RegularizerBuilder.java
package org.drip.learning.regularization;
/*
* -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
*/
/*!
* Copyright (C) 2020 Lakshmi Krishnamurthy
* Copyright (C) 2019 Lakshmi Krishnamurthy
* Copyright (C) 2018 Lakshmi Krishnamurthy
* Copyright (C) 2017 Lakshmi Krishnamurthy
* Copyright (C) 2016 Lakshmi Krishnamurthy
* Copyright (C) 2015 Lakshmi Krishnamurthy
*
* This file is part of DROP, an open-source library targeting analytics/risk, transaction cost analytics,
* asset liability management analytics, capital, exposure, and margin analytics, valuation adjustment
* analytics, and portfolio construction analytics within and across fixed income, credit, commodity,
* equity, FX, and structured products. It also includes auxiliary libraries for algorithm support,
* numerical analysis, numerical optimization, spline builder, model validation, statistical learning,
* and computational support.
*
* https://lakshmidrip.github.io/DROP/
*
* DROP is composed of three modules:
*
* - DROP Product Core - https://lakshmidrip.github.io/DROP-Product-Core/
* - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
* - DROP Computational Core - https://lakshmidrip.github.io/DROP-Computational-Core/
*
* DROP Product Core implements libraries for the following:
* - Fixed Income Analytics
* - Loan Analytics
* - Transaction Cost Analytics
*
* DROP Portfolio Core implements libraries for the following:
* - Asset Allocation Analytics
* - Asset Liability Management Analytics
* - Capital Estimation Analytics
* - Exposure Analytics
* - Margin Analytics
* - XVA Analytics
*
* DROP Computational Core implements libraries for the following:
* - Algorithm Support
* - Computation Support
* - Function Analysis
* - Model Validation
* - Numerical Analysis
* - Numerical Optimizer
* - Spline Builder
* - Statistical Learning
*
* Documentation for DROP is Spread Over:
*
* - Main => https://lakshmidrip.github.io/DROP/
* - Wiki => https://github.com/lakshmiDRIP/DROP/wiki
* - GitHub => https://github.com/lakshmiDRIP/DROP
* - Repo Layout Taxonomy => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
* - Javadoc => https://lakshmidrip.github.io/DROP/Javadoc/index.html
* - Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
* - Release Versions => https://lakshmidrip.github.io/DROP/version.html
* - Community Credits => https://lakshmidrip.github.io/DROP/credits.html
* - Issues Catalog => https://github.com/lakshmiDRIP/DROP/issues
* - JUnit => https://lakshmidrip.github.io/DROP/junit/index.html
* - Jacoco => https://lakshmidrip.github.io/DROP/jacoco/index.html
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
*
* You may obtain a copy of the License at
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
*
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/**
* <i>RegularizerBuilder</i> constructs Custom Regularizers for the different Normed Learner Function Types.
* The References are:
*
* <br><br>
* <ul>
* <li>
* Alon, N., S. Ben-David, N. Cesa Bianchi, and D. Haussler (1997): Scale-sensitive Dimensions, Uniform
* Convergence, and Learnability <i>Journal of Association of Computational Machinery</i> <b>44
* (4)</b> 615-631
* </li>
* <li>
* Anthony, M., and P. L. Bartlett (1999): <i>Artificial Neural Network Learning - Theoretical
* Foundations</i> <b>Cambridge University Press</b> Cambridge, UK
* </li>
* <li>
* Kearns, M. J., R. E. Schapire, and L. M. Sellie (1994): <i>Towards Efficient Agnostic Learning</i>
* Machine Learning <b>17 (2)</b> 115-141
* </li>
* <li>
* Lee, W. S., P. L. Bartlett, and R. C. Williamson (1998): The Importance of Convexity in Learning with
