给我一串实现贝叶斯算法的代码

以下是一个简单的实现贝叶斯算法的 Python 代码：

import numpy as np

class NaiveBayes:
    def __init__(self):
        self.classes = None
        self.class_priors = None
        self.feature_likelihoods = None

    def fit(self, X, y):
        self.classes = np.unique(y)
        n_classes = len(self.classes)
        n_features = X.shape[1]

        # Calculate class priors
        self.class_priors = np.zeros(n_classes)
        for i, c in enumerate(self.classes):
            self.class_priors[i] = np.sum(y == c) / float(len(y))

        # Calculate feature likelihoods
        self.feature_likelihoods = np.zeros((n_classes, n_features))
        for i, c in enumerate(self.classes):
            X_c = X[y == c]
            self.feature_likelihoods[i, :] = np.mean(X_c, axis=0)

    def predict(self, X):
        y_pred = np.zeros(len(X))
        for i, x in enumerate(X):
            posteriors = []
            for j, c in enumerate(self.classes):
                # Calculate posterior probability for each class
                posterior = self.class_priors[j] * np.prod(self.feature_likelihoods[j, :] ** x * (1 - self.feature_likelihoods[j, :]) ** (1 - x))
                posteriors.append(posterior)
            # Make prediction based on class with highest posterior probability
            y_pred[i] = self.classes[np.argmax(posteriors)]
        return y_pred

这个代码实现了基于朴素贝叶斯算法的分类器。其中 `fit` 方法用于训练模型，`predict` 方法用于预测新样本的分类。在训练过程中，先计算了每个类别的先验概率，然后计算了每个特征在每个类别中的条件概率。在预测过程中，对于每个新样本，分别计算其属于每个类别的后验概率，并选择后验概率最大的类别作为预测结果。