給出以上问题的python代码

好的，下面是利用Python实现RBFN逼近函数f的代码，代码中使用了Scikit-learn库中的RBFN模型：

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.metrics import mean_squared_error
from sklearn.metrics.pairwise import euclidean_distances
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
from sklearn.model_selection import GridSearchCV

# 定义函数f
def f(x1, x2):
    return 20 + x1**2 - 10*np.cos(2*np.pi*x1) - 10*np.cos(2*np.pi*x2)

# 生成样本数据
X = np.random.rand(100, 2)
y = np.array([f(x[0], x[1]) for x in X]).reshape(-1, 1)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 使用K-Means算法选择中心点
kmeans = KMeans(n_clusters=10, random_state=0).fit(X_train)
centers = kmeans.cluster_centers_

# 计算径向基函数的输出
rbf = euclidean_distances(X_train, centers)
rbf = np.exp(-0.1*rbf**2)

# 使用最小二乘法拟合RBFN模型
model = LinearRegression().fit(rbf, y_train)

# 在测试集上进行预测
rbf_test = euclidean_distances(X_test, centers)
rbf_test = np.exp(-0.1*rbf_test**2)
y_pred = model.predict(rbf_test)

# 计算R2、MAE和RMSE等指标
r2 = r2_score(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))

print("R2 score:", r2)
print("MAE:", mae)
print("RMSE:", rmse)

在上面的代码中，我们首先定义了函数f，并使用随机数生成器生成了100个样本数据。然后使用K-Means算法选择10个中心点，并计算径向基函数的输出。接着使用最小二乘法拟合RBFN模型，并在测试集上进行预测，最后计算R2、MAE和RMSE等指标来评估模型的性能。