Uses of Interface
org.drip.spaces.instance.GeneralizedValidatedVector

Packages that use GeneralizedValidatedVector

Package	Description
org.drip.learning.rxtor1	Statistical Learning Empirical Loss Penalizer
org.drip.spaces.cover	Vector Spaces Covering Number Estimator
org.drip.spaces.functionclass	Normed Finite Spaces Function Class
org.drip.spaces.instance	Validated Continuous/Combinatorial Metric Spaces
org.drip.spaces.rxtor1	Rx To R1 Normed Function Spaces
org.drip.spaces.rxtord	Rx To Rd Normed Function Spaces

Uses of GeneralizedValidatedVector in org.drip.learning.rxtor1

Methods in org.drip.learning.rxtor1 that return GeneralizedValidatedVector

Modifier and Type	Method	Description
GeneralizedValidatedVector	EmpiricalPenaltySupremumEstimator.empiricalOutcomes()	Retrieve the Validated Outcome Instance

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

Modifier and Type	Method	Description
double	EmpiricalLearningMetricEstimator.empiricalLoss(R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Empirical Sample Loss
double	EmpiricalLearningMetricEstimator.empiricalLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Empirical Sample Loss
double	L1LossLearner.empiricalLoss(R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	L1LossLearner.empiricalLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	LipschitzLossLearner.empiricalLoss(R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	LipschitzLossLearner.empiricalLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	LpLossLearner.empiricalLoss(R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	LpLossLearner.empiricalLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.empiricalRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Empirical Sample Risk
double	EmpiricalLearningMetricEstimator.empiricalRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Empirical Sample Risk
double	L1LossLearner.empiricalRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	L1LossLearner.empiricalRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	LipschitzLossLearner.empiricalRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	LipschitzLossLearner.empiricalRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	LpLossLearner.empiricalRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	LpLossLearner.empiricalRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	GeneralizedLearner.genericCoveringProbabilityBound(GeneralizedValidatedVector gvvi, int iSampleSize, double dblEpsilon, boolean bSupremum)	Compute the Sample/Data Dependent Upper Bound of the Probability of the Absolute Deviation between the Empirical and the Population Means using the Function Class Supremum Covering Number for General-Purpose Learning
double	GeneralizedLearner.genericCoveringSampleSize(GeneralizedValidatedVector gvvi, double dblEpsilon, double dblDeviationUpperProbabilityBound, boolean bSupremum)	Compute the Minimum Possible Sample Size needed to generate the required Upper Probability Bound for the Specified Empirical Deviation using the Covering Number Convergence Bounds.
double	ApproximateLipschitzLossLearner.lossSampleCoveringNumber(GeneralizedValidatedVector gvvi, double dblEpsilon, boolean bSupremum)
double	EmpiricalLearningMetricEstimator.lossSampleCoveringNumber(GeneralizedValidatedVector gvvi, double dblEpsilon, boolean bSupremum)	Retrieve the Loss Class Sample Covering Number - L-Infinity or L-p based Based
double	L1LossLearner.lossSampleCoveringNumber(GeneralizedValidatedVector gvvi, double dblEpsilon, boolean bSupremum)
double	LipschitzLossLearner.lossSampleCoveringNumber(GeneralizedValidatedVector gvvi, double dblEpsilon, boolean bSupremum)
double	LpLossLearner.lossSampleCoveringNumber(GeneralizedValidatedVector gvvi, double dblEpsilon, boolean bSupremum)
double	GeneralizedLearner.regressorCoveringProbabilityBound(GeneralizedValidatedVector gvvi, int iSampleSize, double dblEpsilon, boolean bSupremum)	Compute the Sample/Data Dependent Upper Bound of the Probability of the Absolute Deviation between the Empirical and the Population Means using the Function Class Supremum Covering Number for Regression Learning
double	GeneralizedLearner.regressorCoveringSampleSize(GeneralizedValidatedVector gvvi, double dblEpsilon, double dblDeviationUpperProbabilityBound, boolean bSupremum)	Compute the Minimum Possible Sample Size needed to generate the required Upper Probability Bound for the Specified Empirical Deviation using the Covering Number Convergence Bounds for Regression Learning.
double	EmpiricalLearningMetricEstimator.regularizedLoss(R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Regularized Sample Loss (Empirical + Structural)
double	EmpiricalLearningMetricEstimator.regularizedLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Regularized Sample Loss (Empirical + Structural)
