#region License Information
/* HeuristicLab
* Copyright (C) 2002-2013 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.Linq;
using System.Threading;
using HeuristicLab.Algorithms.GeneticAlgorithm;
using HeuristicLab.Common;
using HeuristicLab.Data;
using HeuristicLab.Persistence.Default.Xml;
using Microsoft.VisualStudio.TestTools.UnitTesting;
using HeuristicLab.Algorithms.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis;
using System.Collections.Generic;
using HeuristicLab.Core;
namespace HeuristicLab_33.Tests {
[TestClass]
public class SupportVectorMachineTest {
public SupportVectorMachineTest() { }
private TestContext testContextInstance;
///
///Gets or sets the test context which provides
///information about and functionality for the current test run.
///
public TestContext TestContext {
get {
return testContextInstance;
}
set {
testContextInstance = value;
}
}
private EventWaitHandle trigger = new AutoResetEvent(false);
private Exception ex;
[TestMethod]
public void SupportVectorMachinePerformanceTest() {
ex = null;
var cv = new CrossValidation();
cv.Algorithm = new SupportVectorRegression();
var rand = new HeuristicLab.Random.MersenneTwister();
double[,] data = GenerateData(1000, rand);
List variables = new List() { "x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "x9", "x10", "y" };
Dataset ds = new Dataset(variables, data);
cv.Problem.ProblemDataParameter.ActualValue = new RegressionProblemData(ds, variables.Take(10), variables.Last());
cv.Folds.Value = 5;
cv.SamplesStart.Value = 0;
cv.SamplesEnd.Value = 999;
cv.ExceptionOccurred += new EventHandler>(cv_ExceptionOccurred);
cv.Stopped += new EventHandler(cv_Stopped);
cv.Prepare();
cv.Start();
trigger.WaitOne();
if (ex != null) throw ex;
TestContext.WriteLine("Runtime: {0}", cv.ExecutionTime.ToString());
}
// poly-10: y = x1 x2 + x3 x4 + x5 x6 + x1 x7 x9 + x3 x6 x10
private double[,] GenerateData(int n, IRandom random) {
double[,] data = new double[n, 11];
for (int i = 0; i < n; i++) {
for (int c = 0; c < 10; c++) {
data[i, c] = random.NextDouble() * 2.0 - 1.0;
}
data[i, 10] =
data[i, 0] * data[i, 1] +
data[i, 2] * data[i, 3] +
data[i, 4] * data[i, 5] +
data[i, 0] * data[i, 6] * data[i, 8] +
data[i, 2] * data[i, 5] * data[i, 9];
}
return data;
}
private void cv_ExceptionOccurred(object sender, EventArgs e) {
ex = e.Value;
}
private void cv_Stopped(object sender, EventArgs e) {
trigger.Set();
}
}
}