Uses of Class
libsvm.svm_node

Packages that use svm_node

featRep
libsvm

Uses of svm_node in featRep

Methods in featRep with parameters of type svm_node

double	FeatureRepresentation.dotProduct(svm_node[] x) Computes the expected dot product
double	FeatureRepresentation.dotProduct(svm_node[] x, svm_node[] y) Computes the expected dot product
double	FeatureRepresentation.dotProduct(svm_node[] x, svm_node[] y) Computes the expected dot product
private double	FeatureRepresentation.dotProductPDF(svm_node[] x) Computes the expected dot product based on QI-statistics
private double	FeatureRepresentation.dotProductPDF(svm_node[] x, svm_node[] y) Computes the expected dot product based on QI-statistics
private double	FeatureRepresentation.dotProductPDF(svm_node[] x, svm_node[] y) Computes the expected dot product based on QI-statistics
private double	FeatureRepresentation.dotProductUni(svm_node[] x) Computes the expected dot product based on the assumption that values of a generalization are distributed uniformly
private double	FeatureRepresentation.dotProductUni(svm_node[] x, svm_node[] y) Computes the expected dot product based on the assumption that values of a generalization are distributed uniformly
private double	FeatureRepresentation.dotProductUni(svm_node[] x, svm_node[] y) Computes the expected dot product based on the assumption that values of a generalization are distributed uniformly
double	FeatureRepresentation.squareDistance(svm_node[] x, svm_node[] y) Computes the expected square distance
double	FeatureRepresentation.squareDistance(svm_node[] x, svm_node[] y) Computes the expected square distance
private double	FeatureRepresentation.squareDistancePDF(svm_node[] x, svm_node[] y) Computes the expected square distance based on QI-statistics
private double	FeatureRepresentation.squareDistancePDF(svm_node[] x, svm_node[] y) Computes the expected square distance based on QI-statistics
private double	FeatureRepresentation.squareDistanceUni(svm_node[] x, svm_node[] y) Computes the expected square distance based on the assumption that values of a generalization are distributed uniformly
private double	FeatureRepresentation.squareDistanceUni(svm_node[] x, svm_node[] y) Computes the expected square distance based on the assumption that values of a generalization are distributed uniformly

Uses of svm_node in libsvm

Fields in libsvm declared as svm_node

(package private) svm_node[][]	svm_model.SV
private svm_node[][]	Kernel.x
svm_node[][]	svm_problem.x

Methods in libsvm with parameters of type svm_node

(package private) static double	Kernel.dot(svm_node[] x, svm_node[] y, FeatureRepresentation fr, boolean useExpectedValues)
(package private) static double	Kernel.dot(svm_node[] x, svm_node[] y, FeatureRepresentation fr, boolean useExpectedValues)
(package private) static double	Kernel.k_function(svm_node[] x, svm_node[] y, svm_parameter param)
(package private) static double	Kernel.k_function(svm_node[] x, svm_node[] y, svm_parameter param)
static double	svm.svm_predict_probability(svm_model model, svm_node[] x, double[] prob_estimates)
static void	svm.svm_predict_values(svm_model model, svm_node[] x, double[] dec_values)
static double	svm.svm_predict(svm_model model, svm_node[] x)

Constructors in libsvm with parameters of type svm_node

Kernel(int l, svm_node[][] x_, svm_parameter param)