MonotoneConvexHaganWest.java
package org.drip.spline.pchip;
/*
* -*- 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>MonotoneConvexHaganWest</i> implements the regime using the Hagan and West (2006) Estimator. It
* provides the following functionality:
*
* <br><br>
* <ul>
* <li>
* Static Method to Create an instance of MonotoneConvexHaganWest
* </li>
* <li>
* Ensure that the estimated regime is monotone an convex
* </li>
* <li>
* If need be, enforce positivity and/or apply amelioration
* </li>
* <li>
* Apply segment-by-segment range bounds as needed
* </li>
* <li>
* Retrieve predictor ordinates/response values
* </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/pchip/README.md">Monotone Convex Themed PCHIP Splines</a></li>
* </ul>
* <br><br>
*
* @author Lakshmi Krishnamurthy
*/
public class MonotoneConvexHaganWest extends org.drip.function.definition.R1ToR1 {
private double[] _adblObservation = null;
private double[] _adblResponseValue = null;
private boolean _bLinearNodeInference = true;
private double[] _adblPredictorOrdinate = null;
private double[] _adblResponseZScoreLeft = null;
private double[] _adblResponseZScoreRight = null;
private org.drip.function.definition.R1ToR1[] _aAU = null;
class Case1Univariate extends org.drip.function.definition.R1ToR1 {
private double _dblResponseZScoreLeft = java.lang.Double.NaN;
private double _dblResponseZScoreRight = java.lang.Double.NaN;
private double _dblPredictorOrdinateLeft = java.lang.Double.NaN;
private double _dblPredictorOrdinateRight = java.lang.Double.NaN;
Case1Univariate (
final double dblPredictorOrdinateLeft,
final double dblPredictorOrdinateRight,
final double dblResponseZScoreLeft,
final double dblResponseZScoreRight)
{
super (null);
_dblResponseZScoreLeft = dblResponseZScoreLeft;
_dblResponseZScoreRight = dblResponseZScoreRight;
_dblPredictorOrdinateLeft = dblPredictorOrdinateLeft;
_dblPredictorOrdinateRight = dblPredictorOrdinateRight;
}
@Override public double evaluate (
final double dblPredictorOrdinate)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (dblPredictorOrdinate) || dblPredictorOrdinate <
_dblPredictorOrdinateLeft || dblPredictorOrdinate > _dblPredictorOrdinateRight)
throw new java.lang.Exception ("Case1Univariate::evaluate => Invalid Inputs");
double dblX = (dblPredictorOrdinate - _dblPredictorOrdinateLeft) / (_dblPredictorOrdinateRight -
_dblPredictorOrdinateLeft);
return _dblResponseZScoreLeft * (1. - 4. * dblX + 3. * dblX * dblX) + _dblResponseZScoreRight *
(-2. * dblX + 3. * dblX * dblX);
}
@Override public double integrate (
final double dblBegin,
final double dblEnd)
throws java.lang.Exception
{
return org.drip.numerical.integration.R1ToR1Integrator.Boole (this, dblBegin, dblEnd);
}
}
class Case2Univariate extends org.drip.function.definition.R1ToR1 {
private double _dblEta = java.lang.Double.NaN;
private double _dblResponseZScoreLeft = java.lang.Double.NaN;
private double _dblResponseZScoreRight = java.lang.Double.NaN;
private double _dblPredictorOrdinateLeft = java.lang.Double.NaN;
private double _dblPredictorOrdinateRight = java.lang.Double.NaN;
Case2Univariate (
final double dblPredictorOrdinateLeft,
final double dblPredictorOrdinateRight,
final double dblResponseZScoreLeft,
final double dblResponseZScoreRight)
{
super (null);
_dblResponseZScoreLeft = dblResponseZScoreLeft;
_dblResponseZScoreRight = dblResponseZScoreRight;
_dblPredictorOrdinateLeft = dblPredictorOrdinateLeft;
_dblPredictorOrdinateRight = dblPredictorOrdinateRight;
_dblEta = _dblResponseZScoreLeft != _dblResponseZScoreRight ? (_dblResponseZScoreRight + 2. *
_dblResponseZScoreLeft) / (_dblResponseZScoreRight - _dblResponseZScoreLeft) : 0.;
