/// <summary>
/// Private method that tests a fold
/// </summary>
/// <param name="foldDirectories">directory in which the folds are placed</param>
/// <param name="fold">the current fold being tested</param>
/// <returns>the classifier containing accuracy, precision and recall</returns>
private SVMClassify TestFold(string[] foldDirectories, int fold)
{
SVMClassify classifier = new SVMClassify();
classifier.ExecuteClassifier("svm_classify.exe", Path.Combine(foldDirectories[fold], fold + ".data"),
Path.Combine(foldDirectories[fold], fold + ".model"), Path.Combine(foldDirectories[fold], fold + ".output"),
Path.Combine(foldDirectories[fold], fold + ".test.log.txt"), Path.Combine(foldDirectories[fold], fold + ".incorrect.txt"),
false);
return(classifier);
}
/// <summary>
/// Perform n-fold cross validation
/// </summary>
/// <param name="cvFolder">folder that contains the folds</param>
/// <param name="silent">whether or not to be silent on the screen</param>
/// <param name="learner">The svm learner with the relevant parameters</param>
/// <returns>results</returns>
public Results PerformCrossValidation(String cvFolder, bool silent, SVMLearn learner)
{
String[] foldDirectories = Directory.GetDirectories(cvFolder);
double[] precisions = new double[foldDirectories.Length];
double[] recalls = new double[foldDirectories.Length];
double[] accuracies = new double[foldDirectories.Length];
for (int counter = 0; counter < foldDirectories.Length; counter++)
{
String concatenatedFile = Path.Combine(foldDirectories[counter], "combined" + counter + ".data");
using (StreamWriter writer = new StreamWriter(concatenatedFile, false))
{
for (int innerCounter = 0; innerCounter < foldDirectories.Length; innerCounter++)
{
if (innerCounter != counter)
{
using (StreamReader reader =
new StreamReader(Path.Combine(foldDirectories[innerCounter], innerCounter + ".data")))
{
while (!reader.EndOfStream)
{
writer.WriteLine(reader.ReadLine());
}
}
}
}
}
//concatenated file created. now train and test. delete concatenated file
this.TrainFold(foldDirectories, counter, concatenatedFile, silent, learner);
File.Delete(concatenatedFile);
SVMClassify classifier = this.TestFold(foldDirectories, counter);
precisions[counter] = classifier.Precision;
recalls[counter] = classifier.Recall;
accuracies[counter] = classifier.Accuracy;
}
double accuracy = accuracies.Average(),
precision = precisions.Average(),
recall = recalls.Average();
Console.WriteLine("Accuracy: {0}, Precision: {1}, Recall: {2}", accuracy, precision, recall);
return(new Results
{
accuracy = accuracy,
precision = precision,
recall = recall
});
}
/// <summary>
/// Private method that tests a fold
/// </summary>
/// <param name="foldDirectories">directory in which the folds are placed</param>
/// <param name="fold">the current fold being tested</param>
/// <returns>the classifier containing accuracy, precision and recall</returns>
private SVMClassify TestFold(string[] foldDirectories, int fold)
{
SVMClassify classifier = new SVMClassify();
classifier.ExecuteClassifier("svm_classify.exe", Path.Combine(foldDirectories[fold], fold + ".data"),
Path.Combine(foldDirectories[fold], fold + ".model"), Path.Combine(foldDirectories[fold], fold + ".output"),
Path.Combine(foldDirectories[fold], fold + ".test.log.txt"), Path.Combine(foldDirectories[fold], fold + ".incorrect.txt"),
false);
return classifier;
}