/// <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); }
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); }