MultivariateDiscrete.java

  1. package org.drip.measure.statistics;

  3. /*
  4.  * -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
  5.  */

  7. /*!
  8.  * Copyright (C) 2020 Lakshmi Krishnamurthy
  9.  * Copyright (C) 2019 Lakshmi Krishnamurthy
  10.  * Copyright (C) 2018 Lakshmi Krishnamurthy
  11.  * Copyright (C) 2017 Lakshmi Krishnamurthy
  12.  * 
  13.  *  This file is part of DROP, an open-source library targeting analytics/risk, transaction cost analytics,
  14.  *      asset liability management analytics, capital, exposure, and margin analytics, valuation adjustment
  15.  *      analytics, and portfolio construction analytics within and across fixed income, credit, commodity,
  16.  *      equity, FX, and structured products. It also includes auxiliary libraries for algorithm support,
  17.  *      numerical analysis, numerical optimization, spline builder, model validation, statistical learning,
  18.  *      and computational support.
  19.  *  
  20.  *      https://lakshmidrip.github.io/DROP/
  21.  *  
  22.  *  DROP is composed of three modules:
  23.  *  
  24.  *  - DROP Product Core - https://lakshmidrip.github.io/DROP-Product-Core/
  25.  *  - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
  26.  *  - DROP Computational Core - https://lakshmidrip.github.io/DROP-Computational-Core/
  27.  * 
  28.  *  DROP Product Core implements libraries for the following:
  29.  *  - Fixed Income Analytics
  30.  *  - Loan Analytics
  31.  *  - Transaction Cost Analytics
  32.  * 
  33.  *  DROP Portfolio Core implements libraries for the following:
  34.  *  - Asset Allocation Analytics
  35.  *  - Asset Liability Management Analytics
  36.  *  - Capital Estimation Analytics
  37.  *  - Exposure Analytics
  38.  *  - Margin Analytics
  39.  *  - XVA Analytics
  40.  * 
  41.  *  DROP Computational Core implements libraries for the following:
  42.  *  - Algorithm Support
  43.  *  - Computation Support
  44.  *  - Function Analysis
  45.  *  - Model Validation
  46.  *  - Numerical Analysis
  47.  *  - Numerical Optimizer
  48.  *  - Spline Builder
  49.  *  - Statistical Learning
  50.  * 
  51.  *  Documentation for DROP is Spread Over:
  52.  * 
  53.  *  - Main                     => https://lakshmidrip.github.io/DROP/
  54.  *  - Wiki                     => https://github.com/lakshmiDRIP/DROP/wiki
  55.  *  - GitHub                   => https://github.com/lakshmiDRIP/DROP
  56.  *  - Repo Layout Taxonomy     => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
  57.  *  - Javadoc                  => https://lakshmidrip.github.io/DROP/Javadoc/index.html
  58.  *  - Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
  59.  *  - Release Versions         => https://lakshmidrip.github.io/DROP/version.html
  60.  *  - Community Credits        => https://lakshmidrip.github.io/DROP/credits.html
  61.  *  - Issues Catalog           => https://github.com/lakshmiDRIP/DROP/issues
  62.  *  - JUnit                    => https://lakshmidrip.github.io/DROP/junit/index.html
  63.  *  - Jacoco                   => https://lakshmidrip.github.io/DROP/jacoco/index.html
  64.  * 
  65.  *  Licensed under the Apache License, Version 2.0 (the "License");
  66.  *      you may not use this file except in compliance with the License.
  67.  *   
  68.  *  You may obtain a copy of the License at
  69.  *      http://www.apache.org/licenses/LICENSE-2.0
  70.  *  
  71.  *  Unless required by applicable law or agreed to in writing, software
  72.  *      distributed under the License is distributed on an "AS IS" BASIS,
  73.  *      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  74.  *  
  75.  *  See the License for the specific language governing permissions and
  76.  *      limitations under the License.
  77.  */

  79. /**
  80.  * <i>MultivariateDiscrete</i> analyzes and computes the Moment and Metric Statistics for the Realized
  81.  * Multivariate Sequence.
  82.  *
  83.  *  <br><br>
  84.  *  <ul>
  85.  *      <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
  86.  *      <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/NumericalAnalysisLibrary.md">Numerical Analysis Library</a></li>
  87.  *      <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>
  88.  *      <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>
  89.  *  </ul>
  90.  *
  91.  * @author Lakshmi Krishnamurthy
  92.  */

  94. public class MultivariateDiscrete {
  95.     private double[] _adblMean = null;
  96.     private double[] _adblError = null;
  97.     private double[] _adblVariance = null;
  98.     private double[][] _aadblCovariance = null;
  99.     private double[][] _aadblCorrelation = null;
  100.     private double[] _adblStandardDeviation = null;

  102.     /**
  103.      * MultivariateDiscrete Constructor
  104.      * 
  105.      * @param aadblSequence The Array of Multivariate Realizations
  106.      * 
  107.      * @throws java.lang.Exception Thrown if the Inputs are Invalid
  108.      */

  110.     public MultivariateDiscrete (
  111.         final double[][] aadblSequence)
  112.         throws java.lang.Exception
  113.     {
  114.         if (null == aadblSequence)
  115.             throw new java.lang.Exception ("MultivariateDiscrete Constructor => Invalid Inputs");

  117.         int iNumVariate = -1;
  118.         int iSequenceSize = aadblSequence.length;

