liblinear参数及使用方法（原创）

开发语言：JAVA

开发工具：eclipse (下载地址 http://www.eclipse.org/downloads/)

liblinear版本：liblinear-1.94.jar （下载地址：http://liblinear.bwaldvogel.de/）

更多信息请参考：http://www.csie.ntu.edu.tw/~cjlin/liblinear/

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 package liblinear;
 2
 3 import java.io.File;
 4 import java.io.IOException;
 5 import java.util.ArrayList;
 6 import java.util.List;
 7
 8 import de.bwaldvogel.liblinear.Feature;
 9 import de.bwaldvogel.liblinear.FeatureNode;
10 import de.bwaldvogel.liblinear.Linear;
11 import de.bwaldvogel.liblinear.Model;
12 import de.bwaldvogel.liblinear.Parameter;
13 import de.bwaldvogel.liblinear.Problem;
14 import de.bwaldvogel.liblinear.SolverType;
15
16 public class LibLinear{
17     public static void main(String[] args) throws Exception {
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 = new Problem();
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 = new Parameter(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 node
51         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

本程序采用的训练样本如下（5个训练样本，5维特征）：

测试样本为testNode变量：（0.4,0,0.3,0,0）