[7849] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[16140] | 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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[14228] | 25 | using HeuristicLab.Random;
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[7849] | 26 |
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| 27 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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[8238] | 28 | public class KeijzerFunctionFive : ArtificialRegressionDataDescriptor {
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[7849] | 29 |
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[8238] | 30 | public override string Name { get { return "Keijzer 5 f(x) = (30 * x * z) / ((x - 10) * y²)"; } }
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[7849] | 31 | public override string Description {
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| 32 | get {
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| 33 | return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
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| 34 | + "Authors: Maarten Keijzer" + Environment.NewLine
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[8238] | 35 | + "Function: f(x) = (30 * x * z) / ((x - 10) * y²)" + Environment.NewLine
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[7849] | 36 | + "range(train): 1000 points x,z = rnd(-1, 1), y = rnd(1, 2)" + Environment.NewLine
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| 37 | + "range(test): 10000 points x,z = rnd(-1, 1), y = rnd(1, 2)" + Environment.NewLine
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| 38 | + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
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| 39 | }
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| 40 | }
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| 41 | protected override string TargetVariable { get { return "F"; } }
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[8825] | 42 | protected override string[] VariableNames { get { return new string[] { "X", "Y", "Z", "F" }; } }
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[7849] | 43 | protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y", "Z" }; } }
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| 44 | protected override int TrainingPartitionStart { get { return 0; } }
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| 45 | protected override int TrainingPartitionEnd { get { return 1000; } }
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| 46 | protected override int TestPartitionStart { get { return 1000; } }
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| 47 | protected override int TestPartitionEnd { get { return 11000; } }
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[14229] | 48 | public int Seed { get; private set; }
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[7849] | 49 |
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[14228] | 50 | public KeijzerFunctionFive() : this((int)System.DateTime.Now.Ticks) {
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| 51 | }
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| 52 | public KeijzerFunctionFive(int seed) : base() {
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| 53 | Seed = seed;
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| 54 | }
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[7849] | 55 | protected override List<List<double>> GenerateValues() {
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| 56 | List<List<double>> data = new List<List<double>>();
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[14228] | 57 | var rand = new MersenneTwister((uint)Seed);
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[7849] | 58 |
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[14228] | 59 | data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -1, 1).ToList());
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| 60 | data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList());
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| 61 | data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -1, 1).ToList());
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| 62 |
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[7849] | 63 | double x, y, z;
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| 64 | List<double> results = new List<double>();
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| 65 | for (int i = 0; i < data[0].Count; i++) {
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| 66 | x = data[0][i];
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| 67 | y = data[1][i];
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| 68 | z = data[2][i];
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| 69 | results.Add((30 * x * z) / ((x - 10) * Math.Pow(y, 2)));
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| 70 | }
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| 71 | data.Add(results);
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| 72 |
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| 73 | return data;
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| 74 | }
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| 75 | }
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| 76 | }
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