Uses of Class
org.drip.measure.continuousjoint.R1R1

Packages that use R1R1

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

Uses of R1R1 in org.drip.learning.regularization

Methods in org.drip.learning.regularization with parameters of type R1R1

Modifier and Type	Method and Description
double	RegularizerR1ToR1.structuralRisk(R1R1 distR1R1, R1ToR1 funcR1ToR1, double[] adblX, double[] adblY) Compute the Regularization Sample Structural Loss
double	RegularizerR1ContinuousToR1Continuous.structuralRisk(R1R1 distR1R1, R1ToR1 funcR1ToR1, double[] adblX, double[] adblY)
double	RegularizerR1CombinatorialToR1Continuous.structuralRisk(R1R1 distR1R1, R1ToR1 funcR1ToR1, double[] adblX, double[] adblY)

Uses of R1R1 in org.drip.learning.rxtor1

Methods in org.drip.learning.rxtor1 with parameters of type R1R1

Modifier and Type	Method and Description
double	LpLossLearner.empiricalRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	LipschitzLossLearner.empiricalRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	L1LossLearner.empiricalRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.empiricalRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Empirical Sample Risk
double	GeneralizedLearner.regularizedRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.regularizedRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Regularized Sample Risk (Empirical + Structural)
double	GeneralizedLearner.structuralRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.structuralRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Structural Sample Risk
EmpiricalPenaltySupremum	GeneralizedLearner.supremumEmpiricalRisk(R1R1 distR1R1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumEmpiricalRisk(R1R1 distR1R1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Supremum Empirical Sample Risk
EmpiricalPenaltySupremum	GeneralizedLearner.supremumRegularizedRisk(R1R1 distR1R1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumRegularizedRisk(R1R1 distR1R1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Supremum Regularized Sample Risk
EmpiricalPenaltySupremum	GeneralizedLearner.supremumStructuralRisk(R1R1 distR1R1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumStructuralRisk(R1R1 distR1R1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY) Compute the Supremum Structural Sample Risk

Constructors in org.drip.learning.rxtor1 with parameters of type R1R1

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