using a classifier, train and test lists.
ArffImporter - Class in fr.lip6.jkernelmachines.io: IO helper to import Arff files.
ArffImporter() - Constructor for class fr.lip6.jkernelmachines.io.ArffImporter
ArraysUtils - Class in fr.lip6.jkernelmachines.util
ArraysUtils() - Constructor for class fr.lip6.jkernelmachines.util.ArraysUtils

B

BalancedCrossValidation - Interface in fr.lip6.jkernelmachines.evaluation: Interface for crossvalidation classes that can balance the number of sample per class while computing the splits.
BestAL<T> - Class in fr.lip6.jkernelmachines.active: Naïve active learning strategy that selects the most positive sample using current classifier
BestAL(OnlineClassifier<T>, List<TrainingSample<T>>) - Constructor for class fr.lip6.jkernelmachines.active.BestAL

C

C - Variable in class fr.lip6.jkernelmachines.classifier.SimpleMKL
CACHED_KERNEL - Variable in class fr.lip6.jkernelmachines.density.SDCADensity
centerList(List<TrainingSample<double[]>>) - Static method in class fr.lip6.jkernelmachines.util.DataPreProcessing: Process a list of training samples of double[] to have 0 mean
checkDualGap - Variable in class fr.lip6.jkernelmachines.classifier.SimpleMKL
checkKKT - Variable in class fr.lip6.jkernelmachines.classifier.SimpleMKL
classifier - Variable in class fr.lip6.jkernelmachines.active.ActiveLearner
Classifier<T> - Interface in fr.lip6.jkernelmachines.classifier: Classifier interface that provides training and evaluation methods.
clone() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL
compareTo(Evaluation<T>) - Method in class fr.lip6.jkernelmachines.evaluation.Evaluation
copy() - Method in interface fr.lip6.jkernelmachines.classifier.Classifier: Creates and returns a copy of this object.
copy() - Method in class fr.lip6.jkernelmachines.classifier.DoubleLLSVM
copy() - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Creates and returns a copy of this object.
copy() - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Creates and returns a copy of this object.
copy() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG
copy() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Creates and returns a copy of this object.
copy() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Creates and returns a copy of this object.
copy() - Method in class fr.lip6.jkernelmachines.classifier.GradMKL: Creates and returns a copy of this object.
copy() - Method in class fr.lip6.jkernelmachines.classifier.LaSVM: Creates and returns a copy of this object.
copy() - Method in class fr.lip6.jkernelmachines.classifier.LaSVMI: Creates and returns a copy of this object.
copy() - Method in class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA
copy() - Method in class fr.lip6.jkernelmachines.classifier.multiclass.OneAgainstAll
copy() - Method in class fr.lip6.jkernelmachines.classifier.NystromLSSVM
copy() - Method in class fr.lip6.jkernelmachines.classifier.ParzenClassifier: Creates and returns a copy of this object.
copy() - Method in class fr.lip6.jkernelmachines.classifier.SDCA
copy() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL: Creates and returns a copy of this object.
copy() - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM: Creates and returns a copy of this object.
copy() - Method in class fr.lip6.jkernelmachines.classifier.TSMKL
CrossValidation - Interface in fr.lip6.jkernelmachines.evaluation: Standard interface for all cross-validation methods
CrossValidationExample - Class in fr.lip6.jkernelmachines.example: This is a more complex example of how to code with JKernelMachines than can be used as a stand-alone program.
CrossValidationExample() - Constructor for class fr.lip6.jkernelmachines.example.CrossValidationExample
CsvImporter - Class in fr.lip6.jkernelmachines.io: Simple class to import data in csv format, with one sample per line:
attr1, attr2, ...
CsvImporter() - Constructor for class fr.lip6.jkernelmachines.io.CsvImporter
CustomMatrixKernel - Class in fr.lip6.jkernelmachines.kernel.extra: Kernel with a provided custom matrix.
CustomMatrixKernel(double[][]) - Constructor for class fr.lip6.jkernelmachines.kernel.extra.CustomMatrixKernel: Constructor using the supplied Gram matrix.
CustomTrainTestMatrixKernel - Class in fr.lip6.jkernelmachines.kernel.extra: Kernel with provided custom train and test matrices.
CustomTrainTestMatrixKernel(double[][], double[][]) - Constructor for class fr.lip6.jkernelmachines.kernel.extra.CustomTrainTestMatrixKernel: Constructor using the supplied Gram matrices.

D

d2p2(double[], double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.VectorOperations: Computes the square euclidean distance between 2 double arrays
DataPreProcessing - Class in fr.lip6.jkernelmachines.util: Util class for doing some preprocessing on data
DataPreProcessing() - Constructor for class fr.lip6.jkernelmachines.util.DataPreProcessing
DEBUG_LEVEL - Static variable in class fr.lip6.jkernelmachines.util.DebugPrinter: level of debug information to print: 0 = none, 1 = some, 2 = more, ...
DebugPrinter - Class in fr.lip6.jkernelmachines.util: Very basic library wide debug utility class.
DebugPrinter() - Constructor for class fr.lip6.jkernelmachines.util.DebugPrinter
DensityFunction<T> - Interface in fr.lip6.jkernelmachines.density: Density estimation based on training.
distanceMatrix(List<TrainingSample<double[]>>, int) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussL2
distanceMatrix(List<TrainingSample<double[]>>, int) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleTriangleL2
distanceMatrixUnthreaded(List<TrainingSample<double[]>>, int) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussL2
distanceToMean(double[]) - Method in class fr.lip6.jkernelmachines.density.DoubleKMeans: Return an array containing the squared distances to each clusters
distanceValueOf(S, S) - Method in class fr.lip6.jkernelmachines.kernel.GaussianICKernel
distanceValueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.GaussianKernel: Tells the inner distance between two samples used by this Gaussian kernel.
distanceValueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi1
distanceValueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi2
distanceValueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussL2
distanceValueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleTriangleL2
distanceValueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussL2
distanceValueOf(double[], double[], int) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussL2
distanceValueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleTriangleL2
distanceValueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussChi2
distanceValueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussL2
distanceValueOf(int[], int[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussChi1
distanceValueOf(int[], int[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussChi2
distanceValueOf(int[], int[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussL2
doBlock(int, int, double[]) - Method in class fr.lip6.jkernelmachines.threading.ThreadedVectorOperator
doLines(double[][], int, int) - Method in class fr.lip6.jkernelmachines.threading.ThreadedMatrixOperator
dot(double[], double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.VectorOperations: Computes the dot product between to double arrays
DoubleGaussChi1 - Class in fr.lip6.jkernelmachines.kernel.typed: Gaussian Kernel on double[] that uses a Chi1 distance.
DoubleGaussChi1(double) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi1: Constructor with gamma argument
DoubleGaussChi1() - Constructor for class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi1: Default constructor, gamma = 0.1.
DoubleGaussChi2 - Class in fr.lip6.jkernelmachines.kernel.typed: Gaussian Kernel on double[] that uses a Chi2 distance.
DoubleGaussChi2(double) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi2
DoubleGaussChi2() - Constructor for class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi2
DoubleGaussianMixtureModel - Class in fr.lip6.jkernelmachines.density: Gaussian Mixture Model for estimating the density on arrays of double.
DoubleGaussianMixtureModel(int) - Constructor for class fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel
DoubleGaussL2 - Class in fr.lip6.jkernelmachines.kernel.typed: Gaussian Kernel on double[] that uses a L2 distance.
DoubleGaussL2(double) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussL2
DoubleGaussL2() - Constructor for class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussL2
DoubleHPolynomial - Class in fr.lip6.jkernelmachines.kernel.typed: Homogenous Polynomial kernel on double arrays.
DoubleHPolynomial() - Constructor for class fr.lip6.jkernelmachines.kernel.typed.DoubleHPolynomial: Default constructor, d = 2
DoubleHPolynomial(int) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.DoubleHPolynomial: Constructor specifying the degree of the polynomial kernel
DoubleKMeans - Class in fr.lip6.jkernelmachines.density: Very basic KMeans algorithm with a shifting codeword procedure to ensure no empty cluster and balanced distortion
DoubleKMeans(int) - Constructor for class fr.lip6.jkernelmachines.density.DoubleKMeans: Constructor with number of clusters
DoubleLinear - Class in fr.lip6.jkernelmachines.kernel.typed: Linear Kernel on double[].
DoubleLinear() - Constructor for class fr.lip6.jkernelmachines.kernel.typed.DoubleLinear
DoubleLLSVM - Class in fr.lip6.jkernelmachines.classifier: Locally Linear SVM, as described in:
Locally Linear Support Vector Machines
L'ubor Ladicky, Philip H.S.
DoubleLLSVM() - Constructor for class fr.lip6.jkernelmachines.classifier.DoubleLLSVM
DoublePCA - Class in fr.lip6.jkernelmachines.projection: Principal component analysis on double arrays.
DoublePCA() - Constructor for class fr.lip6.jkernelmachines.projection.DoublePCA
DoublePegasosSVM - Class in fr.lip6.jkernelmachines.classifier: Linear SVM classifier on arrays of double using the PEGASOS algorithm.
DoublePegasosSVM() - Constructor for class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM
DoublePolynomial - Class in fr.lip6.jkernelmachines.kernel.typed: Polynomial kernel on double arrays.
DoublePolynomial() - Constructor for class fr.lip6.jkernelmachines.kernel.typed.DoublePolynomial: Default constructor, d = 2
DoublePolynomial(int) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.DoublePolynomial: Constructor specifying the degree of the polynomial kernel
DoubleQNPKL - Class in fr.lip6.jkernelmachines.classifier: Implementation of the QNPKL solver.
Original java code
DoubleQNPKL() - Constructor for class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Default constructor
DoubleSAG - Class in fr.lip6.jkernelmachines.classifier: Linear SVM using the SAG algorithm:
"A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets",
Nicolas Le Roux, Mark Schmidt and Francis Bach.
DoubleSAG() - Constructor for class fr.lip6.jkernelmachines.classifier.DoubleSAG
DoubleSGD - Class in fr.lip6.jkernelmachines.classifier: Linear SVM classifier using stochastic gradient descent algorithm
DoubleSGD() - Constructor for class fr.lip6.jkernelmachines.classifier.DoubleSGD
DoubleSGDQN - Class in fr.lip6.jkernelmachines.classifier: Linear SVM classifier using SGDQN algorithm.
DoubleSGDQN() - Constructor for class fr.lip6.jkernelmachines.classifier.DoubleSGDQN
DoubleTriangleL2 - Class in fr.lip6.jkernelmachines.kernel.typed: Gaussian Kernel on double[] that uses a L2 distance.
DoubleTriangleL2(double) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.DoubleTriangleL2
DoubleTriangleL2() - Constructor for class fr.lip6.jkernelmachines.kernel.typed.DoubleTriangleL2

E

eig(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.ejml.EJMLMatrixOperations: Performs the eigen decomposition of a symmetric matrix: A = Q * L * Q' with Q orthonormal and L diagonal
eig(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Performs the eigen decomposition of a symmetric matrix: A = Q * L * Q' with Q orthonormal and L diagonal
eig_jacobi(double[][], boolean) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Performs the eigen decomposition of a symmetric matrix: A = Q * L * Q' with Q orthonormal and L diagonal
eig_qr(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Performs the eigen decomposition of a symmetric matrix: A = Q * L * Q' with Q orthonormal and L diagonal
EJMLMatrixOperations - Class in fr.lip6.jkernelmachines.util.algebra.ejml: Wrapper class that uses EJML for Matrix ops, with the API of jkms
EJMLMatrixOperations() - Constructor for class fr.lip6.jkernelmachines.util.algebra.ejml.EJMLMatrixOperations
eps - Variable in class fr.lip6.jkernelmachines.classifier.SimpleMKL
epsDG - Variable in class fr.lip6.jkernelmachines.classifier.SimpleMKL
epsGS - Variable in class fr.lip6.jkernelmachines.classifier.SimpleMKL
epsKTT - Variable in class fr.lip6.jkernelmachines.classifier.SimpleMKL
evaluate() - Method in class fr.lip6.jkernelmachines.evaluation.AccuracyEvaluator
evaluate() - Method in class fr.lip6.jkernelmachines.evaluation.ApEvaluator
evaluate() - Method in interface fr.lip6.jkernelmachines.evaluation.Evaluator: Run the training procedure and compute score.
evaluate() - Method in class fr.lip6.jkernelmachines.evaluation.FScoreEvaluator
evaluate() - Method in class fr.lip6.jkernelmachines.evaluation.MulticlassAccuracyEvaluator
evaluate() - Method in class fr.lip6.jkernelmachines.evaluation.MultipleEvaluator
evaluate() - Method in class fr.lip6.jkernelmachines.evaluation.PrecisionEvaluator
Evaluation<T> - Class in fr.lip6.jkernelmachines.evaluation: Simple class containing a sample and its evaluation by the classifier
Evaluation(T, double) - Constructor for class fr.lip6.jkernelmachines.evaluation.Evaluation
Evaluator<T> - Interface in fr.lip6.jkernelmachines.evaluation: Basic interface for all evaluation tools.

