我可以为 ELM 模型应用泛化代码吗？

如何解决我可以为 ELM 模型应用泛化代码吗？

我使用极限学习机 (ELM) 模型进行预测，并将我的数据集划分为 70% 的训练数据和 30% 的测试数据。我想添加用于交叉验证的代码，我尝试这样做，但在这样做时遇到了问题。请任何人都可以帮助我。

我的代码：

#------------------------------import data--------------
train = pd.read_excel('INRStrai.xlsx')
test = pd.read_excel('INRStes.xlsx')

#--------------------------------(scaler data)------------
scaler = MinMaxScaler()
scaler_X = MinMaxScaler()
scaler_Y = MinMaxScaler()
# fit_transform for training data:
X_train = scaler_X.fit_transform(train.values[:,:-1])
y_train = scaler_Y.fit_transform(train.values[:,-1:])
X_test = scaler_X.transform(test.values[:,:-1])
y_test = scaler_Y.transform(test.values[:,-1:])
#----------------------------(input size)-------------
input_size = X_train.shape[1]

#---------------------------(Number of neurons)-------
hidden_size = 17

#---------------------------(To fix the RESULT)-------
seed =16   # can be any number,and the exact value does not matter
np.random.seed(seed)

#---------------------------(weights & biases)------------
input_weights = np.random.normal(size=[input_size,hidden_size])
biases = np.random.normal(size=[hidden_size])

#----------------------(Activation Function)----------
def relu(x):
   return np.maximum(x,x)

#--------------------------(Calculations)----------
def hidden_nodes(X):
    G = np.dot(X,input_weights)
    G = G + biases
    H = relu(G)
    return H

#Output weights 
output_weights = np.dot(pinv2(hidden_nodes(X_train)),y_train)


#------------------------(Def prediction)---------
def predict(X):
    out = hidden_nodes(X)
    out = np.dot(out,output_weights)
    return out


#------------------------------------(Make_PREDICTION)--------------
prediction = predict(X_test)
#unscaler_prediction=prediction*(4.58620-1.15000)+1.15000
#unscaler_y_train=y_train*(4.58620-1.15000)+1.15000
unscaler_prediction=prediction*(4.5862069-1.23333333)+1.23333333
unscaler_y_test=y_test*(4.5862069-1.23333333)+1.23333333