source: trunk/sources/HeuristicLab.SupportVectorMachines/3.2/SupportVectorEvaluator.cs @ 1808

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2009 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 System.Text;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.DataAnalysis;

namespace HeuristicLab.SupportVectorMachines {
  public class SupportVectorEvaluator : OperatorBase {

    public SupportVectorEvaluator()
      : base() {
      //Dataset infos
      AddVariableInfo(new VariableInfo("Dataset", "Dataset with all samples on which to apply the function", typeof(Dataset), VariableKind.In));
      AddVariableInfo(new VariableInfo("AllowedFeatures", "List of indexes of allowed features", typeof(ItemList<IntData>), VariableKind.In));
      AddVariableInfo(new VariableInfo("TargetVariable", "Index of the column of the dataset that holds the target variable", typeof(IntData), VariableKind.In));
      AddVariableInfo(new VariableInfo("SamplesStart", "Start index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));
      AddVariableInfo(new VariableInfo("SamplesEnd", "End index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));

      AddVariableInfo(new VariableInfo("SVMModel", "Represent the model learned by the SVM", typeof(ObjectData), VariableKind.In));
      AddVariableInfo(new VariableInfo("SVMRangeTransform", "The applied transformation during the learning the model", typeof(ObjectData), VariableKind.In));

      AddVariableInfo(new VariableInfo("Values", "Target vs predicted values", typeof(ItemList), VariableKind.New | VariableKind.Out));

    }


    public override IOperation Apply(IScope scope) {
      Dataset dataset = GetVariableValue<Dataset>("Dataset", scope, true);
      ItemList<IntData> allowedFeatures = GetVariableValue<ItemList<IntData>>("AllowedFeatures", scope, true);
      int targetVariable = GetVariableValue<IntData>("TargetVariable", scope, true).Data;
      int start = GetVariableValue<IntData>("SamplesStart", scope, true).Data;
      int end = GetVariableValue<IntData>("SamplesEnd", scope, true).Data;

      SVM.Model model = (SVM.Model)GetVariableValue<ObjectData>("SVMModel", scope, true).Data;
      SVM.RangeTransform rangeTransform = (SVM.RangeTransform)GetVariableValue<ObjectData>("SVMRangeTransform", scope, true).Data;

      SVM.Problem problem = SVMHelper.CreateSVMProblem(dataset, allowedFeatures, targetVariable, start, end);
      SVM.Problem scaledProblem = SVM.Scaling.Scale(problem, rangeTransform);

      ItemList predictedValues = new ItemList();
      ItemList row;
      for (int i = 0; i < end - start; i++) {
        row = new ItemList();
        row.Add(new DoubleData(SVM.Prediction.Predict(model, scaledProblem.X[i])));
        row.Add(new DoubleData(dataset.Samples[(start + i) * dataset.Columns + targetVariable]));
        predictedValues.Add(row);
      }

      scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("Values"), predictedValues));
      return null;
    }
  }
}