开发语言：JAVA

liblinear版本：liblinear-1.94.jar (下载地址：

1.下载 liblinear-1.94.jar，导入工程

在工程上右键---->Properties----->选中Java Build Path----->选中Libraries标签----->点击Add External JARs。

找到需要添加的jar包，确定即可。

2.创建LibLinear类 (类名自选)

代码如下：

1 packageliblinear;2

3 importjava.io.File;4 importjava.io.IOException;5 importjava.util.ArrayList;6 importjava.util.List;7

8 importde.bwaldvogel.liblinear.Feature;9 importde.bwaldvogel.liblinear.FeatureNode;10 importde.bwaldvogel.liblinear.Linear;11 importde.bwaldvogel.liblinear.Model;12 importde.bwaldvogel.liblinear.Parameter;13 importde.bwaldvogel.liblinear.Problem;14 importde.bwaldvogel.liblinear.SolverType;15

16 public classLibLinear{17 public static void main(String[] args) throwsException {18 //loading train data

19 Feature[][] featureMatrix = new Feature[5][];20 Feature[] featureMatrix1 = { new FeatureNode(2, 0.1), new FeatureNode(3, 0.2) };21 Feature[] featureMatrix2 = { new FeatureNode(2, 0.1), new FeatureNode(3, 0.3), new FeatureNode(4, -1.2)};22 Feature[] featureMatrix3 = { new FeatureNode(1, 0.4) };23 Feature[] featureMatrix4 = { new FeatureNode(2, 0.1), new FeatureNode(4, 1.4), new FeatureNode(5, 0.5) };24 Feature[] featureMatrix5 = { new FeatureNode(1, -0.1), new FeatureNode(2, -0.2), new FeatureNode(3, 0.1), new FeatureNode(4, -1.1), new FeatureNode(5, 0.1) };25 featureMatrix[0] =featureMatrix1;26 featureMatrix[1] =featureMatrix2;27 featureMatrix[2] =featureMatrix3;28 featureMatrix[3] =featureMatrix4;29 featureMatrix[4] =featureMatrix5;30 //loading target value

31 double[] targetValue = {1,-1,1,-1,0};32

33 Problem problem = newProblem();34 problem.l = 5; //number of training examples：训练样本数

35 problem.n = 5; //number of features：特征维数

36 problem.x = featureMatrix; //feature nodes：特征数据

37 problem.y = targetValue; //target values：类别

38

39 SolverType solver = SolverType.L2R_LR; //-s 0

40 double C = 1.0; //cost of constraints violation

41 double eps = 0.01; //stopping criteria

42

43 Parameter parameter = newParameter(solver, C, eps);44 Model model =Linear.train(problem, parameter);45 File modelFile = new File("model");46 model.save(modelFile);47 //load model or use it directly

48 model =Model.load(modelFile);49

50 Feature[] testNode = { new FeatureNode(1, 0.4), new FeatureNode(3, 0.3) };//test node51 double prediction =Linear.predict(model, testNode);52 System.out.print("classification result: "+prediction);53 }54 }

运行后得到testNode的分类结果：

3.参数说明

1. SolverType是solver的类型，可以是如下一种：

分类器：

L2R_LR：L2-regularized logistic regression (primal)

L2R_L2LOSS_SVC_DUAL：L2-regularized L2-loss support vector classification (dual)

L2R_L2LOSS_SVC：L2-regularized L2-loss support vector classification (primal)

L2R_L1LOSS_SVC_DUAL：L2-regularized L1-loss support vector classification (dual)

MCSVM_CS：supportvector classification by Crammer and Singer

L1R_L2LOSS_SVC：L1-regularized L2-loss support vector classification

L1R_LR：L1-regularized logistic regression

L2R_LR_DUAL：L2-regularized logistic regression (dual)

回归模型：

L2R_L2LOSS_SVR：L2-regularized L2-loss support vector regression (primal)

L2R_L2LOSS_SVR_DUAL：L2-regularized L2-loss support vector regression (dual)

L2R_L1LOSS_SVR_DUAL：L2-regularized L1-loss support vector regression (dual)

2. C是约束violation的代价参数 (默认为1)

3. eps是迭代停止条件的容忍度tolerance

本程序采用的训练样本如下(5个训练样本，5维特征)：

label

feature1

feature2

feature3

feature4

feature5

1

0

0.1

0.2

0

0

-1

0

0.1

0.3

-1.2

0

1

0.4

0

0

0

0

-1

0

0.1

0

1.4

0.5

0

-0.1

-0.2

0.1

1.1

0.1

测试样本为testNode变量：(0.4,0,0.3,0,0)

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