import cc.mallet.types.GainRatio; //导入依赖的package包/类
public double computeCostAndPrune()
{
	double costS = getMDL();

	if (isLeaf())
		return costS + 1;

	double minCost1 = getLeftChild().computeCostAndPrune();
	double minCost2 = getRightChild().computeCostAndPrune();
	double costSplit = Math.log(m_gainRatio.getNumSplitPointsForBestFeature()) / GainRatio.log2;
	double minCostN = Math.min(costS+1, costSplit+1+minCost1+minCost2);

	if (Maths.almostEquals(minCostN, costS+1))
		m_leftChild = m_rightChild = null;

	return minCostN;
}
 

import cc.mallet.types.GainRatio; //导入依赖的package包/类
public Node(InstanceList ilist, Node parent, int minNumInsts, int[] instIndices)
{
	if (instIndices == null) {
		instIndices = new int[ilist.size()];
		for (int ii = 0; ii < instIndices.length; ii++)
			instIndices[ii] = ii;
	}
	m_gainRatio = GainRatio.createGainRatio(ilist, instIndices, minNumInsts);
	m_ilist = ilist;
	m_instIndices = instIndices;
	m_dataDict = m_ilist.getDataAlphabet();
	m_minNumInsts = minNumInsts;
	m_parent = parent;
	m_leftChild = m_rightChild = null;
}
 

import cc.mallet.types.GainRatio; //导入依赖的package包/类
/**
 * Calculates the minimum description length of this node, i.e., 
 * the length of the binary encoding that describes the feature 
 * and the split value used at this node
 */
public double getMDL()
{
	int numClasses = m_ilist.getTargetAlphabet().size();
	double mdl = getSize() * getGainRatio().getBaseEntropy();
	mdl += ((numClasses-1) * Math.log(getSize() / 2.0)) / (2 * GainRatio.log2);
	double piPow = Math.pow(Math.PI, numClasses/2.0);
	double gammaVal = Maths.gamma(numClasses/2.0);
	mdl += Math.log(piPow/gammaVal) / GainRatio.log2;
	return mdl;
}
 

import cc.mallet.types.GainRatio; //导入依赖的package包/类
public GainRatio getGainRatio() { return m_gainRatio; }