public static void testSVMWrapperPackage() //rafal parsal { libSVM_Problem Problem = libSVM_Problem.Load("../../../LibSVMFull/testSvmWrapper/data/train.dat"); GuiPreferences.Instance.setLog("trainnig data loaded"); //GuiPreferences.Instance.setLog("trainnig data loaded"; Problem.Save("../../../LibSVMFull/testSvmWrapper/data/train_saved"); GuiPreferences.Instance.setLog("training data saved"); libSVM_Parameter Parameter = new libSVM_Parameter(); libSVM_Extension svm = new libSVM_Extension(); Parameter.svm_type = SVM_TYPE.C_SVC; Parameter.kernel_type = KERNEL_TYPE.LINEAR; svm = new libSVM_Extension(); svm.Train(Problem, Parameter); libSVM_Problem Test = libSVM_Problem.Load("../../../LibSVMFull/testSvmWrapper/data/test.dat"); svm.GetAccuracyFromTest(Test); double accuracy = svm.output.accuracy; GuiPreferences.Instance.setLog("Predicted accuracy from testing set: " + accuracy.ToString()); svm.Dispose(); svm = new libSVM_Extension(); //svm.TrainAuto(10, Problem, Parameter, libSVM_Grid.C(), libSVM_Grid.gamma(), libSVM_Grid.p(), libSVM_Grid.nu(), libSVM_Grid.coef0(), libSVM_Grid.degree()); libSVM_Grid grid = new libSVM_Grid(); accuracy = svm.TrainAuto(10, Problem, Parameter, grid, null, null, null, null, null); GuiPreferences.Instance.setLog("Predicted accuracy from 10 cross fold validation: " + accuracy.ToString()); svm.Save("../../../LibSVMFull/testSvmWrapper/data/model_file"); GuiPreferences.Instance.setLog("10 cfv best model saved"); svm.Dispose(); }
///////////////////////////////////////////////////////////////////////////////////////// private void initVariables() { udp = new UDP(); udpsim = new UDPSim(); //holds min max values which prevents the min from being zeroed out after the threhold filter. MinMax = new List <Dictionary <int, MinMax> >(); BrainMean = new List <string>(); //checking for global drift before and after IG filter TrainingEventStartLocationsPerTR = new Dictionary <int, Dictionary <string, List <int> > >(); //run, tr, list of indices validMaxBrainAverage = 1000; corruptedVector = false; PipeServerName = "OBL"; //in here the client uses OBL pipe to communicate to the server, if a communication is needed from server to client, i believe we should use another pipe - check this. PipeClientName = "Unity"; pipeServer = new NamedPipeServer(PipeServerName); pipeClient = new NamedPipeClient(PipeServerName); cumulativeTR = 0; nextEvent = 0; nextTRinEvent = 0; currentRunningEvent = ""; // DirMonitor variables //=========================== masterPath = "/My_Dropbox/VERE/MRI_data/Nir/"; pathName = masterPath + "NIR_run1_rtp/"; dicomMasterPath = masterPath + "NIR_run1_dcm_master/"; dicomTbvWatchPath = masterPath + "NIR_run1_dcm_watch/"; //configuration - 1b - protocol events = new Events(new List <IntIntStr>()); protocolLoaded = false; //configuration - 3 - import, radio buttons binary file types svmLoaded = false; ProblemOriginal = new libSVM_ExtendedProblem(); svmWrapper = new libSVM_Extension(); deleteFiles = new string[] { "TrainSet.libsvm", "TrainSet_3th_vectors.libsvm", "TrainSet_3th_vectors_scale_paramCS.libsvm", "TrainSet_3th_vectors_scaledCS.libsvm", "TrainSet_3th_vectors_scaledCS.libsvm.arff", "TrainSet_3th_vectors_scaledCS_filteredIG.arff", "TrainSet_3th_vectors_scaledCS_filteredIG.model", "TrainSet_3th_vectors_scaledCS_filteredIG_indices.xml", "TrainSet_4th_vectors.libsvm", "TrainSet_4th_vectors_scale_paramCS.libsvm", "TrainSet_4th_vectors_scaledCS.libsvm", "TrainSet_4th_vectors_scaledCS.libsvm.arff", "TrainSet_5th_vectors.libsvm", "TrainSet_5th_vectors_scale_paramCS.libsvm", "TrainSet_5th_vectors_scaledCS.libsvm", "TrainSet_5th_vectors_scaledCS.libsvm.arff", "TrainSet_6th_vectors.libsvm", "TrainSet_6th_vectors_scale_paramCS.libsvm", "TrainSet_6th_vectors_scaledCS.libsvm", "TrainSet_6th_vectors_scaledCS.libsvm.arff" }; //memory capcity test, each array up to 2gb!! /*try * { * int[] ar = new int[1]; * int[] ar2 = new int[1]; * for (int i = 1; i < 100000; i++) * { * Array.Resize(ref ar, ar.Length + 100000000); * Array.Resize(ref ar2, ar.Length + 100000000); * long l = PublicMethods.getRam(); * } * } * catch (Exception e) * { * long l = PublicMethods.getRam(); * string s = e.ToString(); * }*/ classification = new List <string[]>(); //7-plots try { //caused problems in mri's pc //glm = new MatlabGLM(); } catch (Exception e) { MessageBox.Show( "Warning: Matlab Initialization failed, please install matlab 2011a 64bit! (it is SAFE to CONTINUE!)" + e.ToString(), "Warning", MessageBoxButtons.OK, MessageBoxIcon.Information, MessageBoxDefaultButton.Button2); } }