Examples of ResultsTable

bg.smoc.model.web.ResultsTable
ij.measure.ResultsTable
This is a table for storing measurement results as columns of numeric values. Call the static ResultsTable.getResultsTable() method to get a reference to the ResultsTable used by the Analyze/Measure command. @see ij.plugin.filter.Analyzer#getResultsTable
jmt.gui.exact.table.ResultsTable
A "reduced" ExactTable for showing unmodifiable results
org.data2semantics.exp.utils.ResultsTable

Examples of org.data2semantics.exp.utils.ResultsTable


    int[] depths = {1, 2, 3};
    int[] iterations = {0, 2, 4, 6};
    dataset = new RDFFileDataSet(dataDir, RDFFormat.NTRIPLES);


    ResultsTable resTable = new ResultsTable();
    resTable.setManWU(0.05);


    boolean inference = false;
          


    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());


    for (double frac : fractions) {
      createGeoDataSet((int)(1000 * frac), frac, seed, "http://data.bgs.ac.uk/ref/Lexicon/hasTheme");
      List<Double> target = EvaluationUtils.createTarget(labels);
      
      LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
      linParms.setDoCrossValidation(false);
      linParms.setNumFolds(0);
      linParms.setSplitFraction((float) 0.7);
      
      Map<Double, Double> counts = EvaluationUtils.computeClassCounts(target);
      int[] wLabels = new int[counts.size()];
      double[] weights = new double[counts.size()];


      for (double label : counts.keySet()) {
        wLabels[(int) label - 1] = (int) label;
        weights[(int) label - 1] = 1 / counts.get(label);
      }
      linParms.setWeightLabels(wLabels);
      linParms.setWeights(weights);
      


      System.out.println("Running fraction: " + frac);


      
      for (int i : depths) {      
        for (int it : iterations) {
          resTable.newRow("");  


          KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(new RDFWLSubTreeKernel(it, i, inference, true), seeds, linParms, dataset, instances, target, blackList, evalFuncs);
        
          System.out.println("Running WL RDF: " + i + " " + it);
          exp.run();


          for (Result res : exp.getResults()) {
            resTable.addResult(res);
          }  
        }
      }
      


      for (int i : depths) {      
        //for (int it : iterations) {
          resTable.newRow("");  


          //KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(new RDFWLSubTreeKernel(it, i, inference, true), seeds, linParms, dataset, instances, target, blackList, evalFuncs);


          KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(new RDFIntersectionTreeEdgeVertexPathKernel(i, false, inference, true), seeds, linParms, dataset, instances, target, blackList, evalFuncs);


          
          System.out.println("Running EVP: " + i);
          //System.out.println("Running WL RDF: " + i + " " + it);
          exp.run();


          for (Result res : exp.getResults()) {
            resTable.addResult(res);
          }  
        }
      //}
    }


    saveResults(resTable, "geo_theme_" + seed + ".ser");


    resTable.addCompResults(resTable.getBestResults());
    System.out.println(resTable);
    saveResults(resTable.toString(), "geo_theme_full_" + seed + ".txt");
  }

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Examples of org.data2semantics.exp.utils.ResultsTable

    evalFuncs.add(new F1());








    ResultsTable resTable = new ResultsTable();
    resTable.setManWU(0.05);
    resTable.setDigits(3);


    boolean inference = true;
    for (int d : depths) {
      resTable.newRow("");
      for (int it : iterations) {


        List<List<Result>> res = new ArrayList<List<Result>>();
        for (long seed : seeds) {
          long[] s2 = {seed};


          loadDataSet(fraction, seed);


          List<Double> targets = EvaluationUtils.createTarget(labels);


          LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
          linParms.setEvalFunction(new Accuracy());
          linParms.setDoCrossValidation(false);
          linParms.setSplitFraction((float) 0.8);
          linParms.setEps(0.1);


          Map<Double, Double> counts = EvaluationUtils.computeClassCounts(targets);
          int[] wLabels = new int[counts.size()];
          double[] weights = new double[counts.size()];


          for (double label : counts.keySet()) {
            wLabels[(int) label - 1] = (int) label;
            weights[(int) label - 1] = 1 / counts.get(label);
          }
          linParms.setWeightLabels(wLabels);
          linParms.setWeights(weights);




          RDFLinearKernelExperiment exp = new RDFLinearKernelExperiment(new RDFWLSubTreeKernel(it, d, inference, true), s2, linParms, dataset, instances, targets, blackList, evalFuncs);
          res.add(exp.getResults());


          System.out.println("Running WL RDF: " + d + " " + it);
          exp.run();
        }
        for (Result res2 : Result.mergeResultLists(res)) {
          resTable.addResult(res2);
        }
      }
    }
    resTable.addCompResults(resTable.getBestResults());
    System.out.println(resTable);