}
@Override public double evaluate (
final double dblPredictorOrdinate)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (dblPredictorOrdinate) || dblPredictorOrdinate <
_dblPredictorOrdinateLeft || dblPredictorOrdinate > _dblPredictorOrdinateRight)
throw new java.lang.Exception ("Case2Univariate::evaluate => Invalid Inputs");
if (_dblResponseZScoreLeft == _dblResponseZScoreRight) return _dblResponseZScoreRight;
double dblX = (dblPredictorOrdinate - _dblPredictorOrdinateLeft) / (_dblPredictorOrdinateRight -
_dblPredictorOrdinateLeft);
return dblX <= _dblEta ? _dblResponseZScoreLeft : _dblResponseZScoreLeft +
(_dblResponseZScoreRight - _dblResponseZScoreLeft) * (dblX - _dblEta) * (dblX - _dblEta) /
(1. - _dblEta) / (1. - _dblEta);
}
@Override public double integrate (
final double dblBegin,
final double dblEnd)
throws java.lang.Exception
{
return org.drip.numerical.integration.R1ToR1Integrator.Boole (this, dblBegin, dblEnd);
}
}
class Case3Univariate extends org.drip.function.definition.R1ToR1 {
private double _dblEta = java.lang.Double.NaN;
private double _dblResponseZScoreLeft = java.lang.Double.NaN;
private double _dblResponseZScoreRight = java.lang.Double.NaN;
private double _dblPredictorOrdinateLeft = java.lang.Double.NaN;
private double _dblPredictorOrdinateRight = java.lang.Double.NaN;
Case3Univariate (
final double dblPredictorOrdinateLeft,
final double dblPredictorOrdinateRight,
final double dblResponseZScoreLeft,
final double dblResponseZScoreRight)
{
super (null);
_dblResponseZScoreLeft = dblResponseZScoreLeft;
_dblResponseZScoreRight = dblResponseZScoreRight;
_dblPredictorOrdinateLeft = dblPredictorOrdinateLeft;
_dblPredictorOrdinateRight = dblPredictorOrdinateRight;
_dblEta = _dblResponseZScoreLeft != _dblResponseZScoreRight ? 3. * _dblResponseZScoreRight /
(_dblResponseZScoreRight - _dblResponseZScoreLeft) : 0.;
}
@Override public double evaluate (
final double dblPredictorOrdinate)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (dblPredictorOrdinate) || dblPredictorOrdinate <
_dblPredictorOrdinateLeft || dblPredictorOrdinate > _dblPredictorOrdinateRight)
throw new java.lang.Exception ("Case3Univariate::evaluate => Invalid Inputs");
if (_dblResponseZScoreLeft == _dblResponseZScoreRight) return _dblResponseZScoreRight;
double dblX = (dblPredictorOrdinate - _dblPredictorOrdinateLeft) / (_dblPredictorOrdinateRight -
_dblPredictorOrdinateLeft);
return dblX < _dblEta ? _dblResponseZScoreLeft + (_dblResponseZScoreLeft -
_dblResponseZScoreRight) * (_dblEta - dblX) * (_dblEta - dblX) / _dblEta / _dblEta :
_dblResponseZScoreRight;
}
@Override public double integrate (
final double dblBegin,
final double dblEnd)
throws java.lang.Exception
{
return org.drip.numerical.integration.R1ToR1Integrator.Boole (this, dblBegin, dblEnd);
}
}
class Case4Univariate extends org.drip.function.definition.R1ToR1 {
private double _dblA = java.lang.Double.NaN;
private double _dblEta = java.lang.Double.NaN;
private double _dblResponseZScoreLeft = java.lang.Double.NaN;
private double _dblResponseZScoreRight = java.lang.Double.NaN;
private double _dblPredictorOrdinateLeft = java.lang.Double.NaN;
private double _dblPredictorOrdinateRight = java.lang.Double.NaN;
Case4Univariate (
final double dblPredictorOrdinateLeft,
final double dblPredictorOrdinateRight,
final double dblResponseZScoreLeft,
final double dblResponseZScoreRight)
{
super (null);
_dblResponseZScoreLeft = dblResponseZScoreLeft;
_dblResponseZScoreRight = dblResponseZScoreRight;
_dblPredictorOrdinateLeft = dblPredictorOrdinateLeft;
_dblPredictorOrdinateRight = dblPredictorOrdinateRight;
if (_dblResponseZScoreLeft != _dblResponseZScoreRight) {
