#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2016 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.Drawing;
using System.Linq;
using HeuristicLab.Analysis;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;

namespace HeuristicLab.GoalSeeking {
  internal enum GoalSeekingOptimizerAction { None, Prepare, Start, Stop, Pause }

  [StorableClass]
  [Item("GoalSeeking Optimizer", "A wrapper for the GoalSeekingProblem class to facilitate adding models and problem data.")]
  [Creatable("Algorithms")]
  public class GoalSeekingOptimizer : NamedItem, IOptimizer, IStorableContent {
    [Storable]
    private IGoalSeekingProblem problem;
    public IGoalSeekingProblem Problem {
      get { return problem; }
      set {
        if (value == null || value == problem)
          return;

        problem = value;
        // the problem takes precedence over the algorithm. if the algorithm is unsuitable we remove it.
        if (optimizer != null && !optimizer.ProblemType.IsInstanceOfType(problem)) {
          optimizer = null;
          OnAlgorithmChanged();
        }
        if (optimizer != null)
          optimizer.Problem = problem;
      }
    }

    [Storable]
    private IAlgorithm optimizer;
    public IAlgorithm Optimizer {
      get { return optimizer; }
      set {
        if (value == null || value == optimizer)
          return;

        // make sure the algorithm is suitable for the problem.
        if (problem != null && !value.ProblemType.IsInstanceOfType(problem))
          throw new InvalidOperationException("The algorithm is incompatible with the problem.");

        optimizer = value;

        if (problem != null)
          optimizer.Problem = problem;

        RegisterOptimizerEvents();
        OnAlgorithmChanged();
      }
    }

    [Storable]
    private IRegressionProblemData problemData;
    public IRegressionProblemData ProblemData {
      get { return problemData; }
      set {
        if (value == null || value == problemData)
          return;

        if (problem == null)
          throw new InvalidOperationException("Cannot set problem data. Please configure a problem first.");

        problemData = value;
        problem.Configure(problemData, problemData.TrainingIndices.First());
        OnProblemDataChanged();
      }
    }

    [Storable]
    private ResultCollection results;
    public ResultCollection Results {
      get { return results; }
      private set {
        if (value != null)
          results = value;
        OnResultsChanged();
      }
    }

    private GoalSeekingOptimizerAction gsoAction;

    public GoalSeekingOptimizer() {
      Name = "Goal Seeking Optimizer";
    }

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


    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      if (optimizer != null)
        RegisterOptimizerEvents();
    }

    protected GoalSeekingOptimizer(GoalSeekingOptimizer original, Cloner cloner) : base(original, cloner) {
      this.Optimizer = (IAlgorithm)original.Optimizer.Clone(cloner);
      this.Problem = (IGoalSeekingProblem)original.Problem.Clone(cloner);
      this.ProblemData = (IRegressionProblemData)original.ProblemData.Clone(cloner);
      this.Results = (ResultCollection)original.Results.Clone();
    }

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

    public EventHandler ProblemChanged;
    private void OnProblemChanged() {
      var changed = ProblemChanged;
      if (changed != null)
        changed(this, EventArgs.Empty);
    }

    public EventHandler AlgorithmChanged;
    private void OnAlgorithmChanged() {
      var changed = AlgorithmChanged;
      if (changed != null)
        changed(this, EventArgs.Empty);
    }

    public EventHandler ProblemDataChanged;
    private void OnProblemDataChanged() {
      var changed = ProblemDataChanged;
      if (changed != null)
        changed(this, EventArgs.Empty);
    }

    public EventHandler ResultsChanged;
    private void OnResultsChanged() {
      var changed = ResultsChanged;
      if (changed != null)
        changed(this, EventArgs.Empty);
    }

    private int calculatedRows;
    private int currentRow;

    public void Start() {
      if ((ExecutionState != ExecutionState.Prepared) && (ExecutionState != ExecutionState.Paused))
        throw new InvalidOperationException(string.Format("Start not allowed in execution state \"{0}\".", ExecutionState));
      if (optimizer == null) return;
      gsoAction = GoalSeekingOptimizerAction.Start;
      if (Optimizer.ExecutionState == ExecutionState.Stopped) {
        Optimizer.Prepare(clearRuns: true);
        calculatedRows = 0;
        currentRow = problemData.TrainingIndices.Skip(calculatedRows).First();
        problem.Configure(problemData, currentRow);
        optimizer.Prepare();
      }
      optimizer.Start();
    }