  }

View Full Code Here

Examples of org.data2semantics.exp.utils.ResultsTable


    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());


    ResultsTable resTable = new ResultsTable();
    resTable.setManWU(0.05);








    for (int i : depths) {      
      for (int it : iterations) {
        resTable.newRow("");


        LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
        KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(new RDFWLSubTreeKernel(it, i, inference, true), seeds, linParms, dataset, instances, target, blackList, evalFuncs);


        System.out.println("Running WL RDF: " + i + " " + it);
        exp.run();


        for (Result res : exp.getResults()) {
          resTable.addResult(res);
        }  
      }
    }
    System.out.println(resTable);


    for (int i : depths) {      
      resTable.newRow("");


      LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
      KernelExperiment<RDFFeatureVectorKernel> exp = new RDFLinearKernelExperiment(new RDFIntersectionTreeEdgeVertexPathKernel(i, false, inference, true), seeds, linParms, dataset, instances, target, blackList, evalFuncs);


      System.out.println("Running EVP: " + i);
      exp.run();


      for (Result res : exp.getResults()) {
        resTable.addResult(res);
      }  
    }
    System.out.println(resTable);
    
    for (int i : depths) {      
      resTable.newRow("");


      LibSVMParameters svmParms = new LibSVMParameters(LibSVMParameters.C_SVC, cs);
    
      KernelExperiment<RDFGraphKernel> exp = new RDFOldKernelExperiment(new RDFIntersectionSubTreeKernel(i, 1, inference, true), seeds, svmParms, dataset, instances, labels, blackList);


      System.out.println("Running IST: " + i);
      exp.run();


      for (Result res : exp.getResults()) {
        resTable.addResult(res);
      }  
    }




    resTable.addCompResults(resTable.getBestResults());
    System.out.println(resTable);
  }

View Full Code Here

Examples of org.data2semantics.exp.utils.ResultsTable

    Experimenter experimenter = new Experimenter(NUMBER_OF_PROC);
    Thread expT = new Thread(experimenter);
    expT.setDaemon(true);
    expT.start();


    ResultsTable resultsWL = new ResultsTable();
    ResultsTable resultsSTF = new ResultsTable();
    ResultsTable resultsSTP = new ResultsTable();
    ResultsTable resultsIGW = new ResultsTable();
    ResultsTable resultsIGP = new ResultsTable();


    ResultsTable resultsWLadd = new ResultsTable();
    ResultsTable resultsSTFadd = new ResultsTable();
    ResultsTable resultsSTPadd = new ResultsTable();
    ResultsTable resultsIGWadd = new ResultsTable();
    ResultsTable resultsIGPadd = new ResultsTable();


    
    /**  
     * FIRST EXPERIMENT, STANDARD SETTINGS
     * 
     */    
    List<PropertyPredictionDataSetParameters> dataSetsParams = new ArrayList<PropertyPredictionDataSetParameters>();


    
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 1, false, false));
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 2, false, false));
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 1, false, true));
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 2, false, true));
    
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 1, true, false));
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 2, true, false));
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 1, true, true));
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 2, true, true));
    
    /*
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetB, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 1, false, false));
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetB, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 2, false, false));
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetB, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 1, false, true));
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetB, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 2, false, true));
    
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetB, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 1, true, false));
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetB, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 2, true, false));
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetB, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 1, true, true));
    dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetB, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 2, true, true));
    //*/    
  


    try {
      
      
  
  
      for (PropertyPredictionDataSetParameters params : dataSetsParams) {
        dataset = DataSetFactory.createPropertyPredictionDataSet(params);
        dataset.removeSmallClasses(5);
        dataset.removeVertexAndEdgeLabels();


        resultsWL.newRow(dataset.getLabel() + " WLSubTreeKernel");
        for (int i = 0; i < 3; i++) {
          if (experimenter.hasSpace()) {  
            int fileId = (int) (Math.random() * 100000000);  
            File file = new File(DATA_DIR + fileId + "_" + "WL" + "_" + i + ".txt");
            WLSubTreeKernel kernel = new WLSubTreeKernel(i, true);
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), kernel, seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsWL.addResult(exp.getResults().getAccuracy());
            resultsWL.addResult(exp.getResults().getF1());
            
            System.out.println("Running WL, it " + i + " on " + dataset.getLabel());
          }
        }


        
        resultsSTF.newRow(dataset.getLabel() + " IntersectionFullSubTree");
        for (int i = 0; i < 3; i++) {


          if (experimenter.hasSpace()) {    
            int fileId = (int) (Math.random() * 100000000);  
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionFullSubTree" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionSubTreeKernel(i, 1), seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsSTF.addResult(exp.getResults().getAccuracy());
            resultsSTF.addResult(exp.getResults().getF1());
            
            System.out.println("Running STF, it " + i + " on " + dataset.getLabel());
          }


