package test.dr.distibutions;

import dr.math.distributions.LogNormalDistribution;
import dr.math.interfaces.OneVariableFunction;
import dr.math.iterations.BisectionZeroFinder;
import junit.framework.TestCase;

/**
 * @author Alexei Drummond
 */
public class LogNormalDistributionTest extends TestCase {

    LogNormalDistribution logNormal;

    public void setUp() {

        logNormal = new LogNormalDistribution(1.0, 2.0);
    }

    public void testPdf() {

        System.out.println("Testing 10000 random pdf calls");

        for (int i = 0; i < 10000; i++) {
            double M = Math.random() * 10.0 - 5.0;
            double S = Math.random() * 10;

            double x = Math.log(Math.random() * 10);

            logNormal.setM(M);
            logNormal.setS(S);

            double pdf = 1.0 / (x * S * Math.sqrt(2 * Math.PI)) * Math.exp(-Math.pow(Math.log(x) - M, 2) / (2 * S * S));

            //System.out.println("Testing logNormal[M=" + M + " S=" + S + "].pdf(" + x + ")");

            assertEquals(pdf, logNormal.pdf(x), 1e-10);
        }
    }

    public void testMean() {

        for (int i = 0; i < 1000; i++) {
            double M = Math.random() * 10.0 - 5.0;
            double S = Math.random() * 10;

            logNormal.setM(M);
            logNormal.setS(S);

            double mean = Math.exp(M + S * S / 2);

            //System.out.println("Testing logNormal[M=" + M + " S=" + S + "].mean()");

            assertEquals(mean, logNormal.mean(), 1e-10);
        }
    }

    public void testVariance() {

        for (int i = 0; i < 1000; i++) {
            double M = Math.random() * 10.0 - 5.0;
            double S = Math.random() * 10;

            logNormal.setM(M);
            logNormal.setS(S);

            double variance = (Math.exp(S * S) - 1) * Math.exp(2 * M + S * S);

            //System.out.println("Testing logNormal[M=" + M + " S=" + S + "].variance()");

            assertEquals(variance, logNormal.variance(), 1e-10);
        }
    }


    public void testMedian() {

        System.out.println("Testing 10000 random quantile(0.5) calls");

        for (int i = 0; i < 10000; i++) {
            double M = Math.random() * 10.0 - 5.0;
            double S = Math.random() * 10;

            logNormal.setM(M);
            logNormal.setS(S);

            double median = Math.exp(M);

            //System.out.println("Testing logNormal[M=" + M + " S=" + S + "].median()");

            assertEquals(median, logNormal.quantile(0.5), median / 1e6);
        }
    }

    public void testCDFAndQuantile() {

        System.out.println("Testing 10000 random quantile/cdf pairs");

        for (int i = 0; i < 10000; i++) {

            double M = Math.random() * 10.0 - 5.0;
            double S = Math.random() * 10;

            logNormal.setM(M);
            logNormal.setS(S);

            double p = Math.random();
            double quantile = logNormal.quantile(p);

            double cdf = logNormal.cdf(quantile);

            assertEquals(p, cdf, 1e-8);
        }
    }

    public void testCDFAndQuantile2() {

        final LogNormalDistribution f = new LogNormalDistribution(1, 1);
        for (double i = 0.01; i < 0.95; i += 0.01) {
            final double y = i;

            BisectionZeroFinder zeroFinder = new BisectionZeroFinder(new OneVariableFunction() {
                public double value(double x) {
                    return f.cdf(x) - y;
                }
            }, 0.01, 100);
            zeroFinder.evaluate();

            assertEquals(f.quantile(i), zeroFinder.getResult(), 1e-6);
        }
    }

}
