//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); }
/// <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(); }