* Squared Loss <i>IEEE Transactions on Information Theory</i> <b>44</b> 1974-1980
* </li>
* <li>
* Vapnik, V. N. (1998): <i>Statistical learning Theory</i> <b>Wiley</b> New York
* </li>
* </ul>
*
* <br><br>
* <ul>
* <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
* <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/StatisticalLearningLibrary.md">Statistical Learning</a></li>
* <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/learning">Agnostic Learning Bounds under Empirical Loss Minimization Schemes</a></li>
* <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/learning/regularization">Statistical Learning Empirical Loss Regularizer</a></li>
* </ul>
*
* @author Lakshmi Krishnamurthy
*/
public class RegularizerBuilder {
/**
* Construct an Instance of R^1 Combinatorial To R^1 Continuous Regularizer
*
* @param funcRegularizerR1ToR1 The R^1 To R^1 Regularizer Function
* @param funcSpaceR1ToR1 The R^1 Combinatorial To R^1 Continuous Learner Function Space
* @param dblLambda The Regularization Lambda
*
* @return The R^1 Combinatorial To R^1 Continuous Regularizer Instance
*/
public static final org.drip.learning.regularization.RegularizerR1ToR1 R1CombinatorialToR1Continuous (
final org.drip.function.definition.R1ToR1 funcRegularizerR1ToR1,
final org.drip.spaces.rxtor1.NormedR1CombinatorialToR1Continuous funcSpaceR1ToR1,
final double dblLambda)
{
try {
return null == funcSpaceR1ToR1 ? null : new
org.drip.learning.regularization.RegularizerR1CombinatorialToR1Continuous
(funcRegularizerR1ToR1, (org.drip.spaces.metric.R1Combinatorial)
funcSpaceR1ToR1.inputMetricVectorSpace(), (org.drip.spaces.metric.R1Continuous)
funcSpaceR1ToR1.outputMetricVectorSpace(), dblLambda);
} catch (java.lang.Exception e) {
e.printStackTrace();
}
return null;
}
/**
* Construct an Instance of R^1 Continuous To R^1 Continuous Regularizer
*
* @param funcRegularizerR1ToR1 The R^1 To R^1 Regularizer Function
* @param funcSpaceR1ToR1 The R^1 Continuous To R^1 Continuous Learner Function Space
* @param dblLambda The Regularization Lambda
*
* @return The R^1 Continuous To R^1 Continuous Regularizer Instance
*/
public static final org.drip.learning.regularization.RegularizerR1ToR1 R1ContinuousToR1Continuous (
final org.drip.function.definition.R1ToR1 funcRegularizerR1ToR1,
final org.drip.spaces.rxtor1.NormedR1ContinuousToR1Continuous funcSpaceR1ToR1,
final double dblLambda)
{
try {
return null == funcSpaceR1ToR1 ? null : new
org.drip.learning.regularization.RegularizerR1ContinuousToR1Continuous
(funcRegularizerR1ToR1, (org.drip.spaces.metric.R1Continuous)
funcSpaceR1ToR1.inputMetricVectorSpace(), (org.drip.spaces.metric.R1Continuous)
funcSpaceR1ToR1.outputMetricVectorSpace(), dblLambda);
} catch (java.lang.Exception e) {
e.printStackTrace();
}
return null;
}
/**
* Construct an Instance of R^1 Combinatorial/Continuous To R^1 Continuous Regularizer
*
* @param funcRegularizerR1ToR1 The R^1 To R^1 Regularizer Function
* @param r1Input The R^1 Combinatorial/Continuous Input Space
* @param r1ContinuousOutput The R^1 Continuous Output Space
* @param dblLambda The Regularization Lambda
*
* @return The R^1 Combinatorial/Continuous To R^1 Continuous Regularizer Instance
*/
public static final org.drip.learning.regularization.RegularizerR1ToR1 ToR1Continuous (
final org.drip.function.definition.R1ToR1 funcRegularizerR1ToR1,
final org.drip.spaces.metric.R1Normed r1Input,
final org.drip.spaces.metric.R1Continuous r1ContinuousOutput,
final double dblLambda)
{
if (null == r1Input) return null;
try {
if (r1Input instanceof org.drip.spaces.metric.R1Continuous)
return new org.drip.learning.regularization.RegularizerR1ContinuousToR1Continuous
