package SVM;

import libsvm.svm;

import libsvm.svm_model;

import libsvm.svm_node;

import libsvm.svm_parameter;

import libsvm.svm_problem;

public class jmain {

/**

* @param args

*/

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}

svm_node pa0 = new svm_node();

pa0.index = 0;

pa0.value = 176.0;

svm_node pa1 = new svm_node();

pa1.index = 1;

pa1.value = 70.0;

svm_node pb0 = new svm_node();

pb0.index = 0;

pb0.value = 180.0;

svm_node pb1 = new svm_node();

pb1.index = 1;

pb1.value = 80.0;

svm_node pc0 = new svm_node();

pb0.index = 0;

pb0.value = 161.0;

svm_node pc1 = new svm_node();

pb1.index = 1;

pb1.value = 45.0;

svm_node pd0 = new svm_node();

pb0.index = 0;

pb0.value = 163.0;

svm_node pd1 = new svm_node();

pb1.index = 1;

pb1.value = 47.0;

svm_node[] pa = {pa0, pa1}; //点a

svm_node[] pb = {pb0, pb1}; //点b

svm_node[] pc = {pc0, pc1}; //点c

svm_node[] pd = {pd0, pd1}; //点d

svm_node[][] datas = {pa, pb,pc,pd}; //训练集的向量表

double[] lables = {1.0,1.0,2.0,2.0}; //a,b 对应的lable

//定义svm_problem对象

svm_problem problem = new svm_problem();

problem.l = 4; //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.C_SVC;

param.kernel_type = svm_parameter.LINEAR;

param.cache_size = 100;

param.eps = 0.00001;

param.C = 1;

//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

//定义测试数据点c

svm_node pe0 = new svm_node();

pc0.index = 0;

pc0.value = 165.0;

svm_node pe1 = new svm_node();

pc1.index = 1;

pc1.value = 50.0;

svm_node[] pe = {pe0, pe1};

//预测测试数据的lable

System.out.println(svm.svm_predict(model, pe));

}

}