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);
}
}
