#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2011 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using HeuristicLab.Data;

namespace HeuristicLab.Problems.DataAnalysis.Benchmarks {
  public class SalutowiczFunctionOneDimensional : RegressionToyBenchmark {

    public SalutowiczFunctionOneDimensional() {
      Name = "Salutowicz";
      Description = "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
        + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
        + "Function: F2(X) = e^-X * X^3 * cos(X) * sin(X) * (cos(X)sin(X)^2 - 1)" + Environment.NewLine
        + "Training Data: 100 points X = (0.05:0.1:10)" + Environment.NewLine
        + "Test Data: 221 points X = (-0.5:0.05:10.5)" + Environment.NewLine
        + "Function Set: +, -, *, /, sqaure, x^real, x + real, x + real, e^x, e^-x, sin(x), cos(x)";
      targetVariable = "Y";
      inputVariables = new List<string>() { "X" };
      trainingPartition = new IntRange(0, 100);
      testPartition = new IntRange(101, 321);
    }

    protected override List<double> GenerateTarget(List<List<double>> data) {
      double x;
      List<double> results = new List<double>();
      for (int i = 0; i < data[0].Count; i++) {
        x = data[0][i];
        results.Add(Math.Exp(-x) * Math.Pow(x, 3) * Math.Cos(x) * Math.Sin(x) * (Math.Cos(x) * Math.Pow(Math.Sin(x), 2) - 1));
      }
      return results;
    }

    protected override List<List<double>> GenerateInput() {
      List<List<double>> dataList = new List<List<double>>();
      dataList.Add(RegressionBenchmark.GenerateSteps(new DoubleRange(0.05, 10), 0.1));
      dataList[0].AddRange(RegressionBenchmark.GenerateSteps(new DoubleRange(-0.5, 10.5), 0.05));

      return dataList;
    }
  }
}