RegularizerBuilder.java

  1. package org.drip.learning.regularization;

  3. /*
  4.  * -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
  5.  */

  7. /*!
  8.  * Copyright (C) 2020 Lakshmi Krishnamurthy
  9.  * Copyright (C) 2019 Lakshmi Krishnamurthy
  10.  * Copyright (C) 2018 Lakshmi Krishnamurthy
  11.  * Copyright (C) 2017 Lakshmi Krishnamurthy
  12.  * Copyright (C) 2016 Lakshmi Krishnamurthy
  13.  * Copyright (C) 2015 Lakshmi Krishnamurthy
  14.  * 
  15.  *  This file is part of DROP, an open-source library targeting analytics/risk, transaction cost analytics,
  16.  *      asset liability management analytics, capital, exposure, and margin analytics, valuation adjustment
  17.  *      analytics, and portfolio construction analytics within and across fixed income, credit, commodity,
  18.  *      equity, FX, and structured products. It also includes auxiliary libraries for algorithm support,
  19.  *      numerical analysis, numerical optimization, spline builder, model validation, statistical learning,
  20.  *      and computational support.
  21.  *  
  22.  *      https://lakshmidrip.github.io/DROP/
  23.  *  
  24.  *  DROP is composed of three modules:
  25.  *  
  26.  *  - DROP Product Core - https://lakshmidrip.github.io/DROP-Product-Core/
  27.  *  - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
  28.  *  - DROP Computational Core - https://lakshmidrip.github.io/DROP-Computational-Core/
  29.  * 
  30.  *  DROP Product Core implements libraries for the following:
  31.  *  - Fixed Income Analytics
  32.  *  - Loan Analytics
  33.  *  - Transaction Cost Analytics
  34.  * 
  35.  *  DROP Portfolio Core implements libraries for the following:
  36.  *  - Asset Allocation Analytics
  37.  *  - Asset Liability Management Analytics
  38.  *  - Capital Estimation Analytics
  39.  *  - Exposure Analytics
  40.  *  - Margin Analytics
  41.  *  - XVA Analytics
  42.  * 
  43.  *  DROP Computational Core implements libraries for the following:
  44.  *  - Algorithm Support
  45.  *  - Computation Support
  46.  *  - Function Analysis
  47.  *  - Model Validation
  48.  *  - Numerical Analysis
  49.  *  - Numerical Optimizer
  50.  *  - Spline Builder
  51.  *  - Statistical Learning
  52.  * 
  53.  *  Documentation for DROP is Spread Over:
  54.  * 
  55.  *  - Main                     => https://lakshmidrip.github.io/DROP/
  56.  *  - Wiki                     => https://github.com/lakshmiDRIP/DROP/wiki
  57.  *  - GitHub                   => https://github.com/lakshmiDRIP/DROP
  58.  *  - Repo Layout Taxonomy     => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
  59.  *  - Javadoc                  => https://lakshmidrip.github.io/DROP/Javadoc/index.html
  60.  *  - Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
  61.  *  - Release Versions         => https://lakshmidrip.github.io/DROP/version.html
  62.  *  - Community Credits        => https://lakshmidrip.github.io/DROP/credits.html
  63.  *  - Issues Catalog           => https://github.com/lakshmiDRIP/DROP/issues
  64.  *  - JUnit                    => https://lakshmidrip.github.io/DROP/junit/index.html
  65.  *  - Jacoco                   => https://lakshmidrip.github.io/DROP/jacoco/index.html
  66.  * 
  67.  *  Licensed under the Apache License, Version 2.0 (the "License");
  68.  *      you may not use this file except in compliance with the License.
  69.  *   
  70.  *  You may obtain a copy of the License at
  71.  *      http://www.apache.org/licenses/LICENSE-2.0
  72.  *  
  73.  *  Unless required by applicable law or agreed to in writing, software
  74.  *      distributed under the License is distributed on an "AS IS" BASIS,
  75.  *      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  76.  *  
  77.  *  See the License for the specific language governing permissions and
  78.  *      limitations under the License.
  79.  */