    public void Pause() {
      if (ExecutionState != ExecutionState.Started)
        throw new InvalidOperationException(string.Format("Pause not allowed in execution state \"{0}\".", ExecutionState));
      if (optimizer == null) return;
      gsoAction = GoalSeekingOptimizerAction.Pause;
      if (optimizer.ExecutionState != ExecutionState.Started) return;
      // a race-condition may occur when the algorithm has changed the state by itself in the meantime
      try { optimizer.Pause(); }
      catch (InvalidOperationException) { }
    }

    public void Stop() {
      if ((ExecutionState != ExecutionState.Started) && (ExecutionState != ExecutionState.Paused))
        throw new InvalidOperationException(string.Format("Stop not allowed in execution state \"{0}\".", ExecutionState));
      if (optimizer == null) return;
      gsoAction = GoalSeekingOptimizerAction.Stop;
      if (optimizer.ExecutionState != ExecutionState.Started && optimizer.ExecutionState != ExecutionState.Paused) {
        OnStopped();
        return;
      }
      // a race-condition may occur when the algorithm has changed the state by itself in the meantime
      try { optimizer.Stop(); }
      catch (InvalidOperationException) { }
    }

    public void Prepare() {
      Prepare(false);
    }

    public void Prepare(bool clearRuns) {
      if ((ExecutionState != ExecutionState.Prepared) && (ExecutionState != ExecutionState.Paused) && (ExecutionState != ExecutionState.Stopped))
        throw new InvalidOperationException(string.Format("Prepare not allowed in execution state \"{0}\".", ExecutionState));

      if (Results != null)
        Results.Clear();

      if (optimizer != null) {
        ExecutionTime = TimeSpan.Zero;
        if (clearRuns) optimizer.Runs.Clear();
        gsoAction = GoalSeekingOptimizerAction.Prepare;
        // a race-condition may occur when the algorithm has changed the state by itself in the meantime
        try { optimizer.Prepare(clearRuns); }
        catch (InvalidOperationException) { }
      } else {
        ExecutionState = ExecutionState.Stopped;
      }
    }

    [Storable]
    private TimeSpan executionTime;
    public TimeSpan ExecutionTime {
      get {
        if ((Optimizer != null) && (Optimizer.ExecutionState != ExecutionState.Stopped))
          return executionTime + Optimizer.ExecutionTime;
        else
          return executionTime;
      }
      private set {
        executionTime = value;
        OnExecutionTimeChanged();
      }
    }

    public RunCollection Runs {
      get {
        if (optimizer == null)
          return new RunCollection(); // return an empty run collection
        return Optimizer.Runs;
      }
    }

    public IEnumerable<IOptimizer> NestedOptimizers { get; private set; }

    public string Filename { get; set; }

    public new static Image StaticItemImage {
      get { return HeuristicLab.Common.Resources.VSImageLibrary.Event; }
    }
    public override Image ItemImage {
      get {
        if (ExecutionState == ExecutionState.Prepared) return HeuristicLab.Common.Resources.VSImageLibrary.BatchRunPrepared;
        else if (ExecutionState == ExecutionState.Started) return HeuristicLab.Common.Resources.VSImageLibrary.BatchRunStarted;
        else if (ExecutionState == ExecutionState.Paused) return HeuristicLab.Common.Resources.VSImageLibrary.BatchRunPaused;
        else if (ExecutionState == ExecutionState.Stopped) return HeuristicLab.Common.Resources.VSImageLibrary.BatchRunStopped;
        else return base.ItemImage;
      }
    }

    #region Events
    protected override void OnNameChanged() {
      base.OnNameChanged();
    }

