libsvm支持向量机回归示例

libsvm支持向量机算法包的基本使用,此处演示的是支持向量回归机

import java.io.bufferedreader;
import java.io.file;
import java.io.filereader;
import java.util.arraylist;
import java.util.list;

import libsvm.svm;
import libsvm.svm_model;
import libsvm.svm_node;
import libsvm.svm_parameter;
import libsvm.svm_problem;

public class svm {
 public static void main(string[] args) {
  // 定义训练集点a{10.0, 10.0} 和 点b{-10.0, -10.0}，对应lable为{1.0, -1.0}
  list<double> label = new arraylist<double>();
  list<svm_node[]> nodeset = new arraylist<svm_node[]>();
  getdata(nodeset, label, "file/train.txt");

  int datarange=nodeset.get(0).length;
  svm_node[][] datas = new svm_node[nodeset.size()][datarange]; // 训练集的向量表
  for (int i = 0; i < datas.length; i++) {
   for (int j = 0; j < datarange; j++) {
    datas[i][j] = nodeset.get(i)[j];
   }
  }
  double[] lables = new double[label.size()]; // a,b 对应的lable
  for (int i = 0; i < lables.length; i++) {
   lables[i] = label.get(i);
  }

  // 定义svm_problem对象
  svm_problem problem = new svm_problem();
  problem.l = nodeset.size(); // 向量个数
  problem.x = datas; // 训练集向量表
  problem.y = lables; // 对应的lable数组

  // 定义svm_parameter对象
  svm_parameter param = new svm_parameter();
  param.svm_type = svm_parameter.epsilon_svr;
  param.kernel_type = svm_parameter.linear;
  param.cache_size = 100;
  param.eps = 0.00001;
  param.c = 1.9;
  // 训练svm分类模型
  system.out.println(svm.svm_check_parameter(problem, param));
  // 如果参数没有问题，则svm.svm_check_parameter()函数返回null,否则返回error描述。
  svm_model model = svm.svm_train(problem, param);
  // svm.svm_train()训练出svm分类模型

  // 获取测试数据
  list<double> testlabel = new arraylist<double>();
  list<svm_node[]> testnodeset = new arraylist<svm_node[]>();
  getdata(testnodeset, testlabel, "file/test.txt");

  svm_node[][] testdatas = new svm_node[testnodeset.size()][datarange]; // 训练集的向量表
  for (int i = 0; i < testdatas.length; i++) {
   for (int j = 0; j < datarange; j++) {
    testdatas[i][j] = testnodeset.get(i)[j];
   }
  }
  double[] testlables = new double[testlabel.size()]; // a,b 对应的lable
  for (int i = 0; i < testlables.length; i++) {
   testlables[i] = testlabel.get(i);
  }

  // 预测测试数据的lable
  double err = 0.0;
  for (int i = 0; i < testdatas.length; i++) {
   double truevalue = testlables[i];
   system.out.print(truevalue + " ");
   double predictvalue = svm.svm_predict(model, testdatas[i]);
   system.out.println(predictvalue);
   err += math.abs(predictvalue - truevalue);
  }
  system.out.println("err=" + err / datas.length);
 }

 public static void getdata(list<svm_node[]> nodeset, list<double> label,
   string filename) {
  try {

   filereader fr = new filereader(new file(filename));
   bufferedreader br = new bufferedreader(fr);
   string line = null;
   while ((line = br.readline()) != null) {
    string[] datas = line.split(",");
    svm_node[] vector = new svm_node[datas.length - 1];
    for (int i = 0; i < datas.length - 1; i++) {
     svm_node node = new svm_node();
     node.index = i + 1;
     node.value = double.parsedouble(datas[i]);
     vector[i] = node;
    }
    nodeset.add(vector);
    double lablevalue = double.parsedouble(datas[datas.length - 1]);
    label.add(lablevalue);
   }
  } catch (exception e) {
   e.printstacktrace();
  }

 }
}

训练数据，最后一列为目标值

17.6,17.7,17.7,17.7,17.8
17.7,17.7,17.7,17.8,17.8
17.7,17.7,17.8,17.8,17.9
17.7,17.8,17.8,17.9,18
17.8,17.8,17.9,18,18.1
17.8,17.9,18,18.1,18.2
17.9,18,18.1,18.2,18.4
18,18.1,18.2,18.4,18.6
18.1,18.2,18.4,18.6,18.7
18.2,18.4,18.6,18.7,18.9
18.4,18.6,18.7,18.9,19.1
18.6,18.7,18.9,19.1,19.3

测试数据

18.7,18.9,19.1,19.3,19.6
18.9,19.1,19.3,19.6,19.9
19.1,19.3,19.6,19.9,20.2
19.3,19.6,19.9,20.2,20.6
19.6,19.9,20.2,20.6,21
19.9,20.2,20.6,21,21.5
20.2,20.6,21,21.5,22