對應分析Weka中weka.filters.supervised.attribute.Discretize
涉及的其他類包括weka.filters.Filter
分析的巨集觀程式碼

Discretize disc=new Discretize()
disc.setInputFormat(data)
Instances afterDiscretize=Filter.useFilter(data,disc)

Filter.useFilter()

Filter.useFilter(Instances data, Filter filter){
for (int i = 0; i < data.numInstances(); i++) {
      filter
 
.input(data.instance(i));
      //進一步呼叫bufferInput():把instance複製一份後交給Filter的m_InputFormat變數，這相當於輸入資料的一個完整copy
      //
    }
    filter.batchFinished();//呼叫離散化計算的主體部分calculateCutPoints()詳細分析見下文
    Instances newData = filter.getOutputFormat();
    Instance processed;
    while ((processed = filter.output()) != null) {
      newData.add(processed);
    }
}
return
 
 newData

Discretize.calculateCutPoints()

calculateCutPoints(){
    m_CutPoints = new double[getInputFormat().numAttributes()][];
    calculateCutPointsByMDL(i, copy);//對第i個屬性進行離散化
}