R1MultivariateNormal.java

  1. package org.drip.measure.gaussian;

  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.  * Copyright (C) 2016 Lakshmi Krishnamurthy
  13.  * 
  14.  *  This file is part of DROP, an open-source library targeting analytics/risk, transaction cost analytics,
  15.  *      asset liability management analytics, capital, exposure, and margin analytics, valuation adjustment
  16.  *      analytics, and portfolio construction analytics within and across fixed income, credit, commodity,
  17.  *      equity, FX, and structured products. It also includes auxiliary libraries for algorithm support,
  18.  *      numerical analysis, numerical optimization, spline builder, model validation, statistical learning,
  19.  *      and computational support.
  20.  *  
  21.  *      https://lakshmidrip.github.io/DROP/
  22.  *  
  23.  *  DROP is composed of three modules:
  24.  *  
  25.  *  - DROP Product Core - https://lakshmidrip.github.io/DROP-Product-Core/
  26.  *  - DROP Portfolio Core - https://lakshmidrip.github.io/DROP-Portfolio-Core/
  27.  *  - DROP Computational Core - https://lakshmidrip.github.io/DROP-Computational-Core/
  28.  * 
  29.  *  DROP Product Core implements libraries for the following:
  30.  *  - Fixed Income Analytics
  31.  *  - Loan Analytics
  32.  *  - Transaction Cost Analytics
  33.  * 
  34.  *  DROP Portfolio Core implements libraries for the following:
  35.  *  - Asset Allocation Analytics
  36.  *  - Asset Liability Management Analytics
  37.  *  - Capital Estimation Analytics
  38.  *  - Exposure Analytics
  39.  *  - Margin Analytics
  40.  *  - XVA Analytics
  41.  * 
  42.  *  DROP Computational Core implements libraries for the following:
  43.  *  - Algorithm Support
  44.  *  - Computation Support
  45.  *  - Function Analysis
  46.  *  - Model Validation
  47.  *  - Numerical Analysis
  48.  *  - Numerical Optimizer
  49.  *  - Spline Builder
  50.  *  - Statistical Learning
  51.  * 
  52.  *  Documentation for DROP is Spread Over:
  53.  * 
  54.  *  - Main                     => https://lakshmidrip.github.io/DROP/
  55.  *  - Wiki                     => https://github.com/lakshmiDRIP/DROP/wiki
  56.  *  - GitHub                   => https://github.com/lakshmiDRIP/DROP
  57.  *  - Repo Layout Taxonomy     => https://github.com/lakshmiDRIP/DROP/blob/master/Taxonomy.md
  58.  *  - Javadoc                  => https://lakshmidrip.github.io/DROP/Javadoc/index.html
  59.  *  - Technical Specifications => https://github.com/lakshmiDRIP/DROP/tree/master/Docs/Internal
  60.  *  - Release Versions         => https://lakshmidrip.github.io/DROP/version.html
  61.  *  - Community Credits        => https://lakshmidrip.github.io/DROP/credits.html
  62.  *  - Issues Catalog           => https://github.com/lakshmiDRIP/DROP/issues
  63.  *  - JUnit                    => https://lakshmidrip.github.io/DROP/junit/index.html
  64.  *  - Jacoco                   => https://lakshmidrip.github.io/DROP/jacoco/index.html
  65.  * 
  66.  *  Licensed under the Apache License, Version 2.0 (the "License");
  67.  *      you may not use this file except in compliance with the License.
  68.  *   
  69.  *  You may obtain a copy of the License at
  70.  *      http://www.apache.org/licenses/LICENSE-2.0
  71.  *  
  72.  *  Unless required by applicable law or agreed to in writing, software
  73.  *      distributed under the License is distributed on an "AS IS" BASIS,
  74.  *      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  75.  *  
  76.  *  See the License for the specific language governing permissions and
  77.  *      limitations under the License.
  78.  */

  80. /**
  81.  * <i>R1MultivariateNormal</i> contains the Generalized Joint Multivariate R<sup>1</sup> Normal
  82.  * Distributions.
  83.  *
  84.  *  <br><br>
  85.  *  <ul>
  86.  *      <li><b>Module </b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/ComputationalCore.md">Computational Core Module</a></li>
  87.  *      <li><b>Library</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/NumericalAnalysisLibrary.md">Numerical Analysis Library</a></li>
  88.  *      <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>
  89.  *      <li><b>Package</b> = <a href = "https://github.com/lakshmiDRIP/DROP/tree/master/src/main/java/org/drip/measure/gaussian/README.md">R<sup>1</sup> R<sup>d</sup> Covariant Gaussian Quadrature</a></li>
  90.  *  </ul>
  91.  *
  92.  * @author Lakshmi Krishnamurthy
  93.  */

  95. public class R1MultivariateNormal extends org.drip.measure.continuous.R1Multivariate {
  96.     private double[] _adblMean = null;
  97.     private org.drip.measure.gaussian.Covariance _covariance = null;

