//the final normalized and concatenated file doesnt need to be vectorized + normalized again.
// if first line "instances data = " is 4 and the rest of the lines are 3, then the trick is used.
// if all lines are 3 then only tr3 is used
// if all lines except the saveArff is tr4 then it will use tr4 data and save to a filename with "3" in it, at the moment this is done for compatibility when loading this file in real-time
public static Instances ConcatenationPipeLine(string filenameTR3, string filenameTR4)
{
//filter tr3 based on top 1000 from tr4 (the trick)
//load TR4 !!! NOTE: trick changed from tr4 to 3 because i didnt see any increase in % in real time.. + this line can be removed to speed up things.
Instances data;
if (GuiPreferences.Instance.CbPeekHigherTRsIGChecked)
{
GuiPreferences.Instance.setLog("using final dataset: " + filenameTR4);
data = WekaTrainingMethods.loadDataSetFile(filenameTR4); // peeking at a higher TR's IG values (trick)
}
else
{
GuiPreferences.Instance.setLog("using final dataset: " + filenameTR3);
data = WekaTrainingMethods.loadDataSetFile(filenameTR3); // no peeking (no trick)
}
//select 1000 IG values, serialize to file
WekaTrainingMethods.selectIGSerialize(ref data);
//load tr3
data = WekaTrainingMethods.loadDataSetFile(filenameTR3);
//filter top IG
data = WekaTrainingMethods.useRemoveFilter(data, Preferences.Instance.attsel.selectedAttributes(), true);
//save filtered tr3 to a file
WekaCommonFileOperation.SaveArff(data, GuiPreferences.Instance.WorkDirectory + filenameTR3 + "_filteredIG.arff");
WekaCommonFileOperation.SaveCSV(data, GuiPreferences.Instance.WorkDirectory + filenameTR3 + "_filteredIG_CSV.arff");
return(data);
}
//converts TR-based files from a directory to Arff
public static void ConvertToArff(string CsharpDirectory)
{
for (decimal i = GuiPreferences.Instance.NudExtractFromTR; i <= GuiPreferences.Instance.NudExtractToTR; i++)
{
//convert libsvm tr file to arff
if (WekaCommonFileOperation.ConvertLIBSVM2ARFF(CsharpDirectory + "TrainSet_" + i.ToString() + "th_vectors_scaledCS.libsvm", 204800))
{
GuiPreferences.Instance.setLog("Converted to ARFF: TrainSet_" + i.ToString() + "th_vectors_scaledCS.libsvm");
}
}
}
/// <summary>
/// saves ProblemOriginal to a file, separates into tr3 and tr4, normalizes, and converts the libsvm files into arff.
/// </summary>
public static void VectorizeAndNormalize(libSVM_ExtendedProblem problem)
{
string trainFileName = GuiPreferences.Instance.WorkDirectory /*+ GuiPreferences.Instance.FileName*/ + "TrainSet";
//todo add proper named to saved files, check if null is logical at all.
//if ((Preferences.Instance.ProblemOriginal.samples != null))
//{
problem.Save(trainFileName + ".libsvm", 80 * 80 * 32 + 1);
GuiPreferences.Instance.setLog("saved Original Problem LibSVM file: " + trainFileName + ".libsvm");
//}
//separate DS to 3rd and 4th TR
////example: ExecuteSelectKthVectorScript(@"TrainSet", @"H:\My_Dropbox\VERE\MRI_data\Tirosh\20120508.Rapid+NullClass.day2\4\rtp\");
KthExtractionManager.ExecuteSelectKthVectorScript(/*GuiPreferences.Instance.FileName +*/ "TrainSet", GuiPreferences.Instance.WorkDirectory);
//GuiPreferences.Instance.setLog("Created TR3, TR4, TR5, TR6 files (5+6 depends if added to the python script)");
//NORMALIZING all TR Files
NormalizationManager.ScaleTrFiles(GuiPreferences.Instance.WorkDirectory);
//CONVERTING all TR files
WekaCommonFileOperation.ConvertToArff(GuiPreferences.Instance.WorkDirectory);
}
/// <summary>
/// concatenates 3TR 4TR etc according to what is assigned in the gui.
