使用python写一段RBF神经网络代码

以下是使用Python编写的RBF神经网络代码：

import numpy as np

class RBF:
    def __init__(self, k, lr=0.1, epochs=100):
        self.k = k
        self.lr = lr
        self.epochs = epochs
        self.centers = None
        self.weights = None

    def fit(self, X, y):
        # 初始化中心点
        self.centers = X[np.random.choice(X.shape[0], self.k, replace=False)]
        # 计算每个样本到中心点的距离
        distances = np.zeros((X.shape[0], self.k))
        for i in range(X.shape[0]):
            for j in range(self.k):
                distances[i][j] = np.linalg.norm(X[i] - self.centers[j])
        # 计算每个样本的输出
        phi = np.exp(-distances ** 2)
        # 使用最小二乘法计算权重
        self.weights = np.linalg.inv(phi.T @ phi) @ phi.T @ y

    def predict(self, X):
        distances = np.zeros((X.shape[0], self.k))
        for i in range(X.shape[0]):
            for j in range(self.k):
                distances[i][j] = np.linalg.norm(X[i] - self.centers[j])
        phi = np.exp(-distances ** 2)
        return phi @ self.weights

这是一个简单的RBF神经网络实现，其中k是中心点的数量，lr是学习率，epochs是训练轮数。fit方法用于训练模型，predict方法用于预测。