ProjectionDistributionLoading.java
package org.drip.measure.bayesian;
/*
* -*- 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
*
* 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>ProjectionDistributionLoading</i> contains the Projection Distribution and its Loadings to the Scoping
* Distribution.
*
* <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/bayesian/README.md">Prior, Conditional, Posterior Theil Bayesian</a></li>
* </ul>
*
* @author Lakshmi Krishnamurthy
*/
public class ProjectionDistributionLoading {
private double[][] _aadblScopingLoading = null;
private org.drip.measure.continuous.R1Multivariate _r1mDistribution = null;
/**
* Generate the Projection Co-variance Matrix from the Confidence Level
*
* @param aadblScopingCovariance The Scoping Co-variance Matrix
* @param aadblScopingLoading The Projection-Scoping Variate Loadings
* @param dblTau The Tau Parameter
*
* @return The Projection Co-variance Matrix
*/
public static final double[][] ProjectionCovariance (
final double[][] aadblScopingCovariance,
final double[][] aadblScopingLoading,
final double dblTau)
{
if (null == aadblScopingCovariance || null == aadblScopingLoading ||
!org.drip.numerical.common.NumberUtil.IsValid (dblTau))
return null;
int iNumProjection = aadblScopingLoading.length;
double[][] aadblProjectionCovariance = 0 == iNumProjection ? null : new
double[iNumProjection][iNumProjection];
if (0 == iNumProjection || iNumProjection != aadblScopingLoading.length) return null;
for (int i = 0; i < iNumProjection; ++i) {
for (int j = 0; j < iNumProjection; ++j) {
try {
aadblProjectionCovariance[i][j] = i != j ? 0. : dblTau *
org.drip.numerical.linearalgebra.Matrix.DotProduct (aadblScopingLoading[i],
org.drip.numerical.linearalgebra.Matrix.Product (aadblScopingCovariance,
aadblScopingLoading[j]));
} catch (java.lang.Exception e) {
e.printStackTrace();
return null;
}
}
}
return aadblProjectionCovariance;
}
/**
* Generate the ProjectionDistributionLoading Instance from the Confidence Level
*
* @param meta The R^1 Multivariate Meta Headers
* @param adblMean Array of the Univariate Means
* @param aadblScopingCovariance The Scoping Co-variance Matrix
* @param aadblScopingLoading The Projection-Scoping Variate Loadings
* @param dblTau The Tau Parameter
*
* @return The ProjectionDistributionLoading Instance
*/
public static final ProjectionDistributionLoading FromConfidence (
final org.drip.measure.continuous.MultivariateMeta meta,
final double[] adblMean,
final double[][] aadblScopingCovariance,
final double[][] aadblScopingLoading,
final double dblTau)
{
try {
return new ProjectionDistributionLoading (new org.drip.measure.gaussian.R1MultivariateNormal
(meta, adblMean, new org.drip.measure.gaussian.Covariance (ProjectionCovariance
(aadblScopingCovariance, aadblScopingLoading, dblTau))), aadblScopingLoading);
} catch (java.lang.Exception e) {
e.printStackTrace();
}
return null;
}
/**
* ProjectionDistributionLoading Constructor
*
* @param r1mDistribution The Projection Distribution Instance
* @param aadblScopingLoading The Projection-Scoping Variate Loadings
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public ProjectionDistributionLoading (
final org.drip.measure.continuous.R1Multivariate r1mDistribution,
final double[][] aadblScopingLoading)
throws java.lang.Exception
{
if (null == (_r1mDistribution = r1mDistribution) || null == (_aadblScopingLoading =
aadblScopingLoading))
throw new java.lang.Exception ("ProjectionDistributionLoading Constructor => Invalid Inputs!");
int iNumProjectionView = _r1mDistribution.meta().numVariable();
if (iNumProjectionView != _aadblScopingLoading.length)
throw new java.lang.Exception ("ProjectionDistributionLoading Constructor => Invalid Inputs!");
for (int i = 0; i < iNumProjectionView; ++i) {
if (null == _aadblScopingLoading[i] || 0 == _aadblScopingLoading[i].length ||
!org.drip.numerical.common.NumberUtil.IsValid (_aadblScopingLoading[i]))
throw new java.lang.Exception
("ProjectionDistributionLoading Constructor => Invalid Inputs!");
}
}
/**
* Retrieve the Projection Distribution
*
* @return The Projection Distribution
*/
public org.drip.measure.continuous.R1Multivariate distribution()
{
return _r1mDistribution;
}
/**
* Retrieve the Matrix of the Scoping Loadings
*
* @return The Matrix of the Scoping Loadings
*/
public double[][] scopingLoading()
{
return _aadblScopingLoading;
}
/**
* Retrieve the Number of the Projection Variates
*
* @return The Number of the Projection Variates
*/
public int numberOfProjectionVariate()
{
return _aadblScopingLoading.length;
}
/**
* Retrieve the Number of the Scoping Variate
*
* @return The Number of the Scoping Variate
*/
public int numberOfScopingVariate()
{
return _aadblScopingLoading[0].length;
}
}