  81. /**
  82.  * <i>RegularizerBuilder</i> constructs Custom Regularizers for the different Normed Learner Function Types.
  83.  * The References are:
  84.  *  
  85.  * <br><br>
  86.  * <ul>
  87.  *  <li>
  88.  *      Alon, N., S. Ben-David, N. Cesa Bianchi, and D. Haussler (1997): Scale-sensitive Dimensions, Uniform
  89.  *          Convergence, and Learnability <i>Journal of Association of Computational Machinery</i> <b>44
  90.  *          (4)</b> 615-631
  91.  *  </li>
  92.  *  <li>
  93.  *      Anthony, M., and P. L. Bartlett (1999): <i>Artificial Neural Network Learning - Theoretical
  94.  *          Foundations</i> <b>Cambridge University Press</b> Cambridge, UK
  95.  *  </li>
  96.  *  <li>
  97.  *      Kearns, M. J., R. E. Schapire, and L. M. Sellie (1994): <i>Towards Efficient Agnostic Learning</i>
  98.  *          Machine Learning <b>17 (2)</b> 115-141
  99.  *  </li>
  100.  *  <li>
  101.  *      Lee, W. S., P. L. Bartlett, and R. C. Williamson (1998): The Importance of Convexity in Learning with
  102.  *          Squared Loss <i>IEEE Transactions on Information Theory</i> <b>44</b> 1974-1980
  103.  *  </li>
  104.  *  <li>
  105.  *      Vapnik, V. N. (1998): <i>Statistical learning Theory</i> <b>Wiley</b> New York
  106.  *  </li>
  107.  * </ul>
  108.  *
  109.  *  <br><br>
  110.  *  <ul>
  111.  *      <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
  112.  *      <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/StatisticalLearningLibrary.md">Statistical Learning</a></li>
  113.  *      <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>
  114.  *      <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>
  115.  *  </ul>
  116.  *
  117.  * @author Lakshmi Krishnamurthy
  118.  */

  120. public class RegularizerBuilder {

  122.     /**
  123.      * Construct an Instance of R^1 Combinatorial To R^1 Continuous Regularizer
  124.      * 
  125.      * @param funcRegularizerR1ToR1 The R^1 To R^1 Regularizer Function
  126.      * @param funcSpaceR1ToR1 The R^1 Combinatorial To R^1 Continuous Learner Function Space
  127.      * @param dblLambda The Regularization Lambda
  128.      * 
  129.      * @return The R^1 Combinatorial To R^1 Continuous Regularizer Instance
  130.      */

  132.     public static final org.drip.learning.regularization.RegularizerR1ToR1 R1CombinatorialToR1Continuous (
  133.         final org.drip.function.definition.R1ToR1 funcRegularizerR1ToR1,
  134.         final org.drip.spaces.rxtor1.NormedR1CombinatorialToR1Continuous funcSpaceR1ToR1,
  135.         final double dblLambda)
  136.     {
  137.         try {
  138.             return null == funcSpaceR1ToR1 ? null : new
  139.                 org.drip.learning.regularization.RegularizerR1CombinatorialToR1Continuous
  140.                     (funcRegularizerR1ToR1, (org.drip.spaces.metric.R1Combinatorial)
  141.                         funcSpaceR1ToR1.inputMetricVectorSpace(), (org.drip.spaces.metric.R1Continuous)
  142.                             funcSpaceR1ToR1.outputMetricVectorSpace(), dblLambda);
  143.         } catch (java.lang.Exception e) {
  144.             e.printStackTrace();
  145.         }

  147.         return null;
  148.     }

  150.     /**
  151.      * Construct an Instance of R^1 Continuous To R^1 Continuous Regularizer
  152.      * 
  153.      * @param funcRegularizerR1ToR1 The R^1 To R^1 Regularizer Function
  154.      * @param funcSpaceR1ToR1 The R^1 Continuous To R^1 Continuous Learner Function Space
  155.      * @param dblLambda The Regularization Lambda
  156.      * 
  157.      * @return The R^1 Continuous To R^1 Continuous Regularizer Instance
  158.      */

