#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.Collections;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Data;
using HeuristicLab.Random;

namespace HeuristicLab.Problems.DataAnalysis.Benchmarks {
  public abstract class RegressionBenchmark : Benchmark, IRegressionBenchmarkProblemDataGenerator {

    #region properties
    public abstract List<string> InputVariable { get; }
    public abstract string TargetVariable { get; }
    public abstract IntRange TrainingPartition { get; }
    public abstract IntRange TestPartition { get; }
    #endregion

    protected RegressionBenchmark() { }
    protected RegressionBenchmark(RegressionBenchmark original, Cloner cloner)
      : base(original, cloner) {
    }

    protected abstract List<double> CalculateFunction(Dictionary<string, IList<double>> data);

    protected abstract Dictionary<string, IList<double>> GenerateInput(Dictionary<string, IList<double>> data);

    public IDataAnalysisProblemData GenerateProblemData() {
      Dictionary<string, IList<double>> data = new Dictionary<string, IList<double>>();
      data.Add(this.TargetVariable, new List<double>());
      foreach (var variable in this.InputVariable) {
        data.Add(variable, new List<double>());
      }

      data = GenerateInput(data);

      List<IList> values = new List<IList>();
      foreach (var valueList in data.Values) {
        values.Add((IList)valueList);
      }

      Dataset dataset = new Dataset(data.Keys, values);
      dataset.Name = this.Name;

      RegressionProblemData problemData = new RegressionProblemData(dataset, dataset.DoubleVariables.Skip(1), dataset.DoubleVariables.First());

      problemData.Name = "Data generated for benchmark problem \"" + this.Name + "\"";


      problemData.TestPartition.Start = this.TestPartition.Start;
      problemData.TestPartition.End = this.TestPartition.End;

      problemData.TrainingPartition.Start = this.TrainingPartition.Start;
      problemData.TrainingPartition.End = this.TrainingPartition.End;

      return problemData;
    }

    //private Dictionary<string, IList<double>> CalculateValues(Dictionary<string, IList<double>> data, DatasetDefinition dataDef) {
    //  Random rand = new Random();
    //  var combinationDataSet = AllCombinationsOf(dataDef.RangeVariables.Values.Select(range => range.Values).ToList());
    //  int index = 0;
    //  var help = dataDef.RangeVariables.Keys;
    //  foreach (var dataSet in combinationDataSet) {
    //    data[help.ElementAt(index)] = dataSet;
    //    index++;
    //  }
    //  List<string> vars = new List<string>(dataDef.RandomVariables.Keys);
    //  for (int i = 0; i < dataDef.AmountOfPoints; i++) {
    //    foreach (var variable in vars) {
    //      data[variable].Add(dataDef.RandomVariables[variable].Next());
    //    }
    //    //  data[TargetVariable].Add(CalculateFunction(data, vars));
    //  }
    //  int bla = 0;
    //  var test = data.Values.Select((ind) => (ind.ElementAt(bla)));

    //  return data;
    //}

    public static List<double> generateSteps(DoubleRange range, double stepWidth) {
      return Enumerable.Range(0, (int)((range.End - range.Start) / stepWidth) + 1)
                                      .Select(i => (range.Start + i * stepWidth))
                                      .ToList<double>();
    }

    public static List<double> generateUniformDistributedValues(int amount, DoubleRange range) {
      List<double> values = new List<double>();
      System.Random rand = new System.Random();
      for (int i = 0; i < amount; i++) {
        values.Add(rand.NextDouble() * (range.End - range.Start) + range.Start);
      }
      return values;
    }

    public static List<double> generateNormalDistributedValues(int amount, double mu, double sigma) {
      List<double> values = new List<double>();
      FastRandom rand = new FastRandom();
      for (int i = 0; i < amount; i++) {
        values.Add(NormalDistributedRandom.NextDouble(rand, mu, sigma));
      }
      return values;
    }

    public static List<List<double>> AllCombinationsOf(List<List<double>> sets) {

      var combinations = new List<List<double>>();

      foreach (var value in sets[0])
        combinations.Add(new List<double> { value });

      foreach (var set in sets.Skip(1))
        combinations = AddExtraSet(combinations, set);

      return combinations;
    }

    private static List<List<double>> AddExtraSet
         (List<List<double>> combinations, List<double> set) {
      var newCombinations = from value in set
                            from combination in combinations
                            select new List<double>(combination) { value };

      return newCombinations.ToList();
    }
  }
}