#region License Information
/* HeuristicLab
 * Copyright (C) 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 HEAL.Attic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.IntegerVectorEncoding;
using HeuristicLab.Parameters;
using HeuristicLab.Random;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
  [Item("SamplingSegmentOptimizationManipulator", "")]
  [StorableType("4D4D8BC9-2C2B-418E-B9AE-5FB5511C70CE")]
  public class SamplingSegmentOptimizationManipulator : SegmentOptimizationMutator {

    [StorableType("65B2E870-7EED-4DD0-918E-94A346671784")]
    public enum DimensionType {
      All,
      Single
    }
    [StorableType("C73064F2-4E2F-473F-8DBA-DD47881726D4")]
    public enum SearchRangeType {
      None, // Fixed on current value
      Full,
      RandomDirection,
      RandomRange,
      DirectedDirection,
      DirectedRange
    }
    [StorableType("A9EBE01E-53DE-4EB7-94B3-26C624034A08")]
    public enum SamplingType {
      Exhaustive,
      RandomSampling,
      LinearSelection // "generalized nth"
    }

    [StorableType("4EBF0DFE-90A1-4DFE-8CC7-D358EF8AF96B")]
    [Flags]
    public enum SamplingPointsType {
      None = 0,
      BeforeCombinations = 1,
      AfterCombinations = 2
    }

    #region Properties
    public DimensionType Dimension { get { return DimensionParameter.Value.Value; } set { DimensionParameter.Value.Value = value; } }
    public SearchRangeType SearchRange { get { return SearchRangeParameter.Value.Value; } set { SearchRangeParameter.Value.Value = value; } }
    public double DirectedRangeStepSize { get { return DirectedRangeStepSizeParameter.Value.Value; } set { DirectedRangeStepSizeParameter.Value.Value = value; } }
    public int DirectedRangeRange { get { return DirectedRangeRangeParameter.Value.Value; } set { DirectedRangeRangeParameter.Value.Value = value; } }
    public SamplingType Sampling { get { return SamplingParameter.Value.Value; } set { SamplingParameter.Value.Value = value; } }
    public int SampleCount { get { return SampleCountParameter.Value.Value; } set { SampleCountParameter.Value.Value = value; } }
    public SamplingPointsType SamplingPoints { get { return SamplingPointsParameter.Value.Value; } set { SamplingPointsParameter.Value.Value = value; } }
    #endregion

    #region Parameter Properties
    public ValueParameter<EnumValue<DimensionType>> DimensionParameter => (ValueParameter<EnumValue<DimensionType>>)Parameters["Dimension"];
    public ValueParameter<EnumValue<SearchRangeType>> SearchRangeParameter => (ValueParameter<EnumValue<SearchRangeType>>)Parameters["SearchRange"];
    public ValueParameter<DoubleValue> DirectedRangeStepSizeParameter => (ValueParameter<DoubleValue>)Parameters["DirectedRangeStepSize"];
    public ValueParameter<IntValue> DirectedRangeRangeParameter => (ValueParameter<IntValue>)Parameters["DirectedRangeRange"];
    public ValueParameter<EnumValue<SamplingType>> SamplingParameter => (ValueParameter<EnumValue<SamplingType>>)Parameters["Sampling"];
    public ValueParameter<IntValue> SampleCountParameter => (ValueParameter<IntValue>)Parameters["SampleCount"];
    public ValueParameter<EnumValue<SamplingPointsType>> SamplingPointsParameter => (ValueParameter<EnumValue<SamplingPointsType>>)Parameters["SamplingPoints"];
    public ValueParameter<BoolValue> CountSamplesAsEvaluationsParameter => (ValueParameter<BoolValue>)Parameters["CountSamplesAsEvaluations"];
    public ValueParameter<BoolValue> CountCacheHitsAsEvaluationsParameter => (ValueParameter<BoolValue>)Parameters["CountCacheHitsAsEvaluations"];

    public LookupParameter<IntValue> EvaluatedSolutionsParameter => (LookupParameter<IntValue>)Parameters["EvaluatedSolutions"];
    #endregion

    private IDictionary<Tuple<int, int>, double> cache;

