#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 SupportVectorCreator : OperatorBase {
public SupportVectorCreator()
: 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));
//SVM parameters
AddVariableInfo(new VariableInfo("SVMType", "String describing which SVM type is used. Valid inputs are: C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR",
typeof(StringData), VariableKind.In));
AddVariableInfo(new VariableInfo("SVMKernelType", "String describing which SVM kernel is used. Valid inputs are: LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED",
typeof(StringData), VariableKind.In));
AddVariableInfo(new VariableInfo("SVMCost", "Cost parameter (C) of C-SVC, epsilon-SVR and nu-SVR", typeof(DoubleData), VariableKind.In));
AddVariableInfo(new VariableInfo("SVMNu", "Nu parameter of nu-SVC, one-class SVM and nu-SVR", typeof(DoubleData), VariableKind.In));
AddVariableInfo(new VariableInfo("SVMGamma", "Gamma parameter in kernel function", typeof(DoubleData), VariableKind.In));
AddVariableInfo(new VariableInfo("SVMModel", "Represent the model learned by the SVM", typeof(SVMModel), VariableKind.New | VariableKind.Out));
AddVariableInfo(new VariableInfo("SVMRangeTransform", "The applied transformation during the learning the model", typeof(SVMRangeTransform), 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;
string svmType = GetVariableValue("SVMType", scope, true).Data;
string svmKernelType = GetVariableValue("SVMKernelType", scope, true).Data;
//extract SVM parameters from scope and set them
SVM.Parameter parameter = new SVM.Parameter();
parameter.SvmType = (SVM.SvmType)Enum.Parse(typeof(SVM.SvmType), svmType, true);
parameter.KernelType = (SVM.KernelType)Enum.Parse(typeof(SVM.KernelType), svmKernelType, true);
parameter.C = GetVariableValue("SVMCost", scope, true).Data;
parameter.Nu = GetVariableValue("SVMNu", scope, true).Data;
parameter.Gamma = GetVariableValue("SVMGamma", scope, true).Data;
SVM.Problem problem = SVMHelper.CreateSVMProblem(dataset, allowedFeatures, targetVariable, start, end);
SVM.RangeTransform rangeTransform = SVM.Scaling.DetermineRange(problem);
SVM.Problem scaledProblem = SVM.Scaling.Scale(problem, rangeTransform);
SVM.Model model = SVM.Training.Train(scaledProblem, parameter);
//persist variables in scope
SVMModel modelData = new SVMModel();
modelData.Data = model;
scope.AddVariable(new Variable(scope.TranslateName("SVMModel"),modelData));
SVMRangeTransform rangeTransformData = new SVMRangeTransform();
rangeTransformData.Data = rangeTransform;
scope.AddVariable(new Variable(scope.TranslateName("SVMRangeTransform"),rangeTransformData));
return null;
}
}
}