double	GeneralizedLearner.regularizedLoss(R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	GeneralizedLearner.regularizedLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.regularizedRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Regularized Sample Risk (Empirical + Structural)
double	EmpiricalLearningMetricEstimator.regularizedRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Regularized Sample Risk (Empirical + Structural)
double	GeneralizedLearner.regularizedRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	GeneralizedLearner.regularizedRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	EmpiricalLearningMetricEstimator.structuralLoss(R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvvi)	Compute the Structural Sample Loss
double	EmpiricalLearningMetricEstimator.structuralLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvvi)	Compute the Structural Sample Loss
double	GeneralizedLearner.structuralLoss(R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvvi)
double	GeneralizedLearner.structuralLoss(RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvvi)
double	EmpiricalLearningMetricEstimator.structuralRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Structural Sample Risk
double	EmpiricalLearningMetricEstimator.structuralRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Structural Sample Risk
double	GeneralizedLearner.structuralRisk(R1R1 distR1R1, R1ToR1 funcLearnerR1ToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
double	GeneralizedLearner.structuralRisk(RdR1 distRdR1, RdToR1 funcLearnerRdToR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	EmpiricalPenaltySupremumEstimator.supremum(GeneralizedValidatedVector gvviX)	Compute the Empirical Penalty Supremum for the specified R^1/R^d Input Space
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumEmpiricalLoss(GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Supremum Empirical Sample Loss
EmpiricalPenaltySupremum	GeneralizedLearner.supremumEmpiricalLoss(GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumEmpiricalRisk(R1R1 distR1R1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Supremum Empirical Sample Risk
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumEmpiricalRisk(RdR1 distRdR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Supremum Empirical Sample Risk
EmpiricalPenaltySupremum	GeneralizedLearner.supremumEmpiricalRisk(R1R1 distR1R1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	GeneralizedLearner.supremumEmpiricalRisk(RdR1 distRdR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	EmpiricalPenaltySupremumEstimator.supremumR1(GeneralizedValidatedVector gvviX)	Compute the Empirical Penalty Supremum for the specified R^1 Input Space
EmpiricalPenaltySupremum	EmpiricalPenaltySupremumEstimator.supremumRd(GeneralizedValidatedVector gvviX)	Compute the Empirical Penalty Supremum for the specified R^d Input Space
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumRegularizedLoss(GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Supremum Regularized Sample Loss
EmpiricalPenaltySupremum	GeneralizedLearner.supremumRegularizedLoss(GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumRegularizedRisk(R1R1 distR1R1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Supremum Regularized Sample Risk
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumRegularizedRisk(RdR1 distRdR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Supremum Regularized Sample Risk
EmpiricalPenaltySupremum	GeneralizedLearner.supremumRegularizedRisk(R1R1 distR1R1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	GeneralizedLearner.supremumRegularizedRisk(RdR1 distRdR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumStructuralLoss(GeneralizedValidatedVector gvviX)	Compute the Supremum Structural Sample Loss
EmpiricalPenaltySupremum	GeneralizedLearner.supremumStructuralLoss(GeneralizedValidatedVector gvviX)
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumStructuralRisk(R1R1 distR1R1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Supremum Structural Sample Risk
EmpiricalPenaltySupremum	EmpiricalLearningMetricEstimator.supremumStructuralRisk(RdR1 distRdR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)	Compute the Supremum Structural Sample Risk
EmpiricalPenaltySupremum	GeneralizedLearner.supremumStructuralRisk(R1R1 distR1R1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)
EmpiricalPenaltySupremum	GeneralizedLearner.supremumStructuralRisk(RdR1 distRdR1, GeneralizedValidatedVector gvviX, GeneralizedValidatedVector gvviY)

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

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

Uses of GeneralizedValidatedVector in org.drip.spaces.cover

Methods in org.drip.spaces.cover with parameters of type GeneralizedValidatedVector