    #region event firing
    public ExecutionState ExecutionState { get; private set; }
    public event EventHandler ExecutionStateChanged;
    private void OnExecutionStateChanged() {
      EventHandler handler = ExecutionStateChanged;
      if (handler != null) handler(this, EventArgs.Empty);
    }

    public event EventHandler ExecutionTimeChanged;
    private void OnExecutionTimeChanged() {
      EventHandler handler = ExecutionTimeChanged;
      if (handler != null) handler(this, EventArgs.Empty);
    }

    public event EventHandler Prepared;
    private void OnPrepared() {
      gsoAction = GoalSeekingOptimizerAction.None;
      ExecutionState = ExecutionState.Prepared;
      EventHandler handler = Prepared;
      if (handler != null) handler(this, EventArgs.Empty);
    }

    public event EventHandler Started;
    private void OnStarted() {
      ExecutionState = ExecutionState.Started;
      EventHandler handler = Started;
      if (handler != null) handler(this, EventArgs.Empty);
    }

    public event EventHandler Paused;
    private void OnPaused() {
      gsoAction = GoalSeekingOptimizerAction.None;
      ExecutionState = ExecutionState.Paused;
      EventHandler handler = Paused;
      if (handler != null) handler(this, EventArgs.Empty);
    }

    public event EventHandler Stopped;
    private void OnStopped() {
      gsoAction = GoalSeekingOptimizerAction.None;
      ExecutionState = ExecutionState.Stopped;
      EventHandler handler = Stopped;
      if (handler != null) handler(this, EventArgs.Empty);
    }

    public event EventHandler<EventArgs<Exception>> ExceptionOccurred;
    private void OnExceptionOccurred(Exception exception) {
      EventHandler<EventArgs<Exception>> handler = ExceptionOccurred;
      if (handler != null) handler(this, new EventArgs<Exception>(exception));
    }

    public event EventHandler OptimizerChanged;
    private void OnOptimizerChanged() {
      EventHandler handler = OptimizerChanged;
      if (handler != null) handler(this, EventArgs.Empty);
    }

    public event EventHandler RepetitionsChanged;
    private void OnRepetitionsChanged() {
      EventHandler handler = RepetitionsChanged;
      if (handler != null) handler(this, EventArgs.Empty);
    }

    public event EventHandler RepetetionsCounterChanged;
    private void OnRepetitionsCounterChanged() {
      EventHandler handler = RepetetionsCounterChanged;
      if (handler != null) handler(this, EventArgs.Empty);
    }
    #endregion

    #region optimizer event handlers
    private void RegisterOptimizerEvents() {
      optimizer.ExceptionOccurred += Optimizer_ExceptionOccurred;
      optimizer.ExecutionTimeChanged += Optimizer_ExecutionTimeChanged;
      optimizer.Paused += Optimizer_Paused;
      optimizer.Prepared += Optimizer_Prepared;
      optimizer.Started += Optimizer_Started;
      optimizer.Stopped += Optimizer_Stopped;
    }

    private void DeregisterOptimizerEvents() {
      optimizer.ExceptionOccurred -= Optimizer_ExceptionOccurred;
      optimizer.ExecutionTimeChanged -= Optimizer_ExecutionTimeChanged;
      optimizer.Paused -= Optimizer_Paused;
      optimizer.Prepared -= Optimizer_Prepared;
      optimizer.Started -= Optimizer_Started;
      optimizer.Stopped -= Optimizer_Stopped;
    }

    private void Optimizer_ExceptionOccurred(object sender, EventArgs<Exception> e) {
      OnExceptionOccurred(e.Value);
    }

    private void Optimizer_ExecutionTimeChanged(object sender, EventArgs e) {
      OnExecutionTimeChanged();
    }

    private void Optimizer_Paused(object sender, EventArgs e) {
      if (ExecutionState == ExecutionState.Started) {
        OnPaused();
      }
    }

    private void Optimizer_Prepared(object sender, EventArgs e) {
      if (gsoAction == GoalSeekingOptimizerAction.Prepare || ExecutionState == ExecutionState.Stopped) {
        calculatedRows = 0;
        ExecutionTime = TimeSpan.Zero;
        OnPrepared();
      }
    }

    private void Optimizer_Started(object sender, EventArgs e) {
      if (ExecutionState != ExecutionState.Started) {
        OnStarted();
      }
    }

    private void Optimizer_Stopped(object sender, EventArgs e) {
      calculatedRows++;
      ExecutionTime += optimizer.ExecutionTime;
      var remainingRows = problemData.TrainingIndices.Skip(calculatedRows);

