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;
    }
}