  99.     /**
  100.      * Construct a Standard R1MultivariateNormal Instance
  101.      * 
  102.      * @param meta The R^1 Multivariate Meta Headers
  103.      * @param adblMean Array of the Univariate Means
  104.      * @param aadblCovariance The Covariance Matrix
  105.      * 
  106.      * @return The Standard Normal Univariate Distribution
  107.      */

  109.     public static final R1MultivariateNormal Standard (
  110.         final org.drip.measure.continuous.MultivariateMeta meta,
  111.         final double[] adblMean,
  112.         final double[][] aadblCovariance)
  113.     {
  114.         try {
  115.             return new R1MultivariateNormal (meta, adblMean, new org.drip.measure.gaussian.Covariance
  116.                 (aadblCovariance));
  117.         } catch (java.lang.Exception e) {
  118.             e.printStackTrace();
  119.         }

  121.         return null;
  122.     }

  124.     /**
  125.      * Construct a Standard R1MultivariateNormal Instance
  126.      * 
  127.      * @param astrVariateID Array of Variate IDs
  128.      * @param adblMean Array of the Univariate Means
  129.      * @param aadblCovariance The Covariance Matrix
  130.      * 
  131.      * @return The Standard Normal Univariate Distribution
  132.      */

  134.     public static final R1MultivariateNormal Standard (
  135.         final java.lang.String[] astrVariateID,
  136.         final double[] adblMean,
  137.         final double[][] aadblCovariance)
  138.     {
  139.         try {
  140.             return new R1MultivariateNormal (new org.drip.measure.continuous.MultivariateMeta
  141.                 (astrVariateID), adblMean, new org.drip.measure.gaussian.Covariance (aadblCovariance));
  142.         } catch (java.lang.Exception e) {
  143.             e.printStackTrace();
  144.         }

  146.         return null;
  147.     }

  149.     /**
  150.      * R1MultivariateNormal Constructor
  151.      * 
  152.      * @param meta The R^1 Multivariate Meta Headers
  153.      * @param adblMean Array of the Univariate Means
  154.      * @param covariance The Multivariate Covariance
  155.      * 
  156.      * @throws java.lang.Exception Thrown if the Inputs are Invalid
  157.      */

  159.     public R1MultivariateNormal (
  160.         final org.drip.measure.continuous.MultivariateMeta meta,
  161.         final double[] adblMean,
  162.         final org.drip.measure.gaussian.Covariance covariance)
  163.         throws java.lang.Exception
  164.     {
  165.         super (meta);

  167.         if (null == (_adblMean = adblMean) || null == (_covariance = covariance))
  168.             throw new java.lang.Exception ("R1MultivariateNormal Constructor => Invalid Inputs!");

  170.         int iNumVariate = meta.numVariable();

  172.         if (iNumVariate != _adblMean.length || iNumVariate != _covariance.numVariate() ||
  173.             !org.drip.numerical.common.NumberUtil.IsValid (_adblMean)) {
  174.             System.out.println ("iNumVariate = " + iNumVariate);

  176.             System.out.println ("_adblMean = " + _adblMean.length);

  178.             throw new java.lang.Exception ("R1MultivariateNormal Constructor => Invalid Inputs!");
  179.         }
  180.     }

  182.     /**
  183.      * Compute the Co-variance of the Distribution
  184.      * 
  185.      * @return The Co-variance of the Distribution
  186.      */

  188.     public org.drip.measure.gaussian.Covariance covariance()
  189.     {
  190.         return _covariance;
  191.     }

  193.     @Override public double density (
  194.         final double[] adblVariate)
  195.         throws java.lang.Exception
  196.     {
  197.         if (null == adblVariate || !org.drip.numerical.common.NumberUtil.IsValid (adblVariate))
  198.             throw new java.lang.Exception ("R1MultivariateNormal::density => Invalid Inputs");

  200.         double dblDensity = 0.;
  201.         int iNumVariate = _adblMean.length;
  202.         double[] adblVariateOffset = new double[iNumVariate];

  204.         if (iNumVariate != adblVariate.length)
  205.             throw new java.lang.Exception ("R1MultivariateNormal Constructor => Invalid Inputs!");

  207.         for (int i = 0; i < iNumVariate; ++i)
  208.             adblVariateOffset[i] = adblVariate[i] - _adblMean[i];

  210.         double[][] aadblPrecision = _covariance.precisionMatrix();

  212.         for (int i = 0; i < iNumVariate; ++i) {
  213.             for (int j = 0; j < iNumVariate; ++j)
  214.                 dblDensity = dblDensity + adblVariateOffset[i] * aadblPrecision[i][j] *
  215.                     adblVariateOffset[j];
  216.         }

  218.         return java.lang.Math.exp (dblDensity) * java.lang.Math.pow (2. * java.lang.Math.PI, -0.5 *
  219.             iNumVariate);
  220.     }

  222.     @Override public double[] mean()
  223.     {
  224.         return _adblMean;
  225.     }

  227.     @Override public double[] variance()
  228.     {
  229.         return _covariance.variance();
  230.     }
  231. }
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