Nonuniform.java

package tutorial;
 
import umontreal.ssj.rng.*;
import java.io.*;
import umontreal.ssj.charts.HistogramChart;
import umontreal.ssj.probdist.*;
import umontreal.ssj.randvar.*;
import umontreal.ssj.stat.*;
 
public class Nonuniform {
   // The parameter values are hardcoded here to simplify the program.
   double lambda = 5.0;
   double p = 0.2;
   double alpha = 2.0;
   double beta = 1.0;
   double mu = 5.0;
   double sigma = 0.5;
 
   RandomStream stream = new LFSR113();
   RandomVariateGenInt genN = new RandomVariateGenInt(stream, new PoissonDist(lambda)); // For N
   RandomVariateGen genY = new GammaAcceptanceRejectionGen(stream, new GammaDist(alpha, beta)); // For Y_j
   RandomVariateGen genW = new RandomVariateGen(stream, new LognormalDist(mu, sigma)); // For W_j
 
   // Generates and returns X.
   public double simulate() {
      int N;
      int M;
      int j;
      double X = 0.0;
      N = genN.nextInt();
      M = GeometricDist.inverseF(p, stream.nextDouble()); // Uses static method
      for (j = 0; j < N; j++)
         X += genY.nextDouble();
      for (j = 0; j < M; j++)
         X += genW.nextDouble();
      return X;
   }
 
   // Performs n indep. runs and collects statistics in statX.
   public void simulateRuns(int n, TallyStore statX) {
      for (int i = 0; i < n; i++)
         statX.add(simulate());
   }
 
   public static void main(String[] args) throws IOException {
      int n = 100000;
      TallyStore statX = new TallyStore(n); // To store the n observations of X.
      (new Nonuniform()).simulateRuns(n, statX); // Simulate X n times.
      System.out.println(statX.report(0.95, 1));
 
      // Compute and print the empirical quantiles.
      statX.quickSort();
      double[] data = statX.getArray(); // The sorted observations.
      System.out.printf(" 0.10 quantile: %9.1f%n", data[(int) (0.10 * n)]);
      System.out.printf(" 0.50 quantile: %9.1f%n", data[(int) (0.50 * n)]);
      System.out.printf(" 0.90 quantile: %9.1f%n", data[(int) (0.90 * n)]);
      System.out.printf(" 0.99 quantile: %9.1f%n", data[(int) (0.99 * n)]);
 
      // Make a histogram of the empirical distribution of X.
      HistogramChart hist = new HistogramChart("Histogram of distribution of $X$", "Values of $X$", "Frequency",
            statX.getArray(), n);
      double[] bounds = { 0, 4000, 0, 25000 }; // Range of x and y to be displayed.
      hist.setManualRange(bounds);
      (hist.getSeriesCollection()).setBins(0, 40, 0, 4000); // 40 bins over [0, 4000].
      hist.view(800, 500); // View on screen.
 
      // Make a Latex file that contains the histogram.
      String histLatex = hist.toLatex(12.0, 8.0); // Width and height of plot in cm.
      Writer file = new FileWriter("src/main/docs/examples/tutorial/NonuniformHist.tex");
      file.write(histLatex);
      file.close();
   }
}