        }


        resultsSTP.newRow(dataset.getLabel() + " IntersectionPartialSubTree");
        for (int i = 0; i < 3; i++) {
          if (experimenter.hasSpace()) {    
            int fileId = (int) (Math.random() * 100000000);  
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionPartialSubTree" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionPartialSubTreeKernel(i, 0.01), seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsSTP.addResult(exp.getResults().getAccuracy());
            resultsSTP.addResult(exp.getResults().getF1());
            
            System.out.println("Running STP, it " + i + " on " + dataset.getLabel());
          }
        }




        
        resultsIGP.newRow(dataset.getLabel() + " IntersectionGraphPath");
        for (int i = 1; i < 3; i++) {
          if (experimenter.hasSpace()) {    
            int fileId = (int) (Math.random() * 100000000);  
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionGraphPath" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionGraphPathKernel(i, 1), seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsIGP.addResult(exp.getResults().getAccuracy());
            resultsIGP.addResult(exp.getResults().getF1());
            
            System.out.println("Running IGP, it " + i + " on " + dataset.getLabel());
          }
        }        


        resultsIGW.newRow(dataset.getLabel() + " IntersectionGraphWalk");
        for (int i = 1; i < 3; i++) {
          if (experimenter.hasSpace()) {    
            int fileId = (int) (Math.random() * 100000000);  
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionGraphWalk" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionGraphWalkKernel(i, 1), seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsIGW.addResult(exp.getResults().getAccuracy());
            resultsIGW.addResult(exp.getResults().getF1());
            
            System.out.println("Running IGW, it " + i + " on " + dataset.getLabel());
          }
        }
        
      }
      
      //*/
      


      /******
       * ADDITIONAL EXPERIMENTS
       */
      dataSetsParams = new ArrayList<PropertyPredictionDataSetParameters>();
      
      
      
      dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 1, false, false));
      dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 2, false, false));
      dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 3, false, false));
      //dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 4, false, false));


      
      dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 1, false, true));
      dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 2, false, true));
      dataSetsParams.add(new PropertyPredictionDataSetParameters(testSetA, "http://swrc.ontoware.org/ontology#affiliation", "http://swrc.ontoware.org/ontology#employs", 3, false, true));
      


      
      for (PropertyPredictionDataSetParameters params : dataSetsParams) {
        dataset = DataSetFactory.createPropertyPredictionDataSet(params);
        dataset.removeSmallClasses(5);
        dataset.removeVertexAndEdgeLabels();


        resultsWLadd.newRow(dataset.getLabel() + " WLSubTreeKernel");
        for (int i = 0; i < 4; i++) {
          if (experimenter.hasSpace()) {  
            int fileId = (int) (Math.random() * 100000000);  
            File file = new File(DATA_DIR + fileId + "_" + "WL" + "_" + i + ".txt");
            WLSubTreeKernel kernel = new WLSubTreeKernel(i, true);
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), kernel, seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsWLadd.addResult(exp.getResults().getAccuracy());
            resultsWLadd.addResult(exp.getResults().getF1());
            
            System.out.println("Running WL, it " + i + " on " + dataset.getLabel());
          }
        }


        
        resultsSTFadd.newRow(dataset.getLabel() + " IntersectionFullSubTree");
        for (int i = 0; i < 4; i++) {


          if (experimenter.hasSpace()) {    
            int fileId = (int) (Math.random() * 100000000);  
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionFullSubTree" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionSubTreeKernel(i, 1), seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsSTFadd.addResult(exp.getResults().getAccuracy());
            resultsSTFadd.addResult(exp.getResults().getF1());
            
            System.out.println("Running STF, it " + i + " on " + dataset.getLabel());
          }


        }


        resultsSTPadd.newRow(dataset.getLabel() + " IntersectionPartialSubTree");
        for (int i = 0; i < 4; i++) {
          if (experimenter.hasSpace()) {    
            int fileId = (int) (Math.random() * 100000000);  
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionPartialSubTree" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionPartialSubTreeKernel(i, 0.01), seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsSTPadd.addResult(exp.getResults().getAccuracy());
            resultsSTPadd.addResult(exp.getResults().getF1());
            
            System.out.println("Running STP, it " + i + " on " + dataset.getLabel());
          }
        }
        
        resultsIGPadd.newRow(dataset.getLabel() + " IntersectionGraphPath");
        for (int i = 1; i < 3; i++) {
          if (experimenter.hasSpace()) {    
            int fileId = (int) (Math.random() * 100000000);  
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionGraphPath" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionGraphPathKernel(i, 1), seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsIGPadd.addResult(exp.getResults().getAccuracy());
            resultsIGPadd.addResult(exp.getResults().getF1());
            
