[7849] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[15584] | 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[7849] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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[14305] | 25 | using HeuristicLab.Core;
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[7849] | 26 | using HeuristicLab.Random;
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| 27 |
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| 28 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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| 29 | public class BreimanOne : ArtificialRegressionDataDescriptor {
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| 30 |
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| 31 | public override string Name { get { return "Breiman - I"; } }
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| 32 | public override string Description {
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| 33 | get {
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| 34 | return "Paper: Classification and Regression Trees" + Environment.NewLine
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| 35 | + "Authors: Leo Breiman, Jerome H. Friedman, Charles J. Stone and R. A. Olson";
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| 36 | }
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| 37 | }
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| 38 | protected override string TargetVariable { get { return "Y"; } }
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[8825] | 39 | protected override string[] VariableNames { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "Y" }; } }
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[7849] | 40 | protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10" }; } }
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| 41 | protected override int TrainingPartitionStart { get { return 0; } }
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| 42 | protected override int TrainingPartitionEnd { get { return 5001; } }
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| 43 | protected override int TestPartitionStart { get { return 5001; } }
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| 44 | protected override int TestPartitionEnd { get { return 10001; } }
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| 45 |
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[14305] | 46 | public int Seed { get; private set; }
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[7849] | 47 |
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[14305] | 48 | public BreimanOne() : this((int)DateTime.Now.Ticks) { }
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| 49 | public BreimanOne(int seed) : base() {
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| 50 | Seed = seed;
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| 51 | }
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| 52 |
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[7849] | 53 | protected override List<List<double>> GenerateValues() {
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| 54 | List<List<double>> data = new List<List<double>>();
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| 55 | List<int> values = new List<int>() { -1, 1 };
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[14305] | 56 | var rand = new MersenneTwister((uint)Seed);
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| 57 | data.Add(GenerateUniformIntegerDistribution(rand, values, TestPartitionEnd));
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[7849] | 58 | values.Add(0);
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| 59 | for (int i = 0; i < AllowedInputVariables.Count() - 1; i++) {
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[14305] | 60 | data.Add(GenerateUniformIntegerDistribution(rand, values, TestPartitionEnd));
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[7849] | 61 | }
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| 62 | double x1, x2, x3, x4, x5, x6, x7;
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| 63 | double f;
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| 64 | List<double> results = new List<double>();
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| 65 | double sigma = Math.Sqrt(2);
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| 66 | for (int i = 0; i < data[0].Count; i++) {
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| 67 | x1 = data[0][i];
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| 68 | x2 = data[1][i];
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| 69 | x3 = data[2][i];
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| 70 | x4 = data[3][i];
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| 71 | x5 = data[4][i];
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| 72 | x6 = data[5][i];
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| 73 | x7 = data[6][i];
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| 74 |
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| 75 | if (x1.Equals(1))
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| 76 | f = 3 + 3 * x2 + 2 * x3 + x4;
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| 77 | else
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| 78 | f = -3 + 3 * x5 + 2 * x6 + x7;
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| 79 |
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| 80 | results.Add(f + NormalDistributedRandom.NextDouble(rand, 0, sigma));
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| 81 | }
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| 82 | data.Add(results);
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| 83 |
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| 84 | return data;
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| 85 | }
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| 86 |
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[14305] | 87 | private List<double> GenerateUniformIntegerDistribution(IRandom rand, List<int> classes, int amount) {
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[7849] | 88 | List<double> values = new List<double>();
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| 89 | for (int i = 0; i < amount; i++) {
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| 90 | values.Add(classes[rand.Next(0, classes.Count)]);
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| 91 | }
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| 92 | return values;
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| 93 | }
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| 94 | }
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| 95 | }
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