MultivariateDiscrete.java
package org.drip.measure.statistics;
/*
* -*- 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
*
* 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>MultivariateDiscrete</i> analyzes and computes the Moment and Metric Statistics for the Realized
* Multivariate Sequence.
*
* <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/NumericalAnalysisLibrary.md">Numerical Analysis Library</a></li>
* <li><b>Project</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/measure/README.md">R<sup>d</sup> Continuous/Discrete Probability Measures</a></li>
* <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/measure/statistics/README.md">R<sup>1</sup> R<sup>d</sup> Thin Thick Moments</a></li>
* </ul>
*
* @author Lakshmi Krishnamurthy
*/
public class MultivariateDiscrete {
private double[] _adblMean = null;
private double[] _adblError = null;
private double[] _adblVariance = null;
private double[][] _aadblCovariance = null;
private double[][] _aadblCorrelation = null;
private double[] _adblStandardDeviation = null;
/**
* MultivariateDiscrete Constructor
*
* @param aadblSequence The Array of Multivariate Realizations
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public MultivariateDiscrete (
final double[][] aadblSequence)
throws java.lang.Exception
{
if (null == aadblSequence)
throw new java.lang.Exception ("MultivariateDiscrete Constructor => Invalid Inputs");
int iNumVariate = -1;
int iSequenceSize = aadblSequence.length;
if (0 == iSequenceSize)
throw new java.lang.Exception ("MultivariateDiscrete Constructor => Invalid Inputs");
for (int iSequence = 0; iSequence < iSequenceSize; ++iSequence) {
if (null == aadblSequence[iSequence] || !org.drip.numerical.common.NumberUtil.IsValid
(aadblSequence[iSequence]))
throw new java.lang.Exception ("MultivariateDiscrete Constructor => Invalid Inputs");
if (0 == iSequence) {
if (0 == (iNumVariate = aadblSequence[0].length))
throw new java.lang.Exception ("MultivariateDiscrete Constructor => Invalid Inputs");
_adblMean = new double[iNumVariate];
_adblError = new double[iNumVariate];
_adblVariance = new double[iNumVariate];
_adblStandardDeviation = new double[iNumVariate];
_aadblCovariance = new double[iNumVariate][iNumVariate];
_aadblCorrelation = new double[iNumVariate][iNumVariate];
for (int iVariate = 0; iVariate < iNumVariate; ++iVariate) {
_adblMean[iVariate] = 0.;
_adblError[iVariate] = 0.;
for (int iVariateOther = 0; iVariateOther < iNumVariate; ++iVariateOther)
_aadblCovariance[iVariate][iVariateOther] = 0.;
}
} else if (iNumVariate != aadblSequence[iSequence].length)
throw new java.lang.Exception ("MultivariateDiscrete Constructor => Invalid Inputs");
for (int iVariate = 0; iVariate < iNumVariate; ++iVariate)
_adblMean[iVariate] += aadblSequence[iSequence][iVariate];
}
for (int iVariate = 0; iVariate < iNumVariate; ++iVariate)
_adblMean[iVariate] /= iSequenceSize;
for (int iSequence = 0; iSequence < iSequenceSize; ++iSequence) {
for (int iVariate = 0; iVariate < iNumVariate; ++iVariate) {
double dblOffsetFromMean = aadblSequence[iSequence][iVariate] - _adblMean[iVariate];
_adblError[iVariate] += java.lang.Math.abs (dblOffsetFromMean);
for (int iVariateOther = 0; iVariateOther < iNumVariate; ++iVariateOther)
_aadblCovariance[iVariate][iVariateOther] += dblOffsetFromMean *
(aadblSequence[iSequence][iVariateOther] - _adblMean[iVariateOther]);
}
}
for (int iVariate = 0; iVariate < iNumVariate; ++iVariate) {
_adblError[iVariate] /= iSequenceSize;
for (int iVariateOther = 0; iVariateOther < iNumVariate; ++iVariateOther)
_aadblCovariance[iVariate][iVariateOther] /= iSequenceSize;
_adblStandardDeviation[iVariate] = java.lang.Math.sqrt (_adblVariance[iVariate] =
_aadblCovariance[iVariate][iVariate]);
}
for (int iVariate = 0; iVariate < iNumVariate; ++iVariate) {
for (int iVariateOther = 0; iVariateOther < iNumVariate; ++iVariateOther)
_aadblCorrelation[iVariate][iVariateOther] = _aadblCovariance[iVariate][iVariateOther] /
(_adblStandardDeviation[iVariate] * _adblStandardDeviation[iVariateOther]);
}
}
/**
* Retrieve the Multivariate Means
*
* @return The Multivariate Means
*/
public double[] mean()
{
return _adblMean;
}
/**
* Retrieve the Multivariate Sequence "Error"
*
* @return The Multivariate Sequence "Error"
*/
public double[] error()
{
return _adblError;
}
/**
* Retrieve the Multivariate Covariance
*
* @return The Multivariate Covariance
*/
public double[][] covariance()
{
return _aadblCovariance;
}
/**
* Retrieve the Multivariate Correlation
*
* @return The Multivariate Correlation
*/
public double[][] correlation()
{
return _aadblCorrelation;
}
/**
* Retrieve the Multivariate Variance
*
* @return The Multivariate Variance
*/
public double[] variance()
{
return _adblVariance;
}
/**
* Retrieve the Multivariate Standard Deviation
*
* @return The Multivariate Standard Deviation
*/
public double[] standardDeviation()
{
return _adblStandardDeviation;
}
}