Esempio n. 1
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  @Override
  public void trainC(ClassificationDataSet dataSet, ExecutorService threadPool) {
    final int models = baseClassifiers.size();
    final int C = dataSet.getClassSize();
    weightsPerModel = C == 2 ? 1 : C;
    ClassificationDataSet metaSet =
        new ClassificationDataSet(
            weightsPerModel * models, new CategoricalData[0], dataSet.getPredicting());

    List<ClassificationDataSet> dataFolds = dataSet.cvSet(folds);
    // iterate in the order of the folds so we get the right dataum weights
    for (ClassificationDataSet cds : dataFolds)
      for (int i = 0; i < cds.getSampleSize(); i++)
        metaSet.addDataPoint(
            new DenseVector(weightsPerModel * models),
            cds.getDataPointCategory(i),
            cds.getDataPoint(i).getWeight());

    // create the meta training set
    for (int c = 0; c < baseClassifiers.size(); c++) {
      Classifier cl = baseClassifiers.get(c);
      int pos = 0;
      for (int f = 0; f < dataFolds.size(); f++) {
        ClassificationDataSet train = ClassificationDataSet.comineAllBut(dataFolds, f);
        ClassificationDataSet test = dataFolds.get(f);
        if (threadPool == null) cl.trainC(train);
        else cl.trainC(train, threadPool);
        for (int i = 0;
            i < test.getSampleSize();
            i++) // evaluate and mark each point in the held out fold.
        {
          CategoricalResults pred = cl.classify(test.getDataPoint(i));
          if (C == 2)
            metaSet.getDataPoint(pos).getNumericalValues().set(c, pred.getProb(0) * 2 - 1);
          else {
            Vec toSet = metaSet.getDataPoint(pos).getNumericalValues();
            for (int j = weightsPerModel * c; j < weightsPerModel * (c + 1); j++)
              toSet.set(j, pred.getProb(j - weightsPerModel * c));
          }

          pos++;
        }
      }
    }

    // train the meta model
    if (threadPool == null) aggregatingClassifier.trainC(metaSet);
    else aggregatingClassifier.trainC(metaSet, threadPool);

    // train the final classifiers, unless folds=1. In that case they are already trained
    if (folds != 1) {
      for (Classifier cl : baseClassifiers)
        if (threadPool == null) cl.trainC(dataSet);
        else cl.trainC(dataSet, threadPool);
    }
  }
Esempio n. 2
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  public CategoricalResults classify(DataPoint data) {
    if (coefficents == null) throw new UntrainedModelException("Model has not yet been trained");
    else if (shift != 0 || scale != 1)
      throw new UntrainedModelException("Model was trained for regression, not classifiaction");
    CategoricalResults results = new CategoricalResults(2);

    // It looks a little backwards. But if the true class is 0, and we are accurate, then we expect
    // regress to return a value near zero.
    results.setProb(1, regress(data));
    results.setProb(0, 1.0 - results.getProb(1));
    return results;
  }
Esempio n. 3
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  @Override
  public CategoricalResults classify(DataPoint data) {
    Vec w = new DenseVector(weightsPerModel * baseClassifiers.size());
    if (weightsPerModel == 1)
      for (int i = 0; i < baseClassifiers.size(); i++)
        w.set(i, baseClassifiers.get(i).classify(data).getProb(0) * 2 - 1);
    else {
      for (int i = 0; i < baseClassifiers.size(); i++) {
        CategoricalResults pred = baseClassifiers.get(i).classify(data);
        for (int j = 0; j < weightsPerModel; j++) w.set(i * weightsPerModel + j, pred.getProb(j));
      }
    }

    return aggregatingClassifier.classify(new DataPoint(w));
  }