      // extract results from the optimizer 
      GetResults();

      if (gsoAction == GoalSeekingOptimizerAction.Stop)
        OnStopped();
      else if (!remainingRows.Any()) {
        OnStopped();
      } else switch (gsoAction) {
          case GoalSeekingOptimizerAction.Pause:
            OnPaused();
            break;
          case GoalSeekingOptimizerAction.Start:
            currentRow = remainingRows.First();
            problem.Configure(problemData, currentRow);
            optimizer.Prepare();
            optimizer.Start();
            break;
          default:
            if (ExecutionState == ExecutionState.Started) {
              OnPaused();
            } else OnStopped();
            break;
        }
    }
    #endregion
    #endregion

    #region aggregate results
    private void GetResults() {
      if (Problem is MultiObjectiveGoalSeekingProblem) {
        var m = (DoubleMatrix)optimizer.Results["Pareto Front Solutions"].Value;
        AggregateMultiObjectiveResults(m);
      } else if (Problem is SingleObjectiveGoalSeekingProblem) {
        var m = (DoubleMatrix)optimizer.Results["Best Solution"].Value;
        AggregateSingleObjectiveResults(m);
      }
    }

    private void AggregateSingleObjectiveResults(DoubleMatrix solutions) {
      var indices = solutions.ColumnNames.Select((x, i) => Tuple.Create(x, i)).ToDictionary(x => x.Item1, x => x.Item2);

      var goals = Problem.Goals.Where(x => x.Active).ToList();
      var inputs = Problem.Inputs.Where(x => x.Active).ToList();

      if (Results == null)
        Results = new ResultCollection();
      // goals
      if (!Results.ContainsKey("Goals"))
        Results.Add(new Result("Goals", new ResultCollection()));
      var goalResults = (ResultCollection)Results["Goals"].Value;
      foreach (var goal in goals) {
        var estimatedGoalName = goal.Name + " (estimated)";
        if (!goalResults.ContainsKey(goal.Name)) {
          goalResults.Add(new Result(goal.Name, new ScatterPlot(goal.Name, "")));
        }

        var plot = (ScatterPlot)goalResults[goal.Name].Value;
        if (!plot.Rows.ContainsKey(goal.Name)) {
          plot.Rows.Add(new ScatterPlotDataRow { Name = goal.Name });
          plot.Rows.Add(new ScatterPlotDataRow { Name = estimatedGoalName });
        }

        plot.Rows[goal.Name].Points.Add(new Point2D<double>(currentRow, solutions[0, indices[goal.Name]]));
        plot.Rows[estimatedGoalName].Points.Add(new Point2D<double>(currentRow, solutions[0, indices[estimatedGoalName]]));
      }

      // inputs
      if (!Results.ContainsKey("Inputs"))
        Results.Add(new Result("Inputs", new ResultCollection()));
      var inputResults = (ResultCollection)Results["Inputs"].Value;
      foreach (var input in inputs) {
        var estimatedInputName = input.Name + " (estimated)";
        if (!inputResults.ContainsKey(input.Name)) {
          inputResults.Add(new Result(input.Name, new ScatterPlot(input.Name, "")));
        }

        var plot = (ScatterPlot)inputResults[input.Name].Value;
        if (!plot.Rows.ContainsKey(input.Name)) {
          plot.Rows.Add(new ScatterPlotDataRow { Name = input.Name });
          plot.Rows.Add(new ScatterPlotDataRow { Name = estimatedInputName });
        }

        plot.Rows[input.Name].Points.Add(new Point2D<double>(currentRow, solutions[0, indices[input.Name]]));
        plot.Rows[estimatedInputName].Points.Add(new Point2D<double>(currentRow, solutions[0, indices[estimatedInputName]]));
      }
      // aggregate results as a matrix
      UpdateResultsMatrix(solutions);
    }

    private void AggregateMultiObjectiveResults(DoubleMatrix solutions) {
      int maxSolutionsCount = Math.Min(solutions.Rows, 10);