  120.         if (0 == iSequenceSize)
  121.             throw new java.lang.Exception ("MultivariateDiscrete Constructor => Invalid Inputs");

  123.         for (int iSequence = 0; iSequence < iSequenceSize; ++iSequence) {
  124.             if (null == aadblSequence[iSequence] || !org.drip.numerical.common.NumberUtil.IsValid
  125.                 (aadblSequence[iSequence]))
  126.                 throw new java.lang.Exception ("MultivariateDiscrete Constructor => Invalid Inputs");

  128.             if (0 == iSequence) {
  129.                 if (0 == (iNumVariate = aadblSequence[0].length))
  130.                     throw new java.lang.Exception ("MultivariateDiscrete Constructor => Invalid Inputs");

  132.                 _adblMean = new double[iNumVariate];
  133.                 _adblError = new double[iNumVariate];
  134.                 _adblVariance = new double[iNumVariate];
  135.                 _adblStandardDeviation = new double[iNumVariate];
  136.                 _aadblCovariance = new double[iNumVariate][iNumVariate];
  137.                 _aadblCorrelation = new double[iNumVariate][iNumVariate];

  139.                 for (int iVariate = 0; iVariate < iNumVariate; ++iVariate) {
  140.                     _adblMean[iVariate] = 0.;
  141.                     _adblError[iVariate] = 0.;

  143.                     for (int iVariateOther = 0; iVariateOther < iNumVariate; ++iVariateOther)
  144.                         _aadblCovariance[iVariate][iVariateOther] = 0.;
  145.                 }
  146.             } else if (iNumVariate != aadblSequence[iSequence].length)
  147.                 throw new java.lang.Exception ("MultivariateDiscrete Constructor => Invalid Inputs");

  149.             for (int iVariate = 0; iVariate < iNumVariate; ++iVariate)
  150.                 _adblMean[iVariate] += aadblSequence[iSequence][iVariate];
  151.         }

  153.         for (int iVariate = 0; iVariate < iNumVariate; ++iVariate)
  154.             _adblMean[iVariate] /= iSequenceSize;

  156.         for (int iSequence = 0; iSequence < iSequenceSize; ++iSequence) {
  157.             for (int iVariate = 0; iVariate < iNumVariate; ++iVariate) {
  158.                 double dblOffsetFromMean = aadblSequence[iSequence][iVariate] - _adblMean[iVariate];

  160.                 _adblError[iVariate] += java.lang.Math.abs (dblOffsetFromMean);

  162.                 for (int iVariateOther = 0; iVariateOther < iNumVariate; ++iVariateOther)
  163.                     _aadblCovariance[iVariate][iVariateOther] += dblOffsetFromMean *
  164.                         (aadblSequence[iSequence][iVariateOther] - _adblMean[iVariateOther]);
  165.             }
  166.         }

  168.         for (int iVariate = 0; iVariate < iNumVariate; ++iVariate) {
  169.             _adblError[iVariate] /= iSequenceSize;

  171.             for (int iVariateOther = 0; iVariateOther < iNumVariate; ++iVariateOther)
  172.                 _aadblCovariance[iVariate][iVariateOther] /= iSequenceSize;

  174.             _adblStandardDeviation[iVariate] = java.lang.Math.sqrt (_adblVariance[iVariate] =
  175.                 _aadblCovariance[iVariate][iVariate]);
  176.         }

  178.         for (int iVariate = 0; iVariate < iNumVariate; ++iVariate) {
  179.             for (int iVariateOther = 0; iVariateOther < iNumVariate; ++iVariateOther)
  180.                 _aadblCorrelation[iVariate][iVariateOther] = _aadblCovariance[iVariate][iVariateOther] /
  181.                     (_adblStandardDeviation[iVariate] * _adblStandardDeviation[iVariateOther]);
  182.         }
  183.     }

  185.     /**
  186.      * Retrieve the Multivariate Means
  187.      * 
  188.      * @return The Multivariate Means
  189.      */

  191.     public double[] mean()
  192.     {
  193.         return _adblMean;
  194.     }

  196.     /**
  197.      * Retrieve the Multivariate Sequence "Error"
  198.      * 
  199.      * @return The Multivariate Sequence "Error"
  200.      */

  202.     public double[] error()
  203.     {
  204.         return _adblError;
  205.     }

  207.     /**
  208.      * Retrieve the Multivariate Covariance
  209.      * 
  210.      * @return The Multivariate Covariance
  211.      */

  213.     public double[][] covariance()
  214.     {
  215.         return _aadblCovariance;
  216.     }

  218.     /**
  219.      * Retrieve the Multivariate Correlation
  220.      * 
  221.      * @return The Multivariate Correlation
  222.      */

  224.     public double[][] correlation()
  225.     {
  226.         return _aadblCorrelation;
  227.     }

  229.     /**
  230.      * Retrieve the Multivariate Variance
  231.      * 
  232.      * @return The Multivariate Variance
  233.      */

  235.     public double[] variance()
  236.     {
  237.         return _adblVariance;
  238.     }

  240.     /**
  241.      * Retrieve the Multivariate Standard Deviation
  242.      * 
  243.      * @return The Multivariate Standard Deviation
  244.      */

  246.     public double[] standardDeviation()
  247.     {
  248.         return _adblStandardDeviation;
  249.     }
  250. }
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