public void GenerateJobs(ValidationSuite vs, List<Instance> instances, string notes)
{
ValidationJob job;
int folds = 10, reps = 2;
job = new ValidationJob();
job.Validator = new KFoldValidation(instances, folds, reps);
job.Classifier = new NaiveBayesClassifier();
job.Notes = notes;
vs.Jobs.Add(job);
/*
for (int k = 1; k <= 4; k++)
{
foreach (Metric metric in Enum.GetValues(typeof(Metric)))
{
if (metric == Metric.HammingDistance || metric == Metric.ChebyshevDistance
|| metric == Metric.JaccardSimilarity || metric == Metric.DistanceSquared) continue;
WeightMode mode = WeightMode.InverseDistance;
job = new ValidationJob();
job.Validator = new KFoldValidation(instances, folds, reps);
job.Classifier = new KNearestClassifier(k, metric, mode);
job.Notes = notes;
vs.Jobs.Add(job);
}
}*/
}
public void RunTests(string method, string inPath, string outPath)
{
List<Instance> training;
ValidationSuite vs = new ValidationSuite();
string path = inPath;
string fr = method + ",";
vs.Notes = "Instances loaded from: " + path;
vs.Notes += "\nFeature Reduction: " + fr;
int[] ns = new int[] {16, 32, 64, 128, 256, 512, 768, 1024};
//int[] ns = new int[] { 16, 32, 64, 128, 256, 512 };
foreach(int n in ns)
{
training = LoadInstancesFromBitmaps(path, n, method);
GenerateJobs(vs, training, fr+" "+n);
}
vs.PerformAllJobs();
vs.SaveToFile(outPath);
Print(vs.Print());
}