朴素贝叶斯分类器python

下面是一个简单的朴素贝叶斯分类器的python实现：

import numpy as np

class NaiveBayes:
    def fit(self, X, y):
        self.classes = np.unique(y)
        n_classes = len(self.classes)
        n_features = X.shape[1]
        self.mean = np.zeros((n_classes, n_features))
        self.var = np.zeros((n_classes, n_features))
        self.prior = np.zeros(n_classes)
        for i, c in enumerate(self.classes):
            X_c = X[y == c]
            self.mean[i, :] = X_c.mean(axis=0)
            self.var[i, :] = X_c.var(axis=0)
            self.prior[i] = X_c.shape[0] / X.shape[0]
            
    def predict(self, X):
        posteriors = []
        for i, c in enumerate(self.classes):
            prior = np.log(self.prior[i])
            likelihood = np.sum(np.log(self.pdf(X, self.mean[i, :], self.var[i, :])), axis=1)
            posterior = prior + likelihood
            posteriors.append(posterior)
        return self.classes[np.argmax(posteriors, axis=0)]
    
    def pdf(self, X, mean, var):
        eps = 1e-4
        numerator = np.exp(-0.5 * ((X - mean) ** 2) / (var + eps))
        denominator = np.sqrt(2 * np.pi * (var + eps))
        return numerator / denominator

这个实现包括两个方法：`fit`和`predict`。`fit`方法用于拟合训练数据，计算每个类别的均值和方差，并计算每个类别的先验概率。`predict`方法用于预测新数据的类别，计算每个类别的后验概率，然后选择具有最大后验概率的类别作为预测结果。

在`pdf`方法中，我们使用了一个小常数`eps`来避免分母为零的情况。这个常数的值可以根据具体应用进行调整。