#region License Information
/* HeuristicLab
* Copyright (C) 2002-2008 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.Text;
using System.Xml;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.DataAnalysis;
using System.Linq;
namespace HeuristicLab.SupportVectorMachines {
public class VariableEvaluationImpactCalculator : HeuristicLab.Modeling.VariableEvaluationImpactCalculator {
public VariableEvaluationImpactCalculator()
: base() {
AddVariableInfo(new VariableInfo("SVMModel", "The model that should be evaluated", typeof(SVMModel), VariableKind.In));
}
protected override double[] GetOutputs(IScope scope, Dataset dataset, int targetVariable, ItemList allowedFeatures, int start, int end) {
SVMModel model = GetVariableValue("SVMModel", scope, true);
SVM.Problem problem = SVMHelper.CreateSVMProblem(dataset, allowedFeatures, targetVariable, start, end);
SVM.Problem scaledProblem = SVM.Scaling.Scale(problem, model.RangeTransform);
double[] values = new double[end - start];
for (int i = 0; i < end - start; i++) {
values[i] = SVM.Prediction.Predict(model.Model, scaledProblem.X[i]);
}
return values;
}
}
}