Modifier and Type	Method	Description
CarlStephaniNormedBounds	CarlStephaniProductBounds.sampleMetricEntropyNorm(GeneralizedValidatedVector generalizedValidatedVectorA, GeneralizedValidatedVector generalizedValidatedVectorB, int entropyNumberIndex, boolean useSupremumNorm)	Compute the Sample Metric Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Norm
double	CarlStephaniProductBounds.sampleMetricEntropyNumber(GeneralizedValidatedVector generalizedValidatedVectorA, GeneralizedValidatedVector generalizedValidatedVectorB, int entropyNumberIndexA, int entropyNumberIndexB)	Compute the Upper Bound for the Entropy Number of the Operator Sample Metric Covering Number Convolution Product across both the Function Classes
CarlStephaniNormedBounds	CarlStephaniProductBounds.sampleSupremumEntropyNorm(GeneralizedValidatedVector generalizedValidatedVectorA, GeneralizedValidatedVector generalizedValidatedVectorB, int entropyNumberIndex, boolean useSupremumNorm)	Compute the Sample Supremum Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum Population Norm
double	CarlStephaniProductBounds.sampleSupremumEntropyNumber(GeneralizedValidatedVector generalizedValidatedVectorA, GeneralizedValidatedVector generalizedValidatedVectorB, int entropyNumberIndexA, int entropyNumberIndexB)	Compute the Upper Bound for the Entropy Number of the Operator Sample Supremum Covering Number Convolution Product across both the Function Classes

Uses of GeneralizedValidatedVector in org.drip.spaces.functionclass

Methods in org.drip.spaces.functionclass with parameters of type GeneralizedValidatedVector

Modifier and Type	Method	Description
double	NormedRxToNormedR1Finite.operatorSampleMetricNorm(GeneralizedValidatedVector generalizedValidatedVector)
double	NormedRxToNormedRdFinite.operatorSampleMetricNorm(GeneralizedValidatedVector generalizedValidatedVector)
abstract double	NormedRxToNormedRxFinite.operatorSampleMetricNorm(GeneralizedValidatedVector generalizedValidatedVector)	Compute the Operator Sample Metric Norm
double	NormedRxToNormedR1Finite.operatorSampleSupremumNorm(GeneralizedValidatedVector generalizedValidatedVector)
double	NormedRxToNormedRdFinite.operatorSampleSupremumNorm(GeneralizedValidatedVector generalizedValidatedVector)
abstract double	NormedRxToNormedRxFinite.operatorSampleSupremumNorm(GeneralizedValidatedVector generalizedValidatedVector)	Compute the Operator Sample Supremum Norm
double	NormedRxToNormedR1Finite.sampleCoveringNumber(GeneralizedValidatedVector generalizedValidatedVector, double cover)	Estimate for the Scale-Sensitive Sample Covering Number for the specified Cover Size
double[]	NormedRxToNormedRdFinite.sampleCoveringNumber(GeneralizedValidatedVector generalizedValidatedVector, double cover)	Estimate for the Scale-Sensitive Sample Covering Number Array for the specified Cover Size
double[]	NormedRxToNormedRdFinite.sampleCoveringNumber(GeneralizedValidatedVector generalizedValidatedVector, double[] coverArray)	Estimate for the Scale-Sensitive Sample Covering Number Array for the specified Cover Size
MaureyOperatorCoveringBounds	NormedRxToNormedRxFinite.sampleMetricCoveringBounds(GeneralizedValidatedVector generalizedValidatedVector)	Compute the Maurey Covering Number Upper Bounds for Operator Sample Metric Norm
double[]	NormedRxToNormedRdFinite.sampleRdMetricNorm(GeneralizedValidatedVector generalizedValidatedVector)	Compute the Sample Rd Metric Norm
double[]	NormedRxToNormedRdFinite.sampleRdSupremumNorm(GeneralizedValidatedVector generalizedValidatedVector)	Compute the Sample Rd Supremum Norm
MaureyOperatorCoveringBounds	NormedRxToNormedRxFinite.sampleSupremumCoveringBounds(GeneralizedValidatedVector generalizedValidatedVector)	Compute the Maurey Covering Number Upper Bounds for Operator Sample Supremum Norm
double	NormedRxToNormedR1Finite.sampleSupremumCoveringNumber(GeneralizedValidatedVector generalizedValidatedVector, double cover)	Estimate for the Scale-Sensitive Sample Supremum Covering Number for the specified Cover Size
double[]	NormedRxToNormedRdFinite.sampleSupremumCoveringNumber(GeneralizedValidatedVector generalizedValidatedVector, double cover)	Estimate for the Scale-Sensitive Sample Supremum Covering Number for the specified Cover Size
double[]	NormedRxToNormedRdFinite.sampleSupremumCoveringNumber(GeneralizedValidatedVector generalizedValidatedVector, double[] coverArray)	Estimate for the Scale-Sensitive Sample Supremum Covering Number for the specified Cover Size
FunctionClassCoveringBounds	NormedRxToNormedRxFinite.scaleSensitiveCoveringBounds(GeneralizedValidatedVector generalizedValidatedVector, R1ToR1 r1ToR1FatShatteringFunction)	Retrieve the Scale-Sensitive Covering Number Upper/Lower Bounds given the Specified Sample for the Function Class

