用python代码实现朴素贝叶斯算法

好的，下面是 Python 实现的朴素贝叶斯算法示例代码：

import numpy as np

class NaiveBayes:
    def __init__(self):
        self.classes = None
        self.means = None
        self.std_devs = None

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

        for i, c in enumerate(self.classes):
            X_c = X[y == c]
            self.means[i, :] = X_c.mean(axis=0)
            self.std_devs[i, :] = X_c.std(axis=0)

    def predict(self, X):
        y_pred = np.zeros(X.shape[0])
        for i, x in enumerate(X):
            posterior_probs = []
            for j, c in enumerate(self.classes):
                prior_prob = np.log(self.prior_prob(c))
                posterior_prob = np.sum(np.log(self.likelihood(x, j)))
                posterior_probs.append(prior_prob + posterior_prob)
            y_pred[i] = self.classes[np.argmax(posterior_probs)]
        return y_pred

    def prior_prob(self, class_value):
        return np.mean(self.y_train == class_value)

    def likelihood(self, x, class_index):
        mean = self.means[class_index]
        std_dev = self.std_devs[class_index]
        numerator = np.exp(-((x - mean) ** 2) / (2 * (std_dev ** 2)))
        denominator = np.sqrt(2 * np.pi) * std_dev
        return numerator / denominator

这里实现了一个简单的朴素贝叶斯分类器，包括拟合和预测方法。其中，拟合方法用于计算训练数据的均值和标准差，预测方法用于预测新数据的类别。在预测时，首先计算每个类别的先验概率和样本的似然概率，然后将它们相加得到后验概率最大的类别作为预测结果。