package com.imsl.test.example.stat;

import com.imsl.stat.*;

/**
 * <p>
 * Performs a Kolmogorov one-sample test.
 * </p>
 * In this example, a random sample of size \(n=100\) is generated using class
 * <code>Random</code> for the uniform \((0, 1)\) distribution. We want to test the
 * null hypothesis that the cdf is the standard normal distribution with a mean
 * of \(0.5\) and a variance equal to the uniform \((0, 1)\) variance \((1/12)\).
 *
 * @see <a href="KolmogorovOneSampleEx1.java">Code</a>
 * @see <a href="doc-files/KolmogorovOneSampleEx1.out">Output</a>
 */

public class KolmogorovOneSampleEx1 {

    static public void main(String arg[]) {
        CdfFunction cdf = new CdfFunction() {
            public double cdf(double x) {
                double mean = 0.5;
                double std = 0.2886751;
                double z = (x - mean) / std;
                return Cdf.normal(z);
            }
        };

        double x[] = new double[100];
        Random random = new Random(123457);
        random.setMultiplier(16807);
        for (int i = 0; i < x.length; i++) {
            x[i] = random.nextDouble();
        }

        KolmogorovOneSample kos = new KolmogorovOneSample(cdf, x);
        System.out.println("D  = " + kos.getTestStatistic());
        System.out.println("D+ = " + kos.getMaximumDifference());
        System.out.println("D- = " + kos.getMinimumDifference());
        System.out.println("Z   = " + kos.getZ());
        System.out.println("Prob greater D one sided = "
                + kos.getOneSidedPValue());
        System.out.println("Prob greater D two sided = "
                + kos.getTwoSidedPValue());
        System.out.println("N missing = " + kos.getNumberMissing());
    }
}