      var indices = solutions.ColumnNames.Select((x, i) => Tuple.Create(x, i)).ToDictionary(x => x.Item1, x => x.Item2);

      var goals = Problem.Goals.Where(x => x.Active).ToList();
      var inputs = Problem.Inputs.Where(x => x.Active).ToList();

      if (Results == null)
        Results = new ResultCollection();
      // goals
      if (!Results.ContainsKey("Goals"))
        Results.Add(new Result("Goals", new ResultCollection()));
      var goalResults = (ResultCollection)Results["Goals"].Value;
      foreach (var goal in goals) {
        var estimatedGoalName = goal.Name + " (estimated)";
        if (!goalResults.ContainsKey(goal.Name)) {
          goalResults.Add(new Result(goal.Name, new ScatterPlot(goal.Name, "")));
        }

        var plot = (ScatterPlot)goalResults[goal.Name].Value;
        if (!plot.Rows.ContainsKey(goal.Name)) {
          plot.Rows.Add(new ScatterPlotDataRow { Name = goal.Name, VisualProperties = { PointSize = 5 } });
        }

        plot.Rows[goal.Name].Points.Add(new Point2D<double>(currentRow, solutions[0, indices[goal.Name]]));
        for (int i = 0; i < maxSolutionsCount; ++i) {
          if (!plot.Rows.ContainsKey(estimatedGoalName + i))
            plot.Rows.Add(new ScatterPlotDataRow { Name = estimatedGoalName + i, VisualProperties = { PointSize = 5 } });
          plot.Rows[estimatedGoalName + i].Points.Add(new Point2D<double>(currentRow, solutions[i, indices[estimatedGoalName]]));
        }
      }
      // inputs
      if (!Results.ContainsKey("Inputs"))
        Results.Add(new Result("Inputs", new ResultCollection()));
      var inputResults = (ResultCollection)Results["Inputs"].Value;
      foreach (var input in inputs) {
        var estimatedInputName = input.Name + " (estimated)";
        if (!inputResults.ContainsKey(input.Name)) {
          inputResults.Add(new Result(input.Name, new ScatterPlot(input.Name, "")));
        }

        var plot = (ScatterPlot)inputResults[input.Name].Value;
        if (!plot.Rows.ContainsKey(input.Name)) {
          plot.Rows.Add(new ScatterPlotDataRow { Name = input.Name, VisualProperties = { PointSize = 5 } });
        }

        plot.Rows[input.Name].Points.Add(new Point2D<double>(currentRow, solutions[0, indices[input.Name]])); // it stays the same for all the rows
        for (int i = 0; i < maxSolutionsCount; ++i) {
          if (!plot.Rows.ContainsKey(estimatedInputName + i))
            plot.Rows.Add(new ScatterPlotDataRow { Name = estimatedInputName + i, VisualProperties = { PointSize = 5 } });
          plot.Rows[estimatedInputName + i].Points.Add(new Point2D<double>(currentRow, solutions[i, indices[estimatedInputName]]));
        }
      }
      // also add a matrix containing the whole solution data including the row
      UpdateResultsMatrix(solutions);
    }

    private void UpdateResultsMatrix(DoubleMatrix solutions) {
      // also add a matrix containing the whole solution data including the row
      var rows = problemData.TrainingIndices.Take(calculatedRows).ToList();
      var m = new DoubleMatrix(calculatedRows, solutions.Columns + 1) {
        RowNames = rows.Select(x => x.ToString()),
        ColumnNames = new[] { "Row" }.Concat(solutions.ColumnNames)
      };
      DoubleMatrix old = null;
      if (Results.ContainsKey("Solutions"))
        old = (DoubleMatrix)Results["Solutions"].Value;
      else
        Results.Add(new Result("Solutions", m));

      if (old != null && calculatedRows > 0) {
        for (int i = 0; i < old.Rows; ++i) {
          for (int j = 0; j < old.Columns; ++j) {
            m[i, j] = old[i, j];
          }
        }
      }
      m[rows.Count - 1, 0] = currentRow;
      for (int i = 1; i < m.Columns; ++i) {
        m[rows.Count - 1, i] = solutions[0, i - 1];
      }
      Results["Solutions"].Value = m;
    }
    #endregion
  }
}