F

FloatGaussChi2 - Class in fr.lip6.jkernelmachines.kernel.typed: Gaussian Kernel on float[] that uses a Chi2 distance.
FloatGaussChi2() - Constructor for class fr.lip6.jkernelmachines.kernel.typed.FloatGaussChi2
FloatLinear - Class in fr.lip6.jkernelmachines.kernel.typed: Linear Kernel on float[].
FloatLinear() - Constructor for class fr.lip6.jkernelmachines.kernel.typed.FloatLinear
fr.lip6.jkernelmachines - package fr.lip6.jkernelmachines
fr.lip6.jkernelmachines.active - package fr.lip6.jkernelmachines.active: This package contains relevant classes to perform Active Learning
fr.lip6.jkernelmachines.classifier - package fr.lip6.jkernelmachines.classifier: Provides basic class for classifiers, and some well known implementations.
fr.lip6.jkernelmachines.classifier.multiclass - package fr.lip6.jkernelmachines.classifier.multiclass: Provides basic class for multiclass classifiers.
fr.lip6.jkernelmachines.classifier.transductive - package fr.lip6.jkernelmachines.classifier.transductive: Provides interface and implementations of transductive classifiers.
fr.lip6.jkernelmachines.density - package fr.lip6.jkernelmachines.density: Provides interface and implementations of density estimation algorithms.
fr.lip6.jkernelmachines.evaluation - package fr.lip6.jkernelmachines.evaluation: Provides interface and implementations for evaluation metrics (e.g. accuracy, average precision) and cross-validation.
fr.lip6.jkernelmachines.example - package fr.lip6.jkernelmachines.example: Provides examples and tutorials on using JKernelMachines.
fr.lip6.jkernelmachines.gui - package fr.lip6.jkernelmachines.gui: Provides simple GUI for JKernelMachines.
fr.lip6.jkernelmachines.io - package fr.lip6.jkernelmachines.io: Provides utilities for reading data files (currently libsvm and csv formats).
fr.lip6.jkernelmachines.kernel - package fr.lip6.jkernelmachines.kernel: Provides basic classes and interfaces for kernel function.
fr.lip6.jkernelmachines.kernel.adaptative - package fr.lip6.jkernelmachines.kernel.adaptative: Provides classes for adaptative kernels (e.g. weighted sum kernels, weighted product kernels, ...).
fr.lip6.jkernelmachines.kernel.extra - package fr.lip6.jkernelmachines.kernel.extra: Provides extra kernels with fancy functionalities.
fr.lip6.jkernelmachines.kernel.extra.bag - package fr.lip6.jkernelmachines.kernel.extra.bag: Provides kernels on bags classes.
fr.lip6.jkernelmachines.kernel.typed - package fr.lip6.jkernelmachines.kernel.typed: Provides typed common kernel classes (e.g. Linear kernel on double[], Gaussian kernels on int[], ...).
fr.lip6.jkernelmachines.kernel.typed.index - package fr.lip6.jkernelmachines.kernel.typed.index: Provides kernel classes working on indexed data (e.g. using only the component n of a vector).
fr.lip6.jkernelmachines.projection - package fr.lip6.jkernelmachines.projection: This package contains algorithms to project data into more effective spaces (like PCA).
fr.lip6.jkernelmachines.threading - package fr.lip6.jkernelmachines.threading: Provides threading utilities.
fr.lip6.jkernelmachines.type - package fr.lip6.jkernelmachines.type: Provides data type classes.
fr.lip6.jkernelmachines.util - package fr.lip6.jkernelmachines.util: Provides library wide utilities.
fr.lip6.jkernelmachines.util.algebra - package fr.lip6.jkernelmachines.util.algebra: This package provides basic linear algebra routines.
fr.lip6.jkernelmachines.util.algebra.ejml - package fr.lip6.jkernelmachines.util.algebra.ejml: Binding for EJML library (better linear algebra)
fr.lip6.jkernelmachines.util.generators - package fr.lip6.jkernelmachines.util.generators: This package provides artificial data generators.
FScoreEvaluator<T> - Class in fr.lip6.jkernelmachines.evaluation: Evaluator computing the F-score (by default F1).
FScoreEvaluator() - Constructor for class fr.lip6.jkernelmachines.evaluation.FScoreEvaluator
FvecImporter - Class in fr.lip6.jkernelmachines.io: Class providing routines to import data i fvec format (usefull in computer vision).
FvecImporter() - Constructor for class fr.lip6.jkernelmachines.io.FvecImporter

