PredictorResponseRelationSetup.java

package org.drip.state.estimator;

/*
 * -*- 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
 * Copyright (C) 2014 Lakshmi Krishnamurthy
 * Copyright (C) 2013 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>PredictorResponseRelationSetup</i> holds the Linearized Constraints (and, optionally, their quote
 * sensitivities) necessary needed for the Linear Calibration. Linearized Constraints are expressed as
 * 
 *          Sum_i[Predictor Weight_i * Function (Response_i)] = Constraint Value
 * 
 * where Function can either be univariate function, or weighted spline basis set. To this end, it implements
 * the following functionality:
 *
 *  <br><br>
 *  <ul>
 *      <li>
 *          Update/Retrieve Predictor/Response Weights and their Quote Sensitivities
 *      </li>
 *      <li>
 *          Update/Retrieve Predictor/Response Constraint Values and their Quote Sensitivities
 *      </li>
 *      <li>
 *          Display the contents of PredictorResponseRelationSetup
 *      </li>
 *  </ul>
 *
 *  <br><br>
 *  <ul>
 *      <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ProductCore.md">Product Core Module</a></li>
 *      <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/FixedIncomeAnalyticsLibrary.md">Fixed Income Analytics</a></li>
 *      <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/state/README.md">Latent State Inference and Creation Utilities</a></li>
 *      <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/state/estimator/README.md">Multi-Pass Customized Stretch Curve</a></li>
 *  </ul>
 * <br><br>
 * 
 * @author Lakshmi Krishnamurthy
 */

public class PredictorResponseRelationSetup {
    private double _dblValue = 0.;

    private java.util.TreeMap<java.lang.Double, java.lang.Double> _mapPredictorResponseWeight = new
        java.util.TreeMap<java.lang.Double, java.lang.Double>();

    /**
     * Empty PredictorResponseRelationSetup constructor
     */

    public PredictorResponseRelationSetup()
    {
    }

    /**
     * Update the Constraint Value
     * 
     * @param dblValue The Constraint Value Update Increment
     * 
     * @return TRUE - This Update Succeeded
     */

    public boolean updateValue (
        final double dblValue)
    {
        if (!org.drip.numerical.common.NumberUtil.IsValid (dblValue)) return false;

        _dblValue += dblValue;
        return true;
    }

    /**
     * Add a Predictor/Response Weight entry to the Linearized Constraint
     * 
     * @param dblPredictor The Predictor Node
     * @param dblResponseWeight The Response Weight at the Node
     * 
     * @return TRUE - Successfully added
     */

    public boolean addPredictorResponseWeight (
        final double dblPredictor,
        final double dblResponseWeight)
    {
        if (!org.drip.numerical.common.NumberUtil.IsValid (dblPredictor) ||
            !org.drip.numerical.common.NumberUtil.IsValid (dblResponseWeight))
            return false;

        double dblResponseWeightPrior = _mapPredictorResponseWeight.containsKey (dblPredictor) ?
            _mapPredictorResponseWeight.get (dblPredictor) : 0.;

        _mapPredictorResponseWeight.put (dblPredictor, dblResponseWeight + dblResponseWeightPrior);

        return true;
    }

    /**
     * Retrieve the Constraint Value
     * 
     * @return The Constraint Value
     */

    public double getValue()
    {
        return _dblValue;
    }

    /**
     * Retrieve the Predictor To-From Response Weight Map
     * 
     * @return The Predictor To-From Response Weight Map
     */

    public java.util.TreeMap<java.lang.Double, java.lang.Double> getPredictorResponseWeight()
    {
        return _mapPredictorResponseWeight;
    }

    /**
     * Absorb the "Other" PRRS onto the current one
     * 
     * @param prrsOther The "Other" PRRS
     * 
     * @return TRUE - At least one Entry was absorbed
     */

    public boolean absorb (
        final PredictorResponseRelationSetup prrsOther)
    {
        if (null == prrsOther || !updateValue (prrsOther.getValue())) return false;

        java.util.TreeMap<java.lang.Double, java.lang.Double> mapPRWOther =
            prrsOther.getPredictorResponseWeight();

        if (null == mapPRWOther || 0 == mapPRWOther.size()) return true;

        for (java.util.Map.Entry<java.lang.Double, java.lang.Double> me : mapPRWOther.entrySet()) {
            if (null != me && !addPredictorResponseWeight (me.getKey(), me.getValue())) return false;
        }

        return true;
    }
}