KernelDensity.java

/*
 * Class:        KernelDensity
 * Description:  Kernel density estimators
 * 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.gof;
 
import umontreal.ssj.probdist.*;
// import umontreal.ssj.randvar.KernelDensityGen;

public class KernelDensity {

   private static double estimate(EmpiricalDist dist, ContinuousDistribution kern, double h, double y) {
      // Computes and returns the kernel density estimate at $y$, where the
      // kernel is the density kern.density(x), and the bandwidth is $h$.
      double z;
      double a = kern.getXinf(); // lower limit of density
      double b = kern.getXsup(); // upper limit of density
      double sum = 0;
      int n = dist.getN();
      for (int i = 0; i < n; i++) {
         z = (y - dist.getObs(i)) / h;
         if ((z >= a) && (z <= b))
            sum += kern.density(z);
      }
      sum /= (h * n);
      return sum;
   }

   public static double[] computeDensity(EmpiricalDist dist, ContinuousDistribution kern, double h, double[] Y) {
      int m = Y.length;
      double[] u = new double[m];
      for (int j = 0; j < m; j++)
         u[j] = estimate(dist, kern, h, Y[j]);
      return u;
   }

   public void evalDensity(double data[], ContinuousDistribution kern, double h, double[] evalPoints, double[] density,
         double epsilon0) {
      int m = evalPoints.length;
      // density = new double[m]; // Maybe not needed, but perhaps safer.
      int n = data.length;
      double invhn = 1.0 / (h * n);
      double invh = 1.0 / h;
      double y;
      double sum = 0.0;
      double term; // A term to be added to the sum that defines the density estimate.
      int imin = 0; // We know that the terms for i < imin do not contribute significantly.
      for (int j = 0; j < m; j++) { // Evaluation points are indexed by j.
         y = evalPoints[j];
         term = kern.density((y - data[imin]) * invh);
         while ((term < epsilon0) & (imin < n - 1) && (data[imin] < y))
            term = kern.density((y - data[++imin]) * invh);
         sum = term; // The first significant term.
         for (int i = imin + 1; (i < n) && ((term > epsilon0) || (data[i] < y)); i++)
            // Data indexed by i.
            sum += (term = kern.density((y - data[i]) * invh));
         density[j] = sum * invhn;
      }
   }
 
}