58 lines
2.2 KiB
Python
58 lines
2.2 KiB
Python
import numpy as np
|
||
import pandas as pd
|
||
import numpy as np
|
||
from vmdpy import VMD
|
||
from skopt import gp_minimize
|
||
from skopt.space import Real, Integer, Categorical
|
||
from skopt.utils import use_named_args
|
||
|
||
|
||
# 加载数据
|
||
def do_vmd(dataframe):
|
||
signal = dataframe['power'].values
|
||
# 定义VMD函数，用于优化过程
|
||
def vmd_objective(params):
|
||
alpha, tau, K, DC, init, tol = params
|
||
u, u_hat, omega = VMD(signal, alpha, tau, K, DC, init, tol)
|
||
# 定义目标函数，这里使用模态的平均带宽作为优化目标
|
||
# 你可以根据实际需求定义其他目标函数
|
||
objective = np.mean([np.diff(np.where(um != 0)) for um in u])
|
||
return -objective # gp_minimize用于最小化，所以取负值
|
||
|
||
# 定义参数空间
|
||
space = [
|
||
Real(low=2, high=100, prior='log-uniform', name='alpha'),
|
||
Real(low=0, high=1, name='tau'),
|
||
Integer(low=2, high=10, name='K'),
|
||
Categorical(categories=[0, 1], name='DC'),
|
||
Categorical(categories=[0, 1], name='init'),
|
||
Real(low=1e-6, high=1e-2, prior='log-uniform', name='tol')
|
||
]
|
||
|
||
# 使用贝叶斯优化
|
||
@use_named_args(space)
|
||
def objective_function(alpha, tau, K, DC, init, tol):
|
||
return vmd_objective((alpha, tau, K, DC, init, tol))
|
||
|
||
result = gp_minimize(objective_function, space, n_calls=50, random_state=0)
|
||
|
||
print('最优参数:', result.x)
|
||
print('最优目标函数值:', -result.fun) # 取负值回到原始目标函数的值
|
||
|
||
# 使用找到的最优参数进行VMD分解
|
||
alpha_opt, tau_opt, K_opt, DC_opt, init_opt, tol_opt = result.x
|
||
u_opt, u_hat_opt, omega_opt = VMD(signal, alpha_opt, tau_opt, K_opt, DC_opt, init_opt, tol_opt)
|
||
|
||
# 保存或处理分解得到的模态函数
|
||
best_params = [alpha_opt, tau_opt, K_opt, DC_opt, init_opt, tol_opt]
|
||
vmd_rst = u_opt.T
|
||
|
||
|
||
vmd_train_data = pd.concat([data2vmd, pd.DataFrame.from_records(vmd_rst, index=data2vmd.index, columns=[f"vmd_{x}"for x in range(vmd_rst.shape[1])])], axis=1)
|
||
|
||
return vmd_train_data
|
||
|
||
if __name__ == '__main__':
|
||
data2vmd = pd.read_csv('./data/pv_data_hourly.csv', index_col=0)
|
||
train_data = do_vmd(data2vmd)
|
||
train_data.to_csv('./data/vmd_train.csv', index=False, encoding='utf-8-sig') |