IntegerRandomSequenceBound.java

package org.drip.sample.sequence;

import org.drip.measure.continuous.R1Univariate;
import org.drip.measure.discrete.*;
import org.drip.numerical.common.FormatUtil;
import org.drip.sequence.metrics.*;
import org.drip.sequence.random.*;
import org.drip.service.env.EnvManager;

/*
 * -*- mode: java; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
 */

/*!
 * Copyright (C) 2018 Lakshmi Krishnamurthy
 * Copyright (C) 2017 Lakshmi Krishnamurthy
 * Copyright (C) 2016 Lakshmi Krishnamurthy
 * Copyright (C) 2015 Lakshmi Krishnamurthy
 * 
 *  This file is part of DRIP, a free-software/open-source library for buy/side financial/trading model
 *      libraries targeting analysts and developers
 *      https://lakshmidrip.github.io/DRIP/
 *  
 *  DRIP is composed of four main libraries:
 *  
 *  - DRIP Fixed Income - https://lakshmidrip.github.io/DRIP-Fixed-Income/
 *  - DRIP Asset Allocation - https://lakshmidrip.github.io/DRIP-Asset-Allocation/
 *  - DRIP Numerical Optimizer - https://lakshmidrip.github.io/DRIP-Numerical-Optimizer/
 *  - DRIP Statistical Learning - https://lakshmidrip.github.io/DRIP-Statistical-Learning/
 * 
 *  - DRIP Fixed Income: Library for Instrument/Trading Conventions, Treasury Futures/Options,
 *      Funding/Forward/Overnight Curves, Multi-Curve Construction/Valuation, Collateral Valuation and XVA
 *      Metric Generation, Calibration and Hedge Attributions, Statistical Curve Construction, Bond RV
 *      Metrics, Stochastic Evolution and Option Pricing, Interest Rate Dynamics and Option Pricing, LMM
 *      Extensions/Calibrations/Greeks, Algorithmic Differentiation, and Asset Backed Models and Analytics.
 * 
 *  - DRIP Asset Allocation: Library for model libraries for MPT framework, Black Litterman Strategy
 *      Incorporator, Holdings Constraint, and Transaction Costs.
 * 
 *  - DRIP Numerical Optimizer: Library for Numerical Optimization and Spline Functionality.
 * 
 *  - DRIP Statistical Learning: Library for Statistical Evaluation and Machine Learning.
 * 
 *  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.
 */

/**
 * IntegerRandomSequenceBound demonstrates the Computation of the Probabilistic Bounds for a Sample Random
 *  Integer Sequence.
 *
 * @author Lakshmi Krishnamurthy
 */

public class IntegerRandomSequenceBound {

    private static final void IntegerBounds (
        final UnivariateSequenceGenerator iidsg,
        final R1Univariate dist,
        final int[] aiSampleSize)
        throws Exception
    {
        for (int iSampleSize : aiSampleSize) {
            IntegerSequenceAgnosticMetrics ssamDist = (IntegerSequenceAgnosticMetrics) iidsg.sequence (
                iSampleSize,
                dist
            );

            String strDump = "\t| " + FormatUtil.FormatDouble (iSampleSize, 3, 0, 1) + " => ";

            strDump +=
                FormatUtil.FormatDouble (ssamDist.probGreaterThanZeroUpperBound(), 1, 9, 1.) + " | " +
                FormatUtil.FormatDouble (ssamDist.probEqualToZeroUpperBound(), 1, 9, 1.) + " | ";

            System.out.println (strDump);
        }
    }

    public static void main (
        final String[] args)
        throws Exception
    {
        EnvManager.InitEnv ("");

        BoundedUniformInteger buiSequence = new BoundedUniformInteger (
            0,
            100
        );

        BoundedUniformIntegerDistribution buiDistribution = new BoundedUniformIntegerDistribution (
            0,
            100
        );

        int[] aiSampleSize = new int[] {
            10, 20, 50, 100, 250
        };

        System.out.println();

        System.out.println ("\t|----------------------------------------------------------------------------------|");

        System.out.println ("\t| Generating Integer Random Sequence Metrics");

        System.out.println ("\t| \tL -> R:");

        System.out.println ("\t| \t\tSample Size");

        System.out.println ("\t| \t\tUpper Probability Bound for X != 0");

        System.out.println ("\t| \t\tUpper Probability Bound for X = 0");

        System.out.println ("\t|----------------------------------------------------------------------------------|");

        System.out.println ("\t| Generating Metrics off of Underlying Distribution");

        System.out.println ("\t|----------------------------------------------------------------------------------|");

        IntegerBounds (
            buiSequence,
            buiDistribution,
            aiSampleSize
        );

        System.out.println ("\t|----------------------------------------------------------------------------------|");

        System.out.println();

        System.out.println ("\t|----------------------------------------------------------------------------------|");

        System.out.println ("\t| Generating Metrics off of Empirical Distribution");

        System.out.println ("\t|----------------------------------------------------------------------------------|");

        IntegerBounds (
            buiSequence,
            null,
            aiSampleSize
        );

        System.out.println ("\t|----------------------------------------------------------------------------------|");
    }
}