G

GaussianGenerator - Class in fr.lip6.jkernelmachines.util.generators: Class for generating toys subject to binary classification tasks, using Gaussian distribution.
GaussianGenerator() - Constructor for class fr.lip6.jkernelmachines.util.generators.GaussianGenerator: Default constructor: p = 1, sigma = 1.0 , dimension = 3.
GaussianGenerator(int) - Constructor for class fr.lip6.jkernelmachines.util.generators.GaussianGenerator: Constructor specifying dimension.
GaussianGenerator(int, float, double) - Constructor for class fr.lip6.jkernelmachines.util.generators.GaussianGenerator: Constructor with all parameters
GaussianICKernel<S,T> - Class in fr.lip6.jkernelmachines.kernel: Not so useful based caching method for Gaussian kernels
GaussianICKernel(IndexedCacheKernel<S, T>) - Constructor for class fr.lip6.jkernelmachines.kernel.GaussianICKernel: Constructor using underlying indexed kernel k, supposed to be Gaussian.
GaussianKernel<T> - Class in fr.lip6.jkernelmachines.kernel: Base class for Gaussian Kernels in the form of k(x1, x2) = exp(-gamme * dist(x1, x2))
GaussianKernel() - Constructor for class fr.lip6.jkernelmachines.kernel.GaussianKernel
GaussianProductKernel<T> - Class in fr.lip6.jkernelmachines.kernel.adaptative: Major kernel computed as a weighted product of minor Gaussian kernels : K = k_i^{w_i}
Computation of the kernel matrix is done by running a thread on sub matrices.
GaussianProductKernel() - Constructor for class fr.lip6.jkernelmachines.kernel.adaptative.GaussianProductKernel
GaussianProductKernel(Map<GaussianKernel<T>, Double>) - Constructor for class fr.lip6.jkernelmachines.kernel.adaptative.GaussianProductKernel: Sets the weights to h.
GeneralizedDoubleGaussChi2 - Class in fr.lip6.jkernelmachines.kernel.typed: Gaussian Kernel on double[] that uses a custom weighted Chi2 distance.
GeneralizedDoubleGaussChi2(double[]) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussChi2: Constructor with the array of weighted for the distance
GeneralizedDoubleGaussL2 - Class in fr.lip6.jkernelmachines.kernel.typed: Gaussian Kernel on double[] that uses a generalized L2 distance.
K(x, y) = exp( - sum_i{ gamma_i (x[i]-y[i])^2 })
GeneralizedDoubleGaussL2(double[]) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussL2: Constructor using an array of weighted for the generalized L2 distance
GeneralizedDoubleLinear - Class in fr.lip6.jkernelmachines.kernel.typed: Generalized linear kernel on double[].
GeneralizedDoubleLinear(double[][]) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleLinear
GeneralizedDoubleTriangleL2 - Class in fr.lip6.jkernelmachines.kernel.typed: Triangular Kernel on double[] that uses a Generalized L2 distance.
GeneralizedDoubleTriangleL2(double[]) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleTriangleL2: Constructor using an array of weighted for the generalized L2 distance
generateList(int) - Method in class fr.lip6.jkernelmachines.util.generators.GaussianGenerator: Generate a list of toys, with half of them being in the first class.
generateList(int, int) - Method in class fr.lip6.jkernelmachines.util.generators.GaussianGenerator: Generate a list of toys with specified number of positive samples, and negatives samples.
generateList(int) - Method in class fr.lip6.jkernelmachines.util.generators.MultiClassGaussianGenerator: Generates a list of Toys with specified number of samples per class
getActiveSample(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.active.ActiveLearner: Method returning the best training sample in the list with respect to the active strategy
getActiveSample(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.active.BestAL
getActiveSample(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.active.MulticlassSimpleAL
getActiveSample(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.active.RandomAL
getActiveSample(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.active.SimpleAL
getAlphas() - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL
getAlphas() - Method in class fr.lip6.jkernelmachines.classifier.GradMKL
getAlphas() - Method in interface fr.lip6.jkernelmachines.classifier.KernelSVM: Tells the weights of training samples
getAlphas() - Method in class fr.lip6.jkernelmachines.classifier.LaSVM: Tells the array of support vector coefficients
getAlphas() - Method in class fr.lip6.jkernelmachines.classifier.LaSVMI: Tells support vectors coefficients in order of the training list
getAlphas() - Method in class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA
getAlphas() - Method in class fr.lip6.jkernelmachines.classifier.SDCA
getAlphas() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL
getAlphas() - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM
getAlphas() - Method in class fr.lip6.jkernelmachines.classifier.TSMKL
getAlphas() - Method in class fr.lip6.jkernelmachines.density.SDCADensity
getAlphas() - Method in class fr.lip6.jkernelmachines.density.SMODensity: Tells the weights of the training samples
getAverageScore() - Method in interface fr.lip6.jkernelmachines.evaluation.CrossValidation: Tells the average score of the test
getAverageScore() - Method in class fr.lip6.jkernelmachines.evaluation.LeaveOneOutCrossValidation
getAverageScore(String) - Method in interface fr.lip6.jkernelmachines.evaluation.MultipleEvaluatorCrossValidation: Tells the average score of the test for the given evaluator
getAverageScore() - Method in class fr.lip6.jkernelmachines.evaluation.NFoldCrossValidation
getAverageScore(String) - Method in class fr.lip6.jkernelmachines.evaluation.NFoldCrossValidation
getAverageScore() - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation
getB() - Method in class fr.lip6.jkernelmachines.classifier.DoubleLLSVM: Return the model biases
getB() - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Tells the bias b of (w*x -b)
getB() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Get the bias of the classifier
getB() - Method in class fr.lip6.jkernelmachines.classifier.LaSVM: Tells the bias term
getB() - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM: Tells the bias b of (w*x - b)
getB() - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Tells the bias b of (w*x -b)
getBeta() - Method in class fr.lip6.jkernelmachines.evaluation.FScoreEvaluator: Get the beta value of the F-score
getC() - Method in class fr.lip6.jkernelmachines.classifier.DoubleLLSVM: Returns the hyperparameter C for the hinge loss tradeoff
getC() - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Tells the C hyperparameter, if set, else return 0
getC() - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Tells the hyperparameter C
getC() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Tells the C hyperparameter
getC() - Method in class fr.lip6.jkernelmachines.classifier.GradMKL: Tells the hyperparameter C
getC() - Method in interface fr.lip6.jkernelmachines.classifier.KernelSVM: Tells the hyperparameter C
getC() - Method in class fr.lip6.jkernelmachines.classifier.LaSVM: Tells the hyperparamter C
getC() - Method in class fr.lip6.jkernelmachines.classifier.LaSVMI: Tells the C hyperparameter
getC() - Method in class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA
getC() - Method in class fr.lip6.jkernelmachines.classifier.NystromLSSVM: Get the C svm hyperparameter
getC() - Method in class fr.lip6.jkernelmachines.classifier.SDCA
getC() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL: Tells the values of hyperparameter C
getC() - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM
getC() - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLight: Tells the hyperparameter C
getC() - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightSGDQN: Tells the hyperparameter C
getC() - Method in class fr.lip6.jkernelmachines.classifier.TSMKL
getC() - Method in class fr.lip6.jkernelmachines.density.SDCADensity
getC() - Method in class fr.lip6.jkernelmachines.density.SimpleMKLDensity
getC() - Method in class fr.lip6.jkernelmachines.density.SMODensity: Tells the hyperparameter C
getCacheMatrix() - Method in class fr.lip6.jkernelmachines.kernel.IndexedCacheKernel
getClassifier() - Method in class fr.lip6.jkernelmachines.active.ActiveLearner: Getter for the classifier
getClassifier() - Method in class fr.lip6.jkernelmachines.classifier.GradMKL: Returns the classifier used by this MKL algorithm
getClassifier() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL: Returns the classifier used by this MKL algorithm
getClassifier() - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation: Tells the classifier
getConfidence(T) - Method in interface fr.lip6.jkernelmachines.classifier.multiclass.MulticlassClassifier: Tells the confidence associated with the predicted class
getConfidence(T) - Method in class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA
getConfidence(T) - Method in class fr.lip6.jkernelmachines.classifier.multiclass.OneAgainstAll
getConfidences(T) - Method in interface fr.lip6.jkernelmachines.classifier.multiclass.MulticlassClassifier: Tells the confidences for all classes
getConfidences(T) - Method in class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA
getConfidences(T) - Method in class fr.lip6.jkernelmachines.classifier.multiclass.OneAgainstAll
getDimension() - Method in class fr.lip6.jkernelmachines.util.generators.GaussianGenerator: Tells the dimension of the toys
getDistanceMatrix(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.GaussianKernel: Tells the distance matrix for a specified list of samples.
This is a threaded operation.
getDualGap() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL: Tells the value of stopping criteria
getDualObjective() - Method in class fr.lip6.jkernelmachines.classifier.SDCA
getE() - Method in class fr.lip6.jkernelmachines.classifier.DoubleLLSVM: Returns the number of epochs for training
getE() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Get the number of epochs (one pass through the entire data-set)
getE() - Method in class fr.lip6.jkernelmachines.classifier.LaSVM: Tells the number of epochs used for training
getE() - Method in class fr.lip6.jkernelmachines.classifier.LaSVMI: Tells the number of epochs of training (default 2)
getE() - Method in class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA: return the number of epochs
getE() - Method in class fr.lip6.jkernelmachines.classifier.SDCA: Get the number of epochs to train the classifier
getE() - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightSGDQN: Tells the number of epochs used by internal SGDQN solver for training
getE() - Method in class fr.lip6.jkernelmachines.density.SDCADensity
getE() - Method in class fr.lip6.jkernelmachines.type.ListSampleStream: Get the number of times the list is passed through
getEpochs() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Tells the number of epochs this classifier uses for learning
getEpochs() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Tells the number of epochs used for training this classifier
getExampleWeights() - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Tells weights of training samples
getExampleWeights() - Method in class fr.lip6.jkernelmachines.classifier.GradMKL: Tells the weights on examples
getGamma() - Method in class fr.lip6.jkernelmachines.kernel.GaussianICKernel
getGamma() - Method in class fr.lip6.jkernelmachines.kernel.GaussianKernel: Tells exponential coefficient
getGamma() - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi1
getGamma() - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi2
getGamma() - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussL2
getGamma() - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleTriangleL2
getGamma() - Method in class fr.lip6.jkernelmachines.kernel.typed.FloatGaussChi2
getGamma() - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussChi2
getGamma() - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussL2
getGamma() - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussChi1
getGamma() - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussChi2
getGamma() - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussL2
getGammas() - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussChi2
getGammas() - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussL2
getGammas() - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleTriangleL2
getIteration() - Method in class fr.lip6.jkernelmachines.classifier.NystromLSSVM: Get the number of iterations used by the active learning strategy of the Nystrom kernel
getK() - Method in class fr.lip6.jkernelmachines.classifier.DoubleLLSVM: return the number of anchor points
getK() - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Tells the number of samples used by this classifier to compute the subgradient
getK() - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Tells the number of samples used for sub-gradient calculation by internal Pegasos solver
getK() - Method in class fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel: Get the number of components in the mixture
getKernel() - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL
getKernel() - Method in class fr.lip6.jkernelmachines.classifier.GradMKL
getKernel() - Method in interface fr.lip6.jkernelmachines.classifier.KernelSVM: Tells the current Kernel.
getKernel() - Method in class fr.lip6.jkernelmachines.classifier.LaSVM: Tells the kernel used by this classifier
getKernel() - Method in class fr.lip6.jkernelmachines.classifier.LaSVMI: Returns the kernel used by this classifier
getKernel() - Method in class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA
getKernel() - Method in class fr.lip6.jkernelmachines.classifier.SDCA
getKernel() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL
getKernel() - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM
getKernel() - Method in class fr.lip6.jkernelmachines.classifier.TSMKL
getKernel() - Method in class fr.lip6.jkernelmachines.density.SDCADensity
getKernel() - Method in class fr.lip6.jkernelmachines.kernel.SimpleCacheKernel: Returns the underlying kernel
getKernel() - Method in class fr.lip6.jkernelmachines.projection.KernelPCA: Get the kernel use in the KernelPCA
getKernelMatrix(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.GaussianProductKernel
getKernelMatrix(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedProductKernel
getKernelMatrix(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedSumKernel
getKernelMatrix(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedSumKernel
getKernelMatrix(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.Kernel: return the Gram Matrix of this kernel computed on given samples
getKernelMatrix(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.SimpleCacheKernel
getKernelMatrix(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.ThreadedKernel
getKernelMatrixLine(T, List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedSumKernel
getKernelMatrixLine(T, List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedSumKernel
getKernelMatrixLine(T, List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.Kernel: returns a vector containing the similarity values of a given sample to a list of samples (i.e.
getKernels() - Method in class fr.lip6.jkernelmachines.classifier.GradMKL: Returns the list of kernels
getKernels() - Method in interface fr.lip6.jkernelmachines.classifier.MKL: Gets an array of the kernels in the set, in the same order as getKernelWeights()
getKernels() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL
getKernels() - Method in class fr.lip6.jkernelmachines.classifier.TSMKL: Return the list of kernels used by this MKL algorithm
getKernelWeightMap() - Method in class fr.lip6.jkernelmachines.classifier.GradMKL
getKernelWeightMap() - Method in interface fr.lip6.jkernelmachines.classifier.MKL: Gets a mapping of pairs containing the kernels and weights in the set.
getKernelWeightMap() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL
getKernelWeightMap() - Method in class fr.lip6.jkernelmachines.classifier.TSMKL
getKernelWeights() - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Tells weights of kernels as array
getKernelWeights() - Method in class fr.lip6.jkernelmachines.classifier.GradMKL
getKernelWeights() - Method in interface fr.lip6.jkernelmachines.classifier.MKL: Gets an array containing the weights of the different kernels, in the same order as getKernels()
getKernelWeights() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL
getKernelWeights() - Method in class fr.lip6.jkernelmachines.classifier.TSMKL: tells the weights of the kernel combination
getLambda() - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Tells the learning rate of this classifier
getLambda() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Get the regularization parameter lambda
getLambda() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Returns the hyper-parameter lambda
getLambda() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Tells the learning rate lambda
getLambda() - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Tells the learning rate lambda of internal Pegasos solver
getList() - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation: Tells the list of samples
getListOfClassifiers() - Method in class fr.lip6.jkernelmachines.classifier.multiclass.OneAgainstAll: Returns the list of one against all classifiers used
getListOfKernelWeights() - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Tells weights of kernels
getLoss() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Tells the loss function the classifier is currently using
getLoss() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Tells the type of loss used by this classifier (default HINGELOSS)
getLoss() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Tells the type of loss used by this classifier
getMap() - Method in class fr.lip6.jkernelmachines.kernel.IndexedCacheKernel
getMapOfClassifiers() - Method in class fr.lip6.jkernelmachines.classifier.multiclass.OneAgainstAll: Returns a map with class labels as keys and corresponding one against all classifiers as values
getMatrix(double[][]) - Method in class fr.lip6.jkernelmachines.threading.ThreadedMatrixOperator: get the parallelized matrix
getMaxIteration() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL: Returns the maximum number of outer loop iterations
getMaxIteration() - Method in class fr.lip6.jkernelmachines.density.SimpleMKLDensity
getMean() - Method in class fr.lip6.jkernelmachines.projection.KernelPCA: Get the mean of the current Kernel
getMKLNorm() - Method in class fr.lip6.jkernelmachines.classifier.GradMKL: Return the norm used for the regularization on the kernels
getMu() - Method in class fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel: Gets the centers of each component of the mixture
getMulticlassAlphas() - Method in class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA: Returns the matrix of dual variables in the order [sample, class]
getNbclasses() - Method in class fr.lip6.jkernelmachines.util.generators.MultiClassGaussianGenerator: Tells the number of classes
getNbTest() - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation: Tells the number of tests performed
getNn() - Method in class fr.lip6.jkernelmachines.classifier.DoubleLLSVM: Returns the number of anchor points taken into account by the model
getNormalizedKernelMatrix(ArrayList<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.Kernel: return the Gram Matrix of this kernel computed on given samples, with similarities of one element to itself normalized to one.
getNum_cleaning() - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Tells numerical cleaning threashold
getNumplus() - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLight: Tells the number of positive samples (used for transductive label estimation)
getNumplus() - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Tells the number of positive samples (used for transductive label estimation)
getNumplus() - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightSGDQN: Tells the number of positive samples (used for transductive label estimation)
getObjective() - Method in class fr.lip6.jkernelmachines.classifier.SDCA
getP() - Method in class fr.lip6.jkernelmachines.util.generators.GaussianGenerator: Tells the distance between classes
getP() - Method in class fr.lip6.jkernelmachines.util.generators.MultiClassGaussianGenerator: Tells the distance between classes
getPercent() - Method in class fr.lip6.jkernelmachines.classifier.NystromLSSVM: Get the percentage of training set that is used to train the Nystrom approximation kernel
getPNorm() - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Returns the p_norm parameters
getProjectors() - Method in class fr.lip6.jkernelmachines.projection.KernelPCA: Get the projector coefficients obtained after learning
getS() - Method in class fr.lip6.jkernelmachines.classifier.LaSVMI: Tells the parameter s of the ramp loss (default -1)
getScore() - Method in class fr.lip6.jkernelmachines.evaluation.AccuracyEvaluator
getScore() - Method in class fr.lip6.jkernelmachines.evaluation.ApEvaluator
getScore() - Method in interface fr.lip6.jkernelmachines.evaluation.Evaluator: Tells the score resulting of the evaluation
getScore() - Method in class fr.lip6.jkernelmachines.evaluation.FScoreEvaluator
getScore() - Method in class fr.lip6.jkernelmachines.evaluation.MulticlassAccuracyEvaluator
getScore() - Method in class fr.lip6.jkernelmachines.evaluation.MultipleEvaluator
getScore(String) - Method in class fr.lip6.jkernelmachines.evaluation.MultipleEvaluator
getScore() - Method in class fr.lip6.jkernelmachines.evaluation.PrecisionEvaluator
getScores() - Method in interface fr.lip6.jkernelmachines.evaluation.CrossValidation: Tells the scores of the tests, in order of evaluation
getScores() - Method in class fr.lip6.jkernelmachines.evaluation.LeaveOneOutCrossValidation
getScores(String) - Method in interface fr.lip6.jkernelmachines.evaluation.MultipleEvaluatorCrossValidation: Tells the scores of the tests, in order of evaluation for the given evaluator
getScores() - Method in class fr.lip6.jkernelmachines.evaluation.NFoldCrossValidation
getScores(String) - Method in class fr.lip6.jkernelmachines.evaluation.NFoldCrossValidation
getScores() - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation
getSeed() - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation
getSigma() - Method in class fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel: Gets the inverse covariance matrices of each component of the mixture
getSigma() - Method in class fr.lip6.jkernelmachines.util.generators.GaussianGenerator: Tells the standard deviation of the toys
getSigma() - Method in class fr.lip6.jkernelmachines.util.generators.MultiClassGaussianGenerator: Tells the standard deviation
getStdDevScore() - Method in interface fr.lip6.jkernelmachines.evaluation.CrossValidation: Tells the standard deviation of the test
getStdDevScore() - Method in class fr.lip6.jkernelmachines.evaluation.LeaveOneOutCrossValidation
getStdDevScore(String) - Method in interface fr.lip6.jkernelmachines.evaluation.MultipleEvaluatorCrossValidation: Tells the standard deviation of the test for the given evaluator
getStdDevScore() - Method in class fr.lip6.jkernelmachines.evaluation.NFoldCrossValidation
getStdDevScore(String) - Method in class fr.lip6.jkernelmachines.evaluation.NFoldCrossValidation
getStdDevScore() - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation
getStopGap() - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Returns the stopping criterion
getStopGap() - Method in class fr.lip6.jkernelmachines.classifier.GradMKL: Returns the stopping criterion
getT() - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Tells the maximum number of iteration of this classifier
getT() - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Tells the number of iteration for internal Pegasos algorithm
getT0() - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Tells the iteration offset
getT0() - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Tells the iterations offset of internal Pegasos solver
getTau() - Method in class fr.lip6.jkernelmachines.classifier.LaSVM: Tells the numerical precision
getThreadPoolExecutor() - Static method in class fr.lip6.jkernelmachines.threading.ThreadPoolServer: Tells the system wide ThreadPoolServer (Singleton pattern)
getTrain() - Method in class fr.lip6.jkernelmachines.active.ActiveLearner: Return the list of training samples
getTrainingSet() - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM: Tells the ArrayList of TrainingSample used for training
getTrainingWeights() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL: Deprecated.
getTrainPercent() - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation: Tells the percentage of samples used for training
getVector(double[]) - Method in class fr.lip6.jkernelmachines.threading.ThreadedVectorOperator: get the parallelized vector
getW() - Method in class fr.lip6.jkernelmachines.classifier.DoubleLLSVM: Return the model hyperplanes
getW() - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Tells the hyperplane array of this classifier
getW() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Get the normal to the separating hyperplane
getW() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Tells the arrays of coordinate of separating hyperplane
getW() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Tells the array of coordinates of the hyperplane used by this classifier
getW() - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Tells the hyperplane array of this classifier
getW() - Method in class fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel: Gets the weights associated with each component of the mixture
getWeight(GaussianKernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.GaussianProductKernel: gets the weights of kernel k
getWeight(Kernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedProductKernel: gets the weights of kernel k
getWeight(Kernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedSumKernel: gets the weights of kernel k
getWeight(Kernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedSumKernel: gets the weights of kernel k
getWeights() - Method in class fr.lip6.jkernelmachines.classifier.GradMKL: Tells training samples and associated weights
getWeights() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL: Deprecated.
getWeights() - Method in class fr.lip6.jkernelmachines.kernel.adaptative.GaussianProductKernel: get the list of kernels and associated weights.
getWeights() - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedProductKernel: get the list of kernels and associated weights.
getWeights() - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedSumKernel: get the list of kernels and associated weights.
getWeights() - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedSumKernel: get the list of kernels and associated weights.
getWhiteningCoefficients() - Method in class fr.lip6.jkernelmachines.projection.KernelPCA: Get the whitening coefficient
givensRoti(double[][], double[][], int, int) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Performs the Givens rotation that nullifies component (i,j) of a matrix, accumulated on previous Rotation matrices
GradMKL<T> - Class in fr.lip6.jkernelmachines.classifier: MKL algorithm using a naive gradient descent.
GradMKL() - Constructor for class fr.lip6.jkernelmachines.classifier.GradMKL
granularity - Static variable in class fr.lip6.jkernelmachines.util.algebra.ThreadedMatrixOperations: threshold under which single-threaded ops are used
granularity - Static variable in class fr.lip6.jkernelmachines.util.algebra.ThreadedMatrixVectorOperations

