提供一段朴素贝叶斯分类的代码

以下是一个简单的朴素贝叶斯分类器实现的示例代码：

import numpy as np

class NaiveBayesClassifier():
    def fit(self, X, y):
        n_samples, n_features = X.shape
        self._classes = np.unique(y)
        n_classes = len(self._classes)

        # 初始化参数
        self._mean = np.zeros((n_classes, n_features), dtype=np.float64)
        self._var = np.zeros((n_classes, n_features), dtype=np.float64)
        self._priors = np.zeros(n_classes, dtype=np.float64)

        for c in self._classes:
            X_c = X[c==y]
            self._mean[c,:] = X_c.mean(axis=0)
            self._var[c,:] = X_c.var(axis=0)
            self._priors[c] = X_c.shape[0] / float(n_samples)

    def predict(self, X):
        y_pred = [self._predict(x) for x in X]
        return np.array(y_pred)

    def _predict(self, x):
        posteriors = []

        for idx, c in enumerate(self._classes):
            prior = np.log(self._priors[idx])
            class_conditional = np.sum(np.log(self._pdf(idx, x)))
            posterior = prior + class_conditional
            posteriors.append(posterior)

        return self._classes[np.argmax(posteriors)]

    def _pdf(self, class_idx, x):
        mean = self._mean[class_idx]
        var = self._var[class_idx]
        numerator = np.exp(-(x-mean)**2 / (2*var))
        denominator = np.sqrt(2*np.pi*var)
        return numerator / denominator

这个分类器支持多分类和多元特征，使用高斯分布作为类条件概率的估计方法。`fit` 方法用于训练模型，`predict` 方法用于对新数据进行预测。