#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2010 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.Collections.Generic;
using System.Linq;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Interfaces;
using HeuristicLab.Problems.DataAnalysis.Regression.Symbolic;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Common;

namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
  [Item("SymbolicVectorRegressionNormalizedMseEvaluator", "Represents an operator that calculates the sum of the normalized mean squared error over all components.")]
  [StorableClass]
  public class SymbolicVectorRegressionNormalizedMseEvaluator : SingleObjectiveSymbolicVectorRegressionEvaluator {


    [StorableConstructor]
    protected SymbolicVectorRegressionNormalizedMseEvaluator(bool deserializing) : base(deserializing) { }
    protected SymbolicVectorRegressionNormalizedMseEvaluator(SymbolicVectorRegressionNormalizedMseEvaluator original, Cloner cloner)
      : base(original, cloner) {
    }
    public SymbolicVectorRegressionNormalizedMseEvaluator()
      : base() {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new SymbolicVectorRegressionNormalizedMseEvaluator(this, cloner);
    }

    public override double Evaluate(SymbolicExpressionTree tree, ISymbolicExpressionTreeInterpreter interpreter, MultiVariateDataAnalysisProblemData problemData, IEnumerable<string> targetVariables, IEnumerable<int> rows, DoubleArray lowerEstimationBound, DoubleArray upperEstimationBound) {
      return Calculate(tree, interpreter, problemData, targetVariables, rows, lowerEstimationBound, upperEstimationBound);
    }

    public static double Calculate(SymbolicExpressionTree tree, ISymbolicExpressionTreeInterpreter interpreter, MultiVariateDataAnalysisProblemData problemData, IEnumerable<string> targetVariables, IEnumerable<int> rows, DoubleArray lowerEstimationBound, DoubleArray upperEstimationBound) {
      List<string> targetVariablesList = targetVariables.ToList();
      double nmseSum = 0.0;
      // use only the i-th vector component
      List<SymbolicExpressionTreeNode> componentBranches = new List<SymbolicExpressionTreeNode>(tree.Root.SubTrees[0].SubTrees);
      while (tree.Root.SubTrees[0].SubTrees.Count > 0) tree.Root.SubTrees[0].RemoveSubTree(0);

      for (int i = 0; i < targetVariablesList.Count; i++) {
        tree.Root.SubTrees[0].AddSubTree(componentBranches[i]);
        double nmse = SymbolicRegressionNormalizedMeanSquaredErrorEvaluator.Calculate(interpreter, tree,
          lowerEstimationBound[i], upperEstimationBound[i],
          problemData.Dataset, targetVariablesList[i], rows);
        tree.Root.SubTrees[0].RemoveSubTree(0);
        nmseSum += nmse;
      }
      // restore tree
      foreach (var treeNode in componentBranches) {
        tree.Root.SubTrees[0].AddSubTree(treeNode);
      }
      return nmseSum;
    }
  }
}