| Package | Description |
|---|---|
| org.drip.learning.rxtor1 | |
| org.drip.spaces.cover | |
| org.drip.spaces.functionclass | |
| org.drip.spaces.instance | |
| org.drip.spaces.rxtor1 | |
| org.drip.spaces.rxtord |
| Modifier and Type | Method and Description |
|---|---|
GeneralizedValidatedVector |
EmpiricalPenaltySupremumEstimator.empiricalOutcomes()
Retrieve the Validated Outcome Instance
|
| Modifier and Type | Method and Description |
|---|---|
double |
LpLossLearner.empiricalLoss(R1ToR1 funcLearnerR1ToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
LipschitzLossLearner.empiricalLoss(R1ToR1 funcLearnerR1ToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
L1LossLearner.empiricalLoss(R1ToR1 funcLearnerR1ToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
EmpiricalLearningMetricEstimator.empiricalLoss(R1ToR1 funcLearnerR1ToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Empirical Sample Loss
|
double |
LpLossLearner.empiricalLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
LipschitzLossLearner.empiricalLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
L1LossLearner.empiricalLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
EmpiricalLearningMetricEstimator.empiricalLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Empirical Sample Loss
|
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 |
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.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 |
LpLossLearner.lossSampleCoveringNumber(GeneralizedValidatedVector gvvi,
double dblEpsilon,
boolean bSupremum) |
double |
LipschitzLossLearner.lossSampleCoveringNumber(GeneralizedValidatedVector gvvi,
double dblEpsilon,
boolean bSupremum) |
double |
L1LossLearner.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 |
ApproximateLipschitzLossLearner.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 |
GeneralizedLearner.regularizedLoss(R1ToR1 funcLearnerR1ToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
EmpiricalLearningMetricEstimator.regularizedLoss(R1ToR1 funcLearnerR1ToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Regularized Sample Loss (Empirical + Structural)
|
double |
GeneralizedLearner.regularizedLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
double |
EmpiricalLearningMetricEstimator.regularizedLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Regularized Sample Loss (Empirical + Structural)
|
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.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.structuralLoss(R1ToR1 funcLearnerR1ToR1,
GeneralizedValidatedVector gvvi) |
double |
EmpiricalLearningMetricEstimator.structuralLoss(R1ToR1 funcLearnerR1ToR1,
GeneralizedValidatedVector gvvi)
Compute the Structural Sample Loss
|
double |
GeneralizedLearner.structuralLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvvi) |
double |
EmpiricalLearningMetricEstimator.structuralLoss(RdToR1 funcLearnerRdToR1,
GeneralizedValidatedVector gvvi)
Compute the Structural Sample Loss
|
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
|
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 |
EmpiricalPenaltySupremumEstimator.supremum(GeneralizedValidatedVector gvviX)
Compute the Empirical Penalty Supremum for the specified R^1/R^d Input Space
|
EmpiricalPenaltySupremum |
GeneralizedLearner.supremumEmpiricalLoss(GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
EmpiricalPenaltySupremum |
EmpiricalLearningMetricEstimator.supremumEmpiricalLoss(GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Supremum Empirical Sample Loss
|
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.supremumEmpiricalRisk(RdR1 distRdR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
EmpiricalPenaltySupremum |
EmpiricalLearningMetricEstimator.supremumEmpiricalRisk(RdR1 distRdR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Supremum Empirical Sample Risk
|
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 |
GeneralizedLearner.supremumRegularizedLoss(GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
EmpiricalPenaltySupremum |
EmpiricalLearningMetricEstimator.supremumRegularizedLoss(GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Supremum Regularized Sample Loss
|
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.supremumRegularizedRisk(RdR1 distRdR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
EmpiricalPenaltySupremum |
EmpiricalLearningMetricEstimator.supremumRegularizedRisk(RdR1 distRdR1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Supremum Regularized Sample Risk
|
EmpiricalPenaltySupremum |
GeneralizedLearner.supremumStructuralLoss(GeneralizedValidatedVector gvviX) |
EmpiricalPenaltySupremum |
EmpiricalLearningMetricEstimator.supremumStructuralLoss(GeneralizedValidatedVector gvviX)
Compute the Supremum Structural Sample Loss
|
EmpiricalPenaltySupremum |
