编写python程序实现朴素贝叶斯算法

好的，以下是实现朴素贝叶斯算法的Python代码：

import math

class NaiveBayesClassifier:
    def __init__(self):
        self.classes = {}
        self.class_probabilities = {}

    def fit(self, X, y):
        # 计算每个类别的出现次数
        for i in range(len(y)):
            if y[i] not in self.classes:
                self.classes[y[i]] = []
            self.classes[y[i]].append(X[i])

        # 计算每个类别的概率
        total_samples = len(y)
        for class_name in self.classes:
            class_samples = len(self.classes[class_name])
            self.class_probabilities[class_name] = class_samples / total_samples

    def predict(self, X):
        predictions = []
        for x in X:
            max_probability = -1
            most_likely_class = None

            # 对于每个类别，计算概率并找出概率最大的类别
            for class_name in self.classes:
                class_probability = self.class_probabilities[class_name]
                likelihood = 1.0
                for i in range(len(x)):
                    feature = x[i]
                    feature_occurrences = self.classes[class_name].count(feature)
                    feature_probability = feature_occurrences / len(self.classes[class_name])
                    likelihood *= feature_probability

                probability = class_probability * likelihood
                if probability > max_probability:
                    max_probability = probability
                    most_likely_class = class_name

            predictions.append(most_likely_class)

        return predictions

示例用法：

X_train = [[1, 'S'], [1, 'M'], [1, 'M'], [1, 'S'], [1, 'S'], [2, 'S'], [2, 'M'], [2, 'M'], [2, 'L'], [2, 'L'], [3, 'L'], [3, 'M'], [3, 'M'], [3, 'L'], [3, 'L']]
y_train = ['-', '-', '+', '+', '-', '-', '-', '+', '+', '+', '+', '+', '+', '+', '-']

X_test = [[2, 'S'], [1, 'S']]
y_test = ['-', '-']

# 初始化并训练分类器
clf = NaiveBayesClassifier()
clf.fit(X_train, y_train)

# 使用分类器进行预测
predictions = clf.predict(X_test)

# 输出预测结果
print(predictions) # ['-', '-']