BrownianMotion.java

/*
 * Class:        BrownianMotion
 * Description:  
 * Environment:  Java
 * Software:     SSJ 
 * Copyright (C) 2001  Pierre L'Ecuyer and Universite de Montreal
 * Organization: DIRO, Universite de Montreal
 * @author       
 * @since
 *
 *
 * 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.
 *
 */
package umontreal.ssj.stochprocess;
 
import umontreal.ssj.rng.*;
import umontreal.ssj.probdist.*;
import umontreal.ssj.randvar.*;

public class BrownianMotion extends StochasticProcess {
   protected NormalGen gen;
   protected double mu, sigma;
   // Precomputed values for standard BM
   protected double[] mudt, sigmasqrdt;

   public BrownianMotion(double x0, double mu, double sigma, RandomStream stream) {
      this(x0, mu, sigma, new NormalGen(stream));
   }

   public BrownianMotion(double x0, double mu, double sigma, NormalGen gen) {
      this.mu = mu;
      this.sigma = sigma;
      this.x0 = x0;
      this.gen = gen;
   }
 
   public double nextObservation() {
      double x = path[observationIndex];
      x += mudt[observationIndex] + sigmasqrdt[observationIndex] * gen.nextDouble();
      observationIndex++;
      path[observationIndex] = x;
      return x;
   }

   public double nextObservation(double nextTime) {
      // This method is useful for generating variance gamma processes
      double x = path[observationIndex];
      double previousTime = t[observationIndex];
      observationIndex++;
      t[observationIndex] = nextTime;
      double dt = nextTime - previousTime;
      x += mu * dt + sigma * Math.sqrt(dt) * gen.nextDouble();
      path[observationIndex] = x;
      return x;
   }

   public double nextObservation(double x, double dt) {
      x += mu * dt + sigma * Math.sqrt(dt) * gen.nextDouble();
      return x;
   }
 
   public double[] generatePath() {
      double x = x0;
      for (int j = 0; j < d; j++) {
         x += mudt[j] + sigmasqrdt[j] * gen.nextDouble();
         path[j + 1] = x;
      }
      observationIndex = d;
      observationCounter = d;
      return path;
   }

   public double[] generatePath(double[] uniform01) {
      double x = x0;
      for (int j = 0; j < d; j++) {
         x += mudt[j] + sigmasqrdt[j] * NormalDist.inverseF01(uniform01[j]);
         path[j + 1] = x;
      }
      observationIndex = d;
      observationCounter = d;
      return path;
   }
 
   public double[] generatePath(RandomStream stream) {
      gen.setStream(stream);
      return generatePath();
   }

   public void setParams(double x0, double mu, double sigma) {
      this.x0 = x0;
      this.mu = mu;
      if (sigma <= 0)
         throw new IllegalArgumentException("sigma <= 0");
      this.sigma = sigma;
      if (observationTimesSet)
         init(); // Otherwise not needed.
   }

   public void setStream(RandomStream stream) {
      gen.setStream(stream);
   }

   public RandomStream getStream() {
      return gen.getStream();
   }

   public double getMu() {
      return mu;
   }

   public double getSigma() {
      return sigma;
   }

   public NormalGen getGen() {
      return gen;
   }
 
   // This is called by setObservationTimes to precompute constants
   // in order to speed up the path generation.
   protected void init() {
      super.init();
      mudt = new double[d];
      sigmasqrdt = new double[d];
      for (int j = 0; j < d; j++) {
         double dt = t[j + 1] - t[j];
         mudt[j] = mu * dt;
         sigmasqrdt[j] = sigma * Math.sqrt(dt);
      }
   }
 
}