写一个粒子群算法优化LSTM回归预测的代码

好的，下面是一个用粒子群算法优化LSTM回归预测的Python代码，其中使用了PySwarm库实现粒子群算法：

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM
from sklearn.metrics import mean_squared_error
from pyswarm import pso

# 准备数据
def prepare_data(data, lags):
    X, Y = [], []
    for i in range(len(data)-lags-1):
        X.append(data[i:(i+lags), 0])
        Y.append(data[(i+lags), 0])
    return np.array(X), np.array(Y)

# 创建LSTM模型
def create_model(lags):
    model = Sequential()
    model.add(LSTM(50, input_shape=(1, lags)))
    model.add(Dense(1))
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model

# 粒子群算法优化
def pso_optimization(train_X, train_Y, test_X, test_Y, lags):
    # 定义目标函数
    def objective_function(weights):
        # 将一维向量转为二维矩阵
        weights = np.reshape(weights, (lags, 1))
        # 构建LSTM模型
        model = create_model(lags)
        # 将权重设置到LSTM层
        model.layers[0].set_weights([weights])
        # 训练LSTM模型
        model.fit(train_X, train_Y, epochs=50, batch_size=1, verbose=0)
        # 预测测试集
        y_pred = model.predict(test_X)
        # 计算均方误差
        mse = mean_squared_error(test_Y, y_pred)
        return mse

    # 定义搜索空间
    lb = np.zeros((lags, 1))
    ub = np.ones((lags, 1))
    # 调用粒子群算法
    _, weights = pso(objective_function, lb, ub, swarmsize=10, maxiter=50)
    # 将一维向量转为二维矩阵
    weights = np.reshape(weights, (lags, 1))
    return weights

# 加载数据
data = np.loadtxt('data.csv', delimiter=',')
# 准备数据
lags = 3
train_size = int(len(data) * 0.67)
train, test = data[0:train_size], data[train_size:]
train_X, train_Y = prepare_data(train, lags)
test_X, test_Y = prepare_data(test, lags)
train_X = np.reshape(train_X, (train_X.shape[0], 1, train_X.shape[1]))
test_X = np.reshape(test_X, (test_X.shape[0], 1, test_X.shape[1]))
# 粒子群算法优化
weights = pso_optimization(train_X, train_Y, test_X, test_Y, lags)
# 创建LSTM模型
model = create_model(lags)
# 将权重设置到LSTM层
model.layers[0].set_weights([weights])
# 训练LSTM模型
model.fit(train_X, train_Y, epochs=50, batch_size=1, verbose=0)
# 预测测试集
y_pred = model.predict(test_X)
# 计算