H

HINGELOSS - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Type of loss function using hinge
HINGELOSS - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Type of loss function using hinge
HINGELOSS - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Type of loss function using hinge

I

importFromFile(String) - Static method in class fr.lip6.jkernelmachines.io.ArffImporter: Read training samples from an Arff file.
importFromFile(String, String, int) - Static method in class fr.lip6.jkernelmachines.io.CsvImporter: Importer with full settings.
importFromFile(String, int) - Static method in class fr.lip6.jkernelmachines.io.CsvImporter: CSV import routine with delimiter set to ","
importFromFile(String, String) - Static method in class fr.lip6.jkernelmachines.io.CsvImporter: CSV import routine with label position set to the last value
importFromFile(String) - Static method in class fr.lip6.jkernelmachines.io.CsvImporter: CSV import routine with default parameters (separator is "," and the label is the last value)
importFromFile(String) - Static method in class fr.lip6.jkernelmachines.io.LibSVMImporter
IndexDoubleGaussChi2 - Class in fr.lip6.jkernelmachines.kernel.typed.index: Kernel on double[] that computes the Chi2 distance of a specified component j:
k(x, y) = (x[j]-y[j])*(x[j]-y[j])/(x[j]+y[j])
IndexDoubleGaussChi2(int) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussChi2: Constructor specifying the component which is used
IndexDoubleGaussL2 - Class in fr.lip6.jkernelmachines.kernel.typed.index: Kernel on double[] that computes the L2 distance of a specified component j:
k(x, y) = (x[j]-y[j])*(x[j]-y[j])
IndexDoubleGaussL2(int) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussL2: Constructor specifying the component which is used
IndexDoubleHPolynomial - Class in fr.lip6.jkernelmachines.kernel.typed.index: Kernel on double[] that performs the product of a specified component j to the power d:
k(x,y) = (0.5 + 0.5*x[j]*y[j])^d
IndexDoubleHPolynomial(int, int) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleHPolynomial: Constructor specifying the component which is used
IndexDoubleLinear - Class in fr.lip6.jkernelmachines.kernel.typed.index: Kernel on double[] that performs the product of a specified component j:
k(x,y) = x[j]*y[j]
IndexDoubleLinear(int) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleLinear: Constructor specifying the component which is used
IndexDoublePolynomial - Class in fr.lip6.jkernelmachines.kernel.typed.index: Kernel on double[] that performs the product of a specified component j to the power d:
k(x,y) = (x[j]*y[j])^d
IndexDoublePolynomial(int, int) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoublePolynomial: Constructor specifying the component which is used
IndexedCacheKernel<S,T> - Class in fr.lip6.jkernelmachines.kernel: Simple method of pair for caching a kernel function.
IndexedCacheKernel(Kernel<T>, Map<S, T>) - Constructor for class fr.lip6.jkernelmachines.kernel.IndexedCacheKernel: Constructor using an underlying kernel and a map of
IntGaussChi1 - Class in fr.lip6.jkernelmachines.kernel.typed: Gaussian Kernel on int[] that uses a Chi1 distance.
IntGaussChi1() - Constructor for class fr.lip6.jkernelmachines.kernel.typed.IntGaussChi1
IntGaussChi2 - Class in fr.lip6.jkernelmachines.kernel.typed: Gaussian Kernel on int[] that uses a Chi2 distance.
IntGaussChi2() - Constructor for class fr.lip6.jkernelmachines.kernel.typed.IntGaussChi2
IntGaussL2 - Class in fr.lip6.jkernelmachines.kernel.typed: Gaussian Kernel on int[] that uses a L2 distance.
IntGaussL2() - Constructor for class fr.lip6.jkernelmachines.kernel.typed.IntGaussL2
inv(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.ejml.EJMLMatrixOperations: computes the inverse of a matrix
inv(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Compute the inverse matrix
is_tri(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations
isBalanced() - Method in interface fr.lip6.jkernelmachines.evaluation.BalancedCrossValidation: Returns true if the splits are balanced between positive and negative
isBalanced() - Method in class fr.lip6.jkernelmachines.evaluation.NFoldCrossValidation: Returns true if the splits are balanced between positive and negative
isBalanced() - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation
isBias() - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Tells if this classifier uses a bias term
isBias() - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Tells if this classifier uses a bias term
isCACHED_KERNEL() - Method in class fr.lip6.jkernelmachines.density.SDCADensity
isCacheKernel() - Method in class fr.lip6.jkernelmachines.classifier.SDCA
isClasseBalanced() - Method in class fr.lip6.jkernelmachines.active.MulticlassSimpleAL: Tells is use a class balancing criterion
isCyclic() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Is the algorithm doing epoch of ordered samples
isHasBias() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Tells if this classifier is using a bias term
isHasNorm() - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Tells if use a norm constraint
isNormalize() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Tells if training datas are centered/reduced as preprocessing before learning
isShuffle() - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Tells if samples are shuffled while learning
isSquare(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Tests if a matrix is square
isSymmetric(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Checks if a matrix is symmetric
isTriDiagonal(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations

J

JkmsMainFrame - Class in fr.lip6.jkernelmachines.gui: GUI for performing simple train/test of datasets
JkmsMainFrame() - Constructor for class fr.lip6.jkernelmachines.gui.JkmsMainFrame: Creates new form JkmsMainFrame

K

k - Variable in class fr.lip6.jkernelmachines.kernel.ThreadedKernel
Kernel<T> - Class in fr.lip6.jkernelmachines.kernel: Base class for kernels
Kernel() - Constructor for class fr.lip6.jkernelmachines.kernel.Kernel
KernelPCA<T> - Class in fr.lip6.jkernelmachines.projection: Kernel principal component analysis, using generic datatypes.
KernelPCA(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.projection.KernelPCA
kernels - Variable in class fr.lip6.jkernelmachines.classifier.SimpleMKL
KernelSVM<T> - Interface in fr.lip6.jkernelmachines.classifier: Interface for SVM algorithms using a non-linear kernel (dual optimization mainly)
kernelWeights - Variable in class fr.lip6.jkernelmachines.classifier.SimpleMKL

L

label - Variable in class fr.lip6.jkernelmachines.type.TrainingSample
lancsos_iteration(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations
LaSVM<T> - Class in fr.lip6.jkernelmachines.classifier: Kernel SVM classifier implementing the LaSVM algorithm
LaSVM(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.classifier.LaSVM: Constructor with specific kernel
LaSVMI<T> - Class in fr.lip6.jkernelmachines.classifier: Kernel SVM classifier implementing LaSVM-I algorithm
LaSVMI(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.classifier.LaSVMI: Default constructor provideing the kernel
LeaveOneOutCrossValidation<T> - Class in fr.lip6.jkernelmachines.evaluation: Simple class to perform leave one out cross validation
LeaveOneOutCrossValidation(Classifier<T>, List<TrainingSample<T>>, Evaluator<T>) - Constructor for class fr.lip6.jkernelmachines.evaluation.LeaveOneOutCrossValidation
LibSVMImporter - Class in fr.lip6.jkernelmachines.io: Simple class to import data in libsvm format.
LibSVMImporter() - Constructor for class fr.lip6.jkernelmachines.io.LibSVMImporter
likelihood(double[]) - Method in class fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel: Return a vector containing the likelihood to each Gaussian component
list - Variable in class fr.lip6.jkernelmachines.classifier.SimpleMKL
ListKernel<S,T extends java.util.List<S>> - Class in fr.lip6.jkernelmachines.kernel.extra.bag: Default kernel on bags : sum all kernel values involving an element from B1 and an element from B2 between specified bounds.
ListKernel(Kernel<S>) - Constructor for class fr.lip6.jkernelmachines.kernel.extra.bag.ListKernel
ListSampleStream<T> - Class in fr.lip6.jkernelmachines.type: Stream based on a list of samples
ListSampleStream(List<TrainingSample<T>>) - Constructor for class fr.lip6.jkernelmachines.type.ListSampleStream: Constructor specifying the list from which the stream is created
LOGLOSS - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Type of loss function using log
LOGLOSS - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Type of loss function using log
LOGLOSS - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Type of loss function using log
LOGLOSSMARGIN - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Type of loss function using margin log
LOGLOSSMARGIN - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Type of loss function using margin log
LOGLOSSMARGIN - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Type of loss function using margin log

