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);
}
}