StretchBestFitResponse.java

package org.drip.spline.params;

/*
 * -*- 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>StretchBestFitResponse</i> implements basis per-Stretch Fitness Penalty Parameter Set. Currently it
 * contains the Best Fit Penalty Weight Grid Matrix and the corresponding Local Predictor Ordinate/Response
 * Match Pair. StretchBestFitResponse exports the following methods:
 *
 * <br><br>
 *  <ul>
 *      <li>
 *          Retrieve the Array of the Fitness Weights.
 *      </li>
 *      <li>
 *          Retrieve the Indexed Fitness Weight Element.
 *      </li>
 *      <li>
 *          Retrieve the Array of Predictor Ordinates.
 *      </li>
 *      <li>
 *          Retrieve the Indexed Predictor Ordinate Element.
 *      </li>
 *      <li>
 *          Retrieve the Array of Responses.
 *      </li>
 *      <li>
 *          Retrieve the Indexed Response Element.
 *      </li>
 *      <li>
 *          Retrieve the Number of Fitness Points.
 *      </li>
 *      <li>
 *          Generate the Segment Local Best Fit Weighted Response contained within the specified Segment.
 *      </li>
 *      <li>
 *          Construct the StretchBestFitResponse Instance from the given Inputs.
 *      </li>
 *      <li>
 *          Construct the StretchBestFitResponse Instance from the given Predictor Ordinate/Response Pairs,
 *              using Uniform Weightings.
 *      </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/SplineBuilderLibrary.md">Spline Builder Library</a></li>
 *      <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/spline/README.md">Basis Splines and Linear Compounders across a Broad Family of Spline Basis Functions</a></li>
 *      <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/spline/params/README.md">Spline Segment Construction Control Parameters</a></li>
 *  </ul>
 * <br><br>
 *
 * @author Lakshmi Krishnamurthy
 */

public class StretchBestFitResponse {
    private double[] _adblWeight = null;
    private double[] _adblResponse = null;
    private double[] _adblPredictorOrdinate = null;

    /**
     * Construct the StretchBestFitResponse Instance from the given Inputs
     * 
     * @param adblPredictorOrdinate Array of Predictor Ordinates
     * @param adblResponseValue Array of Response Values
     * @param adblWeight Array of Weights
     * 
     * @return Instance of StretchBestFitResponse
     */

    public static final StretchBestFitResponse Create (
        final double[] adblPredictorOrdinate,
        final double[] adblResponseValue,
        final double[] adblWeight)
    {
        StretchBestFitResponse frp = null;

        try {
            frp = new StretchBestFitResponse (adblWeight, adblResponseValue, adblPredictorOrdinate);
        } catch (java.lang.Exception e) {
            e.printStackTrace();

            return null;
        }

        return frp.normalizeWeights() ? frp : null;
    }

    /**
     * Construct the StretchBestFitResponse Instance from the given Inputs
     * 
     * @param aiPredictorOrdinate Array of Predictor Ordinates
     * @param adblResponseValue Array of Response Values
     * @param adblWeight Array of Weights
     * 
     * @return Instance of StretchBestFitResponse
     */

    public static final StretchBestFitResponse Create (
        final int[] aiPredictorOrdinate,
        final double[] adblResponseValue,
        final double[] adblWeight)
    {
        if (null == aiPredictorOrdinate) return null;

        int iNumPredictorOrdinate = aiPredictorOrdinate.length;
        double[] adblPredictorOrdinate = new double[iNumPredictorOrdinate];

        if (0 == iNumPredictorOrdinate) return null;

        for (int i = 0; i < iNumPredictorOrdinate; ++i)
            adblPredictorOrdinate[i] = aiPredictorOrdinate[i];

        return Create (adblPredictorOrdinate, adblResponseValue, adblWeight);
    }

    /**
     * Construct the StretchBestFitResponse Instance from the given Predictor Ordinate/Response Pairs, using
     *  Uniform Weightings.
     * 
     * @param adblPredictorOrdinate Array of Predictor Ordinates
     * @param adblResponseValue Array of Response Values
     * 
     * @return Instance of StretchBestFitResponse
     */

    public static final StretchBestFitResponse Create (
        final double[] adblPredictorOrdinate,
        final double[] adblResponseValue)
    {
        if (!org.drip.numerical.common.NumberUtil.IsValid (adblPredictorOrdinate)) return null;

        int iNumWeight = adblPredictorOrdinate.length;
        double[] adblWeight = new double[iNumWeight];

        for (int i = 0; i < iNumWeight; ++i)
            adblWeight[i] = 1.;

        return Create (adblPredictorOrdinate, adblResponseValue, adblWeight);
    }

    private StretchBestFitResponse (
        final double[] adblWeight,
        final double[] adblResponse,
        final double[] adblPredictorOrdinate)
        throws java.lang.Exception
    {
        if (!org.drip.numerical.common.NumberUtil.IsValid (_adblWeight = adblWeight) ||
            !org.drip.numerical.common.NumberUtil.IsValid (_adblResponse = adblResponse) ||
                !org.drip.numerical.common.NumberUtil.IsValid (_adblPredictorOrdinate = adblPredictorOrdinate))
            throw new java.lang.Exception ("StretchBestFitResponse ctr: Invalid Inputs");

