#region License Information /* HeuristicLab * Copyright (C) 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 . */ #endregion using System; using System.Collections.Generic; using System.Linq; using HeuristicLab.Random; namespace HeuristicLab.Problems.Instances.DataAnalysis { public class KornsFunctionTwo : ArtificialRegressionDataDescriptor { public override string Name { get { return "Korns 2 y = 0.23 + (14.2 * ((X3 + X1) / (3.0 * X4)))"; } } public override string Description { get { return "Paper: Accuracy in Symbolic Regression" + Environment.NewLine + "Authors: Michael F. Korns" + Environment.NewLine + "Function: y = 0.23 + (14.2 * ((X3 + X1) / (3.0 * X4)))" + Environment.NewLine + "Binary Operators: +, -, *, % (protected division)" + Environment.NewLine + "Unary Operators: sqrt, square, cube, cos, sin, tan, tanh, ln(|x|) (protected log), exp" + Environment.NewLine + "Constants: random finit 64-bit IEEE double" + Environment.NewLine + "\"Our testing regimen uses only statistical best practices out-of-sample testing techniques. " + "We test each of the test cases on matrices of 10000 rows by 1 to 5 columns with no noise. " + "For each test a training matrix is filled with random numbers between -50 and +50. The test case " + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\""; } } protected override string TargetVariable { get { return "Y"; } } protected override string[] VariableNames { get { return new string[] { "X0", "X1", "X2", "X3", "X4", "Y" }; } } protected override string[] AllowedInputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4" }; } } protected override int TrainingPartitionStart { get { return 0; } } protected override int TrainingPartitionEnd { get { return 10000; } } protected override int TestPartitionStart { get { return 10000; } } protected override int TestPartitionEnd { get { return 20000; } } public int Seed { get; private set; } public KornsFunctionTwo() : this((int)System.DateTime.Now.Ticks) { } public KornsFunctionTwo(int seed) : base() { Seed = seed; } protected override List> GenerateValues() { List> data = new List>(); var rand = new MersenneTwister((uint)Seed); for (int i = 0; i < AllowedInputVariables.Count(); i++) { data.Add(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, -50, 50).ToList()); } double x1, x3, x4; List results = new List(); for (int i = 0; i < data[0].Count; i++) { x1 = data[1][i]; x3 = data[3][i]; x4 = data[4][i]; results.Add(0.23 + (14.2 * ((x3 + x1) / (3.0 * x4)))); } data.Add(results); return data; } } }