    public SamplingSegmentOptimizationManipulator() {
      Parameters.Add(new ValueParameter<EnumValue<DimensionType>>("Dimension", new EnumValue<DimensionType>(DimensionType.All)));
      Parameters.Add(new ValueParameter<EnumValue<SearchRangeType>>("SearchRange", new EnumValue<SearchRangeType>(SearchRangeType.Full)));
      Parameters.Add(new ValueParameter<DoubleValue>("DirectedRangeStepSize", new DoubleValue(1.5)));
      Parameters.Add(new ValueParameter<IntValue>("DirectedRangeRange", new IntValue(5)));
      Parameters.Add(new ValueParameter<EnumValue<SamplingType>>("Sampling", new EnumValue<SamplingType>(SamplingType.Exhaustive)));
      Parameters.Add(new ValueParameter<IntValue>("SampleCount")); // only used when sampling != Exhaustive
      Parameters.Add(new ValueParameter<EnumValue<SamplingPointsType>>("SamplingPoints", new EnumValue<SamplingPointsType>(SamplingPointsType.BeforeCombinations | SamplingPointsType.AfterCombinations))); 
      Parameters.Add(new ValueParameter<BoolValue>("CountSamplesAsEvaluations", new BoolValue(false)));
      Parameters.Add(new ValueParameter<BoolValue>("CountCacheHitsAsEvaluations", new BoolValue(true)));
      Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions"));
    }
    public SamplingSegmentOptimizationManipulator(SamplingSegmentOptimizationManipulator original, Cloner cloner)
      : base(original, cloner) { }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new SamplingSegmentOptimizationManipulator(this, cloner);
    }

    [StorableConstructor]
    public SamplingSegmentOptimizationManipulator(StorableConstructorFlag _) : base(_) { }
    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      if (Parameters.ContainsKey("SlopeCalculationRange"))
        Parameters.Remove("SlopeCalculationRange");
      if (!Parameters.ContainsKey("DirectedRangeStepSize"))
        Parameters.Add(new ValueParameter<DoubleValue>("DirectedRangeStepSize", new DoubleValue(1.5)));
      if (!Parameters.ContainsKey("DirectedRangeRange"))
        Parameters.Add(new ValueParameter<IntValue>("DirectedRangeRange", new IntValue(5)));
      if (!Parameters.ContainsKey("CountCacheHitsAsEvaluations"))
        Parameters.Add(new ValueParameter<BoolValue>("CountCacheHitsAsEvaluations", new BoolValue(true)));

    }

    protected override void ManipulateBounded(IRandom random, IntegerVector integerVector, IntMatrix bounds) {
      var solution = new IntegerVector(new[] { integerVector.Min(), integerVector.Max() });

      var indices = CreateIndices(random, solution, bounds, Dimension, SearchRange, EvaluateCached, true,
        this.DirectedRangeStepSize, this.DirectedRangeRange);
      
      if (SamplingPoints.HasFlag(SamplingPointsType.BeforeCombinations))
        indices = indices.Select(i => SampleIndices(i, Sampling, random, SampleCount).ToList()).ToArray();

      var solutions = CreateCombinations(indices[0], indices[1]);
      if (!solutions.Any()) { 
        //if (SamplingPoints.HasFlag(SamplingPointsType.BeforeCombinations))
          return; // no valid combinations -> no mutation
        //throw new InvalidOperationException("no indices!");
      }

      if (SamplingPoints.HasFlag(SamplingPointsType.AfterCombinations))
        solutions = SampleIndices(solutions, Sampling, random, SampleCount);

      //if (CountSamplesAsEvaluationsParameter.Value.Value) {
      //  int moves = solutions.Count();
      //  EvaluatedSolutionsParameter.ActualValue.Value += moves - 1;
      //}

      var best = FindBest(solutions, EvaluateCached);
      if (best != null) {
        CopyTo(best.Item1, integerVector);
      }
    }

    private double EvaluateCached(IntegerVector solution) {
      if (cache == null) cache = new Dictionary<Tuple<int, int>, double>();
      var key = Tuple.Create(solution.Min(), solution.Max());
      if (cache.TryGetValue(key, out double cachedQuality)) {
        if (CountSamplesAsEvaluationsParameter.Value.Value && CountCacheHitsAsEvaluationsParameter.Value.Value)
          EvaluatedSolutionsParameter.ActualValue.Value += 1;
        return cachedQuality;
      }
      var quality = Evaluate(solution);
      if (CountSamplesAsEvaluationsParameter.Value.Value)
        EvaluatedSolutionsParameter.ActualValue.Value += 1;
      cache.Add(key, quality);
      return quality;
    }
    
    public static IEnumerable<int>[] CreateIndices(IRandom random, IntegerVector integerVector, IntMatrix bounds, DimensionType dimension, SearchRangeType indexRange, Func<IntegerVector, double> evaluate, bool minimize,
      double directedRangeStepSize, int directedRangeRange) {
      var indices = new IEnumerable<int>[integerVector.Length];
      int targetIndex = random.Next(indices.Length); // first or second index
      for (int i = 0; i < indices.Length; i++) {
        var searchRange = dimension == DimensionType.All || targetIndex == i ? indexRange : SearchRangeType.None;
        indices[i] = CreateSingleIndices(integerVector, i, bounds, searchRange, evaluate, minimize, random, directedRangeStepSize, directedRangeRange).ToList();
        if (!indices[i].Any()) {
          //throw new InvalidOperationException("no indices!");
          indices[i] = new[] { integerVector[i] };
        }
      }
      return indices;
    }

