R1UnivariateExponential.java
package org.drip.measure.continuous;
/*
* -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
*/
/*!
* Copyright (C) 2020 Lakshmi Krishnamurthy
* Copyright (C) 2019 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>R1UnivariateExponential</i> implements the Univariate R<sup>1</sup> Exponential Distribution. It
* implements the Incremental, the Cumulative, and the Inverse Cumulative Distribution Densities.
*
* <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/continuous/README.md">R<sup>1</sup> and R<sup>d</sup> Continuous Random Measure</a></li>
* </ul>
*
* @author Lakshmi Krishnamurthy
*/
public class R1UnivariateExponential extends org.drip.measure.continuous.R1Univariate
{
private double _lambda = java.lang.Double.NaN;
/**
* Construct the Standard R1UnivariateExponential Distribution (lambda = 1.)
*
* @return The Standard R1UnivariateExponential Distribution
*/
public static final R1UnivariateExponential Standard()
{
try
{
return new R1UnivariateExponential (1.);
}
catch (java.lang.Exception e)
{
e.printStackTrace();
}
return null;
}
/**
* R1UnivariateExponential Constructor
*
* @param lambda Lambda (Inverse Scaling Parameter)
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public R1UnivariateExponential (
final double lambda)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (_lambda = lambda) || 0. >= _lambda)
{
throw new java.lang.Exception ("R1UnivariateExponential Constructor => Invalid Inputs: " + _lambda);
}
}
/**
* Retrieve the Lambda (Inverse Scaling Parameter)
*
* @return The Lambda (Inverse Scaling Parameter)
*/
public double lambda()
{
return _lambda;
}
@Override public double[] support()
{
return new double[]
{
0.,
java.lang.Double.POSITIVE_INFINITY
};
}
@Override public double cumulative (
final double x)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (x) || x < 0.)
throw new java.lang.Exception ("R1UnivariateExponential::cumulative => Invalid Inputs");
return 1. - java.lang.Math.exp (-1. * _lambda * x);
}
@Override public double incremental (
final double xLeft,
final double xRight)
throws java.lang.Exception
{
return cumulative (xLeft) - cumulative (xRight);
}
@Override public double invCumulative (
final double y)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (y) || 1. < y || 0. > y)
throw new java.lang.Exception ("R1UnivariateExponential::invCumulative => Cannot calculate");
return -1. / _lambda * java.lang.Math.log (1. - y);
}
@Override public double density (
final double x)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (x) || x < 0.)
throw new java.lang.Exception ("R1UnivariateExponential::density => Invalid Inputs");
return _lambda * java.lang.Math.exp (-1. * _lambda * x);
}
@Override public double mean()
{
return 1. / _lambda;
}
@Override public double variance()
{
return 1. / (_lambda * _lambda);
}
@Override public org.drip.numerical.common.Array2D histogram()
{
return null;
}
}