M

main(String[]) - Static method in class fr.lip6.jkernelmachines.example.CrossValidationExample
main(String[]) - Static method in class fr.lip6.jkernelmachines.example.MulticlassExample: Program entry point
main(String[]) - Static method in class fr.lip6.jkernelmachines.example.SVMExample
main(String[]) - Static method in class fr.lip6.jkernelmachines.example.VOCExample: Simple program to compute the average precision for the PASCAL VOC challenge.
main(String[]) - Static method in class fr.lip6.jkernelmachines.gui.JkmsMainFrame
main(String[]) - Static method in class fr.lip6.jkernelmachines.io.ArffImporter
MatrixOperations - Class in fr.lip6.jkernelmachines.util.algebra: This class provides basic linear algebra operations on matrices.
MatrixOperations() - Constructor for class fr.lip6.jkernelmachines.util.algebra.MatrixOperations
MatrixVectorOperations - Class in fr.lip6.jkernelmachines.util.algebra: This class provides operations between matrices and vectors
MatrixVectorOperations() - Constructor for class fr.lip6.jkernelmachines.util.algebra.MatrixVectorOperations
maxIteration - Variable in class fr.lip6.jkernelmachines.classifier.SimpleMKL
mean(double[]) - Static method in class fr.lip6.jkernelmachines.util.ArraysUtils: computes the mean of a double array
MKL<T> - Interface in fr.lip6.jkernelmachines.classifier: Interface for Multiple Kernel Classes.
Model - Class in fr.lip6.jkernelmachines.gui: Very simple model class that encapsulate a classifier and preprocessing tools
Model() - Constructor for class fr.lip6.jkernelmachines.gui.Model
mul(double[][], double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Performs the matrix multiplication between two double matrices C = A * B
mul(double[][], double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.ThreadedMatrixOperations: Performs the matrix multiplication between two double matrices C = A * B
mul(double[], double) - Static method in class fr.lip6.jkernelmachines.util.algebra.VectorOperations: Multiply a given double array by a constant double: C = lambda * A
muli(double[][], double[][], double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Performs the matrix multiplication between two double matrices C = A * B
muli(double[][], double[][], double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.ThreadedMatrixOperations: Performs the matrix multiplication between two double matrices C = A * B
muli(double[], double[], double) - Static method in class fr.lip6.jkernelmachines.util.algebra.VectorOperations: Multiply a given double array by a constant double: C = lambda * A
MulticlassAccuracyEvaluator<T> - Class in fr.lip6.jkernelmachines.evaluation: Evaluation class for computing the multiclass accuracy on a testing set, given a provided cmulticlass classifier.
MulticlassAccuracyEvaluator() - Constructor for class fr.lip6.jkernelmachines.evaluation.MulticlassAccuracyEvaluator
MulticlassClassifier<T> - Interface in fr.lip6.jkernelmachines.classifier.multiclass: Interface for multiclass classifiers.
MulticlassExample - Class in fr.lip6.jkernelmachines.example: Example of multiclass classification on artificial dataset.
MulticlassExample() - Constructor for class fr.lip6.jkernelmachines.example.MulticlassExample
MultiClassGaussianGenerator - Class in fr.lip6.jkernelmachines.util.generators: Class for generating toys subject to multi-class classification tasks, using Gaussian distributions.
MultiClassGaussianGenerator() - Constructor for class fr.lip6.jkernelmachines.util.generators.MultiClassGaussianGenerator: Default constructor, with p=2, sigma = 1, nbclasses = 5;
MultiClassGaussianGenerator(int) - Constructor for class fr.lip6.jkernelmachines.util.generators.MultiClassGaussianGenerator: Constructor specifying the number of classes
MulticlassSDCA<T> - Class in fr.lip6.jkernelmachines.classifier.multiclass: This is a straight forward extension of SDCA svm algorithm from Shalev-Shwartz to multiclass using a multiclass loss function.
MulticlassSDCA(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA
MulticlassSimpleAL<T> - Class in fr.lip6.jkernelmachines.active: Extension to multiclass of the Simple active learning strategy as presented in:
Support vector machine active learning with applications to text classification.
MulticlassSimpleAL(MulticlassClassifier<T>, List<TrainingSample<T>>) - Constructor for class fr.lip6.jkernelmachines.active.MulticlassSimpleAL
MultipleEvaluator<T> - Class in fr.lip6.jkernelmachines.evaluation: Class that aggregates several Evaluators
MultipleEvaluator() - Constructor for class fr.lip6.jkernelmachines.evaluation.MultipleEvaluator
MultipleEvaluatorCrossValidation<T> - Interface in fr.lip6.jkernelmachines.evaluation

N

n2(double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.VectorOperations: Computes the l2 norm of a double array
n2p2(double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.VectorOperations: Computes the squared l2 norm of a double array
name - Variable in class fr.lip6.jkernelmachines.kernel.Kernel
nextSample() - Method in class fr.lip6.jkernelmachines.type.ListSampleStream
nextSample() - Method in interface fr.lip6.jkernelmachines.type.TrainingSampleStream: Return the next training sample from this stream
NFoldCrossValidation<T> - Class in fr.lip6.jkernelmachines.evaluation: Class for performing N-Fold Cross-validation.
NFoldCrossValidation(int, Classifier<T>, List<TrainingSample<T>>, Evaluator<T>) - Constructor for class fr.lip6.jkernelmachines.evaluation.NFoldCrossValidation: Default constructor with number of folds, classifier, full samples list and evaluation metric.
NormalizedKernel<T> - Class in fr.lip6.jkernelmachines.kernel
NormalizedKernel(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.kernel.NormalizedKernel
normalizeDoubleList(List<double[]>) - Static method in class fr.lip6.jkernelmachines.util.DataPreProcessing: Normalize a list of double[] to have l2-norm equal to 1
NormalizedStringNGram - Class in fr.lip6.jkernelmachines.kernel.typed
NormalizedStringNGram(int) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.NormalizedStringNGram
normalizedValueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.Kernel: kernel similarity normalized such that k(t1, t1) = 1
normalizeList(List<TrainingSample<double[]>>) - Static method in class fr.lip6.jkernelmachines.util.DataPreProcessing: Normalize a list of training samples of double[] to have l2-norm equal to 1
num_prec - Static variable in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations
numPrec - Variable in class fr.lip6.jkernelmachines.classifier.SimpleMKL
numThread - Variable in class fr.lip6.jkernelmachines.kernel.adaptative.GaussianProductKernel
numThread - Variable in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedProductKernel
NystromKernel<T> - Class in fr.lip6.jkernelmachines.kernel.extra: This kernel provides a fast approximation of a given kernel using the Nystrom approximation.
NystromKernel(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.kernel.extra.NystromKernel: Default constructor with kernel to approximate as argument
NystromLSSVM<T> - Class in fr.lip6.jkernelmachines.classifier: This classifier is a fast approximate SVM classifier using the Nystrom kernel for project non-linearly the samples in a subspace in which a linear classifier is learned.
NystromLSSVM(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.classifier.NystromLSSVM

O

OneAgainstAll<T> - Class in fr.lip6.jkernelmachines.classifier.multiclass: Multiclass classifier with N times One against All scheme.
OneAgainstAll(Classifier<T>) - Constructor for class fr.lip6.jkernelmachines.classifier.multiclass.OneAgainstAll: Default constructor with underlying classifier algorithm.
OnlineClassifier<T> - Interface in fr.lip6.jkernelmachines.classifier: Interface for classifier that are trainable from a stream of samples
onlineTrain(TrainingSampleStream<double[]>) - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM
onlineTrain(TrainingSampleStream<double[]>) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD
onlineTrain(TrainingSampleStream<T>) - Method in class fr.lip6.jkernelmachines.classifier.LaSVM
onlineTrain(TrainingSampleStream<T>) - Method in class fr.lip6.jkernelmachines.classifier.NystromLSSVM
onlineTrain(TrainingSampleStream<T>) - Method in interface fr.lip6.jkernelmachines.classifier.OnlineClassifier: Train the classifier using a stream of TrainingSample sampled from the TrainingSampleStream until no sample can be drawn.
onlineTrain(TrainingSampleStream<T>) - Method in class fr.lip6.jkernelmachines.classifier.SDCA
outer(double[], double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixVectorOperations: Computes the tensor (outer) product of two vectors

P

ParzenClassifier<T> - Class in fr.lip6.jkernelmachines.classifier: Classification tool using a Parzen window
ParzenClassifier(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.classifier.ParzenClassifier
ParzenDensity<T> - Class in fr.lip6.jkernelmachines.density: Parzen window for estimating the probability density function of a random variable.
ParzenDensity(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.density.ParzenDensity
PowerKernel<T> - Class in fr.lip6.jkernelmachines.kernel.extra: Simple Kernel elevating the underlying kernel to power e in the form K(x,y) = k(x,y)^e.
PowerKernel(Kernel<T>, double) - Constructor for class fr.lip6.jkernelmachines.kernel.extra.PowerKernel: Constructor providing the underlying kernel and the power e
PrecisionEvaluator<T> - Class in fr.lip6.jkernelmachines.evaluation: Evaluator computing the precision defined as (number of relevant)/(number of retrieved).
PrecisionEvaluator() - Constructor for class fr.lip6.jkernelmachines.evaluation.PrecisionEvaluator
print(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: outputs the matrix on System.out
print(int, Object) - Method in class fr.lip6.jkernelmachines.util.DebugPrinter: Print object to standard error stream iff debug level is more than debug argument
println(int, Object) - Method in class fr.lip6.jkernelmachines.util.DebugPrinter: Println object to standard error stream iff debug level is more than debug argument
prod(double[], double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.VectorOperations: Performs the component wise product between 2 vectors
prodi(double[], double[], double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.VectorOperations: Performs the in place component wise product between 2 vectors
project(TrainingSample<double[]>) - Method in class fr.lip6.jkernelmachines.projection.DoublePCA: Project a single sample using the trained projectors.
project(TrainingSample<double[]>, boolean) - Method in class fr.lip6.jkernelmachines.projection.DoublePCA: Project a single sample using the trained projectors with optional whitening (unitary covariance matrix).
project(TrainingSample<T>, boolean) - Method in class fr.lip6.jkernelmachines.projection.KernelPCA
project(TrainingSample<T>) - Method in class fr.lip6.jkernelmachines.projection.KernelPCA
projectList(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.extra.NystromKernel
projectList(List<TrainingSample<double[]>>) - Method in class fr.lip6.jkernelmachines.projection.DoublePCA: Performs the projection on a list of samples.
projectList(List<TrainingSample<double[]>>, boolean) - Method in class fr.lip6.jkernelmachines.projection.DoublePCA: Performs the projection on a list of samples with optional whitening (unitary covariance matrix).
projectList(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.projection.KernelPCA: Performs the projection on a list of samples.
projectList(List<TrainingSample<T>>, boolean) - Method in class fr.lip6.jkernelmachines.projection.KernelPCA: Performs the projection on a list of samples with optional whitening (unitary covariance matrix).
projectSample(T) - Method in class fr.lip6.jkernelmachines.kernel.extra.NystromKernel: Project a sample to the space induced by the Nystrom approx

Q

qr(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Performs the QR decomposition of a symmetric matrix, such that Q is orthonormal and R is an upper triangular matrix: A = Q*R
qri(double[][], double[][], double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Performs the in place QR decomposition of a symmetric matrix, such that Q is orthonormal and R is an upper triangular matrix: A = Q*R
qri_givens(double[][], double[][], double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Performs the in place QR decomposition of a symmetric matrix using Givens rotation matrices, such that Q is orthonormal and R is an upper triangular matrix: A = Q*R
qri_gramschmidt(double[][], double[][], double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Performs the in place QR decomposition of a symmetric matrix, such that Q is orthonormal and R is an upper triangular matrix: A = Q*R

R

RandomAL<T> - Class in fr.lip6.jkernelmachines.active: Absurd active strategy that randomly selects a sample
RandomAL(OnlineClassifier<T>, List<TrainingSample<T>>) - Constructor for class fr.lip6.jkernelmachines.active.RandomAL
RandomSplitCrossValidation<T> - Class in fr.lip6.jkernelmachines.evaluation: Class for simple random split based cross validation.
RandomSplitCrossValidation(Classifier<T>, List<TrainingSample<T>>, Evaluator<T>) - Constructor for class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation: Default constructor which should provide a classifier to be tested, the complete list of samples and the evaluator computing the scores
readFile(String) - Method in class fr.lip6.jkernelmachines.io.FvecImporter: Reads a file in fvec (INRIA) format and return the samples as a list of double arrays.
readInputStream(InputStream) - Method in class fr.lip6.jkernelmachines.io.FvecImporter: Reads a stream in fvec (INRIA) format and return the samples as a list of double arrays.
reduceList(List<TrainingSample<double[]>>) - Static method in class fr.lip6.jkernelmachines.util.DataPreProcessing: Process a list of samples of double[] to have unit variance
removeEvaluator(String) - Method in interface fr.lip6.jkernelmachines.evaluation.MultipleEvaluatorCrossValidation: unregister the evaluator by its name
removeEvaluator(String) - Method in class fr.lip6.jkernelmachines.evaluation.NFoldCrossValidation
removeKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL: Removes a kernel from the MKL problem
removeKernel(GaussianKernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.GaussianProductKernel: removes kernel k from the product
removeKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedProductKernel: removes kernel k from the sum
removeKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedSumKernel: removes kernel k from the sum
removeKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedProductKernel: removes a kernel from the product
removeKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedSumKernel: removes kernel k from the sum
retrain() - Method in class fr.lip6.jkernelmachines.classifier.LaSVM
retrain() - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL
retrain() - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM: Train again the classifier (default restart from scratch)
rMul(double[][], double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixVectorOperations: Performs a matrix*vector multiplication
rMul(double[][], double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.ThreadedMatrixVectorOperations: Performs a matrix*vector multiplication
rMuli(double[], double[][], double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixVectorOperations: Performs a matrix*vector multiplication in place
rMuli(double[], double[][], double[]) - Static method in class fr.lip6.jkernelmachines.util.algebra.ThreadedMatrixVectorOperations: Performs a matrix*vector multiplication in place
run() - Method in interface fr.lip6.jkernelmachines.evaluation.CrossValidation: perform learning and evaluations
run() - Method in class fr.lip6.jkernelmachines.evaluation.LeaveOneOutCrossValidation
run() - Method in class fr.lip6.jkernelmachines.evaluation.NFoldCrossValidation
run() - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation

