#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; using System.Collections.Generic; using System.Linq; using HeuristicLab.Core; using HeuristicLab.Problems.DataAnalysis; using HeuristicLab.Random; namespace HeuristicLab.Problems.Instances.DataAnalysis { public class VectorDataTestOne : ArtificialRegressionDataDescriptor { public override string Name { get { return "Vector Data Test - I (Y = X1 * sum(V1) + X2 * avg(V2)"; } } public override string Description { get { return ""; } } protected override string TargetVariable { get { return "Y"; } } protected override string[] VariableNames { get { return new string[] { "X1", "X2", "X3", "V1", "V2", "Y" }; } } protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3", "V1", "V2" }; } } protected override int TrainingPartitionStart { get { return 0; } } protected override int TrainingPartitionEnd { get { return 80; } } protected override int TestPartitionStart { get { return 80; } } protected override int TestPartitionEnd { get { return 100; } } public int Seed { get; private set; } public VectorDataTestOne() : this((int)DateTime.Now.Ticks) { } public VectorDataTestOne(int seed) : base() { Seed = seed; } protected override List> GenerateValues() { return null; } protected override List GenerateValuesExtended() { var rand = new MersenneTwister((uint)Seed); double x1, x2, x3; DoubleVector v1, v2; double y; var x1Column = new List(100); var x2Column = new List(100); var x3Column = new List(100); var v1Column = new List(100); var v2Column = new List(100); var yColumn = new List(100); for (int i = 0; i < 100; i++) { x1 = GetRandomDouble(-2, 2, rand); x2 = GetRandomDouble(2, 6, rand); x3 = GetRandomDouble(0, 1, rand); int length = rand.Next(3, 6); v1 = GetRandomDoubleVector(-2, 2, length, rand); v2 = GetRandomDoubleVector(3, 5, length, rand); y = x1 * v1.Sum() + x2 * v2.Average(); x1Column.Add(x1); x2Column.Add(x2); x3Column.Add(x3); v1Column.Add(v1); v2Column.Add(v2); yColumn.Add(y); } return new List { x1Column, x2Column, x3Column, v1Column, v2Column, yColumn }; } private static double GetRandomDouble(double min, double max, IRandom rand) { return (max - min) * rand.NextDouble() + min; } private static DoubleVector GetRandomDoubleVector(double min, double max, int length, IRandom rand) { var values = new double[length]; for (int i = 0; i < values.Length; i++) { values[i] = GetRandomDouble(min, max, rand); } return new DoubleVector(values); } } }