R1UnivariateUniform.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>R1UnivariateUniform</i> implements the Univariate R<sup>1</sup> Uniform 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 R1UnivariateUniform extends org.drip.measure.continuous.R1Univariate
{
private double _leftSupport = java.lang.Double.NaN;
private double _rightSupport = java.lang.Double.NaN;
/**
* Construct a Standard (0, 1) R<sup>1</sup> Univariate Uniform Distribution
*
* @return Standard (0, 1) R<sup>1</sup> Univariate Uniform Distribution
*/
public static final R1UnivariateUniform Standard()
{
try
{
return new R1UnivariateUniform (
0.,
1.
);
}
catch (java.lang.Exception e)
{
e.printStackTrace();
}
return null;
}
/**
* R1UnivariateUniform Constructor
*
* @param leftSupport The Left Support
* @param rightSupport The Right Support
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public R1UnivariateUniform (
final double leftSupport,
final double rightSupport)
throws java.lang.Exception
{
if (!org.drip.numerical.common.NumberUtil.IsValid (_leftSupport = leftSupport) ||
!org.drip.numerical.common.NumberUtil.IsValid (_rightSupport = rightSupport) ||
_leftSupport >= _rightSupport)
{
throw new java.lang.Exception ("R1UnivariateUniform Constructor => Invalid Inputs");
}
}
/**
* Retrieve the Left Support
*
* @return The Left Support
*/
public double leftSupport()
{
return _leftSupport;
}
/**
* Retrieve the Right Support
*
* @return The Right Support
*/
public double rightSupport()
{
return _rightSupport;
}
/**
* Indicate if the specified x Value stays inside the Support
*
* @param x X
*
* @return The Value stays in Support
*/
public boolean supported (
final double x)
{
return org.drip.numerical.common.NumberUtil.IsValid (x) && x >= _leftSupport || x <= _rightSupport;
}
@Override public double[] support()
{
return new double[]
{
_leftSupport,
_rightSupport
};
}
@Override public double cumulative (
final double x)
throws java.lang.Exception
{
if (!supported (x))
throw new java.lang.Exception ("R1UnivariateUniform::cumulative => Invalid Inputs");
return (x - _leftSupport) / (_rightSupport - _leftSupport);
}
@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 ("R1UnivariateUniform::invCumulative => Cannot calculate");
return y * (_rightSupport - _leftSupport) + _leftSupport;
}
@Override public double density (
final double x)
throws java.lang.Exception
{
if (!supported (x)) throw new java.lang.Exception ("R1UnivariateUniform::density => Invalid Inputs");
return 1. / (_rightSupport - _leftSupport);
}
@Override public double mean()
{
return 0.5 * (_rightSupport + _leftSupport);
}
@Override public double variance()
{
double support = _rightSupport - _leftSupport;
return support * support / 12.;
}
@Override public double random()
{
return java.lang.Math.random() * (_rightSupport - _leftSupport) + _leftSupport;
}
@Override public org.drip.numerical.common.Array2D histogram()
{
return null;
}
}