import java.io.File;import java.io.FileNotFoundException;import java.util.ArrayList;import java.util.Scanner;public class Data {	private ArrayList<ArrayList<String>> trainData=http://www.mamicode.com/new ArrayList>();"D://javajavajava//dbdt//src//script//data//adult.data.csv";		Scanner in;		try {			in = new Scanner(new File(dataPath));			while (in.hasNext()) {				String line=in.nextLine();				String []strs=line.trim().split(",");				ArrayList<String> tmp=new ArrayList<>();				for(int i=0;i<strs.length;i++)				{					tmp.add(strs[i]);					}				this.trainData.add(tmp);			}		} catch (FileNotFoundException e) {			// TODO Auto-generated catch block			e.printStackTrace();		}			}	public static void main(String[] args) {		// TODO Auto-generated method stub		Data d =new Data();			}}

import java.util.ArrayList;import java.util.HashSet;import java.util.Iterator;import java.util.Random;import java.util.spi.TimeZoneNameProvider;public class Tree {	private Tree leftTree=new Tree();	private Tree rightTree=new Tree();	private double loss=-1;	private int attributeSplit=0;	private String attributeSplitType="";	boolean isLeaf;	double leafValue;	private ArrayList<Integer> leafNodeSet=new ArrayList<>();		public ArrayList<String> getAttributeSet(ArrayList<ArrayList<String>> trainData,int idx)	{		HashSet<String> mySet=new HashSet<>();		ArrayList<String> ans =new ArrayList<>();		for(int i=0;i<trainData.size();i++)		{			mySet.add(trainData.get(i).get(idx));		}				Iterator<String> it=mySet.iterator();				while(it.hasNext())		{			ans.add(it.next());		}				return ans;	}	public boolean myCmpLess(String str1,String str2)	{		if(Integer.parseInt(str1.trim())<=Integer.parseInt(str2.trim()))			return true;		else return false;			}	public double computeLoss(ArrayList<Double> values)	{		double loss=0;		for(int i=0;i<values.size();i++)		{			loss+=values.get(i);		}		double mean=loss/values.size();		loss=0;		for(int i=0;i<values.size();i++)		{			loss+=Math.pow(values.get(i)-mean,2);		}		return Math.sqrt(loss);	}	public double getPredictValue(int K, ArrayList<Integer> subIdx,ArrayList<Double> target) {		double ans=0;		double sum=0,sum1=0;		for(int i=0;i<subIdx.size();i++)		{			sum+=target.get(subIdx.get(i));		}		for(int i=0;i<subIdx.size();i++)		{			sum1+=target.get(subIdx.get(i))*(1-target.get(subIdx.get(i)));		}		ans=(K-1)/K*sum/sum1;		return ans;	}	public double getPredictValue(Tree root)	{		return root.leafValue;	}	public double getPredictValue(Tree root,ArrayList<String> instance,Boolean isDigit[])	{				if(root.isLeaf)			return root.leafValue;		else if(isDigit[root.attributeSplit])		{			if(myCmpLess(instance.get(root.attributeSplit).trim(),root.attributeSplitType))				return getPredictValue(root.leftTree, instance, isDigit);			return getPredictValue(root.rightTree, instance, isDigit);		}		else		{			if(instance.get(root.attributeSplit).trim().equals(root.attributeSplitType))				return getPredictValue(root.leftTree, instance, isDigit);			return getPredictValue(root.rightTree, instance, isDigit);		}			}	public Tree constructTree(ArrayList<ArrayList<Integer>> leafNodes,ArrayList<Double> leafValues,int K,int splitPoints, Boolean isDigit[],ArrayList<Integer> subIdx,ArrayList<ArrayList<String>> trainData,ArrayList<Double> target,int maxDepth[],int depth)	{				int n=trainData.size();		int dim=trainData.get(0).size();		ArrayList<Integer> leftTreeIdx=new ArrayList<>();		ArrayList<Integer> rightTreeIdx=new ArrayList<>();				if(depth<maxDepth[0])		{			/*			 * 从所有的attribute中选取最佳的attribute，并且attribute中最佳的分割点，对数据进行分割			 * */			double loss=-1;			ArrayList<Integer> leftNodes=new ArrayList<>();			ArrayList<Integer> rightNodes=new ArrayList<>();			int attributeSplit=0;			String