使用Mallet加载模型并对输入进行分类

import cc.mallet.classify.Classifier;
import cc.mallet.pipe.iterator.CsvIterator;
import cc.mallet.types.Instance;
import cc.mallet.types.InstanceList;

import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.ObjectInputStream;
import java.util.Iterator;

public class ModelLoader {
    public static void main(String[] args) {
        String modelPath = "path/to/model";
        String inputPath = "path/to/input";

        // 加载模型
        Classifier classifier = loadModel(modelPath);

        // 加载输入数据
        InstanceList instances = loadInputData(inputPath);

        // 对输入进行分类
        for (Instance instance : instances) {
            double[] probabilities = classifier.classify(instance).getValues();
            // 根据概率值确定分类结果
            int predictedLabel = classifier.classify(instance).getLabeling().getBestIndex();
            System.out.println("Predicted label: " + predictedLabel);
            System.out.println("Probabilities: " + Arrays.toString(probabilities));
        }
    }

    private static Classifier loadModel(String modelPath) {
        try {
            FileInputStream fileInputStream = new FileInputStream(new File(modelPath));
            ObjectInputStream objectInputStream = new ObjectInputStream(fileInputStream);
            Classifier classifier = (Classifier) objectInputStream.readObject();
            objectInputStream.close();
            fileInputStream.close();
            return classifier;
        } catch (IOException | ClassNotFoundException e) {
            e.printStackTrace();
        }
        return null;
    }

    private static InstanceList loadInputData(String inputPath) {
        try {
            Iterator<Instance> iterator = new CsvIterator(new File(inputPath), "(\\w+)\\s+(\\w+)\\s+(.*)", 3, 2, 1);
            InstanceList instances = new InstanceList(iterator);
            return instances;
        } catch (IOException e) {
            e.printStackTrace();
        }
        return null;
    }
}