#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 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.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
  [Item("ReplaceBranchMoveEvaluator", "")]
  [StorableClass]
  public class ReplaceBranchMoveEvaluator : SingleSuccessorOperator, ISymbolicRegressionMoveEvaluator {

    public override bool CanChangeName {
      get { return false; }
    }

    public ILookupParameter<ReplaceBranchMove> MoveParameter {
      get { return (ILookupParameter<ReplaceBranchMove>)Parameters["ReplaceBranchMove"]; }
    }
    public ILookupParameter<DoubleValue> QualityParameter {
      get { return (ILookupParameter<DoubleValue>)Parameters["Quality"]; }
    }

    public ILookupParameter<DoubleValue> MoveQualityParameter {
      get { return (ILookupParameter<DoubleValue>)Parameters["MoveQuality"]; }
    }

    public ILookupParameter<ISymbolicRegressionSingleObjectiveEvaluator> EvaluatorParameter {
      get { return (ILookupParameter<ISymbolicRegressionSingleObjectiveEvaluator>)Parameters["Evaluator"]; }
    }
    public ILookupParameter<IRegressionProblemData> ProblemDataParameter {
      get { return (ILookupParameter<IRegressionProblemData>)Parameters["ProblemData"]; }
    }
    public ILookupParameter<IntRange> FitnessCalculationPartitionParameter {
      get { return (ILookupParameter<IntRange>)Parameters["FitnessCalculationPartition"]; }
    }

    public ILookupParameter<ISymbolicExpressionTree> SymbolicExpressionTreeParameter {
      get { return (ILookupParameter<ISymbolicExpressionTree>)Parameters["SymbolicExpressionTree"]; }
    }
    [StorableConstructor]
    protected ReplaceBranchMoveEvaluator(bool deserializing) : base(deserializing) { }
    protected ReplaceBranchMoveEvaluator(ReplaceBranchMoveEvaluator original, Cloner cloner) : base(original, cloner) { }
    public ReplaceBranchMoveEvaluator()
      : base() {
      Parameters.Add(new LookupParameter<DoubleValue>("Quality", "The quality of a symbolic regression solution."));
      Parameters.Add(new LookupParameter<DoubleValue>("MoveQuality", "The evaluated quality of a move on a symbolic regression solution."));
      Parameters.Add(new LookupParameter<ISymbolicRegressionSingleObjectiveEvaluator>("Evaluator", ""));
      Parameters.Add(new LookupParameter<ReplaceBranchMove>("ReplaceBranchMove", ""));
      Parameters.Add(new LookupParameter<IRegressionProblemData>("ProblemData", ""));
      Parameters.Add(new LookupParameter<IntRange>("FitnessCalculationPartition", ""));
      Parameters.Add(new LookupParameter<ISymbolicExpressionTree>("SymbolicExpressionTree", ""));
    }

    public override IOperation Apply() {
      var evaluator = EvaluatorParameter.ActualValue;

      var move = MoveParameter.ActualValue;

      // calculate linear scaling
      OnlineCalculatorError errorState;
      double alpha, beta;
      OnlineLinearScalingParameterCalculator.Calculate(move.NewOutput, move.OriginalOutput, out alpha, out beta, out errorState);

      // prevent scaling to a constant value
      if (beta.IsAlmost(0.0)) beta = 1.0;

      move.Alpha = alpha;
      move.Beta = beta;

      var oldBranch = move.Parent.GetSubtree(move.SubtreeIndex);
      move.Parent.RemoveSubtree(move.SubtreeIndex);
      move.Parent.InsertSubtree(move.SubtreeIndex, Scale(move.NewBranch, alpha, beta));

      var problemData = ProblemDataParameter.ActualValue;
      IExecutionContext childContext = (IExecutionContext)ExecutionContext.CreateChildOperation(evaluator);

      var fitnessPartition = FitnessCalculationPartitionParameter.ActualValue;
      var rows = Enumerable.Range(fitnessPartition.Start, fitnessPartition.End - fitnessPartition.Start);
      var quality = evaluator.Evaluate(childContext, move.Tree, problemData, rows);

      // revert changes to tree
      move.Parent.RemoveSubtree(move.SubtreeIndex);
      move.Parent.InsertSubtree(move.SubtreeIndex, oldBranch);

      MoveQualityParameter.ActualValue = new DoubleValue(quality);
      return base.Apply();
    }

    private Addition add = new Addition();
    private Multiplication mul = new Multiplication();
    private Constant constant = new Constant();

    private ISymbolicExpressionTreeNode Scale(ISymbolicExpressionTreeNode node, double alpha, double beta) {
      var constAlpha = (ConstantTreeNode)constant.CreateTreeNode();
      var constBeta = (ConstantTreeNode)constant.CreateTreeNode();
      constAlpha.Value = alpha;
      constBeta.Value = beta;
      var sum = add.CreateTreeNode();
      var prod = mul.CreateTreeNode();
      prod.AddSubtree(node); prod.AddSubtree(constBeta);
      sum.AddSubtree(prod); sum.AddSubtree(constAlpha);
      return sum;
    }

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