编程实现贝叶斯分类算法，并对简单应用样本数据实现预测分类，添加准确度度计算函数，编写出java代码

以下是Java代码实现贝叶斯分类算法并进行准确度计算：

import java.util.HashMap;
import java.util.Map;

public class NaiveBayesClassifier {

    private Map<String, Integer> classCountMap;
    private Map<String, Map<String, Integer>> wordCountMap;

    public NaiveBayesClassifier() {
        classCountMap = new HashMap<>();
        wordCountMap = new HashMap<>();
    }

    public void train(String document, String label) {
        String[] words = document.split(" ");
        if (!classCountMap.containsKey(label)) {
            classCountMap.put(label, 0);
            wordCountMap.put(label, new HashMap<>());
        }
        classCountMap.put(label, classCountMap.get(label) + 1);
        for (String word : words) {
            if (!wordCountMap.get(label).containsKey(word)) {
                wordCountMap.get(label).put(word, 0);
            }
            wordCountMap.get(label).put(word, wordCountMap.get(label).get(word) + 1);            
        }
    }

    public String predict(String document) {
        String[] words = document.split(" ");
        String maxLabel = null;
        double maxProb = Double.NEGATIVE_INFINITY;
        for (String label : classCountMap.keySet()) {
            double prob = Math.log((double) classCountMap.get(label) / getTotalClassCount());
            for (String word : words) {
                int count = wordCountMap.get(label).containsKey(word) ? wordCountMap.get(label).get(word) : 0;
                prob += Math.log((double) (count + 1) / (getTotalWordCount(label) + getVocabularySize()));
            }
            if (prob > maxProb) {
                maxProb = prob;
                maxLabel = label;
            }
        }
        return maxLabel;
    }

    public double getAccuracy(String[] documents, String[] labels) {
        if (documents.length != labels.length) {
            throw new IllegalArgumentException("The number of documents must match the number of labels");
        }
        int correctCount = 0;
        for (int i = 0; i < documents.length; i++) {
            String prediction = predict(documents[i]);
            if (prediction.equals(labels[i])) {
                correctCount++;
            }
        }
        return (double) correctCount / documents.length;
    }

    private int getTotalClassCount() {
        int count = 0;
        for (int c : classCountMap.values()) {
            count += c;
        }
        return count;
    }

    private int getTotalWordCount(String label) {
        int count = 0;
        for (int c : wordCountMap.get(label).values()) {
            count += c;
        }
        return count;
    }

    private int getVocabularySize() {
        int size = 0;
        for (Map<String, Integer> wordCount : wordCountMap.values()) {
            size += wordCount.size();
        }
        return size;
    }

    public static void main(String[] args) {
        NaiveBayesClassifier classifier = new NaiveBayesClassifier();
        classifier.train("Chinese Beijing Chinese", "China");
        classifier.train("Chinese Chinese Shanghai", "China");
        classifier.train("Chinese Macao", "China");
        classifier.train("Tokyo Japan Chinese", "Japan");

        String[] documents = { "Chinese Chinese Chinese Tokyo Japan", "Chinese Tokyo Japan" };
        String[] labels = { "China", "Japan" };

        double accuracy = classifier.getAccuracy(documents, labels);
        System.out.println("Accuracy: " + accuracy);
    }
}

以上代码中的训练样本包括4个文档，标签分别为“China”和“Japan”。在`main`函数中，我们使用两个新的文档来测试分类器的准确度。具体而言，我们先训练分类器，然后对新的文档进行分类并计算分类器的准确度。在上面的代码中，我们使用 Laplace 平滑技术来处理不出现在训练样本中的词。对于每个文档，我们首先计算每个标签的概率，并使用对数可能性加法避免数值下溢。接着，我们计算每个单词在每个标签类别中的概率，并将它们相乘。最后，我们选择具有最大概率的标签作为预测。注意，在预测过程中，我们使用了相同的 Laplace 平滑技术来避免未知单词的影响。