MarkovChainDouble.java

package umontreal.ssj.markovchainrqmc;
 
import umontreal.ssj.rng.*;
import umontreal.ssj.stat.Tally;

public abstract class MarkovChainDouble extends MarkovChainComparable {
 
   protected double state = 0; // State of this chain.
   protected int step = 0; // Current step.
   protected double perf = 0;


   public abstract double initialStateDouble();
 
   public void initialState() {
      step = 0;
      state = initialStateDouble();
      stopped = false;
   }

   public abstract double nextStepDouble(int step, double s, RandomStream stream);
 
   public void nextStep(RandomStream stream) {
      state = nextStepDouble(step, state, stream);
      step++;
   }

   public abstract double getPerformanceDouble(double state, int numSteps);



   public double getPerformance(int numSteps) {
      return getPerformanceDouble(state, numSteps);
   }

   public double getPerformance() {
      return perf;
   }

   public boolean hasStopped() {
      return state == Double.POSITIVE_INFINITY;
   }
 
   public int getStateDimension() {
      return 1;
   }
 
   public int compareTo(MarkovChainComparable other, int i) {
      double os = ((MarkovChainDouble) other).state;
      return (state < os ? -1 : (state > os ? 1 : 0));
   }

   public double simulStepsDouble(int numSteps, RandomStream stream) {
      initialState();
      for (step = 0; step < numSteps && !hasStopped(); ++step) {
         state = nextStepDouble(step, state, stream);
      }
      return state;
   }

   public void simulRunsWithSubstreams(int n, int numSteps, RandomStream stream, Tally statRuns) {
      statRuns.init();
      stream.resetStartStream();
      for (int i = 0; i < n; i++) {
         simulStepsDouble(numSteps, stream);
         statRuns.add(getPerformanceDouble(state, numSteps));
         stream.resetNextSubstream();
      }
   }
 
}