package liblinear;

 import java.io.File;

 import java.io.IOException;

 import java.util.ArrayList;

 import java.util.List;

 import de.bwaldvogel.liblinear.Feature;

 import de.bwaldvogel.liblinear.FeatureNode;

 import de.bwaldvogel.liblinear.Linear;

 import de.bwaldvogel.liblinear.Model;

 import de.bwaldvogel.liblinear.Parameter;

 import de.bwaldvogel.liblinear.Problem;

 import de.bwaldvogel.liblinear.SolverType;

 public class LibLinear{

     public static void main(String[] args) throws Exception {

         //loading train data

         Feature[][] featureMatrix = new Feature[5][];

         Feature[] featureMatrix1 = { new FeatureNode(2, 0.1), new FeatureNode(3, 0.2) };

         Feature[] featureMatrix2 = { new FeatureNode(2, 0.1), new FeatureNode(3, 0.3), new FeatureNode(4, -1.2)};

         Feature[] featureMatrix3 = { new FeatureNode(1, 0.4) };

         Feature[] featureMatrix4 = { new FeatureNode(2, 0.1), new FeatureNode(4, 1.4), new FeatureNode(5, 0.5) };

         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) };

         featureMatrix[0] = featureMatrix1;

         featureMatrix[1] = featureMatrix2;

         featureMatrix[2] = featureMatrix3;

         featureMatrix[3] = featureMatrix4;

         featureMatrix[4] = featureMatrix5;

         //loading target value

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

         Problem problem = new Problem();

         problem.l = 5; // number of training examples：训练样本数

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

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

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

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

         double C = 1.0;    // cost of constraints violation

         double eps = 0.01; // stopping criteria

         Parameter parameter = new Parameter(solver, C, eps);

         Model model = Linear.train(problem, parameter);

         File modelFile = new File("model");

         model.save(modelFile);

         // load model or use it directly

         model = Model.load(modelFile);

         Feature[] testNode = { new FeatureNode(1, 0.4), new FeatureNode(3, 0.3) };//test node

         double prediction = Linear.predict(model, testNode);

         System.out.print("classification result: "+prediction);

     }

 }