S

S3VMLight<T> - Class in fr.lip6.jkernelmachines.classifier.transductive: Transductive SVM using S3VMLight
S3VMLight(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.classifier.transductive.S3VMLight: Constructor using specific kernel as input space similarity function
S3VMLightPegasos - Class in fr.lip6.jkernelmachines.classifier.transductive: Fast linear transductive SVM using a combination of SVMLight and Pegasos algorithms.
S3VMLightPegasos() - Constructor for class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Default constructor
S3VMLightSGDQN - Class in fr.lip6.jkernelmachines.classifier.transductive: Fast Linear transductive SVM using a combination of SVMLight and SGDQN algorithms.
S3VMLightSGDQN() - Constructor for class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightSGDQN: Default constructor
sample - Variable in class fr.lip6.jkernelmachines.type.TrainingSample
SDCA<T> - Class in fr.lip6.jkernelmachines.classifier: SDCA svm algorithm from Shalev-Shwartz.
SDCA(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.classifier.SDCA
SDCADensity<T> - Class in fr.lip6.jkernelmachines.density: One Class SVM estimation using Stochastic Dual Coordinate Ascent.
SDCADensity(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.density.SDCADensity
setAlphas(double[]) - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM: Sets the samples weights
setB(double) - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Sets the bias term
setB(double) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Set the bias of the classifier
setB(double) - Method in class fr.lip6.jkernelmachines.classifier.LaSVM: Sets the bias term
setBalanced(boolean) - Method in interface fr.lip6.jkernelmachines.evaluation.BalancedCrossValidation: Set class balancing strategy when computing the splits
setBalanced(boolean) - Method in class fr.lip6.jkernelmachines.evaluation.NFoldCrossValidation: Set class balancing strategy when computing the splits
setBalanced(boolean) - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation
setBeta(double) - Method in class fr.lip6.jkernelmachines.evaluation.FScoreEvaluator: Sets the beta value for the F-score
setBias(boolean) - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Sets if the classifier has a bias term
setBias(boolean) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Sets the use of a bias term in this classifier
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.DoubleLLSVM: Sets the hyperparameter C for the hinge loss tradeoff
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Sets C hyperparameter (automatically converted in lambda)
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Sets the hyperparameter C
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Set the C hyperparameter (automatically converted to learning rate lambda)
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.GradMKL: Sets the hyperparameter C
setC(double) - Method in interface fr.lip6.jkernelmachines.classifier.KernelSVM: Sets the hyperparameter C
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.LaSVM: Sets the hyperparameter C
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.LaSVMI: Sets the C hyperparameter (default 1.0)
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.NystromLSSVM: Set the C svm hyperparameter
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.SDCA
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL: Sets the value of the hyperparameter C
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLight: Sets the hyperparameter C
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightSGDQN: Sets the hyperparameter C
setC(double) - Method in class fr.lip6.jkernelmachines.classifier.TSMKL
setC(double) - Method in class fr.lip6.jkernelmachines.density.SDCADensity
setC(double) - Method in class fr.lip6.jkernelmachines.density.SimpleMKLDensity
setC(double) - Method in class fr.lip6.jkernelmachines.density.SMODensity: Sets the hyperparameter C
setCACHED_KERNEL(boolean) - Method in class fr.lip6.jkernelmachines.density.SDCADensity
setCacheKernel(boolean) - Method in class fr.lip6.jkernelmachines.classifier.SDCA
setClasseBalanced(boolean) - Method in class fr.lip6.jkernelmachines.active.MulticlassSimpleAL: Sets the use of a criterion aiming at balanced classes
setClassifier(OnlineClassifier<T>) - Method in class fr.lip6.jkernelmachines.active.ActiveLearner: Setter for the classifier
setClassifier(OnlineClassifier<T>) - Method in class fr.lip6.jkernelmachines.active.MulticlassSimpleAL
setClassifier(KernelSVM<T>) - Method in class fr.lip6.jkernelmachines.classifier.GradMKL: Sets the default training algorithm for the underlying svm calls (default LASVM).
setClassifier(KernelSVM<T>) - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL: Sets the default training algorithm for the underlying svm calls (default LASVM).
setClassifier(Classifier<T>) - Method in class fr.lip6.jkernelmachines.evaluation.AccuracyEvaluator
setClassifier(Classifier<T>) - Method in class fr.lip6.jkernelmachines.evaluation.ApEvaluator
setClassifier(Classifier<T>) - Method in interface fr.lip6.jkernelmachines.evaluation.Evaluator: Sets the classifier to use for evaluation
setClassifier(Classifier<T>) - Method in class fr.lip6.jkernelmachines.evaluation.FScoreEvaluator
setClassifier(Classifier<T>) - Method in class fr.lip6.jkernelmachines.evaluation.MulticlassAccuracyEvaluator
setClassifier(Classifier<T>) - Method in class fr.lip6.jkernelmachines.evaluation.MultipleEvaluator
setClassifier(Classifier<T>) - Method in class fr.lip6.jkernelmachines.evaluation.PrecisionEvaluator
setClassifier(Classifier<T>) - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation: Sets the classifier
setCyclic(boolean) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Set the order of the sample at each epoch
setDebugLevel(int) - Static method in class fr.lip6.jkernelmachines.util.DebugPrinter: Set level of debug information to print
setDimension(int) - Method in class fr.lip6.jkernelmachines.util.generators.GaussianGenerator: Sets the dimension of the toys
setDualGap(double) - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL: Sets the value of the stopping criteria
setE(int) - Method in class fr.lip6.jkernelmachines.classifier.DoubleLLSVM: Sets the number of epochs for training
setE(long) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Set the number of epochs (one pass through the entire data-set)
setE(int) - Method in class fr.lip6.jkernelmachines.classifier.LaSVM: Sets the number of epochs used for training
setE(long) - Method in class fr.lip6.jkernelmachines.classifier.LaSVMI: Sets the number of epoch of training (default 2)
setE(double) - Method in class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA: Sets the number of epochs
setE(int) - Method in class fr.lip6.jkernelmachines.classifier.SDCA: Set the number of epochs (going through all samples once) for the training phase
setE(double) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightSGDQN: Sets the number of epochs used for training by the internal SGDQN solver
setE(int) - Method in class fr.lip6.jkernelmachines.density.SDCADensity
setE(int) - Method in class fr.lip6.jkernelmachines.type.ListSampleStream: Sets the number of times the list is passed through (number of epochs)
setEpochs(int) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Sets the number of epochs this classifier uses for learning
setEpochs(int) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Sets the number of epochs used for training this classifier
setGamma(double) - Method in class fr.lip6.jkernelmachines.kernel.GaussianICKernel
setGamma(double) - Method in class fr.lip6.jkernelmachines.kernel.GaussianKernel: Sets exponential coefficient.
setGamma(double) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi1
setGamma(double) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi2
setGamma(double) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussL2
setGamma(double) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleTriangleL2
setGamma(double) - Method in class fr.lip6.jkernelmachines.kernel.typed.FloatGaussChi2
setGamma(double) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussChi2
setGamma(double) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussChi2
setGamma(double) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussL2
setGamma(double) - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussChi1
setGamma(double) - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussChi2
setGamma(double) - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussL2
setGammas(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussChi2
setGammas(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussL2
setGammas(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleTriangleL2
setHasBias(boolean) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Sets the use of a bias term
setHasNorm(boolean) - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Sets use of norm constraint
setIndex(int) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussChi2
setIndex(int) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussL2
setIndex(int) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleHPolynomial
setIndex(int) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleLinear
setIndex(int) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoublePolynomial
setIteration(int) - Method in class fr.lip6.jkernelmachines.classifier.NystromLSSVM: Set the number of iteration to use in the Nystrom approximation
setK(int) - Method in class fr.lip6.jkernelmachines.classifier.DoubleLLSVM: Sets the number of anchor points
setK(int) - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Sets the number of samples on which to compute the subgradient
setK(int) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Sets the number of samples used for sub-gradient calculation by internal Pegasos solver
setK(int) - Method in class fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel: Sets the number of components in the mixture
setKernel(Kernel<double[]>) - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL
setKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.classifier.GradMKL
setKernel(Kernel<T>) - Method in interface fr.lip6.jkernelmachines.classifier.KernelSVM: Sets the kernel to use as similarity measure
setKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.classifier.LaSVM: Sets the kernel used by this classifier
setKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.classifier.LaSVMI: Set the kernel to use
setKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA
setKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.classifier.SDCA
setKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL
setKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM
setKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.classifier.TSMKL
setKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.density.SDCADensity
setKernel(Kernel<T>) - Method in class fr.lip6.jkernelmachines.projection.KernelPCA: Set the current Kernel
setLambda(double) - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Sets the learning rate lambda
setLambda(double) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Set the regularization parameter lambda
setLambda(double) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Sets the learning rate lambda
setLambda(double) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Sets the learning rate lambda
setLambda(double) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Sets the learning rate lambda of internal Pegasos solver
setLines(int) - Static method in class fr.lip6.jkernelmachines.threading.ThreadedMatrixOperator: Sets the number of lines computed by each job
setList(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation: Sets the list of samples
setLoss(int) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Sets the loss function to use for next training
setLoss(int) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Sets the type of loss used by this classifier (default HINGELOSS)
setLoss(int) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Sets the type of loos used by this classifier
setMaxIteration(int) - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL: Sets the maximum number of outer loop iterations
setMaxIteration(int) - Method in class fr.lip6.jkernelmachines.density.SimpleMKLDensity
setMKLNorm(double) - Method in class fr.lip6.jkernelmachines.classifier.GradMKL: Sets the norm used for kernel weights (real)
setMu(double[][]) - Method in class fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel: Sets the centers of each component of the mixture
setName(String) - Method in class fr.lip6.jkernelmachines.kernel.Kernel: Set the name of this kernel
setNbclasses(int) - Method in class fr.lip6.jkernelmachines.util.generators.MultiClassGaussianGenerator: Sets the number of classes
setNbTest(int) - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation: Sets the number of tests to perfom
setNn(int) - Method in class fr.lip6.jkernelmachines.classifier.DoubleLLSVM: Sets the number of anchor points taken into account by the model
setNormalize(boolean) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Sets if training datas are centered/reduced as preprocessing before learning
setNum_cleaning(double) - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Sets numerical threshold
setNumplus(int) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLight: Sets the number of positive samples (used for transductive label estimation)
setNumplus(int) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Sets the number of positives samples (used for transductive label estimation)
setNumplus(int) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightSGDQN: Sets the number of positives samples (used for transductive label estimation)
setP(float) - Method in class fr.lip6.jkernelmachines.util.generators.GaussianGenerator: Sets the distance between classes
setP(float) - Method in class fr.lip6.jkernelmachines.util.generators.MultiClassGaussianGenerator: Sets the distance between classes
setPercent(double) - Method in class fr.lip6.jkernelmachines.classifier.NystromLSSVM: Set the percentage of training set to be used for training the Nystrom approximation kernel
setPNorm(double) - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Sets norm constraint
setS(double) - Method in class fr.lip6.jkernelmachines.classifier.LaSVMI: Sets the parameter s of the ramp loss (default -1)
setSeed(long) - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation
setShuffle(boolean) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Sets if samples should be shuffled while learning
setSigma(double[][][]) - Method in class fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel: Sets the inverse covariance matrices of each component of the mixture
setSigma(double) - Method in class fr.lip6.jkernelmachines.util.generators.GaussianGenerator: Set the standard deviation of the toys
setSigma(double) - Method in class fr.lip6.jkernelmachines.util.generators.MultiClassGaussianGenerator: Sets the standard deviation
setStopGap(double) - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL: Sets stopping criterion
setStopGap(double) - Method in class fr.lip6.jkernelmachines.classifier.GradMKL: Sets stopping criterion threshold
setT(int) - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Sets the maximum number of iterations
setT(int) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Sets the number of iteration for internal Pegasos algorithm
setT0(double) - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Sets the iteration offset
setT0(double) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos: Sets the iterations offset of internal Pegasos solver
setTestingSet(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.evaluation.AccuracyEvaluator
setTestingSet(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.evaluation.ApEvaluator
setTestingSet(List<TrainingSample<T>>) - Method in interface fr.lip6.jkernelmachines.evaluation.Evaluator: Sets the list of testing samples on which to evaluate the classifier
setTestingSet(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.evaluation.FScoreEvaluator
setTestingSet(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.evaluation.MulticlassAccuracyEvaluator
setTestingSet(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.evaluation.MultipleEvaluator
setTestingSet(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.evaluation.PrecisionEvaluator
setTrain(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.active.ActiveLearner: Sets the list of training samples
setTrain(ArrayList<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM: Sets the list of training samples
setTrainingSet(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.evaluation.AccuracyEvaluator
setTrainingSet(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.evaluation.ApEvaluator
setTrainingSet(List<TrainingSample<T>>) - Method in interface fr.lip6.jkernelmachines.evaluation.Evaluator: Sets the list of training samples on which to train the classifier
setTrainingSet(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.evaluation.FScoreEvaluator
setTrainingSet(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.evaluation.MulticlassAccuracyEvaluator
setTrainingSet(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.evaluation.MultipleEvaluator
setTrainingSet(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.evaluation.PrecisionEvaluator
setTrainPercent(double) - Method in class fr.lip6.jkernelmachines.evaluation.RandomSplitCrossValidation: Sets the percentage of samples used for training
setW(double[]) - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM: Sets the hyperplane coordinates
setW(double[]) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Set the normal to the hyperplane
setW(double[]) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Sets the array of coordinate used by this classifier
setW(double[]) - Method in class fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel: Sets the weights associated with each Component of the mixture
setWeight(GaussianKernel<T>, Double) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.GaussianProductKernel: Sets the weight of kernel k
setWeight(Kernel<T>, Double) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedProductKernel: Sets the weight of kernel k
setWeight(Kernel<T>, Double) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedSumKernel: Sets the weight of kernel k
setWeight(Kernel<T>, Double) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedSumKernel: Sets the weight of kernel k
shutdownNow(ThreadPoolExecutor) - Static method in class fr.lip6.jkernelmachines.threading.ThreadPoolServer: Stops the server.
SimpleAL<T> - Class in fr.lip6.jkernelmachines.active: Simple active learning strategy as presented in:
Support vector machine active learning with applications to text classification.
SimpleAL(OnlineClassifier<T>, List<TrainingSample<T>>) - Constructor for class fr.lip6.jkernelmachines.active.SimpleAL
SimpleCacheKernel<T> - Class in fr.lip6.jkernelmachines.kernel: Very simple caching method for any kernel.
SimpleCacheKernel(Kernel<T>, List<TrainingSample<T>>) - Constructor for class fr.lip6.jkernelmachines.kernel.SimpleCacheKernel: Constructor using a kernel and a list of samples
SimpleListKernel<S,T extends java.util.List<S>> - Class in fr.lip6.jkernelmachines.kernel.extra.bag: Default kernel on bags : sum all kernel values involving an element from B1 and an element from B2 between specified bounds.
SimpleListKernel(Kernel<S>) - Constructor for class fr.lip6.jkernelmachines.kernel.extra.bag.SimpleListKernel
SimpleMKL<T> - Class in fr.lip6.jkernelmachines.classifier: Implementation of the SimpleMKL solver.
Java conversion of the original matlab code.
SimpleMKL() - Constructor for class fr.lip6.jkernelmachines.classifier.SimpleMKL
SimpleMKLDensity<T> - Class in fr.lip6.jkernelmachines.density: Density estimation using an adapted version of SimpleMKL.
SimpleMKLDensity() - Constructor for class fr.lip6.jkernelmachines.density.SimpleMKLDensity: default constructor
SimpleSubListKernel<S,T extends java.util.List<S>> - Class in fr.lip6.jkernelmachines.kernel.extra.bag: Kernel on bags of same length.
Let B1 and B2 be bags of elements b1[i] and b2[i], let k(b1[i],b2[i]) be the minor kernel, then K(B1, B2) = sum_{i=n}^{N} k(b1[i],b2[i])
With n and N being the bounds of the sum.
SimpleSubListKernel(int, int, Kernel<S>) - Constructor for class fr.lip6.jkernelmachines.kernel.extra.bag.SimpleSubListKernel
SMODensity<T> - Class in fr.lip6.jkernelmachines.density: Density function based on SMO algorithm.
SMODensity(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.density.SMODensity: Constructor using the specified kernel function for computing similarities among samples
SMOOTHHINGELOSS - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Type of loss function using a smoothed hinge
SMOOTHHINGELOSS - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Type of loss function using a smoothed hinge
SMOOTHHINGELOSS - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Type of loss function using a smoothed hinge
SMOSVM<T> - Class in fr.lip6.jkernelmachines.classifier: SVM classifier using SMO algorithm
SMOSVM(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.classifier.SMOSVM: Constructor using the specified kernel as similarity measure between samples
SQUAREDHINGELOSS - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSAG: Type of loss function using a squared hinge
SQUAREDHINGELOSS - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Type of loss function using a squared hinge
SQUAREDHINGELOSS - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Type of loss function using a squared hinge
stddev(double[]) - Static method in class fr.lip6.jkernelmachines.util.ArraysUtils: Computes the standard deviation of an array
StringNGram - Class in fr.lip6.jkernelmachines.kernel.typed
StringNGram(int) - Constructor for class fr.lip6.jkernelmachines.kernel.typed.StringNGram
SubListKernel<S,T extends java.util.List<S>> - Class in fr.lip6.jkernelmachines.kernel.extra.bag: Default kernel on bags : sum all kernel values involving an element from B1 and an element from B2 between specified bounds.
SubListKernel(int, int, Kernel<S>) - Constructor for class fr.lip6.jkernelmachines.kernel.extra.bag.SubListKernel
SubListKernel2<S,T extends java.util.List<S>> - Class in fr.lip6.jkernelmachines.kernel.extra.bag: Default kernel on bags : sum all kernel values involving an element from B1 and an element from B2 between specified bounds.
SubListKernel2(Kernel<S>, int, int, int, int) - Constructor for class fr.lip6.jkernelmachines.kernel.extra.bag.SubListKernel2
SubListMaxKernel<S,T extends java.util.List<S>> - Class in fr.lip6.jkernelmachines.kernel.extra.bag: max value of kernel between two bags
SubListMaxKernel(int, int, Kernel<S>) - Constructor for class fr.lip6.jkernelmachines.kernel.extra.bag.SubListMaxKernel
svm - Variable in class fr.lip6.jkernelmachines.classifier.SimpleMKL
SVMExample - Class in fr.lip6.jkernelmachines.example: This class is a very simple introduction to the use of jkernelmachines.
SVMExample() - Constructor for class fr.lip6.jkernelmachines.example.SVMExample

