Examples of weka.core.Attribute.type()

weka.core.Attribute.type()
Returns the attribute's type as an integer. @return the attribute's type.

    boolean bHasMissingValues = false;


    Enumeration enu = instances.enumerateAttributes();    
    while (enu.hasMoreElements()) {
      Attribute attribute = (Attribute) enu.nextElement();
      if (attribute.type() != Attribute.NOMINAL) {
  m_nNonDiscreteAttribute = attribute.index();
  bHasNonNominal = true;
  //throw new UnsupportedAttributeTypeException("BayesNet handles nominal variables only. Non-nominal variable in dataset detected.");
      }
      Enumeration enum2 = instances.enumerateInstances();

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          Attribute modelAtt = m_modelHeader.attribute(i);
          Attribute incomingAtt = m_inputHeader.attribute(j);
          
          // check types
          if (modelAtt.type() != incomingAtt.type()) {
            m_attributeStatus[i] = TYPE_MISMATCH;
            break;
          }          
          
          // now check nominal values (number, names...)

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      Attribute attribute = (Attribute) enu.nextElement();


      // If the attribute is numeric, determine the estimator 
      // numeric precision from differences between adjacent values
      double numPrecision = DEFAULT_NUM_PRECISION;
      if (attribute.type() == Attribute.NUMERIC) {
  m_Instances.sort(attribute);
  if ((m_Instances.numInstances() > 0)
      && !m_Instances.instance(0).isMissing(attribute)) {
    double lastVal = m_Instances.instance(0).value(attribute);
    double currentVal, deltaSum = 0;

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  }
      }




      for (int j = 0; j < m_Instances.numClasses(); j++) {
  switch (attribute.type()) {
  case Attribute.NUMERIC: 
    if (m_UseKernelEstimator) {
      m_Distributions[attIndex][j] = 
        new KernelEstimator(numPrecision);
    } else {

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      for (int j = 0; j < dataSet.numAttributes(); j++) {
        if (dataSet.attribute(j).name().equals(schemaAttName)) {
          Attribute miningSchemaAtt = fieldsI.attribute(i);
          Attribute incomingAtt = dataSet.attribute(j);
          // check type match
          if (miningSchemaAtt.type() != incomingAtt.type()) {
            if (miningSchemaAtt.isString() && incomingAtt.isNominal()) {
              // don't worry about String attributes in the mining schema
              // (as long as the corresponding incoming is a String or nominal),
              // since values for the String attributes are more than likely revealed
              // by FieldRef elements in the actual model itself

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