GeneralizedLearner.supremumStructuralRisk(R1R1 distR1R1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY) |
EmpiricalPenaltySupremum |
EmpiricalLearningMetricEstimator.supremumStructuralRisk(R1R1 distR1R1,
GeneralizedValidatedVector gvviX,
GeneralizedValidatedVector gvviY)
Compute the Supremum Structural 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
|
| Constructor and Description |
|---|
EmpiricalPenaltySupremumEstimator(int iSupremumPenaltyLossMode,
EmpiricalLearningMetricEstimator elme,
GeneralizedValidatedVector gvviY,
R1R1 distR1R1,
RdR1 distRdR1)
EmpiricalPenaltySupremumEstimator Constructor
|
| Modifier and Type | Method and Description |
|---|---|
CarlStephaniNormedBounds |
CarlStephaniProductBounds.sampleMetricEntropyNorm(GeneralizedValidatedVector gvviA,
GeneralizedValidatedVector gvviB,
int iEntropyNumberIndex,
boolean bUseSupremumNorm)
Compute the Sample Metric Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum
Population Norm
|
double |
CarlStephaniProductBounds.sampleMetricEntropyNumber(GeneralizedValidatedVector gvviA,
GeneralizedValidatedVector gvviB,
int iEntropyNumberIndexA,
int iEntropyNumberIndexB)
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 gvviA,
GeneralizedValidatedVector gvviB,
int iEntropyNumberIndex,
boolean bUseSupremumNorm)
Compute the Sample Supremum Carl-Stephani Entropy Number Upper Bound using either the Metric/Supremum
Population Norm
|
double |
CarlStephaniProductBounds.sampleSupremumEntropyNumber(GeneralizedValidatedVector gvviA,
GeneralizedValidatedVector gvviB,
int iEntropyNumberIndexA,
int iEntropyNumberIndexB)
Compute the Upper Bound for the Entropy Number of the Operator Sample Supremum Covering Number
Convolution Product across both the Function Classes
|
| Modifier and Type | Method and Description |
|---|---|
abstract double |
NormedRxToNormedRxFinite.operatorSampleMetricNorm(GeneralizedValidatedVector gvvi)
Compute the Operator Sample Metric Norm
|
double |
NormedRxToNormedRdFinite.operatorSampleMetricNorm(GeneralizedValidatedVector gvvi) |
double |
NormedRxToNormedR1Finite.operatorSampleMetricNorm(GeneralizedValidatedVector gvvi) |
abstract double |
NormedRxToNormedRxFinite.operatorSampleSupremumNorm(GeneralizedValidatedVector gvvi)
Compute the Operator Sample Supremum Norm
|
double |
NormedRxToNormedRdFinite.operatorSampleSupremumNorm(GeneralizedValidatedVector gvvi) |
double |
NormedRxToNormedR1Finite.operatorSampleSupremumNorm(GeneralizedValidatedVector gvvi) |
double[] |
NormedRxToNormedRdFinite.sampleCoveringNumber(GeneralizedValidatedVector gvvi,
double dblCover)
Estimate for the Scale-Sensitive Sample Covering Number Array for the specified Cover Size
|
double |
NormedRxToNormedR1Finite.sampleCoveringNumber(GeneralizedValidatedVector gvvi,
double dblCover)
Estimate for the Scale-Sensitive Sample Covering Number for the specified Cover Size
|
double[] |
NormedRxToNormedRdFinite.sampleCoveringNumber(GeneralizedValidatedVector gvvi,
double[] adblCover)
Estimate for the Scale-Sensitive Sample Covering Number Array for the specified Cover Size
|
MaureyOperatorCoveringBounds |
NormedRxToNormedRxFinite.sampleMetricCoveringBounds(GeneralizedValidatedVector gvvi)
Compute the Maurey Covering Number Upper Bounds for Operator Sample Metric Norm
|
double[] |
NormedRxToNormedRdFinite.sampleRdMetricNorm(GeneralizedValidatedVector gvvi)
Compute the Sample R^d Metric Norm
|
double[] |
NormedRxToNormedRdFinite.sampleRdSupremumNorm(GeneralizedValidatedVector gvvi)
Compute the Sample R^d Supremum Norm
|
MaureyOperatorCoveringBounds |
NormedRxToNormedRxFinite.sampleSupremumCoveringBounds(GeneralizedValidatedVector gvvi)
Compute the Maurey Covering Number Upper Bounds for Operator Sample Supremum Norm
|
double[] |
NormedRxToNormedRdFinite.sampleSupremumCoveringNumber(GeneralizedValidatedVector gvvi,
double dblCover)
Estimate for the Scale-Sensitive Sample Supremum Covering Number for the specified Cover Size
|
double |
NormedRxToNormedR1Finite.sampleSupremumCoveringNumber(GeneralizedValidatedVector gvvi,
double dblCover)
Estimate for the Scale-Sensitive Sample Supremum Covering Number for the specified Cover Size
|
double[] |
NormedRxToNormedRdFinite.sampleSupremumCoveringNumber(GeneralizedValidatedVector gvvi,
double[] adblCover)
Estimate for the Scale-Sensitive Sample Supremum Covering Number for the specified Cover Size
|
FunctionClassCoveringBounds |
NormedRxToNormedRxFinite.scaleSensitiveCoveringBounds(GeneralizedValidatedVector gvvi,
R1ToR1 funcR1ToR1FatShatter)
Retrieve the Scale-Sensitive Covering Number Upper/Lower Bounds given the Specified Sample for the
Function Class
|
| Modifier and Type | Class and Description |
|---|---|
class |
ValidatedR1
ValidatedR1 holds the Validated R^1 Vector Instance Sequence and the Corresponding Generalized Vector
Space Type.