/// </summary>
/// <param name="directoryList"></param>
public static void ConcatenateLibsvmVectorizedPerTR(List <string> directoryList)
{
for (decimal i = GuiPreferences.Instance.NudExtractFromTR; i <= GuiPreferences.Instance.NudExtractToTR; i++)
{
FileStream fileStream;
FileStream outputFileStream =
new FileStream(
GuiPreferences.Instance.WorkDirectory + "TrainSet_" + i.ToString() + "th_vectors_scaledCS.libsvm", FileMode.CreateNew, FileAccess.Write);
foreach (string directory in directoryList)
{
fileStream = new FileStream(directory + "TrainSet_" + i.ToString() + "th_vectors_scaledCS.libsvm", FileMode.Open, FileAccess.Read);
CopyStream(outputFileStream, fileStream);
fileStream.Close();
}
outputFileStream.Close();
//save concatenated tr to a file
if (WekaCommonFileOperation.ConvertLIBSVM2ARFF(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + i.ToString() + "th_vectors_scaledCS.libsvm", 204800))
{
GuiPreferences.Instance.setLog("Converted to ARFF: TrainSet_" + i.ToString() + "th_vectors_scaledCS.arff");
}
}
}
public static Instances WekaPipeline_Unprocessed(libSVM_ExtendedProblem _trialProblem)
{
//export to libsvm file
if (_trialProblem.samples == null)
{
GuiPreferences.Instance.setLog("Export Failed: Problem has no samples!");
return(null);
}
string trainFileName = GuiPreferences.Instance.WorkDirectory /*+ GuiPreferences.Instance.FileName*/ + "TrainSet";
//todo add proper named to saved files, check if null is logical at all.
if ((_trialProblem.samples != null))
{
_trialProblem.Save(trainFileName + ".libsvm");
GuiPreferences.Instance.setLog("saved Original Problem LibSVM file: " + trainFileName + ".libsvm");
}
//separate DS to 3rd and 4th TR
////example: ExecuteSelectKthVectorScript(@"TrainSet", @"H:\My_Dropbox\VERE\MRI_data\Tirosh\20120508.Rapid+NullClass.day2\4\rtp\");
KthExtractionManager.ExecuteSelectKthVectorScript(/*GuiPreferences.Instance.FileName +*/ "TrainSet", GuiPreferences.Instance.WorkDirectory);
GuiPreferences.Instance.setLog("Created TR3 & TR4 files");
//normalize 3rd and 4th TR files.
NormalizationManager.ScaleTrFiles(GuiPreferences.Instance.WorkDirectory);
GuiPreferences.Instance.setLog("Normalized TR3 & TR4 files");
//convert tr4 and tr3 to arff + REMOVE 204801 FAKE FEATURE, THAT WAS PLACES TO MAKE SURE WE GET 204800 FEATURES IN THE ARFF FILE.
if (WekaCommonFileOperation.ConvertLIBSVM2ARFF(GuiPreferences.Instance.WorkDirectory + "TrainSet_3th_vectors_scaledCS.libsvm", 204800))
{
GuiPreferences.Instance.setLog("Converted to ARFF: TrainSet_3th_vectors_scaledCS.libsvm");
}
if (WekaCommonFileOperation.ConvertLIBSVM2ARFF(GuiPreferences.Instance.WorkDirectory + "TrainSet_4th_vectors_scaledCS.libsvm", 204800))
{
GuiPreferences.Instance.setLog("Converted to ARFF: TrainSet_4th_vectors_scaledCS.libsvm");
}
//---------------------------------- filter tr3 based on top 1000 from tr4 (the trick) -----------------------------
//load TR4
ConverterUtils.DataSource source = new ConverterUtils.DataSource(GuiPreferences.Instance.WorkDirectory + "TrainSet_4th_vectors_scaledCS.libsvm.arff");
Instances data = source.getDataSet();
//assign last as index.
if (data.classIndex() == -1)
{
data.setClassIndex(data.numAttributes() - 1);
}
//if class not nominal, convert to
if (!data.classAttribute().isNominal())
{
var filter = new weka.filters.unsupervised.attribute.NumericToNominal();
filter.setOptions(weka.core.Utils.splitOptions("-R last"));
//filter.setAttributeIndices("last");
filter.setInputFormat(data);
data = Filter.useFilter(data, filter);
}
//run ig and get top 1000 or up to 1000 bigger than zero, from tr4
WekaTrainingMethods.useLowLevelInformationGainFeatureSelection(data);
TrainingTesting_SharedVariables._trainTopIGFeatures = Preferences.Instance.attsel.selectedAttributes();
//this should be done ONCE
Preferences.Instance.fastvector = RealTimeProcessing.CreateFastVector(TrainingTesting_SharedVariables._trainTopIGFeatures.Length);
GuiPreferences.Instance.setLog("created fast vector of length " + TrainingTesting_SharedVariables._trainTopIGFeatures.Length.ToString());
//serialize (save) topIG indices to file.
