UnivariateDiscreteThin.java
package org.drip.measure.statistics;
/*
* -*- 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
*
* 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>UnivariateDiscreteThin</i> analyzes and computes the "Thin" Statistics for the Realized Univariate
* Sequence.
*
* <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/statistics/README.md">R<sup>1</sup> R<sup>d</sup> Thin Thick Moments</a></li>
* </ul>
*
* @author Lakshmi Krishnamurthy
*/
public class UnivariateDiscreteThin {
private double _dblError = java.lang.Double.NaN;
private double _dblAverage = java.lang.Double.NaN;
private double _dblMaximum = java.lang.Double.NaN;
private double _dblMinimum = java.lang.Double.NaN;
/**
* Generate a UnivariateDiscreteThin Instance from the specified List of Double's
*
* @param doubleList The List of Doubles
*
* @return The UnivariateDiscreteThin Instance
*/
public static final UnivariateDiscreteThin FromList (
final java.util.List<java.lang.Double> doubleList)
{
if (null == doubleList)
{
return null;
}
int listSize = doubleList.size();
if (0 == listSize)
{
return null;
}
double[] sequence = new double[listSize];
for (int index = 0; index < listSize; ++index)
{
sequence[index] = doubleList.get (index);
}
try
{
return new UnivariateDiscreteThin (sequence);
}
catch (java.lang.Exception e)
{
e.printStackTrace();
}
return null;
}
/**
* UnivariateDiscreteThin Constructor
*
* @param adblSequence The Univariate Sequence
*
* @throws java.lang.Exception Thrown if the Inputs are Invalid
*/
public UnivariateDiscreteThin (
final double[] adblSequence)
throws java.lang.Exception
{
if (null == adblSequence)
throw new java.lang.Exception ("UnivariateDiscreteThin Constructor => Invalid Inputs");
_dblError = 0.;
_dblAverage = 0.;
_dblMaximum = 0.;
_dblMinimum = 0.;
int iSequenceSize = adblSequence.length;
if (0 == iSequenceSize)
throw new java.lang.Exception ("UnivariateDiscreteThin Constructor => Invalid Inputs");
for (int i = 0; i < iSequenceSize; ++i) {
if (!org.drip.numerical.common.NumberUtil.IsValid (adblSequence[i]))
throw new java.lang.Exception ("UnivariateDiscreteThin Constructor => Invalid Inputs");
if (0 == i) {
_dblMaximum = adblSequence[0];
_dblMinimum = adblSequence[0];
} else {
if (_dblMaximum < adblSequence[i]) _dblMaximum = adblSequence[i];
if (_dblMinimum > adblSequence[i]) _dblMinimum = adblSequence[i];
}
_dblAverage = _dblAverage + adblSequence[i];
}
_dblAverage /= iSequenceSize;
for (int i = 0; i < iSequenceSize; ++i)
_dblError = _dblError + java.lang.Math.abs (_dblAverage - adblSequence[i]);
_dblError /= iSequenceSize;
}
/**
* Retrieve the Sequence Average
*
* @return The Sequence Average
*/
public double average()
{
return _dblAverage;
}
/**
* Retrieve the Sequence Error
*
* @return The Sequence Error
*/
public double error()
{
return _dblError;
}
/**
* Retrieve the Sequence Maximum
*
* @return The Sequence Maximum
*/
public double maximum()
{
return _dblMaximum;
}
/**
* Retrieve the Sequence Minimum
*
* @return The Sequence Minimum
*/
public double minimum()
{
return _dblMinimum;
}
}