实现贝叶斯分类算法，对简单应用样本数据实现预测分类，并计算条件概率，预测精度的计算与评估，写出java代码

好的，以下是实现贝叶斯分类算法的Java代码示例：

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

public class NaiveBayesClassifier {

private Map<String, Map<String, Integer>> frequencyTable;

private Map<String, Integer> classCountTable;

private Map<String, Double> classProbabilityTable;

private int totalDocCount;

public NaiveBayesClassifier() {
frequencyTable = new HashMap<>();
classCountTable = new HashMap<>();
classProbabilityTable = new HashMap<>();
totalDocCount = 0;
}

public void train(String[] documents, String[] labels) {
for (int i = 0; i < documents.length; i++) {
String document = documents[i];
String label = labels[i];

// 更新类别计数
classCountTable.put(label, classCountTable.getOrDefault(label, 0) + 1);

// 更新文档总数
totalDocCount++;

// 统计单词词频
String[] words = document.split(" ");
for (String word : words) {
Map<String, Integer> classFrequencyTable = frequencyTable.getOrDefault(word, new HashMap<>());
classFrequencyTable.put(label, classFrequencyTable.getOrDefault(label, 0) + 1);
frequencyTable.put(word, classFrequencyTable);
}
}

// 计算类别概率
for (String label : classCountTable.keySet()) {
int count = classCountTable.get(label);
double probability = (double) count / totalDocCount;
classProbabilityTable.put(label, probability);
}
}

public String classify(String document) {
String[] words = document.split(" ");

Map<String, Double> classScoreTable = new HashMap<>();

for (String label : classCountTable.keySet()) {
double score = classProbabilityTable.get(label);

for (String word : words) {
Map<String, Integer> classFrequencyTable = frequencyTable.getOrDefault(word, new HashMap<>());
double wordProb = (double) classFrequencyTable.getOrDefault(label, 0) / classCountTable.get(label);
score *= wordProb;
}

classScoreTable.put(label, score);
}

// 找到分数最大的类别
String maxClass = null;
double maxScore = Double.NEGATIVE_INFINITY;

for (String label : classScoreTable.keySet()) {
double score = classScoreTable.get(label);
if (score > maxScore) {
maxScore = score;
maxClass = label;
}
}

return maxClass;
}

public double evaluate(String[] documents, String[] labels) {
int correctCount = 0;

for (int i = 0; i < documents.length; i++) {
String document = documents[i];
String label = labels[i];
String predictedLabel = classify(document);

if (label.equals(predictedLabel)) {
correctCount++;
}
}

return (double) correctCount / documents.length;
}

public static void main(String[] args) {
String[] documents = {
"Chinese Beijing Chinese",
"Chinese Chinese Shanghai",
"Chinese Macao",
"Tokyo Japan Chinese"
};
String[] labels = {
"China",
"China",
"China",
"Japan"
};

NaiveBayesClassifier classifier = new NaiveBayesClassifier();
classifier.train(documents, labels);

String document = "Chinese Chinese Chinese Tokyo Japan";
String predictedLabel = classifier.classify(document);

System.out.println("Predicted label: " + predictedLabel);

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

该代码实现了贝叶斯分类算法，并对简单应用样本数据进行了预测分类，并计算了条件概率，预测精度的计算与评估。但请注意，该示例代码并不是针对特定应用场景的，需要根据使用场景进行相应的修改和优化。