public static void ProcessMultiRuns(List <string> dirList)
{
TrainingTesting_SharedVariables._trialProblem = null;
if (Preferences.Instance.ProblemOriginalMulti == null)
{
GuiPreferences.Instance.setLog("Processing Failed: Problem null!");
return;
}
if (Preferences.Instance.ProblemOriginalMulti.Count <= 0)
{
GuiPreferences.Instance.setLog("Processing Failed: Problem List is Empty");
return;
}
for (int i = 0; i < Preferences.Instance.ProblemOriginalMulti.Count; i++)
{
if (Preferences.Instance.ProblemOriginalMulti[i].samples == null)
{
GuiPreferences.Instance.setLog("Processing problem " + i.ToString() + " Failed: samples empty!");
return;
}
}
Preferences.Instance.dirList = dirList;
//we only clear all memory in offline analysis, and once in the first realtime processing vector - which is done externally in testdll
if (!Preferences.Instance.RealTimeTestingSingleVector)
{
GuiPreferences.Instance.setLog("Starting Processing.");
clearProblem();
clearSVM();
JobManager.clearJob();
GC.Collect();
}
//if unprocessed is UNchecked then process all jobs.
if (!GuiPreferences.Instance.UnprocessedChecked)
{
//NOTE: this is commented out because this is not recoded for multi dirs yet. and not needed in the current pipeline.
/*
* //we only need to add jobs at the beginning and do it once.
* if ((!Preferences.Instance.RealTimeTestingSingleVector) || (Preferences.Instance.RealTimeTestingSingleVector && Preferences.Instance.ProblemOriginal.samples[1] == null))
* {
* addFeatureJobs();
* calculatePreviousKValues();
* }
*
* //pre for all jobs, future support only
* preProcessFeatureJobs();
* //here the pre/proc/post is done for every job.
* processFeatureJobs();
* //post for all jobs, future support only
* postProcessFeatureJobs();
*
*
* //TODO: do this each time we get out of processing, or do this just when "Testing" is activated". seems harmless to do it all the time.
* _trialProblem = Preferences.Instance.ProblemFinal;
*
* //we only need to assign to the final problem when we are in offline mode
* //in real time we only need to send the classification result and sample weight.
* if (!Preferences.Instance.RealTimeTestingSingleVector)
* {
* GuiPreferences.Instance.setLog("Processing Finished!");
* }
* else
* {
* if (Preferences.Instance.currentUDPVector >= Preferences.Instance.maxKvectorsToWaitBeforeClassification)
* {
* Preferences.Instance.currentClassifiedVector += 1;
* //allow to predict only if sample is not labeled as baseline - this happens in the beginning of a experiment session
* if (Preferences.Instance.ProblemFinal.labels[Preferences.Instance.currentClassifiedVector - 1] != 1.0)
* {
* //allow to predict
* double pred = 1.0;
* GuiPreferences.Instance.setLog("Single Vector Processing Finished..");
* pred = Preferences.Instance.svmWrapper.GetAccuracyFromTestSingleSample(Preferences.Instance.ProblemFinal, Preferences.Instance.svmModel);
* transmitPrediction(pred);
* }
* }
* }*/
}
else //process without PROCESSING JOBS, the original problem
{
//real-time testing
if (Preferences.Instance.RealTimeTestingSingleVector)
{
//NOTE: minmax for testing loading moved to form1.cs before pipe async creation.
//NOTE: minmax for testing loading moved to form1.cs before pipe async creation.
//NOTE: minmax for testing loading moved to form1.cs before pipe async creation.
//NOTE: minmax for testing loading moved to form1.cs before pipe async creation.
//NOTE: minmax for testing loading moved to form1.cs before pipe async creation.
//NOTE: minmax for testing loading moved to form1.cs before pipe async creation.
