EmpiricalLearnerLoss.java

package org.drip.learning.bound;

/*
 * -*- 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>EmpiricalLearnerLoss</i> Function computes the Empirical Loss of a Learning Operation resulting from
 * the Use of a Learning Function in Conjunction with the corresponding Empirical Realization. The References
 * are:
 * <br><br>
 * <ul>
 *  <li>
 *      Boucheron, S., G. Lugosi, and P. Massart (2003): Concentration Inequalities Using the Entropy Method
 *          <i>Annals of Probability</i> <b>31</b> 1583-1614
 *  </li>
 *  <li>
 *      Lugosi, G. (2002): Pattern Classification and Learning Theory, in: <i>L. Györ, editor, Principles
 *          of Non-parametric Learning</i> <b>Springer</b> Wien 5-62
 *  </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/bound">Covering Numbers, Concentration, Lipschitz Bounds</a></li>
 *  </ul>
 *
 * @author Lakshmi Krishnamurthy
 */

public class EmpiricalLearnerLoss extends org.drip.function.definition.R1ToR1 {
    private double _dblRealization = java.lang.Double.NaN;
    private org.drip.function.definition.R1ToR1 _learner = null;

    /**
     * EmpiricalLearnerLoss Constructor
     * 
     * @param learner The Learning Function
     * @param dblRealization The Empirical Outcome
     * 
     * @throws java.lang.Exception Thrown if the Inputs are Invalid
     */

    public EmpiricalLearnerLoss (
        final org.drip.function.definition.R1ToR1 learner,
        final double dblRealization)
        throws java.lang.Exception
    {
        super (null);

        if (null == (_learner = learner) || !org.drip.numerical.common.NumberUtil.IsValid (_dblRealization =
            dblRealization))
            throw new java.lang.Exception ("EmpiricalLearnerLoss ctr: Invalid Inputs");
    }

    /**
     * Retrieve the Empirical Realization
     * 
     * @return The Empirical Realization
     */

    public double empiricalRealization()
    {
        return _dblRealization;
    }

    /**
     * Retrieve the Learning Function
     * 
     * @return The Learning Function
     */

    public org.drip.function.definition.R1ToR1 learner()
    {
        return _learner;
    }

    /**
     * Compute the Loss for the specified Variate
     * 
     * @param dblVariate The Variate
     * 
     * @return Loss for the specified Variate
     * 
     * @throws java.lang.Exception Thrown if the Loss cannot be computed
     */

    public double loss (
        final double dblVariate)
        throws java.lang.Exception
    {
        return _dblRealization - _learner.evaluate (dblVariate);
    }

    @Override public double evaluate (
        final double dblVariate)
        throws java.lang.Exception
    {
        return loss (dblVariate);
    }
}