rbf神经网络python代码

对于RBF（Radial Basis Function）神经网络的Python代码，你可以使用一些库来实现，例如scikit-learn。

下面是一个示例代码：

from sklearn.cluster import KMeans
from sklearn.metrics.pairwise import euclidean_distances
from sklearn.preprocessing import OneHotEncoder
import numpy as np

class RBFNetwork:
    def __init__(self, n_centers, sigma=1.0):
        self.n_centers = n_centers
        self.sigma = sigma
        self.centers = None
        self.weights = None
        self.encoder = OneHotEncoder(sparse=False)

    def fit(self, X, y):
        kmeans = KMeans(n_clusters=self.n_centers)
        kmeans.fit(X)
        self.centers = kmeans.cluster_centers_

        distances = euclidean_distances(X, self.centers)
        phi = np.exp(-(distances ** 2) / (2 * self.sigma ** 2))

        self.encoder.fit(np.array([range(self.n_centers)]).T)
        phi_encoded = self.encoder.transform(np.argmax(phi, axis=1).reshape(-1, 1))

        self.weights = np.linalg.pinv(phi_encoded.T @ phi_encoded) @ phi_encoded.T @ y

    def predict(self, X):
        distances = euclidean_distances(X, self.centers)
        phi = np.exp(-(distances ** 2) / (2 * self.sigma ** 2))
        phi_encoded = self.encoder.transform(np.argmax(phi, axis=1).reshape(-1, 1))
        return phi_encoded @ self.weights

# 示例用法
X_train = np.array([[0], [1], [2], [3], [4], [5]])
y_train = np.array([0, 0, 1, 1, 2, 2])

rbf_net = RBFNetwork(n_centers=3)
rbf_net.fit(X_train, y_train)

X_test = np.array([[1.5], [3.5]])
predicted_labels = rbf_net.predict(X_test)
print(predicted_labels)

这个示例代码使用了scikit-learn库中的KMeans聚类算法来确定RBF网络的中心，然后使用欧几里得距离计算样本到中心的距离，并使用高斯函数（指数函数）作为激活函数。最后，使用OneHotEncoder对激活值进行编码，并使用线性回归来确定权重。