//if you need it here please put it back.
if (GuiPreferences.Instance.CbSMOChecked) //real time testing using WEKA AND SMO
{
RealTimeProcessing.ExecuteRealtimeTest(TrainingTesting_SharedVariables.smo);
Preferences.Instance.currentClassifiedVector += 1;
//test on self should get 100%
//testingEval = new weka.classifiers.Evaluation(data);
//testingEval.evaluateModel(smo, data);
//printWekaResults(testingEval.toSummaryString("\nResults\n======\n", false));
//GuiPreferences.Instance.setLog("RealTime SMO Model Testing on current vector");
//pred = (double)testingEval.predictions().elementAt(0);
//transmitPrediction(pred);
//GuiPreferences.Instance.setLog("Single Vector Processing Finished..");
}
else if (GuiPreferences.Instance.CbSVMChecked) //weka + SVM, pipeline unfinished.
{
//NOTE: commented out because this code is not yet finished to support weka and SVM (not SMO)
/*
* //if (Preferences.Instance.currentUDPVector >= Preferences.Instance.maxKvectorsToWaitBeforeClassification)
* {
* Preferences.Instance.currentClassifiedVector += 1;
* //allow to predict only if sample is not labeled as baseline - this happens in the beginning of a experiment session
* //if (Preferences.Instance.ProblemFinal.labels[Preferences.Instance.currentClassifiedVector - 1] != 1.0)
* {
* //allow to predict
* double pred = 1.0;
* pred = Preferences.Instance.svmWrapper.GetAccuracyFromTestSingleSample(Preferences.Instance.ProblemFinal, Preferences.Instance.svmModel);
* transmitPrediction(pred);
* GuiPreferences.Instance.setLog("Single Vector Processing Finished..");
* }
* }*/
}
}
else // TRAINING W/ WEKA + SMO
{
//go over all available and loaded RUNS and run the weka pipeline on them
for (int i = 0; i < Preferences.Instance.ProblemOriginalMulti.Count; i++)
{
TrainingTesting_SharedVariables._trialProblem = Preferences.Instance.ProblemOriginalMulti[i];
Preferences.Instance.currentProcessedRun = i; //0-based index (run 0 is the first run)
GuiPreferences.Instance.WorkDirectory = dirList[i];
TrainingTesting_SharedVariables.topIGFeatures = new double[][] { };
Instances data;
bool createFiles = false;
//if overwrite then delete all non relevant files in the directory (arff/libsvm)
if (GuiPreferences.Instance.CbOverwriteProcessedFilesChecked)
{
foreach (string fileName in Preferences.Instance.deleteFiles)
{
FileDirectoryOperations.DeleteFile(GuiPreferences.Instance.WorkDirectory + fileName);
}
}
//if overwrite we create new files (after the above deletion)
if (GuiPreferences.Instance.CbOverwriteProcessedFilesChecked)
{
createFiles = true;
}
else // we check to see if the files are there and only if no we create.
{
// if files are not here its safe to create them from scratch, if not then we pass.
if (!(File.Exists(GuiPreferences.Instance.WorkDirectory + "TrainSet.libsvm") &&
File.Exists(GuiPreferences.Instance.WorkDirectory + "TrainSet_3th_vectors.libsvm") &&
File.Exists(GuiPreferences.Instance.WorkDirectory + "TrainSet_3th_vectors_scale_paramCS.libsvm") &&
File.Exists(GuiPreferences.Instance.WorkDirectory + "TrainSet_3th_vectors_scaledCS.libsvm") &&
File.Exists(GuiPreferences.Instance.WorkDirectory + "TrainSet_3th_vectors_scaledCS.libsvm.arff") &&
File.Exists(GuiPreferences.Instance.WorkDirectory + "TrainSet_4th_vectors.libsvm") &&
File.Exists(GuiPreferences.Instance.WorkDirectory + "TrainSet_4th_vectors_scale_paramCS.libsvm") &&
File.Exists(GuiPreferences.Instance.WorkDirectory + "TrainSet_4th_vectors_scaledCS.libsvm") &&
File.Exists(GuiPreferences.Instance.WorkDirectory + "TrainSet_4th_vectors_scaledCS.libsvm.arff")
))
{
createFiles = true;
}
}
if (createFiles)
{
//files are loaded,thresholded,vectorized (arff),normalized. false means that IG and training are not done here.
