libsvm支持向量机回归示例

  • Z时代
  • 2024-01-10
  • 分类：IT

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

代码如下:
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

代码如下:
17.6,17.7,17.7,17.7,17.817.7,17.7,17.7,17.8,17.817.7,17.7,17.8,17.8,17.917.7,17.8,17.8,17.9,1817.8,17.8,17.9,18,18.117.8,17.9,18,18.1,18.217.9,18,18.1,18.2,18.418,18.1,18.2,18.4,18.618.1,18.2,18.4,18.6,18.718.2,18.4,18.6,18.7,18.918.4,18.6,18.7,18.9,19.118.6,18.7,18.9,19.1,19.3

测试数据

代码如下:
18.7,18.9,19.1,19.3,19.618.9,19.1,19.3,19.6,19.919.1,19.3,19.6,19.9,20.219.3,19.6,19.9,20.2,20.619.6,19.9,20.2,20.6,2119.9,20.2,20.6,21,21.520.2,20.6,21,21.5,22

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