_dblEta = _dblResponseZScoreRight / (_dblResponseZScoreRight - _dblResponseZScoreLeft);
_dblA = -1. * _dblResponseZScoreLeft * _dblResponseZScoreRight / (_dblResponseZScoreRight -
_dblResponseZScoreLeft);
} else {
_dblA = 0.;
_dblEta = 0.;
}
}
@Override public double evaluate (
final double dblPredictorOrdinate)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (dblPredictorOrdinate) || dblPredictorOrdinate <
_dblPredictorOrdinateLeft || dblPredictorOrdinate > _dblPredictorOrdinateRight)
throw new java.lang.Exception ("Case4Univariate::evaluate => Invalid Inputs");
if (_dblResponseZScoreLeft == _dblResponseZScoreRight) return _dblResponseZScoreRight;
double dblX = (dblPredictorOrdinate - _dblPredictorOrdinateLeft) / (_dblPredictorOrdinateRight -
_dblPredictorOrdinateLeft);
return dblX < _dblEta ? _dblA + (_dblResponseZScoreLeft - _dblA) * (_dblEta - dblX) * (_dblEta -
dblX) / _dblEta / _dblEta : _dblA + (_dblResponseZScoreRight - _dblA) * (dblX - _dblEta) *
(dblX - _dblEta) / (1. - _dblEta) / (1. - _dblEta);
}
@Override public double integrate (
final double dblBegin,
final double dblEnd)
throws java.lang.Exception
{
return org.drip.numerical.integration.R1ToR1Integrator.Boole (this, dblBegin, dblEnd);
}
}
/**
* Create an instance of MonotoneConvexHaganWest
*
* @param adblPredictorOrdinate Array of Predictor Ordinates
* @param adblObservation Array of Observations
* @param bLinearNodeInference Apply Linear Node Inference from Observations
*
* @return Instance of MonotoneConvexHaganWest
*/
public static final MonotoneConvexHaganWest Create (
final double[] adblPredictorOrdinate,
final double[] adblObservation,
final boolean bLinearNodeInference)
{
MonotoneConvexHaganWest mchw = null;
try {
mchw = new MonotoneConvexHaganWest (adblPredictorOrdinate, adblObservation,
bLinearNodeInference);
} catch (java.lang.Exception e) {
e.printStackTrace();
return null;
}
return mchw.inferResponseValues() && mchw.inferResponseZScores() && mchw.generateUnivariate() ? mchw
: null;
}
private MonotoneConvexHaganWest (
final double[] adblPredictorOrdinate,
final double[] adblObservation,
final boolean bLinearNodeInference)
throws java.lang.Exception
{
super (null);
if (null == (_adblObservation = adblObservation) || null == (_adblPredictorOrdinate =
adblPredictorOrdinate))
throw new java.lang.Exception ("MonotoneConvexHaganWest ctr: Invalid Inputs!");
_bLinearNodeInference = bLinearNodeInference;
int iNumObservation = _adblObservation.length;
if (1 >= iNumObservation || iNumObservation + 1 != _adblPredictorOrdinate.length)
throw new java.lang.Exception ("MonotoneConvexHaganWest ctr: Invalid Inputs!");
}
private boolean inferResponseValues()
{
int iNumPredictorOrdinate = _adblPredictorOrdinate.length;
_adblResponseValue = new double[iNumPredictorOrdinate];
for (int i = 1; i < iNumPredictorOrdinate - 1; ++i) {
if (_bLinearNodeInference)
_adblResponseValue[i] = (_adblPredictorOrdinate[i] - _adblPredictorOrdinate[i - 1]) /
(_adblPredictorOrdinate[i + 1] - _adblPredictorOrdinate[i - 1]) * _adblObservation[i] +
(_adblPredictorOrdinate[i + 1] - _adblPredictorOrdinate[i]) /
(_adblPredictorOrdinate[i + 1] - _adblPredictorOrdinate[i - 1]) *
_adblObservation[i - 1];
else {
_adblResponseValue[i] = 0.;
if (_adblObservation[i - 1] * _adblObservation[i] > 0.) {
_adblResponseValue[i] = (_adblPredictorOrdinate[i] - _adblPredictorOrdinate[i - 1] + 2. *
(_adblPredictorOrdinate[i + 1] - _adblPredictorOrdinate[i])) / (3. *
(_adblPredictorOrdinate[i + 1] - _adblPredictorOrdinate[i])) /
_adblObservation[i - 1];
_adblResponseValue[i] += (_adblPredictorOrdinate[i + 1] - _adblPredictorOrdinate[i] + 2.
