帮我写一个朴素贝叶斯算法用python

好的，我可以帮您写一个朴素贝叶斯算法，以下是代码示例：

import pandas as pd
import numpy as np

class NaiveBayes:

    def __init__(self):
        self.class_priors = None
        self.conditional_prob = None

    def fit(self, X, y):
        n_samples, n_features = X.shape

        # 计算先验概率
        self.class_priors = {}
        for c in np.unique(y):
            self.class_priors[c] = np.mean(y == c)

        # 计算条件概率
        self.conditional_prob = {}
        for f in range(n_features):
            self.conditional_prob[f] = {}
            for c in np.unique(y):
                X_c = X[y == c]
                self.conditional_prob[f][c] = {
                    'mean': X_c[:, f].mean(),
                    'std': X_c[:, f].std(),
                }

    def predict(self, X):
        n_samples, n_features = X.shape
        classes = list(self.class_priors.keys())
        n_classes = len(classes)

        # 计算后验概率
        posterior_prob = np.zeros((n_samples, n_classes))
        for i in range(n_samples):
            for j in range(n_classes):
                class_prior = self.class_priors[classes[j]]
                posterior_prob[i, j] = np.log(class_prior)
                for f in range(n_features):
                    mean = self.conditional_prob[f][classes[j]]['mean']
                    std = self.conditional_prob[f][classes[j]]['std']
                    x = X[i, f]
                    likelihood = self._gaussian_pdf(x, mean, std)
                    posterior_prob[i, j] += np.log(likelihood)

        # 返回预测结果
        return [classes[np.argmax(posterior_prob[i, :])] for i in range(n_samples)]

    @staticmethod
    def _gaussian_pdf(x, mean, std):
        eps = 1e-4
        variance = np.maximum(std ** 2, eps)
        return np.exp(-0.5 * ((x - mean) ** 2) / variance) / np.sqrt(2 * np.pi * variance)

此处为一个朴素贝叶斯分类器的示例代码，您可以根据自己的需求调整和扩展代码。