InverseGaussianProcessPCA.java

/*
 * Class:        InverseGaussianProcessPCA
 * 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 InverseGaussianProcessPCA extends InverseGaussianProcess {
 
   protected BrownianMotionPCA bmPCA;

   public InverseGaussianProcessPCA(double s0, double delta, double gamma, RandomStream stream) {
      super(s0, delta, gamma, stream);
      bmPCA = new BrownianMotionPCA(0., 0., delta, stream);
      numberOfRandomStreams = 1;
   }
 
   public double[] generatePath() {
      double[] uniformIncrement = new double[d];
      double[] BMpath = bmPCA.generatePath();
 
      for (int i = 0; i < d; i++) {
         double dt = bmPCA.mudt[i]; // bmTime[i + 1] - bmTime[i];
         double sigma = bmPCA.sigmasqrdt[i];// Math.sqrt(dt) * bmSigma;
         uniformIncrement[i] = NormalDistQuick.cdf01((BMpath[i + 1] - BMpath[i] - bmPCA.mu * dt) / sigma);
      }
      path[0] = x0;
      for (int i = 0; i < d; i++)
         path[i + 1] = path[i] + InverseGaussianDist.inverseF(imu[i], ilam[i], uniformIncrement[i]);
 
      observationIndex = d;
      observationCounter = d;
      return path;
   }

   public double[] generatePath(double[] uniforms01) {
      double[] uniformIncrement = new double[d];
      double[] BMpath = bmPCA.generatePath(uniforms01);
 
      for (int i = 0; i < d; i++) {
         double dt = bmPCA.mudt[i]; // bmTime[i + 1] - bmTime[i];
         double sigma = bmPCA.sigmasqrdt[i];// Math.sqrt(dt) * bmSigma;
         uniformIncrement[i] = NormalDistQuick.cdf01((BMpath[i + 1] - BMpath[i] - bmPCA.mu * dt) / sigma);
      }
      path[0] = x0;
      for (int i = 0; i < d; i++)
         path[i + 1] = path[i] + InverseGaussianDist.inverseF(imu[i], ilam[i], uniformIncrement[i]);
 
      observationIndex = d;
      observationCounter = d;
      return path;
   }

   public double nextObservation() {
      throw new UnsupportedOperationException("Not implementable for PCA.");
   }

   public void setObservationTimes(double t[], int d) {
      super.setObservationTimes(t, d);
      bmPCA.setObservationTimes(t, d);
   }
 
   public RandomStream getStream() {
      if (stream != bmPCA.getStream())
         throw new IllegalStateException("Two different streams or more are present");
      return stream;
   }
 
   public void setStream(RandomStream stream) {
      super.setStream(stream);
      bmPCA.setStream(stream);
   }

   public void setBrownianMotionPCA(BrownianMotionPCA bmPCA) {
      this.bmPCA = bmPCA;
   }

   public BrownianMotion getBrownianMotionPCA() {
      return bmPCA;
   }
 
}