import weka.classifiers.rules.ZeroR; //導入方法依賴的package包/類

/**

* Build the classifier on the supplied data

*

* @param data the training data

* @throws Exception if the classifier could not be built successfully

*/

public void buildClassifier(Instances data) throws Exception {

super.buildClassifier(data);

// can classifier handle the data?

getCapabilities().testWithFail(data);

// remove instances with missing class

Instances newData = new Instances(data);

newData.deleteWithMissingClass();

double sum = 0;

double temp_sum = 0;

// Add the model for the mean first

m_zeroR = new ZeroR();

m_zeroR.buildClassifier(newData);

// only class? -> use only ZeroR model

if (newData.numAttributes() == 1) {

System.err.println(

"Cannot build model (only class attribute present in data!), "

+ "using ZeroR model instead!");

m_SuitableData = false;

return;

}

else {

m_SuitableData = true;

}

newData = residualReplace(newData, m_zeroR, false);

for (int i = 0; i < newData.numInstances(); i++) {

sum += newData.instance(i).weight() *

newData.instance(i).classValue() * newData.instance(i).classValue();

}

if (m_Debug) {

System.err.println("Sum of squared residuals "

+"(predicting the mean) : " + sum);

}

m_NumIterationsPerformed = 0;

do {

temp_sum = sum;

// Build the classifier

m_Classifiers[m_NumIterationsPerformed].buildClassifier(newData);

newData = residualReplace(newData, m_Classifiers[m_NumIterationsPerformed], true);

sum = 0;

for (int i = 0; i < newData.numInstances(); i++) {

sum += newData.instance(i).weight() *

newData.instance(i).classValue() * newData.instance(i).classValue();

}

if (m_Debug) {

System.err.println("Sum of squared residuals : "+sum);

}

m_NumIterationsPerformed++;

} while (((temp_sum - sum) > Utils.SMALL) &&

(m_NumIterationsPerformed < m_Classifiers.length));

}

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