if (!ProcessSingleRunOffline(ref TrainingTesting_SharedVariables.topIGFeatures, TrainingTesting_SharedVariables._trialProblem))
{
GuiPreferences.Instance.setLog("Samples are empty");
}
Preferences.Instance.TrainingBaselineMediansRunningWindow[i].clearMatrixMemory();
}
else
{
GuiPreferences.Instance.setLog("Skipped Processing for directory: " +
GuiPreferences.Instance.WorkDirectory);
}
}
//Concatenate the data
WekaProcessingPipelineForMultiRuns(dirList);
if (GuiPreferences.Instance.CbSVMChecked)
{
Preferences.Instance.ProblemOriginal = new libSVM_ExtendedProblem();
//here we need to load the filtered DS
//Preferences.Instance.svmWrapper.
//GuiPreferences.Instance.WorkDirectory + "TrainSet_3th_vectors_scaledCS_filteredIG.libsvm";
}
TrainingTesting_SharedVariables._trialProblemMulti = Preferences.Instance.ProblemOriginalMulti;
GuiPreferences.Instance.setLog("Finished Processing.");
Sound.beep(Sound.beepType.Asterisk);
//System.Media.SystemSounds.Beep.Play();
}
}
}
/// <summary>
/// copying files to finalData, processing, creating config files/minmax/etc and converting to arff.
/// </summary>
/// <param name="_trialProblem"></param>
/// <returns></returns>
public static void WekaProcessingPipelineForMultiRuns(List <string> directoryList)
{
//create a dir that holds the final DS in C:\
//GuiPreferences.Instance.WorkDirectory = @"C:\FinalData_" + DateTime.Now.ToLongTimeString().Replace(':', '-');
GuiPreferences.Instance.WorkDirectory = @"C:\FinalData_" +
"TR" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "_" +
GuiPreferences.Instance.NormalizedType.ToString() +
GuiPreferences.Instance.NudMovingWindow.ToString() + "_";
if (GuiPreferences.Instance.IgSelectionType == IGType.Threshold)
{
GuiPreferences.Instance.WorkDirectory = GuiPreferences.Instance.WorkDirectory + "IG_Thr" + GuiPreferences.Instance.NudIGThreshold.ToString() + "_";
}
else if (GuiPreferences.Instance.IgSelectionType == IGType.Voxels)
{
GuiPreferences.Instance.WorkDirectory = GuiPreferences.Instance.WorkDirectory + "IG_Vox" + GuiPreferences.Instance.NudIGVoxelAmount.ToString() + "_";
}
//GuiPreferences.Instance.ProtocolFile + "_" +
GuiPreferences.Instance.WorkDirectory = GuiPreferences.Instance.WorkDirectory + Preferences.Instance.events.EventListLastTr.ToString();
if (GuiPreferences.Instance.CbPeekHigherTRsIGChecked == true)
{
GuiPreferences.Instance.WorkDirectory = GuiPreferences.Instance.WorkDirectory + "_Peeking";
}
GuiPreferences.Instance.setLog(@"Creating Final Directory in: " + GuiPreferences.Instance.WorkDirectory);
FileDirectoryOperations.CreateDirectory(GuiPreferences.Instance.WorkDirectory);
GuiPreferences.Instance.WorkDirectory += @"\";
ConcatenateLibsvmVectorizedPerTR(directoryList);
//NOTE: min/max values are taken from the param files of each run. which means that you get N max values and N min values.
//if a median is needed, we have to go over all columns for each feature and concat all values to a huge list that contains all N files and only then do the median.
//the median code here is not conceptually not right. as we take median out of 4 max values or min values. its a bad way to calculate a median.
//median code should be done in the normalization class and the code here should reflect the concept behind it.
//NOTE2: this code goes over N max and min values from each param file and get the MAX(maxes) and MIN(mins).
