static void Main(string[] args)
{
//ENTER FULL PATH OF EXECUTABLES.
SVMLearn learner = new SVMLearn();
learner.mode = Mode.Classification;
learner.kernelType = Kernel.Linear ;
//learner.ParamC = 2;
//learner.ParamD = 2;
//learner.LeaveOneOutCrossValidation = true;
//learner.ParamG = 5;
//learner.TrainingErrorAndMarginTradeoff = 10;
learner.Cost = 0.55; //high cost model
learner.RemoveInconsistentTrainingExamples = true;
Utilities u = new Utilities();
SVMLearn[] learners = new SVMLearn[10];
Dictionary<double, Results> cvResults = new Dictionary<double, Results>(10);
String outputFolder = @"";
u.ConstructNFolds(@"", 10, outputFolder, r => r.StartsWith("-"));
for (int counter = 0; counter < 10; counter++)
{
learners[counter] = new SVMLearn();
learners[counter].mode = Mode.Classification;
learners[counter].kernelType = Kernel.Linear;
learners[counter].Cost = 0.35 + counter * 0.20;
learners[counter].RemoveInconsistentTrainingExamples = true;
cvResults.Add(learners[counter].Cost, u.PerformCrossValidation(outputFolder, false, learners[counter]));
}
StreamWriter learningRate = new StreamWriter(Path.Combine(outputFolder, "learning.txt"));
foreach (KeyValuePair<double, Results> entry in cvResults)
{
Console.WriteLine("Cost: {0}: Accuracy {1}, Precision {2}, Recall {3}",
entry.Key, entry.Value.accuracy, entry.Value.precision, entry.Value.recall);
learningRate.WriteLine(entry.Key + ", " + entry.Value.accuracy + ", " +
entry.Value.precision + ", " + entry.Value.recall);
}
Console.ReadLine();
}
static void Main(string[] args)
{
//ENTER FULL PATH OF EXECUTABLES.
SVMLearn learner = new SVMLearn();
learner.mode = Mode.Classification;
learner.kernelType = Kernel.Linear ;
//learner.ParamC = 2;
//learner.ParamD = 2;
//learner.LeaveOneOutCrossValidation = true;
//learner.ParamG = 5;
//learner.TrainingErrorAndMarginTradeoff = 10;
learner.Cost = 0.55; //high cost model
learner.RemoveInconsistentTrainingExamples = true;
//String keyword = "kv";
//String logFileName = keyword + learner.kernelType.ToString() + learner.ParamC + learner.ParamD + ".log.txt";
//learner.ExecuteLearner("svm_learn.exe",
// @"E:\kv\My Dropbox\code\SocialGraphLinking\" + keyword + @"\entitylinking-" +
// keyword + ".data",
// @"E:\kv\My Dropbox\code\SocialGraphLinking\" + keyword + "\\" + keyword + ".model",
// @"E:\kv\My Dropbox\code\SocialGraphLinking\" + keyword + "\\" + logFileName, false);
//Utilities utility = new Utilities();
//utility.ConstructTrainingAndTestSets
// (@"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\entitylinking-kv-akshaya-varish.data",
// @"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\kv-akshaya-varish-training.arff",
// @"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\kv-akshaya-varish-test.data.arff", 0.7, true,
// line => line.StartsWith("@"), line => line.Split(',')[5].Trim().Equals("0"));
//learner.ExecuteLearner("svm_learn.exe", @"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\kv-akshaya-varish-reduced-training.data",
// @"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\kv-akshaya-varish-reduced.model",
// @"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\kv-akshaya-varish-reduced-training.log.txt", false);
//SVMClassify classifier = new SVMClassify();
//classifier.ExecuteClassifier("svm_classify.exe", @"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\entitylinking-kv-akshaya-varish.data",
// @"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\combined-linear-highcost.model",
// @"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\whatever.output",
// @"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\whatever-log.txt",
// @"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\whatever-incorrect.txt",
// false);
//classifier.FindIncorrectlyClassifiedInstances(@"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\kv-akshaya-varish-reduced-test.data",
// @"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\kv-akshaya-varish-reduced.output",
// @"E:\kv\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\kv-akshaya-varish-reduced-incorrect.txt");
//IncorrectFileParser.ParseIncorrectFile(@"D:\kv\official\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\combined-linear-incorrect.txt");
//Utilities.ConstructTrainingAndTestSets(@"D:\kv\official\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\entitylinking-kv-akshaya-varish.data",
// @"D:\kv\official\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\training1.data",
// @"D:\kv\official\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\test1.data", 0.7, false);
//int pos, neg;
//Utilities.GetNumberOfTrainingExamples(@"D:\kv\official\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\training.data",
// out pos, out neg);
//Console.WriteLine("positive: {0}, negative:{1}", pos, neg);
Utilities u = new Utilities();
u.EntryPoint(@"D:\kv\official\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\combined.data",
10, @"D:\kv\official\My Dropbox\code\SocialGraphLinking\kv-akshaya-varish\10foldcv",
line => line.StartsWith("-"));
//Random random = new Random();
//for (int counter = 0; counter < 20; counter++)
//{
// Console.WriteLine(random.Next(5));
// Console.ReadKey();
//}
//Console.ReadLine();
}
