随机森林 RandomForest java

随机森林是由多棵树组成的分类或回归方法。主要思想来源于Bagging算法，Bagging技术思想主要是给定一弱分类器及训练集，让该学习算法训练多轮，每轮的训练集由原始训练集中有放回的随机抽取，大小一般跟原始训练集相当，这样依次训练多个弱分类器，最终的分类由这些弱分类器组合，对于分类问题一般采用多数投票法，对于回归问题一般采用简单平均法。随机森林在bagging的基础上，每个弱分类器都是决策树，决策树的生成过程中中，在属性的选择上增加了依一定概率选择属性，在这些属性中选择最佳属性及分割点，传统做法一般是全部属性中去选择最佳属性，这样随机森林有了样本选择的随机性，属性选择的随机性，这样一来增加了每个分类器的差异性、不稳定性及一定程度上避免每个分类器的过拟合（一般决策树有过拟合现象），由此组合分类器增加了最终的泛化能力。下面是代码的简单实现

/**
* 随机森林回归问题
* @author ysh   1208706282
*
*/
public class RandomForest {
   List<Sample> mSamples;
   List<Cart> mCarts;
   double mFeatureRate;
   int mMaxDepth;
   int mMinLeaf;
   Random mRandom;
   /**
   * 加载数据   回归树
   * @param path
   * @param regex
   * @throws Exception
   */
   public void loadData(String path,String regex) throws Exception{
       mSamples = new ArrayList<Sample>();
       BufferedReader reader = new BufferedReader(new FileReader(path));
       String line = null;
       String splits[] = null;
       Sample sample = null;
       while(null != (line=reader.readLine())){
           splits = line.split(regex);
           sample = new Sample();
           sample.label = Double.valueOf(splits[0]);
           sample.feature = new ArrayList<Double>(splits.length-1);
           for(int i=0;i<splits.length-1;i++){
               sample.feature.add(new Double(splits[i+1]));
           }
           mSamples.add(sample);
       }
       reader.close();
   }
   public void train(int iters){
       mCarts = new ArrayList<Cart>(iters);
       Cart cart = null;
       for(int iter=0;iter<iters;iter++){
           cart = new Cart();
           cart.mFeatureRate = mFeatureRate;
           cart.mMaxDepth = mMaxDepth;
           cart.mMinLeaf = mMinLeaf;
           cart.mRandom = mRandom;
           List<Sample> s = new ArrayList<Sample>(mSamples.size());
           for(int i=0;i<mSamples.size();i++){
               s.add(mSamples.get(cart.mRandom.nextInt(mSamples.size())));
           }
           cart.setData(s);
           cart.train();
           mCarts.add(cart);
           System.out.println("iter: "+iter);
           s = null;
       }
   }
   /**
   * 回归问题简单平均法分类问题多数投票法
   * @param sample
   * @return
   */
   public double classify(Sample sample){
       double val = 0;
       for(Cart cart:mCarts){
           val += cart.classify(sample);
       }
       return val/mCarts.size();
   }
   /**
   * @param args
   * @throws Exception
   */
   public static void main(String[] args) throws Exception {
       // TODO Auto-generated method stub
       RandomForest forest = new RandomForest();
       forest.loadData("F:/2016-contest/20161001/train_data_1.csv", ",");
       forest.mFeatureRate = 0.8;
       forest.mMaxDepth = 3;
       forest.mMinLeaf = 1;
       forest.mRandom = new Random();
       forest.mRandom.setSeed(100);
       forest.train(100);

       List<Sample> samples = Cart.loadTestData("F:/2016-contest/20161001/valid_data_1.csv", true, ",");
       double sum = 0;
       for(Sample s:samples){
           double val = forest.classify(s);
           sum += (val-s.label)*(val-s.label);
           System.out.println(val+" "+s.label);
       }
       System.out.println(sum/samples.size()+" "+sum);
       System.out.println(System.currentTimeMillis());
   }

}

随机森林 RandomForest java

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