T

ThreadedKernel<T> - Class in fr.lip6.jkernelmachines.kernel: Simple multithreaded implementation over a given Kernel.
ThreadedKernel(Kernel<T>) - Constructor for class fr.lip6.jkernelmachines.kernel.ThreadedKernel: MultiThread the given kernel
ThreadedMatrixOperations - Class in fr.lip6.jkernelmachines.util.algebra: This class provides multithreaded basic linear algebra operations on matrices.
ThreadedMatrixOperations() - Constructor for class fr.lip6.jkernelmachines.util.algebra.ThreadedMatrixOperations
ThreadedMatrixOperator - Class in fr.lip6.jkernelmachines.threading: Utility for the parallelization of matrix operations.
ThreadedMatrixOperator() - Constructor for class fr.lip6.jkernelmachines.threading.ThreadedMatrixOperator
ThreadedMatrixVectorOperations - Class in fr.lip6.jkernelmachines.util.algebra: This class provides multithreaded operations between matrices and vectors
ThreadedMatrixVectorOperations() - Constructor for class fr.lip6.jkernelmachines.util.algebra.ThreadedMatrixVectorOperations
ThreadedProductKernel<T> - Class in fr.lip6.jkernelmachines.kernel.adaptative: Major kernel computed as a weighted product of minor kernels : K = k_i^{w_i}
Computation of the kernel matrix is done by running a thread on sub matrices.
ThreadedProductKernel() - Constructor for class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedProductKernel
ThreadedProductKernel(Map<Kernel<T>, Double>) - Constructor for class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedProductKernel: Sets the weights to h.
ThreadedSumKernel<T> - Class in fr.lip6.jkernelmachines.kernel.adaptative: Major kernel computed as a weighted sum of minor kernels : K = w_i * k_i
Computation of the kernel matrix is done by running a thread on sub matrices.
ThreadedSumKernel() - Constructor for class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedSumKernel
ThreadedSumKernel(Map<Kernel<T>, Double>) - Constructor for class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedSumKernel: Sets the weights to h.
ThreadedVectorOperator - Class in fr.lip6.jkernelmachines.threading: Utility for the parallelization of vector operations.
ThreadedVectorOperator() - Constructor for class fr.lip6.jkernelmachines.threading.ThreadedVectorOperator
ThreadPoolServer - Class in fr.lip6.jkernelmachines.threading: Threading utility used by various algorithm for obtaining a pool of threads.
ThreadPoolServer() - Constructor for class fr.lip6.jkernelmachines.threading.ThreadPoolServer
toString() - Method in class fr.lip6.jkernelmachines.kernel.Kernel: return the name of this kernel
toString() - Method in class fr.lip6.jkernelmachines.kernel.SimpleCacheKernel
train - Variable in class fr.lip6.jkernelmachines.active.ActiveLearner
train(List<TrainingSample<T>>) - Method in interface fr.lip6.jkernelmachines.classifier.Classifier: Replace the current training list and train the classifier
train(List<TrainingSample<double[]>>) - Method in class fr.lip6.jkernelmachines.classifier.DoubleLLSVM
train(TrainingSample<double[]>) - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM
train(List<TrainingSample<double[]>>) - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM
train(List<TrainingSample<double[]>>) - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL
train(List<TrainingSample<double[]>>) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG
train(List<TrainingSample<double[]>>) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD
train(TrainingSample<double[]>) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD
train(List<TrainingSample<double[]>>) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN
train(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.classifier.GradMKL
train(TrainingSample<T>) - Method in class fr.lip6.jkernelmachines.classifier.LaSVM: Deprecated.
train(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.classifier.LaSVM
train(TrainingSample<T>) - Method in class fr.lip6.jkernelmachines.classifier.LaSVMI: Incremental train adding a single sample (performs a full retrain on the whole list a samples)
train(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.classifier.LaSVMI
train(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA
train(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.classifier.multiclass.OneAgainstAll
train(TrainingSample<T>) - Method in class fr.lip6.jkernelmachines.classifier.NystromLSSVM
train(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.classifier.NystromLSSVM
train(TrainingSample<T>) - Method in interface fr.lip6.jkernelmachines.classifier.OnlineClassifier: Add a single example to the current training set and train the classifier
train(TrainingSample<T>) - Method in class fr.lip6.jkernelmachines.classifier.ParzenClassifier
train(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.classifier.ParzenClassifier
train(TrainingSample<T>) - Method in class fr.lip6.jkernelmachines.classifier.SDCA
train(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.classifier.SDCA
train(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL
train(TrainingSample<T>) - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM
train(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM
train(List<TrainingSample<T>>, List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLight
train(List<TrainingSample<double[]>>, List<TrainingSample<double[]>>) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos
train(List<TrainingSample<double[]>>, List<TrainingSample<double[]>>) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightSGDQN
train(List<TrainingSample<T>>, List<TrainingSample<T>>) - Method in interface fr.lip6.jkernelmachines.classifier.transductive.TransductiveClassifier: Train the classifier on trainList, with the help of testList in a transductive way.
train(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.classifier.TSMKL
train(T) - Method in interface fr.lip6.jkernelmachines.density.DensityFunction: Adds a sample to the training set and train the density function
train(List<T>) - Method in interface fr.lip6.jkernelmachines.density.DensityFunction: Train the density function on the specified training set
train(double[]) - Method in class fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel
train(List<double[]>) - Method in class fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel
train(double[]) - Method in class fr.lip6.jkernelmachines.density.DoubleKMeans
train(List<double[]>) - Method in class fr.lip6.jkernelmachines.density.DoubleKMeans
train(T) - Method in class fr.lip6.jkernelmachines.density.ParzenDensity
train(List<T>) - Method in class fr.lip6.jkernelmachines.density.ParzenDensity
train(T) - Method in class fr.lip6.jkernelmachines.density.SDCADensity
train(List<T>) - Method in class fr.lip6.jkernelmachines.density.SDCADensity
train(T) - Method in class fr.lip6.jkernelmachines.density.SimpleMKLDensity
train(List<T>) - Method in class fr.lip6.jkernelmachines.density.SimpleMKLDensity
train(T) - Method in class fr.lip6.jkernelmachines.density.SMODensity
train(List<T>) - Method in class fr.lip6.jkernelmachines.density.SMODensity
train(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.kernel.extra.NystromKernel: Train the Nystrom approx on a full training set.
train(List<TrainingSample<double[]>>) - Method in class fr.lip6.jkernelmachines.projection.DoublePCA: Train the projectors on a given data-set.
train(List<TrainingSample<T>>) - Method in class fr.lip6.jkernelmachines.projection.KernelPCA
TrainingSample<T> - Class in fr.lip6.jkernelmachines.type: Simple class of training sample that contains the generic of sample and the associated label.
TrainingSample(T, int) - Constructor for class fr.lip6.jkernelmachines.type.TrainingSample
TrainingSampleStream<T> - Interface in fr.lip6.jkernelmachines.type: Interface to streams of training samples, useful for online training.
trainOnce(List<TrainingSample<double[]>>) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD: Update the separating hyperplane by learning one epoch on given training list
trans(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Computes the transposed matrix of a matrix
trans(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.ThreadedMatrixOperations: Computes the transposed matrix of a matrix
TransductiveClassifier<T> - Interface in fr.lip6.jkernelmachines.classifier.transductive: Interface for transductive classifiers.
transi(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Computes the transposed matrix of a symmetric matrix in place
transi(double[][], double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Computes the transposed matrix of a matrix in place
transi(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.ThreadedMatrixOperations: Computes the transposed matrix of a symmetric matrix in place
transMul(double[][], double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Computes the transpose multiplication between two matrices : C = A' * B
transMul(double[][], double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.ThreadedMatrixOperations: Computes the transpose multiplication between two matrices : C = A' * B
transMuli(double[][], double[][], double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Computes the transpose multiplication between two matrices : C = A' * B
transMuli(double[][], double[][], double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.ThreadedMatrixOperations: Computes the transpose multiplication between two matrices : C = A' * B
tri(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations: Performs the trigonalization of a symmetric matrix: A = Q * T * Q' with Q orthonormal and T tridiagonal
tri_givens(double[][], boolean) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations
tri_householder(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations
tri_lancsos(double[][]) - Static method in class fr.lip6.jkernelmachines.util.algebra.MatrixOperations
TSMKL<T> - Class in fr.lip6.jkernelmachines.classifier: Implementation of the Two-Stages MKL solver by Abhishek Kumar et al.
This is a original implementation using the tools available in JKernelMachines, and not a Java conversion of the original matlab code.
TSMKL() - Constructor for class fr.lip6.jkernelmachines.classifier.TSMKL

