#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2018 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.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.IntegerVectorEncoding;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;


namespace HeuristicLab.Problems.Scheduling.MRCPSP {
  [Item("Feasibility Improvement Method (FIM) Improver", "Improves a solution by calling GetNeighbors and Evaluate of the corresponding problem definition.")]
  [StorableClass]
  public abstract class BasicMrcpspImprover : SingleSuccessorOperator, IImprovementOperator, IStochasticOperator {

    public MultiModeResourceConstrainedProjectSchedulingProblem Problem { get; set; }
    public ILookupParameter<IRandom> RandomParameter => (ILookupParameter<IRandom>)Parameters["Random"];

    public ILookupParameter<IEncoding> EncodingParameter => (ILookupParameter<IEncoding>)Parameters["Encoding"];

    public ILookupParameter<DoubleValue> QualityParameter => (ILookupParameter<DoubleValue>)Parameters["Quality"];

    public ILookupParameter<BoolValue> MaximizationParameter => (ILookupParameter<BoolValue>)Parameters["Maximization"];

    public IValueLookupParameter<IntValue> ImprovementAttemptsParameter => (IValueLookupParameter<IntValue>)Parameters["ImprovementAttempts"];

    public IValueLookupParameter<IntValue> SampleSizeParameter => (IValueLookupParameter<IntValue>)Parameters["SampleSize"];

    public ILookupParameter<IntValue> LocalEvaluatedSolutionsParameter => (ILookupParameter<IntValue>)Parameters["LocalEvaluatedSolutions"];

    public Func<Individual, IRandom, double> EvaluateFunc { get; set; }

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

    protected BasicMrcpspImprover(BasicMrcpspImprover original, Cloner cloner) : base(original, cloner) {
      Problem = cloner.Clone(original.Problem);
    }

    protected BasicMrcpspImprover() {
      Parameters.Add(new LookupParameter<IRandom>("Random", "The random number generator to use."));
      Parameters.Add(new LookupParameter<IEncoding>("Encoding", "An item that holds the problem's encoding."));
      Parameters.Add(new LookupParameter<DoubleValue>("Quality", "The quality of the parameter vector."));
      Parameters.Add(new LookupParameter<BoolValue>("Maximization", "Whether the problem should be minimized or maximized."));
      Parameters.Add(new ValueLookupParameter<IntValue>("ImprovementAttempts", "The number of improvement attempts the operator should perform.", new IntValue(100)));
      Parameters.Add(new ValueLookupParameter<IntValue>("SampleSize", "The number of samples to draw from the neighborhood function at maximum.", new IntValue(300)));
      Parameters.Add(new LookupParameter<IntValue>("LocalEvaluatedSolutions", "The number of solution evaluations that have been performed."));
    }

    public override IOperation Apply() {
      var random = RandomParameter.ActualValue;
      var encoding = EncodingParameter.ActualValue;
      var maximize = MaximizationParameter.ActualValue.Value;
      var maxAttempts = ImprovementAttemptsParameter.ActualValue.Value;
      var sampleSize = SampleSizeParameter.ActualValue.Value;
      var individual = encoding.GetIndividual(ExecutionContext.Scope);
      //var quality = QualityParameter.ActualValue?.Value ?? EvaluateFunc(individual, random);

      var count = 0;
      //for (var i = 0; i < maxAttempts; i++) {
      //  Individual best = null;
      //  var bestQuality = quality;
      //  foreach (var neighbor in GetNeighborsFunc(individual, random).Take(sampleSize)) {
      //    var q = EvaluateFunc(neighbor, random);
      //    count++;
      //    if (maximize && bestQuality > q || !maximize && bestQuality < q) continue;
      //    best = neighbor;
      //    bestQuality = q;
      //  }
      //  if (best == null) break;
      //  individual = best;
      //  quality = bestQuality;
      //}

      if (Problem.CalculateERR(individual) <= 0)
        return base.Apply();

      var randomKey = individual.RealVector("RandomKey");
      var modeList = individual.IntegerVector("ModeList");

      var activities = Problem.Activities.Skip(1).Reverse().Skip(1).Reverse().ToList();

      var delta = new Dictionary<int, Dictionary<int, int>>();

      var index = 0;
      foreach (var a in activities) {
        delta[a.Number] = new Dictionary<int, int>();

        foreach (var m in a.Modes) {
          var ml = new IntegerVector(modeList) { [index] = m.Number };
          delta[a.Number][m.Number] = Measure(randomKey, ml);
          count++;
        }

        index++;
      }

      var totalDelta = delta.Sum(a => a.Value.Sum(m => m.Value));
      var deltas = new List<Tuple<int, int, int>>();
      foreach (var a in delta.Keys) {
        foreach (var m in delta[a].Keys) {
          deltas.Add(Tuple.Create(delta[a][m], a, m));
        }
      }

      var selectedQuality = random.NextDouble() * totalDelta;
      index = 0;
      var currentQuality = deltas[index].Item1;
      while (currentQuality < selectedQuality) {
        index++;
        currentQuality += deltas[index].Item1;
      }

      individual.IntegerVector("ModeList")[activities.FindIndex(a => a.Number == deltas[index].Item2)] = deltas[index].Item3;
      var quality = Problem.Evaluate(individual, random);

      LocalEvaluatedSolutionsParameter.ActualValue = new IntValue(count);
      QualityParameter.ActualValue = new DoubleValue(quality);
      //individual.CopyToScope(ExecutionContext.Scope);
      foreach (var val in individual.Values)
        SetScopeValue(val.Key, ExecutionContext.Scope, val.Value);

      return base.Apply();
    }

    protected abstract int Measure(RealVector randomKey, IntegerVector modeList);

    private static void SetScopeValue(string name, IScope scope, IItem value) {
      if (scope == null) throw new ArgumentNullException(nameof(scope));
      if (value == null) throw new ArgumentNullException(nameof(value));

      if (!scope.Variables.ContainsKey(name)) scope.Variables.Add(new Variable(name, value));
      else scope.Variables[name].Value = value;
    }
  }
}