  160.     public static final org.drip.learning.regularization.RegularizerR1ToR1 R1ContinuousToR1Continuous (
  161.         final org.drip.function.definition.R1ToR1 funcRegularizerR1ToR1,
  162.         final org.drip.spaces.rxtor1.NormedR1ContinuousToR1Continuous funcSpaceR1ToR1,
  163.         final double dblLambda)
  164.     {
  165.         try {
  166.             return null == funcSpaceR1ToR1 ? null : new
  167.                 org.drip.learning.regularization.RegularizerR1ContinuousToR1Continuous
  168.                     (funcRegularizerR1ToR1, (org.drip.spaces.metric.R1Continuous)
  169.                         funcSpaceR1ToR1.inputMetricVectorSpace(), (org.drip.spaces.metric.R1Continuous)
  170.                             funcSpaceR1ToR1.outputMetricVectorSpace(), dblLambda);
  171.         } catch (java.lang.Exception e) {
  172.             e.printStackTrace();
  173.         }

  175.         return null;
  176.     }

  178.     /**
  179.      * Construct an Instance of R^1 Combinatorial/Continuous To R^1 Continuous Regularizer
  180.      * 
  181.      * @param funcRegularizerR1ToR1 The R^1 To R^1 Regularizer Function
  182.      * @param r1Input The R^1 Combinatorial/Continuous Input Space
  183.      * @param r1ContinuousOutput The R^1 Continuous Output Space
  184.      * @param dblLambda The Regularization Lambda
  185.      * 
  186.      * @return The R^1 Combinatorial/Continuous To R^1 Continuous Regularizer Instance
  187.      */

  189.     public static final org.drip.learning.regularization.RegularizerR1ToR1 ToR1Continuous (
  190.         final org.drip.function.definition.R1ToR1 funcRegularizerR1ToR1,
  191.         final org.drip.spaces.metric.R1Normed r1Input,
  192.         final org.drip.spaces.metric.R1Continuous r1ContinuousOutput,
  193.         final double dblLambda)
  194.     {
  195.         if (null == r1Input) return null;

  197.         try {
  198.             if (r1Input instanceof org.drip.spaces.metric.R1Continuous)
  199.                 return new org.drip.learning.regularization.RegularizerR1ContinuousToR1Continuous
  200.                     (funcRegularizerR1ToR1, (org.drip.spaces.metric.R1Continuous) r1Input,
  201.                         r1ContinuousOutput, dblLambda);

  203.             return new org.drip.learning.regularization.RegularizerR1CombinatorialToR1Continuous
  204.                 (funcRegularizerR1ToR1, (org.drip.spaces.metric.R1Combinatorial) r1Input,
  205.                     r1ContinuousOutput, dblLambda);
  206.         } catch (java.lang.Exception e) {
  207.             e.printStackTrace();
  208.         }

  210.         return null;
  211.     }

  213.     /**
  214.      * Construct an Instance of R^d Combinatorial To R^1 Continuous Regularizer
  215.      * 
  216.      * @param funcRegularizerRdToR1 The R^d To R^1 Regularizer Function
  217.      * @param funcSpaceRdToR1 The R^d Combinatorial To R^1 Continuous Learner Function Space
  218.      * @param dblLambda The Regularization Lambda
  219.      * 
  220.      * @return The R^d Combinatorial To R^1 Continuous Regularizer Instance
  221.      */