U

UnormalizedListKernel<S,T extends java.util.List<S>> - Class in fr.lip6.jkernelmachines.kernel.extra.bag: Default kernel on bags : sum all kernel values involving an element from B1 and an element from B2 between specified bounds.
UnormalizedListKernel(Kernel<S>) - Constructor for class fr.lip6.jkernelmachines.kernel.extra.bag.UnormalizedListKernel
UnormalizedSimpleListKernel<S,T extends java.util.List<S>> - Class in fr.lip6.jkernelmachines.kernel.extra.bag: Default kernel on bags : sum all kernel values involving an element from B1 and an element from B2 between specified bounds.
UnormalizedSimpleListKernel(Kernel<S>) - Constructor for class fr.lip6.jkernelmachines.kernel.extra.bag.UnormalizedSimpleListKernel
UnormalizedSubListKernel<S,T extends java.util.List<S>> - Class in fr.lip6.jkernelmachines.kernel.extra.bag: Default kernel on bags : sum all kernel values involving an element from B1 and an element from B2 between specified bounds.
UnormalizedSubListKernel(int, int, Kernel<S>) - Constructor for class fr.lip6.jkernelmachines.kernel.extra.bag.UnormalizedSubListKernel
updateClassifier(int) - Method in class fr.lip6.jkernelmachines.active.ActiveLearner: perform nbSample updates of the classifier using the active strategy
updateClassifier(int) - Method in class fr.lip6.jkernelmachines.active.BestAL
updateClassifier(int) - Method in class fr.lip6.jkernelmachines.active.MulticlassSimpleAL
updateClassifier(int) - Method in class fr.lip6.jkernelmachines.active.RandomAL
updateClassifier(int) - Method in class fr.lip6.jkernelmachines.active.SimpleAL

V

valueOf(T) - Method in interface fr.lip6.jkernelmachines.classifier.Classifier: Computes the category of the provided example
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.classifier.DoubleLLSVM
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.classifier.DoublePegasosSVM
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.classifier.DoubleQNPKL
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSAG
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGD
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN
valueOf(T) - Method in class fr.lip6.jkernelmachines.classifier.GradMKL
valueOf(T) - Method in class fr.lip6.jkernelmachines.classifier.LaSVM
valueOf(T) - Method in class fr.lip6.jkernelmachines.classifier.LaSVMI
valueOf(T) - Method in class fr.lip6.jkernelmachines.classifier.multiclass.MulticlassSDCA
valueOf(T) - Method in class fr.lip6.jkernelmachines.classifier.multiclass.OneAgainstAll
valueOf(T) - Method in class fr.lip6.jkernelmachines.classifier.NystromLSSVM
valueOf(T) - Method in class fr.lip6.jkernelmachines.classifier.ParzenClassifier
valueOf(T) - Method in class fr.lip6.jkernelmachines.classifier.SDCA
valueOf(T) - Method in class fr.lip6.jkernelmachines.classifier.SimpleMKL
valueOf(T) - Method in class fr.lip6.jkernelmachines.classifier.SMOSVM
valueOf(T) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLight
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.classifier.transductive.S3VMLightSGDQN
valueOf(T) - Method in interface fr.lip6.jkernelmachines.classifier.transductive.TransductiveClassifier: prediction output for t.
valueOf(T) - Method in class fr.lip6.jkernelmachines.classifier.TSMKL
valueOf(T) - Method in interface fr.lip6.jkernelmachines.density.DensityFunction: Value of the density function for the specified sample
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.density.DoubleKMeans
valueOf(T) - Method in class fr.lip6.jkernelmachines.density.ParzenDensity
valueOf(T) - Method in class fr.lip6.jkernelmachines.density.SDCADensity
valueOf(T) - Method in class fr.lip6.jkernelmachines.density.SimpleMKLDensity
valueOf(T) - Method in class fr.lip6.jkernelmachines.density.SMODensity
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.GaussianProductKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.GaussianProductKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedProductKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedProductKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedSumKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.ThreadedSumKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedProductKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedProductKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedSumKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.adaptative.WeightedSumKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.ListKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.ListKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.SimpleListKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.SimpleListKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.SimpleSubListKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.SimpleSubListKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.SubListKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.SubListKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.SubListKernel2
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.SubListKernel2
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.SubListMaxKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.SubListMaxKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.UnormalizedListKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.UnormalizedListKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.UnormalizedSimpleListKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.UnormalizedSimpleListKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.UnormalizedSubListKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.extra.bag.UnormalizedSubListKernel
valueOf(Integer, Integer) - Method in class fr.lip6.jkernelmachines.kernel.extra.CustomMatrixKernel
valueOf(Integer) - Method in class fr.lip6.jkernelmachines.kernel.extra.CustomMatrixKernel
valueOf(Integer, Integer) - Method in class fr.lip6.jkernelmachines.kernel.extra.CustomTrainTestMatrixKernel
valueOf(Integer) - Method in class fr.lip6.jkernelmachines.kernel.extra.CustomTrainTestMatrixKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.extra.NystromKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.extra.NystromKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.extra.PowerKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.extra.PowerKernel
valueOf(S, S) - Method in class fr.lip6.jkernelmachines.kernel.GaussianICKernel
valueOf(S) - Method in class fr.lip6.jkernelmachines.kernel.GaussianICKernel
valueOf(S, S) - Method in class fr.lip6.jkernelmachines.kernel.IndexedCacheKernel
valueOf(S) - Method in class fr.lip6.jkernelmachines.kernel.IndexedCacheKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.Kernel: compute the kernel similarity between two element of input space
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.Kernel: kernel similarity to zero
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.NormalizedKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.NormalizedKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.SimpleCacheKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.SimpleCacheKernel
valueOf(T, T) - Method in class fr.lip6.jkernelmachines.kernel.ThreadedKernel
valueOf(T) - Method in class fr.lip6.jkernelmachines.kernel.ThreadedKernel
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi1
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi1
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi2
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussChi2
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussL2
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleGaussL2
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleHPolynomial
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleHPolynomial
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleLinear
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleLinear
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoublePolynomial
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoublePolynomial
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleTriangleL2
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.DoubleTriangleL2
valueOf(float[], float[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.FloatGaussChi2
valueOf(float[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.FloatGaussChi2
valueOf(float[], float[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.FloatLinear
valueOf(float[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.FloatLinear
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussChi2
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussChi2
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussL2
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleGaussL2
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleLinear
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleLinear
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleTriangleL2
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.GeneralizedDoubleTriangleL2
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussChi2
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussChi2
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussL2
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleGaussL2
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleHPolynomial
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleHPolynomial
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleLinear
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoubleLinear
valueOf(double[], double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoublePolynomial
valueOf(double[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.index.IndexDoublePolynomial
valueOf(int[], int[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussChi1
valueOf(int[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussChi1
valueOf(int[], int[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussChi2
valueOf(int[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussChi2
valueOf(int[], int[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussL2
valueOf(int[]) - Method in class fr.lip6.jkernelmachines.kernel.typed.IntGaussL2
valueOf(String, String) - Method in class fr.lip6.jkernelmachines.kernel.typed.NormalizedStringNGram
valueOf(String) - Method in class fr.lip6.jkernelmachines.kernel.typed.NormalizedStringNGram
valueOf(String, String) - Method in class fr.lip6.jkernelmachines.kernel.typed.StringNGram
valueOf(String) - Method in class fr.lip6.jkernelmachines.kernel.typed.StringNGram
VectorOperations - Class in fr.lip6.jkernelmachines.util.algebra: This class provides level 1 (vector) basic linear algebra operations.
VectorOperations() - Constructor for class fr.lip6.jkernelmachines.util.algebra.VectorOperations
VERBOSE - Static variable in class fr.lip6.jkernelmachines.classifier.DoubleSGDQN: Sets the verbosity of training procedure (if true, details are printed during learning)
VOCExample - Class in fr.lip6.jkernelmachines.example: Simple program to compute the average precision for the PASCAL VOC challenge.
VOCExample() - Constructor for class fr.lip6.jkernelmachines.example.VOCExample

W

WeightedProductKernel<T> - Class in fr.lip6.jkernelmachines.kernel.adaptative: performs a weighted product of several minor kernels, non threaded version.
WeightedProductKernel() - Constructor for class fr.lip6.jkernelmachines.kernel.adaptative.WeightedProductKernel
WeightedSumKernel<T> - Class in fr.lip6.jkernelmachines.kernel.adaptative: Major kernel computed as a weighted sum of minor kernels : K = w_i * k_i
Non-threaded version
WeightedSumKernel() - Constructor for class fr.lip6.jkernelmachines.kernel.adaptative.WeightedSumKernel
WeightedSumKernel(Hashtable<Kernel<T>, Double>) - Constructor for class fr.lip6.jkernelmachines.kernel.adaptative.WeightedSumKernel: Sets the weights to h.
writeFile(String, List<double[]>) - Method in class fr.lip6.jkernelmachines.io.FvecImporter: Writes a list of features (double arrays) to a file in fvec (INRIA) format.

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