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;
    }
}