  223.     public static final org.drip.learning.regularization.RegularizerRdToR1 RdCombinatorialToR1Continuous (
  224.         final org.drip.function.definition.RdToR1 funcRegularizerRdToR1,
  225.         final org.drip.spaces.rxtor1.NormedRdCombinatorialToR1Continuous funcSpaceRdToR1,
  226.         final double dblLambda)
  227.     {
  228.         try {
  229.             return null == funcSpaceRdToR1 ? null : new
  230.                 org.drip.learning.regularization.RegularizerRdCombinatorialToR1Continuous
  231.                     (funcRegularizerRdToR1, (org.drip.spaces.metric.RdCombinatorialBanach)
  232.                         funcSpaceRdToR1.inputMetricVectorSpace(), (org.drip.spaces.metric.R1Continuous)
  233.                             funcSpaceRdToR1.outputMetricVectorSpace(), dblLambda);
  234.         } catch (java.lang.Exception e) {
  235.             e.printStackTrace();
  236.         }

  238.         return null;
  239.     }

  241.     /**
  242.      * Construct an Instance of R^d Continuous To R^1 Continuous Regularizer
  243.      * 
  244.      * @param funcRegularizerRdToR1 The R^d To R^1 Regularizer Function
  245.      * @param funcSpaceRdToR1 The R^d Continuous To R^1 Continuous Learner Function Space
  246.      * @param dblLambda The Regularization Lambda
  247.      * 
  248.      * @return The R^d Continuous To R^1 Continuous Regularizer Instance
  249.      */

  251.     public static final org.drip.learning.regularization.RegularizerRdToR1 RdContinuousToR1Continuous (
  252.         final org.drip.function.definition.RdToR1 funcRegularizerRdToR1,
  253.         final org.drip.spaces.rxtor1.NormedRdContinuousToR1Continuous funcSpaceRdToR1,
  254.         final double dblLambda)
  255.     {
  256.         try {
  257.             return null == funcSpaceRdToR1 ? null : new
  258.                 org.drip.learning.regularization.RegularizerRdContinuousToR1Continuous
  259.                     (funcRegularizerRdToR1, (org.drip.spaces.metric.RdContinuousBanach)
  260.                         funcSpaceRdToR1.inputMetricVectorSpace(), (org.drip.spaces.metric.R1Continuous)
  261.                             funcSpaceRdToR1.outputMetricVectorSpace(), dblLambda);
  262.         } catch (java.lang.Exception e) {
  263.             e.printStackTrace();
  264.         }

  266.         return null;
  267.     }

  269.     /**
  270.      * Construct an Instance of R^d Combinatorial/Continuous To R^1 Continuous Regularizer
  271.      * 
  272.      * @param funcRegularizerRdToR1 The R^d To R^1 Regularizer Function
  273.      * @param rdInput The R^d Combinatorial/Continuous Input Space
  274.      * @param r1ContinuousOutput The R^1 Continuous Output Space
  275.      * @param dblLambda The Regularization Lambda
  276.      * 
  277.      * @return The R^1 Combinatorial/Continuous To R^1 Continuous Regularizer Instance
  278.      */

  280.     public static final org.drip.learning.regularization.RegularizerRdToR1 ToRdContinuous (
  281.         final org.drip.function.definition.RdToR1 funcRegularizerRdToR1,
  282.         final org.drip.spaces.metric.RdNormed rdInput,
  283.         final org.drip.spaces.metric.R1Continuous r1ContinuousOutput,
  284.         final double dblLambda)
  285.     {
  286.         if (null == rdInput) return null;

  288.         try {
  289.             if (rdInput instanceof org.drip.spaces.metric.RdContinuousBanach)
  290.                 return new org.drip.learning.regularization.RegularizerRdContinuousToR1Continuous
  291.                     (funcRegularizerRdToR1, (org.drip.spaces.metric.RdContinuousBanach) rdInput,
  292.                         r1ContinuousOutput, dblLambda);

  294.             return new org.drip.learning.regularization.RegularizerRdCombinatorialToR1Continuous
  295.                 (funcRegularizerRdToR1, (org.drip.spaces.metric.RdCombinatorialBanach) rdInput,
  296.                     r1ContinuousOutput, dblLambda);
  297.         } catch (java.lang.Exception e) {
  298.             e.printStackTrace();
  299.         }

  301.         return null;
  302.     }
  303. }
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