Package weka.core

Examples of weka.core.Instances.classAttribute()

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      //String relationNameModifier = oldStructure.relationName()
  //+"_with predictions";
      String relationNameModifier = "_with predictions";
  //+"_with predictions";
       if (!m_appendProbabilities
     || oldStructure.classAttribute().isNumeric()) {
   try {
     m_format = makeDataSetClass(oldStructure, classifier,
                 relationNameModifier);
     m_instanceVals = new double [m_format.numAttributes()];
   } catch (Exception ex) {
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    data.deleteWithMissingClass();
   
    if(data.numInstances() < m_Folds)
      throw new Exception("Not enough data for REP.");

    m_ClassAttribute = data.classAttribute();
    if(m_ClassAttribute.isNominal())
      m_NumClasses = m_ClassAttribute.numValues();
    else
      m_NumClasses = 1;
 
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        String results = "=== Performance information ===\n\n"
    +  "Scheme:   " + textTitle + "\n"
    +  "Relation: "+ inst.dataset().relationName() + "\n\n"
    + m_eval.toSummaryString();
              if (inst.classIndex() >= 0 &&
                  inst.classAttribute().isNominal() &&
                  (m_outputInfoRetrievalStats)) {
                results += "\n" + m_eval.toClassDetailsString();
              }

              if (inst.classIndex() >= 0 &&
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                  (m_outputInfoRetrievalStats)) {
                results += "\n" + m_eval.toClassDetailsString();
              }

              if (inst.classIndex() >= 0 &&
                  inst.classAttribute().isNominal()) {
                results += "\n" + m_eval.toMatrixString();
              }
        textTitle = "Results: " + textTitle;
        TextEvent te =
    new TextEvent(this,
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    FastVector predictions = new FastVector();
    Instances runInstances = new Instances(data);
    Random random = new Random(m_Seed);
    runInstances.randomize(random);
    if (runInstances.classAttribute().isNominal() && (numFolds > 1)) {
      runInstances.stratify(numFolds);
    }
    int inst = 0;
    for (int fold = 0; fold < numFolds; fold++) {
      Instances train = runInstances.trainCV(numFolds, fold, random);
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    }
    // Randomize on a copy of the original dataset
    Instances runInstances = new Instances(m_Instances);
    Random random = new Random(run);
    runInstances.randomize(random);
    if (runInstances.classAttribute().isNominal()) {
      runInstances.stratify(m_NumFolds);
    }
    for (int fold = 0; fold < m_NumFolds; fold++) {
      // Add in some fields to the key like run and fold number, dataset name
      Object [] seKey = m_SplitEvaluator.getKey();
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    Random random = new Random(m_seed);
    Instances dataCopy = new Instances(data);
    dataCopy.randomize(random);

    if (dataCopy.classAttribute().isNominal()) {
      dataCopy.stratify(m_numFolds);
    }

    for (int f = 0; f < m_numFolds; f++) {
      trainData[f] = dataCopy.trainCV(m_numFolds, f, random);
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       m_Classifier.buildClassifier(trainData);
       m_BestClassifierOptions = m_InitOptions;
       return;
    }

    if (trainData.classAttribute().isNominal()) {
      trainData.stratify(m_NumFolds);
    }
    m_BestClassifierOptions = null;
   
    // Set up m_ClassifierOptions -- take getOptions() and remove
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    }

    // Randomize on a copy of the original dataset
    Instances runInstances = new Instances(m_Instances);
    runInstances.randomize(new Random(run));
    if (runInstances.classAttribute().isNominal()) {
      runInstances.stratify(m_StepSize);
    }

    // Tell the resultproducer to send results to us
    m_ResultProducer.setResultListener(this);
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            data.setClassIndex(data.numAttributes() - 1);
        } else {
            data.setClassIndex(m_ClassIndex);
        }
       
        if (data.classAttribute().type() != Attribute.NOMINAL) {
            throw new Exception("Class attribute must be nominal");
        }
        int numClasses = data.numClasses();
       
        data.deleteWithMissingClass();
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