|
class |
ValidatedR1Combinatorial
ValidatedR1Combinatorial holds the Validated R^1 Combinatorial Vector Instance Sequence and the
Corresponding Generalized Vector Space Type.
|
class |
ValidatedR1Continuous
ValidatedR1Continuous holds the Validated R^1 Continuous Vector Instance Sequence and the Corresponding
Generalized Vector Space Type.
|
class |
ValidatedRd
ValidatedRd holds the Validated R^d Vector Instance Sequence and the Corresponding Generalized Vector
Space Type.
|
class |
ValidatedRdCombinatorial
ValidatedRdCombinatorial holds the Validated R^d R^d Vector Instance Sequence and the Corresponding
Generalized Vector Space Type.
|
class |
ValidatedRdContinuous
ValidatedRdContinuous holds the Validated R^d Continuous Vector Instance Sequence and the Corresponding
Generalized Vector Space Type.
|
| Modifier and Type | Method and Description |
|---|---|
double |
NormedRxToNormedR1.sampleCoveringNumber(GeneralizedValidatedVector gvvi,
double dblCover)
Retrieve the Sample Covering Number
|
abstract double |
NormedRxToNormedR1.sampleMetricNorm(GeneralizedValidatedVector gvvi)
Retrieve the Sample Metric Norm
|
double |
NormedRdToNormedR1.sampleMetricNorm(GeneralizedValidatedVector gvvi) |
double |
NormedR1ToNormedR1.sampleMetricNorm(GeneralizedValidatedVector gvvi) |
double |
NormedRxToNormedR1.sampleSupremumCoveringNumber(GeneralizedValidatedVector gvvi,
double dblCover)
Retrieve the Sample Supremum Covering Number
|
abstract double |
NormedRxToNormedR1.sampleSupremumNorm(GeneralizedValidatedVector gvvi)
Retrieve the Sample Supremum Norm
|
double |
NormedRdToNormedR1.sampleSupremumNorm(GeneralizedValidatedVector gvvi) |
double |
NormedR1ToNormedR1.sampleSupremumNorm(GeneralizedValidatedVector gvvi) |
| Modifier and Type | Method and Description |
|---|---|
double[] |
NormedRxToNormedRd.sampleCoveringNumber(GeneralizedValidatedVector gvvi,
double dblCover)
Retrieve the Sample Covering Number Array
|
abstract double[] |
NormedRxToNormedRd.sampleMetricNorm(GeneralizedValidatedVector gvvi)
Retrieve the Sample Metric Norm Array
|
double[] |
NormedRdToNormedRd.sampleMetricNorm(GeneralizedValidatedVector gvvi) |
double[] |
NormedR1ToNormedRd.sampleMetricNorm(GeneralizedValidatedVector gvvi) |
double[] |
NormedRxToNormedRd.sampleSupremumCoveringNumber(GeneralizedValidatedVector gvvi,
double dblCover)
Retrieve the Sample Supremum Covering Number Array
|
abstract double[] |
NormedRxToNormedRd.sampleSupremumNorm(GeneralizedValidatedVector gvvi)
Retrieve the Sample Supremum Norm Array
|
double[] |
NormedRdToNormedRd.sampleSupremumNorm(GeneralizedValidatedVector gvvi) |
double[] |
NormedR1ToNormedRd.sampleSupremumNorm(GeneralizedValidatedVector gvvi) |