* (_adblPredictorOrdinate[i] - _adblPredictorOrdinate[i - 1])) / (3. *
(_adblPredictorOrdinate[i + 1] - _adblPredictorOrdinate[i])) /
_adblObservation[i];
_adblResponseValue[i] = 1. / _adblResponseValue[i];
}
}
}
_adblResponseValue[0] = _adblObservation[0] - 0.5 * (_adblResponseValue[1] - _adblObservation[0]);
_adblResponseValue[iNumPredictorOrdinate - 1] = _adblObservation[iNumPredictorOrdinate - 2] - 0.5 *
(_adblResponseValue[iNumPredictorOrdinate - 2] - _adblObservation[iNumPredictorOrdinate - 2]);
return true;
}
private boolean inferResponseZScores()
{
int iNumSegment = _adblPredictorOrdinate.length - 1;
_adblResponseZScoreLeft = new double[iNumSegment];
_adblResponseZScoreRight = new double[iNumSegment];
for (int i = 0; i < iNumSegment; ++i) {
_adblResponseZScoreLeft[i] = _adblResponseValue[i] - _adblObservation[i];
_adblResponseZScoreRight[i] = _adblResponseValue[i + 1] - _adblObservation[i];
}
return true;
}
private boolean generateUnivariate()
{
int iNumSegment = _adblPredictorOrdinate.length - 1;
_aAU = new org.drip.function.definition.R1ToR1[iNumSegment];
for (int i = 0; i < iNumSegment; ++i) {
if ((_adblResponseZScoreLeft[i] > 0. && -0.5 * _adblResponseZScoreLeft[i] >=
_adblResponseZScoreRight[i] && _adblResponseZScoreRight[i] >= -2. *
_adblResponseZScoreLeft[i]) || (_adblResponseZScoreLeft[i] < 0. && -0.5 *
_adblResponseZScoreLeft[i] <= _adblResponseZScoreRight[i] &&
_adblResponseZScoreRight[i] <= -2. * _adblResponseZScoreLeft[i]))
_aAU[i] = new Case1Univariate (_adblPredictorOrdinate[i], _adblPredictorOrdinate[i + 1],
_adblResponseZScoreLeft[i], _adblResponseZScoreRight[i]);
else if ((_adblResponseZScoreLeft[i] < 0. && _adblResponseZScoreRight[i] > -2. *
_adblResponseZScoreLeft[i]) || (_adblResponseZScoreLeft[i] > 0. &&
_adblResponseZScoreRight[i] < -2. * _adblResponseZScoreLeft[i]))
_aAU[i] = new Case2Univariate (_adblPredictorOrdinate[i], _adblPredictorOrdinate[i + 1],
_adblResponseZScoreLeft[i], _adblResponseZScoreRight[i]);
else if ((_adblResponseZScoreLeft[i] > 0. && _adblResponseZScoreRight[i] > -0.5 *
_adblResponseZScoreLeft[i]) || (_adblResponseZScoreLeft[i] < 0. &&
_adblResponseZScoreRight[i] < -0.5 * _adblResponseZScoreLeft[i]))
_aAU[i] = new Case3Univariate (_adblPredictorOrdinate[i], _adblPredictorOrdinate[i + 1],
_adblResponseZScoreLeft[i], _adblResponseZScoreRight[i]);
else if ((_adblResponseZScoreLeft[i] >= 0. && _adblResponseZScoreRight[i] >= 0.) ||
(_adblResponseZScoreLeft[i] <= 0. && _adblResponseZScoreRight[i] <= 0.))
