QingLong/src/main/java/UnitTest/NaiveBayesClassifier.java

package UnitTest;

import org.apache.commons.codec.binary.Base64;

import java.security.KeyFactory;
import java.security.NoSuchAlgorithmException;
import java.security.interfaces.RSAPublicKey;
import java.security.spec.InvalidKeySpecException;
import java.security.spec.X509EncodedKeySpec;
import java.util.HashMap;
import java.util.Map;

public class NaiveBayesClassifier {

    private Map<String, Integer> classCounts;
    private Map<String, Map<String, Integer>> wordCounts;
    private Map<String, Integer> wordTotalCounts;
    private int totalDocuments;

    public NaiveBayesClassifier() {
        classCounts = new HashMap<>();
        wordCounts = new HashMap<>();
        wordTotalCounts = new HashMap<>();
        totalDocuments = 0;
    }

    public void train(String[] document, String className) {
        // 统计类别计数
        classCounts.put(className, classCounts.getOrDefault(className, 0) + 1);

        // 统计每个单词在每个类别中的计数
        if (!wordCounts.containsKey(className)) {
            wordCounts.put(className, new HashMap<>());
        }
        for (String word : document) {
            Map<String, Integer> wordClassCounts = wordCounts.get(className);
            wordClassCounts.put(word, wordClassCounts.getOrDefault(word, 0) + 1);

            // 统计每个单词在所有类别中的计数
            wordTotalCounts.put(word, wordTotalCounts.getOrDefault(word, 0) + 1);
        }

        totalDocuments++;
    }

    public String classify(String[] document) {
        String bestClass = null;
        double bestScore = Double.NEGATIVE_INFINITY;

        for (String className : classCounts.keySet()) {
            double score = calculateClassScore(document, className);

            if (score > bestScore) {
                bestScore = score;
                bestClass = className;
            }
        }

        return bestClass;
    }

    private double calculateClassScore(String[] document, String className) {
        double score = Math.log((double) classCounts.get(className) / totalDocuments);

        for (String word : document) {
            int wordCount = wordCounts.get(className).getOrDefault(word, 0);
            int totalWordCount = wordTotalCounts.getOrDefault(word, 0);
            double wordProbability = (wordCount + 1.0) / (totalWordCount + 2.0);

            score += Math.log(wordProbability);
        }

        return score;
    }

    public static void main(String[] args) throws NoSuchAlgorithmException, InvalidKeySpecException {

        byte[] decoded = Base64.decodeBase64("");
        RSAPublicKey pubKey = (RSAPublicKey)
                KeyFactory.getInstance("RSA").generatePublic(new
                        X509EncodedKeySpec(decoded));
        NaiveBayesClassifier classifier = new NaiveBayesClassifier();

        // 训练数据
        String[] doc1 = {"Chinese", "Beijing", "Chinese"};
        classifier.train(doc1, "China");

        String[] doc2 = {"Chinese", "Chinese", "Shanghai"};
        classifier.train(doc2, "China");

        String[] doc3 = {"Chinese", "Macao"};
        classifier.train(doc3, "China");

        String[] doc4 = {"Tokyo", "Japan", "Chinese"};
        classifier.train(doc4, "Japan");

        String[] doc5 = {"Chinese", "Tokyo", "Japan"};
        classifier.train(doc5, "Japan");

        String[] doc6 = {"Chinese", "Chinese", "Chinese", "Tokyo"};
        classifier.train(doc6, "Japan");

        // 测试数据
        String[] testDoc = {"Chinese", "Chinese", "Chinese", "Shanghai", "Shanghai", "Chinese", "Chinese", "Shanghai", "Shanghai", "Japan"};

        // 分类
        String classifiedClass = classifier.classify(testDoc);
        System.out.println("Classifier predicted class: " + classifiedClass);
    }
}