#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 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.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Classification;

namespace HeuristicLab.Algorithms.DataAnalysis {
  /// <summary>
  /// 0R classification algorithm.
  /// </summary>
  [Item("ZeroR", "0R classification algorithm.")]
  [Creatable("Data Analysis")]
  [StorableClass]
  public sealed class ZeroR : FixedDataAnalysisAlgorithm<IClassificationProblem> {

    [StorableConstructor]
    private ZeroR(bool deserializing) : base(deserializing) { }
    private ZeroR(ZeroR original, Cloner cloner)
      : base(original, cloner) {
    }
    public ZeroR()
      : base() {
      Problem = new ClassificationProblem();
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new ZeroR(this, cloner);
    }

    protected override void Run() {
      var solution = CreateZeroRSolution(Problem.ProblemData);
      Results.Add(new Result("ZeroR solution", "The 0R classifier.", solution));
    }

    public static IClassificationSolution CreateZeroRSolution(IClassificationProblemData problemData) {
      Dataset dataset = problemData.Dataset;
      string target = problemData.TargetVariable;
      var classValuesEnumerator = problemData.ClassValues.GetEnumerator();
      var classValuesInDatasetEnumerator = dataset.GetDoubleValues(target, problemData.TrainingIndices).GetEnumerator();

      Dictionary<double, int> classValuesCount = new Dictionary<double, int>(problemData.ClassValues.Count());

      //initialize
      while (classValuesEnumerator.MoveNext()) {
        classValuesCount[classValuesEnumerator.Current] = 0;
      }

      //count occurence of classes
      while (classValuesInDatasetEnumerator.MoveNext()) {
        classValuesCount[classValuesInDatasetEnumerator.Current] += 1;
      }

      classValuesEnumerator.Reset();
      double mostOccurences = -1;
      double bestClass = double.NaN;
      while (classValuesEnumerator.MoveNext()) {
        if (classValuesCount[classValuesEnumerator.Current] > mostOccurences) {
          mostOccurences = classValuesCount[classValuesEnumerator.Current];
          bestClass = classValuesEnumerator.Current;
        }
      }

      ConstantClassificationModel model = new ConstantClassificationModel(bestClass);
      ConstantClassificationSolution solution = new ConstantClassificationSolution(model, (IClassificationProblemData)problemData.Clone());

      return solution;
    }

    private static SymbolicDiscriminantFunctionClassificationModel CreateDiscriminantFunctionModel(ISymbolicExpressionTree tree,
    ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
    IClassificationProblemData problemData,
    IEnumerable<int> rows,
    IEnumerable<double> classValues) {
      var model = new SymbolicDiscriminantFunctionClassificationModel(tree, interpreter, new AccuracyMaximizationThresholdCalculator());
      IList<double> thresholds = new List<double>();
      double last = 0;
      foreach (double item in classValues) {
        if (thresholds.Count == 0) {
          thresholds.Add(double.NegativeInfinity);
        } else {
          thresholds.Add((last + item) / 2);
        }
        last = item;
      }
      model.SetThresholdsAndClassValues(thresholds, classValues);
      return model;
    }
  }
}