Uses of Class
org.drip.measure.continuousjoint.RdR1

Packages that use RdR1

Package	Description
org.drip.learning.regularization
org.drip.learning.rxtor1

Uses of RdR1 in org.drip.learning.regularization

Methods in org.drip.learning.regularization with parameters of type RdR1

Modifier and Type	Method and Description
double	RegularizerRdToR1.structuralRisk(RdR1 distRdR1, RdToR1 funcRdToR1, double[][] aadblX, double[] adblY) Compute the Regularization Sample Structural Loss
double	RegularizerRdContinuousToR1Continuous.structuralRisk(RdR1 distRdR1, RdToR1 funcRdToR1, double[][] aadblX, double[] adblY)
double	RegularizerRdCombinatorialToR1Continuous.structuralRisk(RdR1 distRdR1, RdToR1 funcRdToR1, double[][] aadblX, double[] adblY)

Uses of RdR1 in org.drip.learning.rxtor1

Methods in org.drip.learning.rxtor1 with parameters of type RdR1

Modifier and Type	Method and Description
double	LpLossLearner.empiricalRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	LipschitzLossLearner.empiricalRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	L1LossLearner.empiricalRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.empiricalRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Empirical Sample Risk
double	GeneralizedLearner.regularizedRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.regularizedRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Regularized Sample Risk (Empirical + Structural)
double	GeneralizedLearner.structuralRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.structuralRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Structural Sample Risk
EmpiricalPenaltySupremum	GeneralizedLearner.supremumEmpiricalRisk(RdR1 distRdR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumEmpiricalRisk(RdR1 distRdR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Supremum Empirical Sample Risk
EmpiricalPenaltySupremum	GeneralizedLearner.supremumRegularizedRisk(RdR1 distRdR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumRegularizedRisk(RdR1 distRdR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Supremum Regularized Sample Risk
EmpiricalPenaltySupremum	GeneralizedLearner.supremumStructuralRisk(RdR1 distRdR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumStructuralRisk(RdR1 distRdR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Supremum Structural Sample Risk

Constructors in org.drip.learning.rxtor1 with parameters of type RdR1

Constructor and Description
EmpiricalPenaltySupremumEstimator(int iSupremumPenaltyLossMode, EmpiricalLearningMetricEstimator elme, GeneralizedValidatedVector gvviY, R1R1 distR1R1, RdR1 distRdR1) EmpiricalPenaltySupremumEstimator Constructor