代码实现朴素贝叶斯分类

以下是Python实现的朴素贝叶斯分类器的示例代码：

import math

class NaiveBayes:
    def __init__(self):
        self.classes = None
        self.class_probabilities = None
        self.conditional_probabilities = None

    def fit(self, X, y):
        self.classes = set(y)
        self.class_probabilities = {}
        self.conditional_probabilities = {}

        # 计算每个类别的概率
        for c in self.classes:
            self.class_probabilities[c] = sum(1 for i in y if i == c) / len(y)

        # 计算每个属性在每个类别下的条件概率
        for i in range(len(X[0])):
            for c in self.classes:
                key = (i, c)
                self.conditional_probabilities[key] = {}
                for xi in set([x[i] for x, yi in zip(X, y) if yi == c]):
                    self.conditional_probabilities[key][xi] = sum(1 for x, yi in zip(X, y) if x[i] == xi and yi == c) / sum(1 for yi in y if yi == c)

    def predict(self, X):
        y_pred = []
        for x in X:
            probabilities = {}
            for c in self.classes:
                probabilities[c] = math.log(self.class_probabilities[c])
                for i, xi in enumerate(x):
                    if (i, c) in self.conditional_probabilities:
                        if xi in self.conditional_probabilities[(i, c)]:
                            probabilities[c] += math.log(self.conditional_probabilities[(i, c)][xi])
                        else:
                            probabilities[c] += math.log(1e-10)
                y_pred.append(max(probabilities, key=probabilities.get))
        return y_pred

这个朴素贝叶斯分类器实现了`fit`和`predict`两个方法。`fit`方法用于训练模型，接受训练数据`X`和标签`y`作为输入。`predict`方法用于预测新的数据，接受测试数据`X`作为输入，返回预测结果。