static public void SVMCrowdScaleForRegression(int trainNumber)
{
string folderName = trainNumber + "Regression";
SVMLearn svmLearn = new SVMLearn();
svmLearn.mode = Mode.Regression;
svmLearn.kernelType = Kernel.Polynomial;
svmLearn.ExecuteLearner("svm_learn.exe", folderName + "CrowdScale/train.dat", folderName + "CrowdScale/model.txt", folderName + "CrowdScale/learnLog.txt", false);
new SVMClassify().ExecuteClassifier("svm_classify.exe", folderName + "CrowdScale/test.dat", folderName + "CrowdScale/model.txt", folderName + "CrowdScale/output.txt", folderName + "CrowdScale/clasifylog.txt", false);
}
static private void SVMExample()
{
SVMLearn svmLearn = new SVMLearn();
svmLearn.ExecuteLearner("svm_learn.exe", "example/train.dat", "example/model.txt", "example/learnLog.txt", false);
SVMClassify svmClassify = new SVMClassify();
svmClassify.ExecuteClassifier("svm_classify.exe", "example/test.dat", "example/model.txt", "example/output.txt", "example/clasifylog.txt", false);
}
static public void SVMCrowdScale(int numberOfTrain, Kernel kernel, int ParamD, double ParamG, double ParamS, double ParamC)
{
string folderName = numberOfTrain + "Classification";
SVMLearn svmLearn0 = new SVMLearn();
svmLearn0.kernelType = kernel;
svmLearn0.ParamD = ParamD;
svmLearn0.ParamG = ParamG;
svmLearn0.ParamS = ParamS;
svmLearn0.ParamC = ParamC;
svmLearn0.ExecuteLearner("svm_learn.exe", folderName + "CrowdScale/0train.dat", folderName + "CrowdScale/0model.txt", folderName + "CrowdScale/0learnLog.txt", false);
new SVMClassify().ExecuteClassifier("svm_classify.exe", folderName + "CrowdScale/0test.dat", folderName + "CrowdScale/0model.txt", folderName + "CrowdScale/0output.txt", folderName + "CrowdScale/0clasifylog.txt", false);
SVMLearn svmLearn1 = new SVMLearn();
svmLearn1.kernelType = kernel;
svmLearn1.ParamD = ParamD;
svmLearn1.ParamG = ParamG;
svmLearn1.ParamS = ParamS;
svmLearn1.ParamC = ParamC;
svmLearn1.ExecuteLearner("svm_learn.exe", folderName + "CrowdScale/1train.dat", folderName + "CrowdScale/1model.txt", folderName + "CrowdScale/1learnLog.txt", false);
new SVMClassify().ExecuteClassifier("svm_classify.exe", folderName + "CrowdScale/1test.dat", folderName + "CrowdScale/1model.txt", folderName + "CrowdScale/1output.txt", folderName + "CrowdScale/1clasifylog.txt", false);
SVMLearn svmLearn2 = new SVMLearn();
svmLearn2.kernelType = kernel;
svmLearn2.ParamD = ParamD;
svmLearn2.ParamG = ParamG;
svmLearn2.ParamS = ParamS;
svmLearn2.ParamC = ParamC;
svmLearn2.ExecuteLearner("svm_learn.exe", folderName + "CrowdScale/2train.dat", folderName + "CrowdScale/2model.txt", folderName + "CrowdScale/2learnLog.txt", false);
new SVMClassify().ExecuteClassifier("svm_classify.exe", folderName + "CrowdScale/2test.dat", folderName + "CrowdScale/2model.txt", folderName + "CrowdScale/2output.txt", folderName + "CrowdScale/2clasifylog.txt", false);
SVMLearn svmLearn3 = new SVMLearn();
svmLearn3.kernelType = kernel;
svmLearn3.ParamD = ParamD;
svmLearn3.ParamG = ParamG;
svmLearn3.ParamS = ParamS;
svmLearn3.ParamC = ParamC;
svmLearn3.ExecuteLearner("svm_learn.exe", folderName + "CrowdScale/3train.dat", folderName + "CrowdScale/3model.txt", folderName + "CrowdScale/3learnLog.txt", false);
new SVMClassify().ExecuteClassifier("svm_classify.exe", folderName + "CrowdScale/3test.dat", folderName + "CrowdScale/3model.txt", folderName + "CrowdScale/3output.txt", folderName + "CrowdScale/3clasifylog.txt", false);
SVMLearn svmLearn4 = new SVMLearn();
svmLearn4.kernelType = kernel;
svmLearn4.ParamD = ParamD;
svmLearn4.ParamG = ParamG;
svmLearn4.ParamS = ParamS;
svmLearn4.ParamC = ParamC;
svmLearn4.ExecuteLearner("svm_learn.exe", folderName + "CrowdScale/4train.dat", folderName + "CrowdScale/4model.txt", folderName + "CrowdScale/4learnLog.txt", false);
new SVMClassify().ExecuteClassifier("svm_classify.exe", folderName + "CrowdScale/4test.dat", folderName + "CrowdScale/4model.txt", folderName + "CrowdScale/4output.txt", folderName + "CrowdScale/4clasifylog.txt", false);
}
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();
}