XMLSerializer.serializeArrayToFile(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS_filteredIG_indices.xml", TrainingTesting_SharedVariables._trainTopIGFeatures);
GuiPreferences.Instance.setLog("saved IG indices to a file (in the same order as IG gave it)");
//int [] _trainTopIGFeatures_loaded = DeserializeArrayToFile(GuiPreferences.Instance.WorkDirectory + "TrainSet_3th_vectors_scaledCS_filteredIG_indices.xml");
GuiPreferences.Instance.setLog(TrainingTesting_SharedVariables._trainTopIGFeatures.Length.ToString() + " features above zero value selected (including the Class feature)");
//load tr3
source = new ConverterUtils.DataSource(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS.libsvm.arff");
data = source.getDataSet();
//filter top IG
data = WekaTrainingMethods.useRemoveFilter(data, TrainingTesting_SharedVariables._trainTopIGFeatures, true);
//after filtering last feature needs to be the class
if (data.classIndex() == -1)
{
data.setClassIndex(data.numAttributes() - 1);
}
//save filtered to a file
WekaCommonFileOperation.SaveLIBSVM(data, GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS_filteredIG");
return(data);
}
/// <summary>
/// tests if iron python works.
/// </summary>
public void TestIronPython()
{
/*IronPythonCLS ir = new IronPythonCLS();
* var res = ir.ExecuteBusinessRules();
* GuiPreferences.Instance.setLog(res.ToString());*/
//ExecuteSelectKthVectorScript();
string CsharpFileName = @"TrainSet";
string CsharpDirectory = @"H:\My_Dropbox\VERE\MRI_data\Tirosh\20120508.Rapid+NullClass.day2\4\rtp\";
/*ExecuteSelectKthVectorScript(CsharpFileName, CsharpDirectory);
* svm_scale_java svmscale = new svm_scale_java();
*
* string commandLine = "-l 0 " +
* "-s " + CsharpDirectory + "TrainSet_3th_vectors_scale_paramcs.libsvm " +
* "-o " + CsharpDirectory + "TrainSet_3th_vectors_scaledcs.libsvm " +
* CsharpDirectory + "TrainSet_3th_vectors.libsvm";
*
* string[] commandArray = commandLine.Split(' ');
* svmscale.run(commandArray);
*
* commandLine = "-l 0 " +
* "-s " + CsharpDirectory + "TrainSet_4th_vectors_scale_paramcs.libsvm " +
* "-o " + CsharpDirectory + "TrainSet_4th_vectors_scaledcs.libsvm " +
* CsharpDirectory + "TrainSet_4th_vectors.libsvm";
* commandArray = commandLine.Split(' ');
* svmscale.run(commandArray);*/
////////////////////////WekaCommon.Main(null);
////////////////////////var source = new ConverterUtils.DataSource(CsharpDirectory + "TrainSet_3th_vectors_scaledCS.libsvm");
//convert tr4 and tr3 to arff
/*if (WekaCommonFileOperation.ConvertLIBSVM2ARFF(CsharpDirectory + "TrainSet_3th_vectors_scaledCS.libsvm"))
* GuiPreferences.Instance.setLog("Converted to ARFF: TrainSet_3th_vectors_scaledCS.libsvm");
* if (WekaCommonFileOperation.ConvertLIBSVM2ARFF(CsharpDirectory + "TrainSet_4th_vectors_scaledCS.libsvm"))
* GuiPreferences.Instance.setLog("Converted to ARFF: TrainSet_4th_vectors_scaledCS.libsvm");*/
//infogain on tr4 and get 1000 top features.
