径向基函数神经网络的中心和核宽由FCM计算，径向基函数神经网络的权重设置为线性函数 ，该径向基函数神经网络的Python代码

抱歉，上面的代码示例中没有包含模糊C均值（FCM）算法的实现。以下是一个完整的径向基函数神经网络的 Python 代码示例，包括了模糊C均值算法的实现：

import numpy as np

def fuzzy_c_means(data, num_centers, max_iter=100, epsilon=1e-6):
    num_samples, num_features = data.shape
    centers = np.random.rand(num_centers, num_features)
    membership = np.random.rand(num_samples, num_centers)
    membership /= np.sum(membership, axis=1)[:, np.newaxis]

    for _ in range(max_iter):
        prev_centers = centers.copy()

        # 更新聚类中心
        for i in range(num_centers):
            centers[i] = np.sum(data * (membership[:, i]**2)[:, np.newaxis], axis=0) / np.sum(membership[:, i]**2)

        # 更新隶属度
        distances = np.zeros((num_samples, num_centers))
        for i in range(num_centers):
            diff = data - centers[i]
            distances[:, i] = np.sum(diff**2, axis=1)
        membership = 1.0 / (distances + epsilon)
        membership /= np.sum(membership, axis=1)[:, np.newaxis]

        # 判断收敛条件
        if np.sum(np.abs(centers - prev_centers)) < epsilon:
            break

    widths = np.zeros(num_centers)
    for i in range(num_centers):
        diff = data - centers[i]
        widths[i] = np.max(np.sqrt(np.sum(diff**2, axis=1)))

    return centers, widths

class RBFN:
    def __init__(self, num_centers, num_features):
        self.num_centers = num_centers
        self.num_features = num_features
        self.centers = None
        self.widths = None
        self.weights = None

    def train(self, data, targets):
        self.centers, self.widths = fuzzy_c_means(data, self.num_centers)
        activation = self._calculate_activation(data)

        # 使用线性回归训练权重
        self.weights = np.linalg.lstsq(activation, targets, rcond=None)[0]

    def _calculate_activation(self, data):
        num_samples = data.shape[0]
        activation = np.zeros((num_samples, self.num_centers))
        for i in range(self.num_centers):
            diff = data - self.centers[i]
            activation[:, i] = np.exp(-0.5 * np.sum(diff**2 / self.widths[i]**2, axis=1))
        return activation

    def predict(self, data):
        activation = self._calculate_activation(data)
        predictions = np.dot(activation, self.weights)
        return predictions

在这个代码中，我们添加了名为 `fuzzy_c_means` 的函数来实现模糊C均值（FCM）算法。这个函数用于计算聚类中心和核宽。其余部分的代码与之前的示例相同。注意，在 `RBFN` 类的构造函数中，我们将聚类中心、核宽和权重初始化为 `None`。这是因为我们需要在训练过程中通过调用 `train` 方法来计算这些值。