#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 .
*/
#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), 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", scope, true);
ItemList allowedFeatures = GetVariableValue>("AllowedFeatures", scope, true);
int targetVariable = GetVariableValue("TargetVariable", scope, true).Data;
int start = GetVariableValue("SamplesStart", scope, true).Data;
int end = GetVariableValue("SamplesEnd", scope, true).Data;
SVM.Model model = (SVM.Model)GetVariableValue("SVMModel", scope, true).Data;
SVM.RangeTransform rangeTransform = (SVM.RangeTransform)GetVariableValue("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;
}
}
}