Java 的svm代码

以下是一个简单的Java SVM分类器的示例代码：

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import libsvm.*;

public class SVMClassifier {
    
    public static void main(String[] args) throws IOException {
        List<Double> y = new ArrayList<Double>();
        List<svm_node[]> x = new ArrayList<svm_node[]>();
        BufferedReader reader = new BufferedReader(new FileReader("data.txt"));
        String line;
        while ((line = reader.readLine()) != null) {
            String[] parts = line.trim().split("\\s+");
            double label = Double.parseDouble(parts[0]);
            svm_node[] features = new svm_node[parts.length - 1];
            for (int i = 1; i < parts.length; i++) {
                String[] indexValue = parts[i].split(":");
                features[i - 1] = new svm_node();
                features[i - 1].index = Integer.parseInt(indexValue[0]);
                features[i - 1].value = Double.parseDouble(indexValue[1]);
            }
            y.add(label);
            x.add(features);
        }
        reader.close();
        
        svm_problem problem = new svm_problem();
        problem.l = y.size();
        problem.x = new svm_node[problem.l][];
        problem.y = new double[problem.l];
        for (int i = 0; i < problem.l; i++) {
            problem.x[i] = x.get(i);
            problem.y[i] = y.get(i);
        }
        
        svm_parameter param = new svm_parameter();
        param.svm_type = svm_parameter.C_SVC;
        param.kernel_type = svm_parameter.RBF;
        param.gamma = 0.5;
        param.nu = 0.5;
        param.cache_size = 20000;
        param.C = 1;
        param.eps = 0.001;
        
        svm_model model = svm.svm_train(problem, param);
        
        // Test the model
        Random rand = new Random();
        int correct = 0;
        for (int i = 0; i < 100; i++) {
            int index = rand.nextInt(y.size());
            svm_node[] testFeatures = x.get(index);
            double trueLabel = y.get(index);
            double predictedLabel = svm.svm_predict(model, testFeatures);
            if (predictedLabel == trueLabel) {
                correct++;
            }
        }
        System.out.println("Accuracy: " + (correct / 100.0));
    }
    
}

在上面的代码中，我们首先读取一个LIBSVM格式的训练数据集，并将其转换为libsvm库使用的数据结构。然后，我们定义一个SVM参数对象，设置一些参数，例如SVM类型、内核类型、惩罚系数等等。最后，我们使用训练数据和SVM参数来训练一个SVM模型，并使用随机生成的测试数据集来测试该模型的准确性。