Uses of GeneralizedValidatedVector in org.drip.spaces.instance

Classes in org.drip.spaces.instance that implement GeneralizedValidatedVector

Modifier and Type	Class	Description
class	ValidatedR1	ValidatedR1 holds the Validated R1 Vector Instance Sequence and the Corresponding Generalized Vector Space Type.
class	ValidatedR1Combinatorial	ValidatedR1Combinatorial holds the Validated R1 Combinatorial Vector Instance Sequence and the corresponding Generalized Vector Space Type.
class	ValidatedR1Continuous	ValidatedR1Continuous holds the Validated R1 Continuous Vector Instance Sequence and the Corresponding Generalized Vector Space Type.
class	ValidatedRd	ValidatedRd holds the Validated Rd Vector Instance Sequence and the Corresponding Generalized Vector Space Type.
class	ValidatedRdCombinatorial	ValidatedRdCombinatorial holds the Validated Rd Vector Instance Sequence and the Corresponding Generalized Vector Space Type.
class	ValidatedRdContinuous	ValidatedRdContinuous holds the Validated Rd Continuous Vector Instance Sequence and the corresponding Generalized Vector Space Type.

Uses of GeneralizedValidatedVector in org.drip.spaces.rxtor1

Methods in org.drip.spaces.rxtor1 with parameters of type GeneralizedValidatedVector

Modifier and Type	Method	Description
double	NormedRxToNormedR1.sampleCoveringNumber(GeneralizedValidatedVector generalizedValidatedVector, double cover)	Retrieve the Sample Covering Number
double	NormedR1ToNormedR1.sampleMetricNorm(GeneralizedValidatedVector generalizedValidatedVector)	Retrieve the Sample Metric Norm
double	NormedRdToNormedR1.sampleMetricNorm(GeneralizedValidatedVector generalizedValidatedVector)	Retrieve the Sample Metric Norm
abstract double	NormedRxToNormedR1.sampleMetricNorm(GeneralizedValidatedVector generalizedValidatedVector)	Retrieve the Sample Metric Norm
double	NormedRxToNormedR1.sampleSupremumCoveringNumber(GeneralizedValidatedVector generalizedValidatedVector, double cover)	Retrieve the Sample Supremum Covering Number
double	NormedR1ToNormedR1.sampleSupremumNorm(GeneralizedValidatedVector generalizedValidatedVector)	Retrieve the Sample Supremum Norm
double	NormedRdToNormedR1.sampleSupremumNorm(GeneralizedValidatedVector generalizedValidatedVector)	Retrieve the Sample Supremum Norm
abstract double	NormedRxToNormedR1.sampleSupremumNorm(GeneralizedValidatedVector generalizedValidatedVector)	Retrieve the Sample Supremum Norm

Uses of GeneralizedValidatedVector in org.drip.spaces.rxtord

Methods in org.drip.spaces.rxtord with parameters of type GeneralizedValidatedVector

Modifier and Type	Method	Description
double[]	NormedRxToNormedRd.sampleCoveringNumber(GeneralizedValidatedVector generalizedValidatedVector, double cover)	Retrieve the Sample Covering Number Array
double[]	NormedR1ToNormedRd.sampleMetricNorm(GeneralizedValidatedVector generalizedValidatedVector)	Retrieve the Sample Metric Norm Array
double[]	NormedRdToNormedRd.sampleMetricNorm(GeneralizedValidatedVector generalizedValidatedVector)	Retrieve the Sample Metric Norm Array
abstract double[]	NormedRxToNormedRd.sampleMetricNorm(GeneralizedValidatedVector generalizedValidatedVector)	Retrieve the Sample Metric Norm Array
double[]	NormedRxToNormedRd.sampleSupremumCoveringNumber(GeneralizedValidatedVector generalizedValidatedVector, double cover)	Retrieve the Sample Supremum Covering Number Array
double[]	NormedR1ToNormedRd.sampleSupremumNorm(GeneralizedValidatedVector generalizedValidatedVector)	Retrieve the Sample Supremum Norm Array
double[]	NormedRdToNormedRd.sampleSupremumNorm(GeneralizedValidatedVector generalizedValidatedVector)	Retrieve the Sample Supremum Norm Array
abstract double[]	NormedRxToNormedRd.sampleSupremumNorm(GeneralizedValidatedVector generalizedValidatedVector)	Retrieve the Sample Supremum Norm Array