attributeSplitType="";						for(int i=0;i<dim;i++)//遍历所有的attribute			{				//得到该attribute下所有的distinct的值				ArrayList<String> myAttributeSet=new ArrayList<>();				ArrayList<String> subDigitAttribute=new ArrayList<>();				myAttributeSet=getAttributeSet(trainData, i);				if(isDigit[i])//如果是数字，就从数组中随机选取splitpoints个节点，代表这个属性可以在这splitpoints下进行分割				{					while(subDigitAttribute.size()<splitPoints)					{						Random r=new Random();						int tmp=r.nextInt(myAttributeSet.size());						subDigitAttribute.add(myAttributeSet.get(tmp));						myAttributeSet.clear();						myAttributeSet=subDigitAttribute;					}				}				for(int j=0;j<myAttributeSet.size();j++)				{					for(int k=0;k<subIdx.size();k++)					{						if((!isDigit[i]&&trainData.get(subIdx.get(k)).get(i).trim().equals(myAttributeSet.get(j)))||(isDigit[i]&&myCmpLess(trainData.get(subIdx.get(k)).get(i),myAttributeSet.get(j))))						{							leftTreeIdx.add(subIdx.get(k));						}						else						{							rightTreeIdx.add(subIdx.get(k));						}					}					ArrayList<Double> leftTarget=new ArrayList<>();					ArrayList<Double> rightTarget=new ArrayList<>();					for(int k=0;k<leftTreeIdx.size();k++)						leftTarget.add(target.get(leftTreeIdx.get(k)));					for(int k=0;k<rightTreeIdx.size();k++)						rightTarget.add(target.get(rightTreeIdx.get(k)));					double lossTmp=computeLoss(leftTarget)+computeLoss(rightTarget);						if(loss<0||loss<lossTmp)					{						leftNodes.clear();						rightNodes.clear();						for(int k=0;k<leftTreeIdx.size();k++)							leftNodes.add(leftTreeIdx.get(k));						for(int k=0;k<rightTreeIdx.size();k++)							rightNodes.add(rightTreeIdx.get(k));						attributeSplit=i;						attributeSplitType=myAttributeSet.get(j);					}									}									}						Tree tmpTree=new Tree();			tmpTree.attributeSplit=attributeSplit;			tmpTree.attributeSplitType=attributeSplitType;			tmpTree.loss=loss;			tmpTree.isLeaf=false;			tmpTree.leftTree=constructTree(leafNodes,leafValues,K,splitPoints, isDigit, leftNodes, trainData, target, maxDepth, depth+1);			tmpTree.leftTree=constructTree(leafNodes,leafValues,K,splitPoints, isDigit, rightNodes, trainData, target, maxDepth, depth+1);			return tmpTree;					}		else		{			Tree tmpTree=new Tree();			tmpTree.isLeaf=true;			tmpTree.leafValue=http://www.mamicode.com/getPredictValue(K, subIdx, target);>

import java.rmi.server.SkeletonNotFoundException;import java.util.ArrayList;import java.util.HashSet;import java.util.Iterator;import java.util.Map;import java.util.Map.Entry;import java.util.Random;import java.util.Set;public class GBDT {		private ArrayList<ArrayList<String>> datas=new ArrayList<ArrayList<String>>();	private ArrayList<String> labelSets=new ArrayList<>();	private ArrayList<ArrayList<Double>> F=new ArrayList<ArrayList<Double>>();	private ArrayList<ArrayList<Double>> residual=new ArrayList<ArrayList<Double>>();	private ArrayList<ArrayList<String>> trainData=http://www.mamicode.com/new ArrayList>();"?"))				{					flag=false;					break;				}			}			if(!flag) continue;			if(datas.get(i).get(this.dim).trim().equals("?")) continue;			trainData.add(tmp);			if(datas.get(i).get(this.dim).trim().equals("<=50K")) 				labelTrainData.add(-1);			else				labelTrainData.add(1);					}		this.n=this.labelTrainData.size();				for(int i=0;i<this.datas.get(0).size()-1;i++)			labelSets.add(this.datas.get(0).get(i));				//初始化F矩阵为全0,F矩阵是n*2，是2分类问题，如果要多分类，改下这里就可以了		for(int i=0;i<this.n;i++)		{			ArrayList<Double> arrTmp=new ArrayList<Double>();			for(int j=0;j<2;j++)			{				arrTmp.add(0.0);			}			this.F.add(arrTmp);			this.residual.add(arrTmp);		}										}		public static void main(String[] args) {		GBDT dGbdt=new GBDT();		dGbdt.getData();		System.err.println(dGbdt.n);			}}