public KNN_SVM(string path, double sigma, double gamma, Kernel kernel, int k, ImageVector[] goodImages, ImageVector[] badImages)
{
type = "KNN & SVM";
svm = new SVM_Matlab(path, sigma, gamma, (SVM_Matlab.Kernel)kernel, goodImages, badImages);
knn = new KNN_Matlab(path, k, goodImages, badImages);
totalImages = 0;
this.goodImages = goodImages;
this.badImages = badImages;
learnedTrue = new bool[goodImages.Length];
learnedFalse = new bool[badImages.Length];
restartTest();
userPath = path + "\\KNN_SVM";
resultPath = userPath + "\\sigma_" + sigma + "_gamma_" + gamma;
learn = GetLearnCommand(userPath, sigma, gamma, kernel);
decideSVM = GetDecideCommandSVM(userPath, kernel);
decideKNN = GetDecideCommandKNN(userPath, k);
matlab = new MLApp.MLApp();
cd = "cd " + smartAlbum.getMatlabDirectory();
matlab.Execute(cd);
}
public bool Learn()
{
// Get list of files in folders
string[] filesAll = Testing.GetFilesFromDirectory(decidePath).ToArray();
if (filesAll.Length <= 0)
return false;
ImageVector[] allImages;
extractAlbum(out allImages, filesAll, false);
//allImages = Internal_GetImages(filesAll);
//ImageVector[] ImagesLearnedTrue = Internal_GetImages(learnedTrue);
//ImageVector[] ImagesLearnedFalse = Internal_GetImages(learnedFalse);
learningAlgoSvm = new SVM_Matlab(userPath, 1, 1, SVM_Matlab.Kernel.SVM_POLY, allImages);
learningAlgoSvm.Learn(subset, isImageGood);
return true;
}
