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();   }  } }

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

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