#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2008 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 System.Text;
using System.Windows.Forms;
using HeuristicLab.PluginInfrastructure;
using System.Net;
using System.ServiceModel;
using HeuristicLab.CEDMA.DB.Interfaces;
using HeuristicLab.CEDMA.DB;
using System.ServiceModel.Description;
using System.Linq;
using HeuristicLab.CEDMA.Core;
using HeuristicLab.GP.StructureIdentification;
using HeuristicLab.Data;
using HeuristicLab.Core;
using HeuristicLab.Modeling;

namespace HeuristicLab.CEDMA.Server {
  public class SimpleDispatcher : DispatcherBase {
    private Random random;
    private IStore store;
    private Dictionary<int, List<string>> finishedAndDispatchedRuns;

    public SimpleDispatcher(IStore store)
      : base(store) {
      this.store = store;
      random = new Random();
      finishedAndDispatchedRuns = new Dictionary<int, List<string>>();
      PopulateFinishedRuns();
    }

    public override IAlgorithm SelectAlgorithm(int targetVariable, LearningTask learningTask) {
      DiscoveryService ds = new DiscoveryService();
      IAlgorithm[] algos = ds.GetInstances<IAlgorithm>();
      IAlgorithm selectedAlgorithm = null;
      switch (learningTask) {
        case LearningTask.Regression: {
            var regressionAlgos = algos.Where(a => (a as IClassificationAlgorithm) == null && (a as ITimeSeriesAlgorithm) == null);
            selectedAlgorithm = ChooseDeterministic(targetVariable, regressionAlgos) ?? ChooseStochastic(regressionAlgos);
            break;
          }
        case LearningTask.Classification: {
            var classificationAlgos = algos.Where(a => (a as IClassificationAlgorithm) != null);
            selectedAlgorithm = ChooseDeterministic(targetVariable, classificationAlgos) ?? ChooseStochastic(classificationAlgos);
            break;
          }
        case LearningTask.TimeSeries: {
            var timeSeriesAlgos = algos.Where(a => (a as ITimeSeriesAlgorithm) != null);
            selectedAlgorithm = ChooseDeterministic(targetVariable, timeSeriesAlgos) ?? ChooseStochastic(timeSeriesAlgos);
            break;
          }
      }
      if (selectedAlgorithm != null) {
        AddDispatchedRun(targetVariable, selectedAlgorithm.Name);
      }
      return selectedAlgorithm;
    }

    private IAlgorithm ChooseDeterministic(int targetVariable, IEnumerable<IAlgorithm> algos) {
      var deterministicAlgos = algos
        .Where(a => (a as IStochasticAlgorithm) == null)
        .Where(a => AlgorithmFinishedOrDispatched(targetVariable, a.Name) == false);

      if (deterministicAlgos.Count() == 0) return null;
      return deterministicAlgos.ElementAt(random.Next(deterministicAlgos.Count()));
    }

    private IAlgorithm ChooseStochastic(IEnumerable<IAlgorithm> regressionAlgos) {
      var stochasticAlgos = regressionAlgos.Where(a => (a as IStochasticAlgorithm) != null);
      if (stochasticAlgos.Count() == 0) return null;
      return stochasticAlgos.ElementAt(random.Next(stochasticAlgos.Count()));
    }

    public override int SelectTargetVariable(int[] targetVariables) {
      return targetVariables[random.Next(targetVariables.Length)];
    }

    private void PopulateFinishedRuns() {
      var datasetBindings = store
        .Query(
        "?Dataset <" + Ontology.InstanceOf + "> <" + Ontology.TypeDataSet + "> .", 0, 1)
        .Select(x => (Entity)x.Get("Dataset"));

      if (datasetBindings.Count() > 0) {
        var datasetEntity = datasetBindings.ElementAt(0);

        DataSet ds = new DataSet(store, datasetEntity);
        var result = store
          .Query(
          "?Model <" + Ontology.TargetVariable + "> ?TargetVariable ." + Environment.NewLine +
          "?Model <" + Ontology.Name + "> ?AlgoName .",
          0, 1000)
          .Select(x => new Resource[] { (Literal)x.Get("TargetVariable"), (Literal)x.Get("AlgoName") });

        foreach (Resource[] row in result) {
          string targetVariable = (string)((Literal)row[0]).Value;
          string algoName = (string)((Literal)row[1]).Value;

          int targetVariableIndex = ds.Problem.Dataset.GetVariableIndex(targetVariable);
          if (!AlgorithmFinishedOrDispatched(targetVariableIndex, algoName))
            AddDispatchedRun(targetVariableIndex, algoName);
        }
      }
    }

    private void AddDispatchedRun(int targetVariable, string algoName) {
      if (!finishedAndDispatchedRuns.ContainsKey(targetVariable)) {
        finishedAndDispatchedRuns[targetVariable] = new List<string>();
      }
      finishedAndDispatchedRuns[targetVariable].Add(algoName);
    }

    private bool AlgorithmFinishedOrDispatched(int targetVariable, string algoName) {
      return
        finishedAndDispatchedRuns.ContainsKey(targetVariable) &&
        finishedAndDispatchedRuns[targetVariable].Contains(algoName);
    }
  }
}