            System.out.println("Running IGP, it " + i + " on " + dataset.getLabel());
          }
        }        


        resultsIGWadd.newRow(dataset.getLabel() + " IntersectionGraphWalk");
        for (int i = 1; i < 3; i++) {
          if (experimenter.hasSpace()) {    
            int fileId = (int) (Math.random() * 100000000);  
            File file = new File(DATA_DIR + fileId + "_" + "IntersectionGraphWalk" + "_" + i + ".txt");
            exp = new PropertyPredictionExperiment(new PropertyPredictionDataSet(dataset), new IntersectionGraphWalkKernel(i, 1), seeds, cs, new FileOutputStream(file));
            experimenter.addExperiment(exp);
            resultsIGWadd.addResult(exp.getResults().getAccuracy());
            resultsIGWadd.addResult(exp.getResults().getF1());
            
            System.out.println("Running IGW, it " + i + " on " + dataset.getLabel());
          }
        }
      }
      


      
      
      
    /***********
     * END OF EXPERIMENTER
     * 
     *  
     */
    } catch (Exception e) {
      e.printStackTrace();
    }


    experimenter.stop();


    while (expT.isAlive()) {
      try {
        Thread.sleep(1000);
      } catch (Exception e) {
        e.printStackTrace();
      }
    }


    /********************************
     * PRINT OUT OF RESULTS
     * 
     **/
    try {
      int fileId = (int) (Math.random() * 100000000);  
      File file = new File(DATA_DIR + fileId + "_" + "all_results" + ".txt");
      PrintWriter fileOut = new PrintWriter(new FileOutputStream(file));


      List<Result> bestResults = new ArrayList<Result>();
      
      bestResults = resultsWL.getBestResults(bestResults);
      bestResults = resultsSTF.getBestResults(bestResults);
      bestResults = resultsSTP.getBestResults(bestResults);
      bestResults = resultsIGW.getBestResults(bestResults);
      bestResults = resultsIGP.getBestResults(bestResults);
      
      bestResults = resultsWLadd.getBestResults(bestResults);
      bestResults = resultsSTFadd.getBestResults(bestResults);
      bestResults = resultsSTPadd.getBestResults(bestResults);
      bestResults = resultsIGWadd.getBestResults(bestResults);
      bestResults = resultsIGPadd.getBestResults(bestResults);
      
      
      resultsWL.addCompResults(bestResults);
      resultsSTF.addCompResults(bestResults);
      resultsSTP.addCompResults(bestResults);
      resultsIGW.addCompResults(bestResults);
      resultsIGP.addCompResults(bestResults);
      
      resultsWLadd.addCompResults(bestResults);
      resultsSTFadd.addCompResults(bestResults);
      resultsSTPadd.addCompResults(bestResults);
      resultsIGWadd.addCompResults(bestResults);
      resultsIGPadd.addCompResults(bestResults);
      
      
      fileOut.println(resultsWL);
      fileOut.println(resultsSTF);
      fileOut.println(resultsSTP);
      fileOut.println(resultsIGW);
      fileOut.println(resultsIGP);


      fileOut.println(resultsWLadd);
      fileOut.println(resultsSTFadd);
      fileOut.println(resultsSTPadd);
      fileOut.println(resultsIGWadd);
      fileOut.println(resultsIGPadd);
      
      
      fileOut.println(resultsWL.allScoresToString());
      fileOut.println(resultsSTF.allScoresToString());
      fileOut.println(resultsSTP.allScoresToString());
      fileOut.println(resultsIGW.allScoresToString());
      fileOut.println(resultsIGP.allScoresToString());


      fileOut.println(resultsWLadd.allScoresToString());
      fileOut.println(resultsSTFadd.allScoresToString());
      fileOut.println(resultsSTPadd.allScoresToString());
      fileOut.println(resultsIGWadd.allScoresToString());
      fileOut.println(resultsIGPadd.allScoresToString());
      
      
      fileOut.close();


      System.out.println(resultsWL);
      System.out.println(resultsSTF);
      System.out.println(resultsSTP);
      System.out.println(resultsIGW);
      System.out.println(resultsIGP);


      System.out.println(resultsWLadd);
      System.out.println(resultsSTFadd);
      System.out.println(resultsSTPadd);
      System.out.println(resultsIGWadd);
      System.out.println(resultsIGPadd);
      
      System.out.println(resultsWL.allScoresToString());
      System.out.println(resultsSTF.allScoresToString());
      System.out.println(resultsSTP.allScoresToString());
      System.out.println(resultsIGW.allScoresToString());
      System.out.println(resultsIGP.allScoresToString());


      System.out.println(resultsWLadd.allScoresToString());
      System.out.println(resultsSTFadd.allScoresToString());
      System.out.println(resultsSTPadd.allScoresToString());
      System.out.println(resultsIGWadd.allScoresToString());
      System.out.println(resultsIGPadd.allScoresToString());
      


    } catch (Exception e) {
      e.printStackTrace();
    }

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