(funcRegularizerR1ToR1, (org.drip.spaces.metric.R1Continuous) r1Input,
r1ContinuousOutput, dblLambda);
return new org.drip.learning.regularization.RegularizerR1CombinatorialToR1Continuous
(funcRegularizerR1ToR1, (org.drip.spaces.metric.R1Combinatorial) r1Input,
r1ContinuousOutput, dblLambda);
} catch (java.lang.Exception e) {
e.printStackTrace();
}
return null;
}
/**
* Construct an Instance of R^d Combinatorial To R^1 Continuous Regularizer
*
* @param funcRegularizerRdToR1 The R^d To R^1 Regularizer Function
* @param funcSpaceRdToR1 The R^d Combinatorial To R^1 Continuous Learner Function Space
* @param dblLambda The Regularization Lambda
*
* @return The R^d Combinatorial To R^1 Continuous Regularizer Instance
*/
public static final org.drip.learning.regularization.RegularizerRdToR1 RdCombinatorialToR1Continuous (
final org.drip.function.definition.RdToR1 funcRegularizerRdToR1,
final org.drip.spaces.rxtor1.NormedRdCombinatorialToR1Continuous funcSpaceRdToR1,
final double dblLambda)
{
try {
return null == funcSpaceRdToR1 ? null : new
org.drip.learning.regularization.RegularizerRdCombinatorialToR1Continuous
(funcRegularizerRdToR1, (org.drip.spaces.metric.RdCombinatorialBanach)
funcSpaceRdToR1.inputMetricVectorSpace(), (org.drip.spaces.metric.R1Continuous)
funcSpaceRdToR1.outputMetricVectorSpace(), dblLambda);
} catch (java.lang.Exception e) {
e.printStackTrace();
}
return null;
}
/**
* Construct an Instance of R^d Continuous To R^1 Continuous Regularizer
*
* @param funcRegularizerRdToR1 The R^d To R^1 Regularizer Function
* @param funcSpaceRdToR1 The R^d Continuous To R^1 Continuous Learner Function Space
* @param dblLambda The Regularization Lambda
*
* @return The R^d Continuous To R^1 Continuous Regularizer Instance
*/
public static final org.drip.learning.regularization.RegularizerRdToR1 RdContinuousToR1Continuous (
final org.drip.function.definition.RdToR1 funcRegularizerRdToR1,
final org.drip.spaces.rxtor1.NormedRdContinuousToR1Continuous funcSpaceRdToR1,
final double dblLambda)
{
try {
return null == funcSpaceRdToR1 ? null : new
org.drip.learning.regularization.RegularizerRdContinuousToR1Continuous
(funcRegularizerRdToR1, (org.drip.spaces.metric.RdContinuousBanach)
funcSpaceRdToR1.inputMetricVectorSpace(), (org.drip.spaces.metric.R1Continuous)
funcSpaceRdToR1.outputMetricVectorSpace(), dblLambda);
} catch (java.lang.Exception e) {
e.printStackTrace();
}
return null;
}
/**
* Construct an Instance of R^d Combinatorial/Continuous To R^1 Continuous Regularizer
*
* @param funcRegularizerRdToR1 The R^d To R^1 Regularizer Function
* @param rdInput The R^d Combinatorial/Continuous Input Space
* @param r1ContinuousOutput The R^1 Continuous Output Space
* @param dblLambda The Regularization Lambda
*
* @return The R^1 Combinatorial/Continuous To R^1 Continuous Regularizer Instance
*/
public static final org.drip.learning.regularization.RegularizerRdToR1 ToRdContinuous (
final org.drip.function.definition.RdToR1 funcRegularizerRdToR1,
final org.drip.spaces.metric.RdNormed rdInput,
final org.drip.spaces.metric.R1Continuous r1ContinuousOutput,
final double dblLambda)
{
if (null == rdInput) return null;
try {
if (rdInput instanceof org.drip.spaces.metric.RdContinuousBanach)
return new org.drip.learning.regularization.RegularizerRdContinuousToR1Continuous
(funcRegularizerRdToR1, (org.drip.spaces.metric.RdContinuousBanach) rdInput,
r1ContinuousOutput, dblLambda);
return new org.drip.learning.regularization.RegularizerRdCombinatorialToR1Continuous
(funcRegularizerRdToR1, (org.drip.spaces.metric.RdCombinatorialBanach) rdInput,
r1ContinuousOutput, dblLambda);
} catch (java.lang.Exception e) {
e.printStackTrace();
}
return null;
}
}