    public static IEnumerable<int> CreateSingleIndices(IntegerVector integerVector, int dim, IntMatrix bounds, SearchRangeType searchRange, Func<IntegerVector, double> evaluate, bool minimize, IRandom random,
      double directedRangeStepSize, int directedRangeRange) {
      int currentIndex = integerVector[dim];
      int length = bounds[dim % bounds.Rows, 1];
      switch (searchRange) {
        case SearchRangeType.Full:
          return Enumerable.Range(0, length);
        case SearchRangeType.None:
          return new[] { currentIndex };
        case SearchRangeType.RandomDirection: // including currentIndex
          return random.Next(2) == 0 
            ? Enumerable.Range(0, currentIndex + 1) // left
            : Enumerable.Range(currentIndex, length - currentIndex); // right
        case SearchRangeType.RandomRange: {
          // random range around current index, not completely random range
          int start = random.Next(0, currentIndex + 1), end = random.Next(currentIndex, length);
          return Enumerable.Range(start, end - start + 1);
        }
        case SearchRangeType.DirectedDirection: {
          var slope = CalculateSlope(integerVector, dim, evaluate);
          if (!minimize) slope = -slope;
          return slope > 0 // assume minimization: positive slope -> try lower indices to reduce objective
            ? Enumerable.Range(0, currentIndex + 1) // left
            : Enumerable.Range(currentIndex, length - currentIndex); // right
        }
        case SearchRangeType.DirectedRange: {
          var slope = CalculateSlope(integerVector, dim, evaluate);
          if (!minimize) slope = -slope;
          
          double stepSize = directedRangeStepSize;
          int range = directedRangeRange;

          double target = currentIndex - stepSize * slope;
          int targetStart = (int)Math.Round(target - range / 2.0);
          int targetEnd = (int)Math.Round(target + range / 2.0);

          int start = Limit(targetStart, 0, currentIndex + 1);
          int end = Limit(targetEnd, currentIndex, length);
          return Enumerable.Range(start, end - start + 1);
        }
        default:
          throw new ArgumentOutOfRangeException(nameof(searchRange), searchRange, null);
      }
    }

    private static double Limit(int x, double min, double max) { return Math.Min(Math.Max(x, min), max); }
    private static int Limit(int x, int min, int max) { return Math.Min(Math.Max(x, min), max); }

    private static double CalculateSlope(IntegerVector position, int dim, Func<IntegerVector, double> evaluate) {
      double f(int i) {
        var modified = new IntegerVector(position);
        modified[dim] = i;
        return evaluate(modified);
      }
      
      int x = position[dim];
      var slope = ( // five-point stencil with h = 1
        + 1 * f(x - 2)
        - 8 * f(x - 1)
        + 8 * f(x + 1)
        - 1 * f(x + 2)
      ) / 12;

      return slope;
    }

    public static IEnumerable<IntegerVector> CreateCombinations(IEnumerable<int> startIndices, IEnumerable<int> endIndices) {
      return 
        from s in startIndices
        from e in endIndices
        where s < e
        select new IntegerVector(new [] { s, e });
    }

    public static IEnumerable<T> SampleIndices<T>(IEnumerable<T> indices, SamplingType sampling, IRandom random, int count) {
      switch (sampling) {
        case SamplingType.Exhaustive:
          return indices;
        case SamplingType.RandomSampling:
          return indices.SampleRandomWithoutRepetition(random, count);
        case SamplingType.LinearSelection:
          var indicesList = indices.ToList();
          // if single sample, the last is always returned
          var selected = MathNet.Numerics.Generate.LinearSpaced(count, 0, indicesList.Count - 1) // LinearSpaced stop is inclusive
            .Select(i => (int)Math.Floor(i))
            .Distinct();
          return selected.Select(i => indicesList[i]);
        default:
          throw new ArgumentOutOfRangeException(nameof(sampling), sampling, null);
      }
    }

    public static Tuple<T, double> FindBest<T>(IEnumerable<T> solutions, Func<T, double> evaluate, bool minimize = true) {
      var evaluatedSolutions = solutions.Select(s => Tuple.Create(s, evaluate(s)));
      var sorted = evaluatedSolutions.OrderBy(t => t.Item2);
      return minimize ? sorted.FirstOrDefault() : sorted.LastOrDefault();
    }

    private static void CopyTo(IntegerVector source, IntegerVector target) {
      for (int i = 0; i < target.Length; i++)
        target[i] = source[i];
    }
  }
}