_aAU[i] = new Case4Univariate (_adblPredictorOrdinate[i], _adblPredictorOrdinate[i + 1],
_adblResponseZScoreLeft[i], _adblResponseZScoreRight[i]);
}
return true;
}
private boolean ameliorate (
final double[] adblResponseLeftMin,
final double[] adblResponseLeftMax,
final double[] adblResponseRightMin,
final double[] adblResponseRightMax)
{
int iNumObservation = _adblObservation.length;
if (iNumObservation != adblResponseLeftMin.length || iNumObservation != adblResponseLeftMax.length ||
iNumObservation != adblResponseRightMin.length || iNumObservation != adblResponseRightMax.length)
return false;
for (int i = 0; i < iNumObservation; ++i) {
if (_adblResponseValue[i] < java.lang.Math.max (adblResponseLeftMin[i], adblResponseRightMin[i])
|| _adblResponseValue[i] > java.lang.Math.min (adblResponseLeftMax[i],
adblResponseRightMax[i])) {
if (_adblResponseValue[i] < java.lang.Math.max (adblResponseLeftMin[i],
adblResponseRightMin[i]))
_adblResponseValue[i] = java.lang.Math.max (adblResponseLeftMin[i],
adblResponseRightMin[i]);
else if (_adblResponseValue[i] > java.lang.Math.min (adblResponseLeftMax[i],
adblResponseRightMax[i]))
_adblResponseValue[i] = java.lang.Math.min (adblResponseLeftMax[i],
adblResponseRightMax[i]);
} else {
if (_adblResponseValue[i] < java.lang.Math.min (adblResponseLeftMax[i],
adblResponseRightMax[i]))
_adblResponseValue[i] = java.lang.Math.min (adblResponseLeftMax[i],
adblResponseRightMax[i]);
else if (_adblResponseValue[i] > java.lang.Math.max (adblResponseLeftMin[i],
adblResponseRightMin[i]))
_adblResponseValue[i] = java.lang.Math.max (adblResponseLeftMin[i],
adblResponseRightMin[i]);
}
}
if (java.lang.Math.abs (_adblResponseValue[0] - _adblObservation[0]) > 0.5 * java.lang.Math.abs
(_adblResponseValue[1] - _adblObservation[0]))
_adblResponseValue[0] = _adblObservation[1] - 0.5 * (_adblResponseValue[1] -
_adblObservation[0]);
if (java.lang.Math.abs (_adblResponseValue[iNumObservation] - _adblObservation[iNumObservation - 1])
> 0.5 * java.lang.Math.abs (_adblResponseValue[iNumObservation - 1] -
_adblObservation[iNumObservation - 1]))
_adblResponseValue[iNumObservation] = _adblObservation[iNumObservation - 1] - 0.5 *
(_adblObservation[iNumObservation - 1] - _adblResponseValue[iNumObservation - 1]);
return inferResponseZScores() && generateUnivariate();
}
private int containingIndex (
final double dblPredictorOrdinate,
final boolean bIncludeLeft,
final boolean bIncludeRight)
throws java.lang.Exception
{
int iNumSegment = _aAU.length;
for (int i = 0 ; i < iNumSegment; ++i) {
boolean bLeftValid = bIncludeLeft ? _adblPredictorOrdinate[i] <= dblPredictorOrdinate :
_adblPredictorOrdinate[i] < dblPredictorOrdinate;
boolean bRightValid = bIncludeRight ? _adblPredictorOrdinate[i + 1] >= dblPredictorOrdinate :
_adblPredictorOrdinate[i + 1] > dblPredictorOrdinate;
if (bLeftValid && bRightValid) return i;
}
throw new java.lang.Exception
("MonotoneConvexHaganWest::containingIndex => Cannot locate Containing Index");
}
@Override public double evaluate (
final double dblPredictorOrdinate)
throws java.lang.Exception
{
int iContainingIndex = containingIndex (dblPredictorOrdinate, true, true);
return _aAU[iContainingIndex].evaluate (dblPredictorOrdinate) + _adblObservation[iContainingIndex];
}
/**
* Enforce the Positivity of the Inferred Response Values
*
* @return TRUE - Positivity Enforcement is successful
*/
public boolean enforcePositivity()
{
try {
_adblResponseValue[0] = org.drip.numerical.common.NumberUtil.Bound (_adblResponseValue[0], 0., 2. *
_adblObservation[0]);
int iNumObservation = _adblObservation.length;
for (int i = 1; i < iNumObservation; ++i)
_adblResponseValue[i] = org.drip.numerical.common.NumberUtil.Bound (_adblResponseValue[i], 0., 2.