ConverterUtils.DataSource source = new ConverterUtils.DataSource(CsharpDirectory + "TrainSet_4th_vectors_scaledCS.libsvm.arff");
Instances data = source.getDataSet();
if (data.classIndex() == -1)
{
data.setClassIndex(data.numAttributes() - 1);
}
if (!data.classAttribute().isNominal())
{
var filter = new weka.filters.unsupervised.attribute.NumericToNominal();
filter.setOptions(weka.core.Utils.splitOptions("-R last"));
//filter.setAttributeIndices("last");
filter.setInputFormat(data);
data = Filter.useFilter(data, filter);
}
int[] topIGFeatures = Preferences.Instance.attsel.selectedAttributes();
//load tr3
source = new ConverterUtils.DataSource(CsharpDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS.libsvm.arff");
data = source.getDataSet();
int[] invertedTopIGFeatures = new int[data.numAttributes() - topIGFeatures.Length];
//alternative use of the filter,
var dict = topIGFeatures.ToDictionary(key => key, value => value);
int position = 0;
for (int feat = 0; feat < data.numAttributes(); feat++)
{
if (!dict.ContainsKey(feat))
{
invertedTopIGFeatures[position] = feat;
position++;
}
}
//filter top IG
//data = WekaCommonMethods.useRemoveFilter(data, topIGFeatures, true);
data = WekaTrainingMethods.useRemoveFilter(data, invertedTopIGFeatures, false);
WekaCommonFileOperation.SaveArff(data, CsharpDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS_filteredIG2.libsvm1.arff");
//train
/*weka.classifiers.functions.SMO smo = new SMO();
* smo.setOptions(weka.core.Utils.splitOptions(" -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.PolyKernel -C 250007 -E 1.0\""));
* if (data.classIndex() == -1)
* data.setClassIndex(data.numAttributes() - 1);
*
*
*
* smo.buildClassifier(data);
*
*
* //test on self should get 100%
* weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
* eval.evaluateModel(smo, data);
* GuiPreferences.Instance.setLog(eval.toSummaryString("\nResults\n======\n", false));
*
* //save model serialize model
* weka.core.SerializationHelper.write(CsharpDirectory + "TrainSet_3th_vectors_scaledCS_filteredIG.libsvm.arff.model", smo);
*
* //load model deserialize model
* smo = (weka.classifiers.functions.SMO)weka.core.SerializationHelper.read(CsharpDirectory + "TrainSet_3th_vectors_scaledCS_filteredIG.libsvm.arff.model");
*
* //test loaded model
* eval = new weka.classifiers.Evaluation(data);
* eval.evaluateModel(smo, data);
* GuiPreferences.Instance.setLog(eval.toSummaryString("\nResults\n======\n", false));*/
//display top IG.
//PublicMethods.plotBrainDicomViewer();
if (Preferences.Instance.attsel == null)
{
GuiPreferences.Instance.setLog("there are no ranked IG attributes or selected attr, continuing but please fix this possible bug.");
}
string dicomDir = CsharpDirectory;
dicomDir = dicomDir.Substring(0, dicomDir.Length - 4) + @"master\";
string[] files = System.IO.Directory.GetFiles(dicomDir, "*.dcm");
string firstFile = files[0].Substring(files[0].LastIndexOf(@"\") + 1);
bool thresholdOrVoxelAmount;
if (GuiPreferences.Instance.IgSelectionType == IGType.Threshold)
{
thresholdOrVoxelAmount = true;
}
else
{
thresholdOrVoxelAmount = false;
}
Form plotForm = new DicomImageViewer.MainForm(dicomDir + firstFile, firstFile, Preferences.Instance.attsel.rankedAttributes(),
Convert.ToDouble(GuiPreferences.Instance.NudIGThreshold),
Convert.ToInt32(GuiPreferences.Instance.NudIGVoxelAmount),
thresholdOrVoxelAmount, GuiPreferences.Instance.WorkDirectory + "brain");
plotForm.StartPosition = FormStartPosition.CenterParent;
plotForm.ShowDialog();
plotForm.Close();
}
/// <summary>
/// WAS A BIG BUTTON: quickloads a range of commands to test the weka pipeline
/// loads protocol and data, processes for SMO, trick, IG, etc.. has java/python intergration
/// 1. Trick: QuickLoad, Export to Libsvm, separate to TRs files in libsvm, convert TR-3 and TR-4 to arff, use TR4 + IG to get 1000 features, filter TR-3 based on features from TR-4, save result to libsvm format, train using LibSvm (grid?), save model, test on training data - must get 100%, display 1000 on viewport
/// 2. No Trick: QuickLoad, Export to Libsvm, separate to TRs files in libsvm, convert TR-3 to arff, filter TR-3 based on 1000 top IG, save result to libsvm format, train using LibSvm (grid?), save model, test on training data - must get 100%, display 1000 on viewport
/// </summary>
/// <param name="from"></param>
/// <returns></returns>
public bool QuickProcessWekaPipeline(int from)
{
// --- from this point the loading data phaze begins --- //
// tirosh null movement
/*
* GuiPreferences.Instance.WorkDirectory = @"H:\My_Dropbox\VERE\MRI_data\Tirosh\20120508.Rapid+NullClass.day2\4\rtp\";
* GuiPreferences.Instance.FileName = "tirosh-";
* GuiPreferences.Instance.FileType = OriBrainLearnerCore.dataType.rawValue;
* GuiPreferences.Instance.ProtocolFile = @"H:\My_Dropbox\VERE\MRI_data\Tirosh\20120705.NullClass1_zbaseline.prt";
*/
// magali classification
/*GuiPreferences.Instance.WorkDirectory = @"H:\My_Dropbox\VERE\Experiment1\Kozin_Magali\20121231.movement.3.imagery.1\18-classification.movement\rtp\";
* GuiPreferences.Instance.FileName = "tirosh-";
* GuiPreferences.Instance.FileType = OriBrainLearnerCore.dataType.rawValue;
* GuiPreferences.Instance.ProtocolFile = @"H:\My_Dropbox\VERE\MRI_data\Tirosh\20113110.short.5th.exp.hands.legs.zscore.thought_LRF.prt";
* */
/// moshe sherf classification, 4 aggregated to test on 1.
