Examples of weka.classifiers.evaluation.output.prediction.AbstractOutput.printClassification()

weka.classifiers.evaluation.output.prediction.AbstractOutput.printClassification()
Prints the classification to the buffer. @param classifier the classifier to use for printing the classification @param inst the instance to print @param index the index of the instance @throws Exception if check fails or error occurs during printing of classification


    for (int i = 0; i < data.numInstances(); i++) {
      predictions[i] = evaluateModelOnceAndRecordPrediction((Classifier)classifier,
          data.instance(i));
      if (classificationOutput != null)
        classificationOutput.printClassification(classifier, data.instance(i), i);
    }


    return predictions;
  }

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        for (int jj=0;jj<inst.numInstances();jj++) {
    plotInstances.process(inst.instance(jj), classifier, eval);
    
    if (outputPredictionsText) {
      classificationOutput.printClassification(classifier, inst.instance(jj), jj);
    }
    if ((jj % 100) == 0) {
      m_Log.statusMessage("Evaluating on training data. Processed "
              +jj+" instances...");
    }

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    m_Log.statusMessage("Evaluating model for fold "
            + (fold + 1) + "...");
    for (int jj=0;jj<test.numInstances();jj++) {
      plotInstances.process(test.instance(jj), current, eval);
      if (outputPredictionsText) {
        classificationOutput.printClassification(current, test.instance(jj), jj);
      }
    }
        }
        if (outputPredictionsText)
    classificationOutput.printFooter();

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        }
     
        for (int jj=0;jj<test.numInstances();jj++) {
    plotInstances.process(test.instance(jj), current, eval);
    if (outputPredictionsText) { 
      classificationOutput.printClassification(current, test.instance(jj), jj);
    }
    if ((jj % 100) == 0) {
      m_Log.statusMessage("Evaluating on test split. Processed "
              +jj+" instances...");
    }

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        int jj = 0;
        while (source.hasMoreElements(userTestStructure)) {
    instance = source.nextElement(userTestStructure);
    plotInstances.process(instance, classifier, eval);
    if (outputPredictionsText) {
      classificationOutput.printClassification(classifier, instance, jj);
    }
    if ((++jj % 100) == 0) {
      m_Log.statusMessage("Evaluating on test data. Processed "
          +jj+" instances...");
    }

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        int jj = 0;
        while (source.hasMoreElements(userTestStructure)) {
    instance = source.nextElement(userTestStructure);
    plotInstances.process(instance, classifierToUse, eval);
    if (outputPredictionsText) {
      classificationOutput.printClassification(classifierToUse, instance, jj);
    }
    if ((++jj % 100) == 0) {
      m_Log.statusMessage("Evaluating on test data. Processed "
          +jj+" instances...");
    }

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    for (int i = 0; i < data.numInstances(); i++) {
      predictions[i] = evaluateModelOnceAndRecordPrediction((Classifier)classifier,
          data.instance(i));
      if (classificationOutput != null)
        classificationOutput.printClassification(classifier, data.instance(i), i);
    }


    return predictions;
  }

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        for (int jj=0;jj<inst.numInstances();jj++) {
    plotInstances.process(inst.instance(jj), classifier, eval);
    
    if (outputPredictionsText) {
      classificationOutput.printClassification(classifier, inst.instance(jj), jj);
    }
    if ((jj % 100) == 0) {
      m_Log.statusMessage("Evaluating on training data. Processed "
              +jj+" instances...");
    }

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    m_Log.statusMessage("Evaluating model for fold "
            + (fold + 1) + "...");
    for (int jj=0;jj<test.numInstances();jj++) {
      plotInstances.process(test.instance(jj), current, eval);
      if (outputPredictionsText) {
        classificationOutput.printClassification(current, test.instance(jj), jj);
      }
    }
        }
        if (outputPredictionsText)
    classificationOutput.printFooter();

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        }
     
        for (int jj=0;jj<test.numInstances();jj++) {
    plotInstances.process(test.instance(jj), current, eval);
    if (outputPredictionsText) { 
      classificationOutput.printClassification(current, test.instance(jj), jj);
    }
    if ((jj % 100) == 0) {
      m_Log.statusMessage("Evaluating on test split. Processed "
              +jj+" instances...");
    }

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