* java.lang.Math.min (_adblObservation[i - 1], _adblObservation[i]));
_adblResponseValue[iNumObservation] = org.drip.numerical.common.NumberUtil.Bound
(_adblResponseValue[iNumObservation], 0., 2. * _adblObservation[iNumObservation - 1]);
return inferResponseZScores() && generateUnivariate();
} catch (java.lang.Exception e) {
e.printStackTrace();
}
return false;
}
/**
* Create an Ameliorated Instance of the Current Instance
*
* @param adblResponseLeftMin Response Left Floor
* @param adblResponseLeftMax Response Left Ceiling
* @param adblResponseRightMin Response Right Floor
* @param adblResponseRightMax Response Right Ceiling
* @param bEnforcePositivity TRUE - Enforce Positivity
*
* @return The Ameliorated Version of the Current Instance
*/
public MonotoneConvexHaganWest generateAmelioratedInstance (
final double[] adblResponseLeftMin,
final double[] adblResponseLeftMax,
final double[] adblResponseRightMin,
final double[] adblResponseRightMax,
final boolean bEnforcePositivity)
{
if (null == adblResponseLeftMin || null == adblResponseLeftMax | null == adblResponseRightMin || null
== adblResponseRightMax)
return null;
int iNumAmelioratedObservation = _adblObservation.length + 2;
int iNumAmelioratedPredicatorOrdinate = _adblPredictorOrdinate.length + 2;
double[] adblAmelioratedObservation = new double[iNumAmelioratedObservation];
double[] adblAmelioratedPredictorOrdinate = new double[iNumAmelioratedPredicatorOrdinate];
for (int i = 0; i < iNumAmelioratedPredicatorOrdinate; ++i) {
if (0 == i)
adblAmelioratedPredictorOrdinate[0] = -1. * _adblPredictorOrdinate[1];
else if (iNumAmelioratedPredicatorOrdinate - 1 == i)
adblAmelioratedPredictorOrdinate[i] = 2. * _adblPredictorOrdinate[i - 1] -
_adblPredictorOrdinate[i - 2];
else
adblAmelioratedPredictorOrdinate[i] = _adblPredictorOrdinate[i - 1];
}
for (int i = 0; i < iNumAmelioratedObservation; ++i) {
if (0 == i)
adblAmelioratedObservation[0] = _adblObservation[0] - (_adblPredictorOrdinate[1] -
_adblPredictorOrdinate[0]) * (_adblObservation[1] - _adblObservation[0]) /
(_adblPredictorOrdinate[2] - _adblPredictorOrdinate[0]);
else if (iNumAmelioratedPredicatorOrdinate - 1 == i)
adblAmelioratedObservation[i] = _adblObservation[i - 1] - (_adblPredictorOrdinate[i - 1] -
_adblPredictorOrdinate[i - 2]) * (_adblObservation[i - 1] - _adblObservation[i - 2]) /
(_adblPredictorOrdinate[i - 1] - _adblPredictorOrdinate[i - 3]);
else
adblAmelioratedObservation[i] = _adblObservation[i - 1];
}
MonotoneConvexHaganWest mchwAmeliorated = Create (adblAmelioratedPredictorOrdinate,
adblAmelioratedObservation, _bLinearNodeInference);
if (null == mchwAmeliorated || mchwAmeliorated.ameliorate (adblResponseLeftMin, adblResponseLeftMax,
adblResponseRightMin, adblResponseRightMax))
return null;
if (bEnforcePositivity) {
if (!mchwAmeliorated.enforcePositivity()) return null;
}
return mchwAmeliorated;
}
/**
* Retrieve the Array of Predictor Ordinates
*
* @return The Array of Predictor Ordinates
*/
public double[] predictorOrdinates()
{
return _adblPredictorOrdinate;
}
/**
* Retrieve the Array of Response Values
*
* @return The Array of Response Values
*/
public double[] responseValues()
{
return _adblResponseValue;
}
}