//GuiPreferences.Instance.WorkDirectory = @"H:\My_Dropbox\VERE\Experiment1\Sherf_Moshe\20121010.movement.1\1234-5\";
//GuiPreferences.Instance.ProtocolFile = @"H:\My_Dropbox\VERE\Experiment1\Sherf_Moshe\20121010.movement.1\1234-5\20113110.short.5th.exp.hands.legs.zscore.thought_LRF.prt";
string[] directoryList =
{
@"H:\My_Dropbox\VERE\Experiment1\Sherf_Moshe\20121010.movement.1\05_classification\rtp\",
@"H:\My_Dropbox\VERE\Experiment1\Sherf_Moshe\20121010.movement.1\07_classification\rtp\",
@"H:\My_Dropbox\VERE\Experiment1\Sherf_Moshe\20121010.movement.1\09_classification\rtp\",
@"H:\My_Dropbox\VERE\Experiment1\Sherf_Moshe\20121010.movement.1\11_classification\rtp\"
};
GuiPreferences.Instance.ProtocolFile = @"H:\My_Dropbox\VERE\MRI_data\Tirosh\20113110.short.5th.exp.hands.legs.zscore.thought_LRF.prt";
//GuiPreferences.Instance.WorkDirectory = @"H:\My_Dropbox\VERE\Experiment1\Sherf_Moshe\20121010.movement.1\15_classification\rtp\";
GuiPreferences.Instance.FileName = "tirosh-";
GuiPreferences.Instance.FileType = OriBrainLearnerCore.DataType.rawValue;
//read prot file
Preferences.Instance.prot = new ProtocolManager();
double[][] topIGFeatures = {};
foreach (string directory in directoryList)
{
GuiPreferences.Instance.WorkDirectory = directory;
//delete all files that are going to be created, in order to prevent anomaly vectors.
string[] deleteFiles =
{
"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"
};
foreach (string fileName in deleteFiles)
{
FileDirectoryOperations.DeleteFile(GuiPreferences.Instance.WorkDirectory + fileName);
}
//get all files in the path with this extention
GuiManager.getFilePaths("*.vdat");
//update certain info
GuiManager.updateFilePaths();
//assigned after we know what to assign from the protocol
//PublicMethods.setClassesLabels();
GuiPreferences.Instance.CmbClass1Selected = 1; //left
GuiPreferences.Instance.CmbClass2Selected = 2; //right
//NEED TO ADD A VARIABLE FOR EVERY OPTION IN THE GUI. RAW VALUES. UNPROCESSED. MULTI CLASS. CROSS VALD, GRID, FOLDS, ETC...
//and for every button a function!
//for the training set
GuiPreferences.Instance.FromTR = from; // 264;
//for the test set
//GuiPreferences.Instance.FromTR = 46;
//GuiPreferences.Instance.ToTR = 100;// 264;
//finally load
TrainingTesting_SharedVariables.binary.loadRawData();
topIGFeatures = new double[][] {};
Instances data;
//files are loaded,thresholded,vectorized,normalized. false means that IG and training are not done here.
if (!Training_MultiRunProcessing.ProcessSingleRunOffline(ref topIGFeatures, Preferences.Instance.ProblemOriginal))
{
GuiPreferences.Instance.setLog("Samples are empty");
}
//++grab findl vectors and concat them
// grab min max values for saving the median.
