[7849] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[14186] | 3 | * Copyright (C) 2002-2016 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.Random;
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[7849] | 26 |
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| 27 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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| 28 | public class KornsFunctionNine : ArtificialRegressionDataDescriptor {
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| 29 |
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[8225] | 30 | public override string Name { get { return "Korns 9 y = ((sqrt(X0) / log(X1)) * (exp(X2) / X3²)"; } }
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[7849] | 31 | public override string Description {
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| 32 | get {
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| 33 | return "Paper: Accuracy in Symbolic Regression" + Environment.NewLine
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| 34 | + "Authors: Michael F. Korns" + Environment.NewLine
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[8225] | 35 | + "Function: y = (sqrt(X0) / log(X1)) * (exp(X2) / X3²)" + Environment.NewLine
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| 36 | + "Binary Operators: +, -, *, % (protected division)" + Environment.NewLine
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| 37 | + "Unary Operators: sqrt, square, cube, cos, sin, tan, tanh, ln(|x|) (protected log), exp" + Environment.NewLine
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| 38 | + "Constants: random finit 64-bit IEEE double" + Environment.NewLine
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[7849] | 39 | + "\"Our testing regimen uses only statistical best practices out-of-sample testing techniques. "
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| 40 | + "We test each of the test cases on matrices of 10000 rows by 1 to 5 columns with no noise. "
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| 41 | + "For each test a training matrix is filled with random numbers between -50 and +50. The test case "
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| 42 | + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"" + Environment.NewLine + Environment.NewLine
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[8900] | 43 | + "Note: Because of the square root and the logarithm only non-negatic values are created for their input variables!" + Environment.NewLine
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| 44 | + "Because of the exponential function only only non-positive values are created for its input variable!";
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[7849] | 45 | }
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| 46 | }
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| 47 | protected override string TargetVariable { get { return "Y"; } }
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[8825] | 48 | protected override string[] VariableNames { get { return new string[] { "X0", "X1", "X2", "X3", "X4", "Y" }; } }
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[7849] | 49 | protected override string[] AllowedInputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4" }; } }
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| 50 | protected override int TrainingPartitionStart { get { return 0; } }
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[8225] | 51 | protected override int TrainingPartitionEnd { get { return 10000; } }
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| 52 | protected override int TestPartitionStart { get { return 10000; } }
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| 53 | protected override int TestPartitionEnd { get { return 20000; } }
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[14305] | 54 | public int Seed { get; private set; }
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[7849] | 55 |
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[14305] | 56 | public KornsFunctionNine() : this((int)System.DateTime.Now.Ticks) {
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| 57 | }
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| 58 | public KornsFunctionNine(int seed) : base() {
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| 59 | Seed = seed;
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| 60 | }
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[7849] | 61 | protected override List<List<double>> GenerateValues() {
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| 62 | List<List<double>> data = new List<List<double>>();
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[14305] | 63 | var rand = new MersenneTwister((uint)Seed);
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[7849] | 64 |
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[14305] | 65 | data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
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| 66 | data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
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| 67 | data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList());
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| 68 | data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList());
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| 69 | data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList());
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| 70 |
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[7849] | 71 | double x0, x1, x2, x3;
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| 72 | List<double> results = new List<double>();
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| 73 | for (int i = 0; i < data[0].Count; i++) {
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| 74 | x0 = data[0][i];
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| 75 | x1 = data[1][i];
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| 76 | x2 = data[2][i];
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| 77 | x3 = data[3][i];
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| 78 | results.Add(((Math.Sqrt(x0) / Math.Log(x1)) * (Math.Exp(x2) / Math.Pow(x3, 2))));
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| 79 | }
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| 80 | data.Add(results);
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| 81 |
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| 82 | return data;
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| 83 | }
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| 84 | }
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| 85 | }
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