GammaProcessPCA.java

/*
 * Class:        GammaProcessPCA
 * Description:
 * Environment:  Java
 * Software:     SSJ
 * Copyright (C) 2001  Pierre L'Ecuyer and Universite de Montreal
 * Organization: DIRO, Universite de Montreal
 * @authors      Jean-Sébastien Parent and Maxime Dion
 * @since        july 2008
 *
 *
 * 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.*;
import umontreal.ssj.stat.*;
import umontreal.ssj.stat.list.*;

public class GammaProcessPCA extends GammaProcess {
   double[] arrayTime;
   BrownianMotionPCA BMPCA;

   public GammaProcessPCA(double s0, double mu, double nu, RandomStream stream) {
      super(s0, mu, nu, new GammaGen(stream, new GammaDist(1.0)));
      this.BMPCA = new BrownianMotionPCA(0.0, 0.0, Math.sqrt(nu), stream);
   }

   public GammaProcessPCA(double s0, double mu, double nu, GammaGen Ggen) {
      super(s0, mu, nu, Ggen);
      this.BMPCA = new BrownianMotionPCA(0.0, 0.0, Math.sqrt(nu), Ggen.getStream());
   }
 
   public double[] generatePath() {
      double[] uniformsV = new double[d];
      arrayTime = BMPCA.getObservationTimes();
      int i;
      double[] BMpath = BMPCA.generatePath();
      double sigma;
      for (i = 0; i < d; i++) {
         sigma = BMPCA.getSigma() * Math.sqrt(arrayTime[i + 1] - arrayTime[i]);
         uniformsV[i] = NormalDist.cdf01((BMpath[i + 1] - BMpath[i]) / sigma);
      }
      path[0] = x0;
      for (i = 0; i < d; i++) {
         path[i + 1] = path[i] + GammaDist.inverseF(mu2dtOverNu[i], muOverNu, 10, uniformsV[i]);
      }
      observationIndex = d;
      observationCounter = d;
      return path;
   }
 
   public double[] generatePath(double[] uniform01) {
      double[] uniformsV = new double[d];
      arrayTime = BMPCA.getObservationTimes();
      int i;
      double[] BMpath = BMPCA.generatePath(uniform01);
      double sigma;
      for (i = 0; i < d; i++) {
         sigma = BMPCA.getSigma() * Math.sqrt(arrayTime[i + 1] - arrayTime[i]);
         uniformsV[i] = NormalDist.cdf01((BMpath[i + 1] - BMpath[i]) / sigma);
      }
      path[0] = x0;
      for (i = 0; i < d; i++) {
         path[i + 1] = path[i] + GammaDist.inverseF(mu2dtOverNu[i], muOverNu, 10, uniformsV[i]);
      }
      observationIndex = d;
      observationCounter = d;
      return path;
   }

   public double nextObservation() {
      throw new UnsupportedOperationException("nextObservation is not implemented in GammaProcessPCA");
   }

   public double nextObservation(double nextT) {
      throw new UnsupportedOperationException("nextObservation is not implemented in GammaProcessPCA");
   }

   public BrownianMotionPCA getBMPCA() {
      return BMPCA;
   }

   public void setObservationTimes(double[] t, int d) {
      super.setObservationTimes(t, d);
      BMPCA.setObservationTimes(t, d);
   }

   public void setParams(double s0, double mu, double nu) {
      super.setParams(s0, mu, nu);
      BMPCA.setParams(0.0, 0.0, Math.sqrt(nu));
   }

   public void setStream(RandomStream stream) {
      super.setStream(stream);
      this.BMPCA.setStream(stream);
   }
 
}