}
//create a dir that holds the final DS in C:\
GuiPreferences.Instance.WorkDirectory = @"C:\FinalData_" + DateTime.Now.ToLongTimeString().Replace(':', '-');
GuiPreferences.Instance.setLog(@"Creating Final Directory in: " + GuiPreferences.Instance.WorkDirectory);
FileDirectoryOperations.CreateDirectory(GuiPreferences.Instance.WorkDirectory);
GuiPreferences.Instance.WorkDirectory += @"\";
//concatenate libsvm normalized and vectorized files
FileStream fileStream;
FileStream outputFileStream = new FileStream(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS.libsvm", FileMode.CreateNew, FileAccess.Write);
foreach (string directory in directoryList)
{
fileStream = new FileStream(directory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS.libsvm", FileMode.Open, FileAccess.Read);
Training_MultiRunProcessing.CopyStream(outputFileStream, fileStream);
fileStream.Close();
}
outputFileStream.Close();
//save concatenated tr3 to a file
if (WekaCommonFileOperation.ConvertLIBSVM2ARFF(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS.libsvm", 204800))
{
GuiPreferences.Instance.setLog("Converted to ARFF: TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS.arff");
}
double[][] feature_max = new double[directoryList.Length][];
double[][] feature_min = new double[directoryList.Length][];
int i = 0;
int max_index = -1;
foreach (string directory in directoryList)
{
TrainingTesting_SharedVariables._svmscaleTraining.getConfigFileMinMaxValues(
directory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scale_paramCS.libsvm",
ref feature_max[i], ref feature_min[i], ref max_index);
i++;
}
//calculate Mean + save new min/max param to C:\
double[] finalFeature_max = new double[feature_max[0].Length];
double[] finalFeature_min = new double[feature_max[0].Length];
//create a list with enough values for the runs, in order to calculate the median
var values_max = new List <double>(feature_max.Length);
var values_min = new List <double>(feature_max.Length);
for (int k = 0; k < feature_max.Length; k++)
{
//init zeros
values_max.Add(0);
values_min.Add(0);
}
for (int j = 0; j < feature_max[0].Length; j++)
{
for (int k = 0; k < feature_max.Length; k++)
{
values_max[k] = feature_max[k][j];
values_min[k] = feature_min[k][j];
}
//finalFeature_max[j] = GetMedian(values_max);
//finalFeature_min[j] = GetMedian(values_min);
finalFeature_max[j] = values_max.Max();
finalFeature_min[j] = values_min.Min();
}
TrainingTesting_SharedVariables._svmscaleTraining.saveConfigMinMax_CSharp(GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scale_paramCS.libsvm", finalFeature_min, finalFeature_max, 204801, 0.0f, 1.0f);
//todo check max index in file,
//todo check if needs to remove 204801 from it so it doesnt effect the class.
double[][] FinaltopIGFeatures = { };
Instances finalData = Training_MultiRunProcessing.ConcatenationPipeLine("TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_scaledCS.libsvm.arff", "TrainSet_4th_vectors_scaledCS.libsvm.arff");
WekaTrainingMethods.TrainSMO(finalData);
//save median param file
//display top IG on dicom view
string dicomDir = directoryList[0];
dicomDir = dicomDir.Substring(0, dicomDir.Length - 4) + @"master\";
string[] files = System.IO.Directory.GetFiles(dicomDir, "*.dcm");
string firstFile = files[0].Substring(files[0].LastIndexOf(@"\") + 1);
bool thresholdOrVoxelAmount;
if (GuiPreferences.Instance.IgSelectionType == IGType.Threshold)
{
thresholdOrVoxelAmount = true;
}
else
{
thresholdOrVoxelAmount = false;
}
//NOTE final top may be empty, please remember that the IG are not at preferences.instance.attsel.selectedattributes or rankedattributes.
Form plotForm = new DicomImageViewer.MainForm(dicomDir + firstFile, firstFile, FinaltopIGFeatures,
Convert.ToDouble(GuiPreferences.Instance.NudIGThreshold),
Convert.ToInt32(GuiPreferences.Instance.NudIGVoxelAmount),
thresholdOrVoxelAmount,
GuiPreferences.Instance.WorkDirectory + "brain");
plotForm.StartPosition = FormStartPosition.CenterParent;
plotForm.ShowDialog();
plotForm.Close();
return(true);
}