source: branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Problems.Instances.DataAnalysis.GaussianProcessRegression/Util.cs @ 9313

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Algorithms.DataAnalysis;
using HeuristicLab.Core;
using HeuristicLab.Random;

namespace HeuristicLab.Problems.Instances.DataAnalysis {
  public class Util {


    public static List<double> SampleGaussianProcess(IRandom random, ParameterizedCovarianceFunction covFunction, List<List<double>> data) {
      int n = data[0].Count;

      var normalRand = new NormalDistributedRandom(random, 0, 1);
      var alpha = (from i in Enumerable.Range(0, n)
                   select normalRand.NextDouble()).ToArray();
      return SampleGaussianProcess(random, covFunction, data, alpha);
    }

    public static List<double> SampleGaussianProcess(IRandom random, ParameterizedCovarianceFunction covFunction, List<List<double>> data, double[] alpha) {
      if (alpha.Length != data[0].Count) throw new ArgumentException();

      double[,] x = new double[data[0].Count, data.Count];
      for (int i = 0; i < x.GetLength(0); i++)
        for (int j = 0; j < x.GetLength(1); j++)
          x[i, j] = data[j][i];
      double[,] K = new double[x.GetLength(0), x.GetLength(0)];
      for (int i = 0; i < K.GetLength(0); i++)
        for (int j = i; j < K.GetLength(1); j++)
          K[i, j] = covFunction.Covariance(x, i, j);

      if (!alglib.spdmatrixcholesky(ref K, K.GetLength(0), true)) throw new ArgumentException();

      List<double> target = new List<double>(K.GetLength(0));
      for (int i = 0; i < K.GetLength(0); i++) {
        double s = 0.0;
        for (int j = K.GetLength(0) - 1; j >= 0; j--) {

          s += K[j, i] * alpha[j];
        }

        target.Add(s);
      }

      return target;
    }
  }
}