        int iNumPointsToFit = _adblWeight.length;

        if (0 == iNumPointsToFit || _adblResponse.length != iNumPointsToFit || _adblPredictorOrdinate.length
            != iNumPointsToFit)
            throw new java.lang.Exception ("StretchBestFitResponse ctr: Invalid Inputs");
    }

    private boolean normalizeWeights()
    {
        double dblCumulativeWeight = 0.;
        int iNumPointsToFit = _adblWeight.length;

        for (int i = 0; i < iNumPointsToFit; ++i) {
            if (_adblWeight[i] < 0.) return false;

            dblCumulativeWeight += _adblWeight[i];
        }

        if (0. >= dblCumulativeWeight) return false;

        for (int i = 0; i < iNumPointsToFit; ++i)
            _adblWeight[i] /= dblCumulativeWeight;

        return true;
    }

    /**
     * Retrieve the Array of the Fitness Weights
     * 
     * @return The Array of the Fitness Weights
     */

    public double[] weight()
    {
        return _adblWeight;
    }

    /**
     * Retrieve the Indexed Fitness Weight Element
     * 
     * @param iIndex The Element Index
     * 
     * @return The Indexed Fitness Weight Element
     * 
     * @throws java.lang.Exception Thrown if the Index is Invalid
     */

    public double weight (
        final int iIndex)
        throws java.lang.Exception
    {
        if (iIndex >= numPoint())
            throw new java.lang.Exception ("StretchBestFitResponse::weight => Invalid Index");

        return _adblWeight[iIndex];
    }

    /**
     * Retrieve the Array of Predictor Ordinates
     * 
     * @return The Array of Predictor Ordinates
     */

    public double[] predictorOrdinate()
    {
        return _adblPredictorOrdinate;
    }

    /**
     * Retrieve the Indexed Predictor Ordinate Element
     * 
     * @param iIndex The Element Index
     * 
     * @return The Indexed Predictor Ordinate Element
     * 
     * @throws java.lang.Exception Thrown if the Index is Invalid
     */

    public double predictorOrdinate (
        final int iIndex)
        throws java.lang.Exception
    {
        if (iIndex >= numPoint())
            throw new java.lang.Exception ("StretchBestFitResponse::predictorOrdinate => Invalid Index");

        return _adblPredictorOrdinate[iIndex];
    }

    /**
     * Retrieve the Array of Responses
     * 
     * @return The Array of Responses
     */

    public double[] response()
    {
        return _adblResponse;
    }

    /**
     * Retrieve the Indexed Response Element
     * 
     * @param iIndex The Element Index
     * 
     * @return The Indexed Response Element
     * 
     * @throws java.lang.Exception Thrown if the Index is Invalid
     */

    public double response (
        final int iIndex)
        throws java.lang.Exception
    {
        if (iIndex >= numPoint())
            throw new java.lang.Exception ("StretchBestFitResponse::response => Invalid Index");

        return _adblResponse[iIndex];
    }

    /**
     * Retrieve the Number of Fitness Points
     * 
     * @return The Number of Fitness Points
     */

    public int numPoint()
    {
        return null == _adblResponse ? 0 : _adblResponse.length;
    }

    /**
     * Generate the Segment Local Best Fit Weighted Response contained within the specified Segment
     * 
     * @param ics The Inelastics Instance to be used for the Localization
     * 
     * @return The Segment Local Best Fit Weighted Response
     */

    public SegmentBestFitResponse sizeToSegment (
        final org.drip.spline.segment.LatentStateInelastic ics)
    {
        if (null == ics) return null;

        int iNumPoint = numPoint();

        java.util.List<java.lang.Integer> lsIndex = new java.util.ArrayList<java.lang.Integer>();

        for (int i = 0; i < iNumPoint; ++i) {
            try {
                if (ics.in (_adblPredictorOrdinate[i])) lsIndex.add (i);
            } catch (java.lang.Exception e) {
                e.printStackTrace();

                return null;
            }
        }

        int iNumLocalPoint = lsIndex.size();

        if (0 == iNumLocalPoint) return null;

        int iIndex = 0;
        double[] adblWeight = new double[iNumLocalPoint];
        double[] adblResponse = new double[iNumLocalPoint];
        double[] adblPredictor = new double[iNumLocalPoint];

        for (int i : lsIndex) {
            adblWeight[iIndex] = _adblWeight[i];
            adblResponse[iIndex] = _adblResponse[i];
            adblPredictor[iIndex++] = _adblPredictorOrdinate[i];
        }

        return org.drip.spline.params.SegmentBestFitResponse.Create (adblPredictor, adblResponse,
            adblWeight);
    }
}