#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 NguyenFunctionTwo : RegressionToyBenchmark {

    public NguyenFunctionTwo() {
      Name = "Nguyen F2 = x^4 + x^3 + x^2 + x";
      Description = "Paper: Semantically-based Crossover in Genetic Programming: Application to Real-valued Symbolic Regression" + Environment.NewLine
        + "Authors: Nguyen Quang Uy · Nguyen Xuan Hoai · Michael O’Neill · R.I. McKay · Edgar Galvan-Lopez" + Environment.NewLine
        + "Function: F2 = x^4 + x^3 + x^2 + x" + Environment.NewLine
        + "Fitcases: 20 random points ⊆ [-1, 1]" + Environment.NewLine
        + "Non-terminals: +, -, *, /, sin, cos, exp, log (protected version)" + Environment.NewLine
        + "Terminals: X, 1 for single variable problems, and X, Y for bivariable problems";
      targetVariable = "Y";
      inputVariables = new List<string>() { "X" };
      trainingPartition = new IntRange(0, 20);
      testPartition = new IntRange(250, 350);
    }

    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.Pow(x, 4) + Math.Pow(x, 3) + Math.Pow(x, 2) + x);
      }
      return results;
    }

    protected override List<List<double>> GenerateInput() {
      List<List<double>> dataList = new List<List<double>>();
      DoubleRange range = new DoubleRange(-1, 1);
      dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(500, range));

      return dataList;
    }
  }
}