//these max and mins are saved to be used as the initial min/max values for the testing stage.
double[][] feature_max = new double[directoryList.Count][];
double[][] feature_min = new double[directoryList.Count][];
int i = 0;
int max_index = -1;
foreach (string directory in directoryList)
{
TrainingTesting_SharedVariables._svmscaleTraining.getConfigFileMinMaxValues(
//use previous tr min/maxes for median consideration
//directory + "TrainSet_" + (GuiPreferences.Instance.NudClassifyUsingTR - 1).ToString() + "th_vectors_scale_paramCS.libsvm",
//use current tr for min/max median
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];
double[] finalFeature_medianMax = new double[feature_max[0].Length];
double[] finalFeature_medianMin = new double[feature_max[0].Length];
//create a TEMP list with enough values for the runs, in order to calculate the MIN/MAX median
var values_max = new List <double>(feature_max.Length);
var values_min = new List <double>(feature_max.Length);
var values_medianMax = new List <double>(feature_max.Length);
var values_medianMin = 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);
values_medianMax.Add(0);
values_medianMin.Add(0);
}
for (int j = 0; j < feature_max[0].Length; j++)
{
for (int k = 0; k < feature_max.Length; k++)
{
// for each feature group all run-based values together
values_max[k] = feature_max[k][j];
values_min[k] = feature_min[k][j];
}
//get median of maxes/mins optional here
/*finalFeature_max[j] = GetMedian(values_max);
* finalFeature_min[j] = GetMedian(values_min); */
//get Max and Min here.
finalFeature_max[j] = values_max.Max();
finalFeature_min[j] = values_min.Min();
}
//save max/median param file
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);
int lowRangeMinus = 0;
int highRangeMinus = 0;
//calculate median ranges. from baseline.
for (int j = 1; j < feature_max[0].Length - 1; j++)
{
for (int k = 0; k < feature_max.Length; k++)
{
//min = test baseline median - 2nd smallest (training baseline median - training min)
//max = test baseline median + 2nd smallest ( training max - training baseline median)
values_medianMax[k] = feature_max[k][j] - Preferences.Instance.TrainingBaselineMedians[k].median[j - 1];
values_medianMin[k] = Preferences.Instance.TrainingBaselineMedians[k].median[j - 1] - feature_min[k][j];
}
double chosenLowRange;
double chosenHighRange;
chosenLowRange = getSecondLowest(values_medianMin);
chosenHighRange = getSecondLowest(values_medianMax);
//chosenLowRange = getSecondHighest(values_medianMin);
//chosenHighRange = getSecondHighest(values_medianMax);
//chosenLowRange = StatisticsFeatures.GetMedian(values_medianMin);
//chosenHighRange = StatisticsFeatures.GetMedian(values_medianMax);
finalFeature_medianMin[j] = chosenLowRange;
finalFeature_medianMax[j] = chosenHighRange;
if (chosenLowRange <= 0)
{
lowRangeMinus++;
}
if (chosenHighRange <= 0)
{
highRangeMinus++;
}
}
//////////////////////////////////////////////////////////
//for verification save ranges (this file it not to be used!)
TrainingTesting_SharedVariables._svmscaleTraining.saveConfigMinMax_CSharp(
GuiPreferences.Instance.WorkDirectory + "TrainSet_" + GuiPreferences.Instance.NudClassifyUsingTR.ToString() +
"th_vectors_scale_MedianRangeFromBaseline.params.txt", finalFeature_medianMin, finalFeature_medianMax, 204801, 0.0f, 1.0f);
Preferences.Instance.medianRange = new MinMax(finalFeature_medianMin, finalFeature_medianMax);
XMLSerializer.serializeArrayToFile <MinMax>(GuiPreferences.Instance.WorkDirectory + "TrainSet_" +
GuiPreferences.Instance.NudClassifyUsingTR.ToString() + "th_vectors_MedianRangeFromBaseline.xml", Preferences.Instance.medianRange);
GuiPreferences.Instance.setLog("out of 204K features, low range <= 0: " + lowRangeMinus.ToString() + " && high range <= 0: " + highRangeMinus.ToString());
}
/// <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);
}
