#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2017 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.Globalization;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Analysis.FitnessLandscape {
  [Item("Problem Instance Similarity Analyzer", "Base class for analyzing whether certain obtained characteristics match the characteristics of already known problem instances.")]
  [StorableClass]
  public abstract class ProblemInstanceAnalyzer : SingleSuccessorOperator, IAnalyzer {

    public bool EnabledByDefault {
      get { return false; }
    }

    [Storable]
    private IResultParameter<StringMatrix> similarInstParam;
    public IResultParameter<StringMatrix> SimilarInstancesParameter {
      get { return similarInstParam; }
    }

    [Storable]
    private IValueParameter<DoubleMatrix> characteristicsParam;
    public IValueParameter<DoubleMatrix> CharacteristicsParameter {
      get { return characteristicsParam; }
    }

    public DoubleMatrix Characteristics {
      get { return CharacteristicsParameter.Value; }
      set { CharacteristicsParameter.Value = value; }
    }

    [StorableConstructor]
    protected ProblemInstanceAnalyzer(bool deserializing) : base(deserializing) { }

    protected ProblemInstanceAnalyzer(ProblemInstanceAnalyzer original, Cloner cloner)
      : base(original, cloner) {
      similarInstParam = cloner.Clone(original.similarInstParam);
      characteristicsParam = cloner.Clone(original.characteristicsParam);
    }

    public ProblemInstanceAnalyzer() {
      Parameters.Add(similarInstParam = new ResultParameter<StringMatrix>("Similar Instances", "Ordered enumeration of similar problem instances to the one currently observed.", "Results", new StringMatrix(new [,] { { "undefined", double.NaN.ToString(CultureInfo.CurrentCulture.NumberFormat) } })));
      Parameters.Add(characteristicsParam = new ValueParameter<DoubleMatrix>("Characteristics", "The matrix that contains the characteristics data and corresponding problem instance as row name."));
    }

    public override IOperation Apply() {
      var kbCharacteristics = Characteristics;
      if (kbCharacteristics == null) throw new InvalidOperationException("No characteristics are given.");

      var currentCharacteristics = GetCharacteristics();
      if (currentCharacteristics == null) return base.Apply();

      var means = kbCharacteristics.GetRow(kbCharacteristics.Rows - 2).ToArray();
      var stdevs = kbCharacteristics.GetRow(kbCharacteristics.Rows - 1).ToArray();

      for (var i = 0; i < means.Length; i++) {
        currentCharacteristics[i] = (currentCharacteristics[i] - means[i]) / stdevs[i];
      }

      var order = Enumerable.Range(0, kbCharacteristics.Rows - 2)
        .Select(row => new { Row = row, MSE = kbCharacteristics.GetRow(row).Zip(currentCharacteristics, (a, b) => (a - b) * (a - b)).Average() })
        .OrderBy(x => x.MSE);

      var instances = kbCharacteristics.RowNames.ToList();
      while (instances.Count < kbCharacteristics.Rows - 2)
        instances.Add(instances.Count.ToString(CultureInfo.CurrentCulture.NumberFormat));

      var result = new StringMatrix(instances.Count, 2);
      var idx = 0;
      foreach (var o in order) {
        result[idx, 0] = instances[o.Row];
        result[idx, 1] = o.MSE.ToString(CultureInfo.CurrentCulture.NumberFormat);
        idx++;
      }

      similarInstParam.ActualValue = result;

      return base.Apply();
    }

    protected abstract DoubleArray GetCharacteristics();
  }
}