Package org.data2semantics.proppred.learners.evaluation

Examples of org.data2semantics.proppred.learners.evaluation.Accuracy

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    boolean inference = true;


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

   
   
    for (int i : depths) { 
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    resTable.setManWU(0.05);
    resTable.setDigits(3);
   
   
    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());

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

    LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
    linParms.setEvalFunction(new Accuracy());
    linParms.setDoCrossValidation(false);
    linParms.setNumFolds(10);

    Map<Double, Double> counts = EvaluationUtils.computeClassCounts(target);
    int[] wLabels = new int[counts.size()];
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    int[] iterations = {0,2,4,6};

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

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

    LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
    linParms.setEvalFunction(new Accuracy());
    linParms.setDoCrossValidation(true);
    linParms.setNumFolds(10);
   
    Map<Double, Double> counts = EvaluationUtils.computeClassCounts(targets);
    int[] wLabels = new int[counts.size()];
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    boolean inference = true;

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

    LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
    linParms.setEvalFunction(new Accuracy());
    linParms.setDoCrossValidation(true);
    linParms.setNumFolds(5);
    linParms.setEps(0.001);
    linParms.setPs(ps1);
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    int[] iterations = {0,2,4,6};

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

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

    LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
    linParms.setEvalFunction(new Accuracy());
    linParms.setDoCrossValidation(true);
    linParms.setNumFolds(10);
   
    Map<Double, Double> counts = EvaluationUtils.computeClassCounts(targets);
    int[] wLabels = new int[counts.size()];
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  }
 
 
  @Out(name="accuracy")
  public double getAccuracy() {
    return new Accuracy().computeScore(targetA, pred);
  }
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    int[] iterations = {0,2,4,6};

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

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

    LibLINEARParameters linParms = new LibLINEARParameters(LibLINEARParameters.SVC_DUAL, cs);
    linParms.setEvalFunction(new Accuracy());
    linParms.setDoCrossValidation(true);
    linParms.setNumFolds(10);
   
    Map<Double, Double> counts = EvaluationUtils.computeClassCounts(target);
    int[] wLabels = new int[counts.size()];
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    linParms.setNumFolds(5);
    linParms.setSplitFraction((float) 0.7);
    linParms.setDoCrossValidation(false);

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


    ResultsTable resTable = new ResultsTable();
    resTable.setDigits(2);
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  @Main
  public void mainKernelMethod(){
   
    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());
    long[] seeds2={seed};
    System.out.println(dataset + " " + labels.size() + " " + instances.size() + " " + blackList.size());
   
    RDFLinearKernelExperiment exp = new RDFLinearKernelExperiment(new RDFWLSubTreeKernel(iteration, depth, true, true), seeds2, linParms, dataset, instances, target, blackList, evalFuncs);
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    boolean inference = true;
    boolean tfidf = false;
    boolean normalize = true;
   
    List<EvaluationFunction> evalFuncs = new ArrayList<EvaluationFunction>();
    evalFuncs.add(new Accuracy());
    evalFuncs.add(new F1());
    List<Double> targets = EvaluationUtils.createTarget(labels);

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

    Map<Double, Double> counts = EvaluationUtils.computeClassCounts(targets);
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