source: branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Benchmarks/3.4/RegressionBenchmarks/Other/BreimanOne.cs @ 7485

#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 HeuristicLab.Data;
using HeuristicLab.Random;

namespace HeuristicLab.Problems.DataAnalysis.Benchmarks {
  public class BreimanOne : RegressionToyBenchmark {

    public BreimanOne() {
      Name = "Breiman - I";
      Description = "Paper: Classification and Regression Trees" + Environment.NewLine
        + "Authors: Leo Breiman, Jerome H. Friedman, Charles J. Stone and R. A. Olson";
      targetVariable = "Y";
      inputVariables = new List<string>() { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10" };
      trainingPartition = new IntRange(0, 5001);
      testPartition = new IntRange(5001, 10001);
    }

    protected override List<double> GenerateTarget(List<List<double>> data) {
      double x1, x2, x3, x4, x5, x6, x7;
      double f;
      List<double> results = new List<double>();
      double sigma = Math.Sqrt(2);
      for (int i = 0; i < data[0].Count; i++) {
        x1 = data[0][i];
        x2 = data[1][i];
        x3 = data[2][i];
        x4 = data[3][i];
        x5 = data[4][i];
        x6 = data[5][i];
        x7 = data[6][i];

        if (x1.Equals(1))
          f = 3 + 3 * x2 + 2 * x3 + x4;
        else
          f = -3 + 3 * x5 + 2 * x6 + x7;

        results.Add(f + NormalDistributedRandom.NextDouble(rand, 0, sigma));
      }
      return results;
    }

    protected override List<List<double>> GenerateInput() {
      List<List<double>> dataList = new List<List<double>>();
      List<int> values = new List<int>() { -1, 1 };
      dataList.Add(BreimanOne.GenerateUniformIntegerDistribution(values, testPartition.End));
      values.Add(0);
      for (int i = 0; i < inputVariables.Count - 1; i++) {
        dataList.Add(BreimanOne.GenerateUniformIntegerDistribution(values, testPartition.End));
      }

      return dataList;
    }

    public static List<double> GenerateUniformIntegerDistribution(List<int> classes, int amount) {
      List<double> values = new List<double>();
      for (int i = 0; i < amount; i++) {
        values.Add(classes[rand.Next(0, classes.Count)]);
      }
      return values;
    }
  }
}