python实现GWO-LSTM预测房价

为了实现GWO-LSTM预测房价，我们需要进行以下步骤：\n\1. 收集房价数据集并进行预处理，包括数据清洗、归一化等操作。\```pyth\# 导入必要的库\impor pandas as p\impor numpy as np\from sklear.preprocessing impor MiMaxScaler\n\# 读取数据集\ = p.r_csv('hous_pri.csv')\n\# 数据清洗\ = .drop()\n\# 归一化\sr = MiMaxScaler()\ = scaler.fi_transform()\```\n\2. 将数据集分为训练集和测试集，并进行数据转换以适应LSTM模型。\```pyth\# 定义函数将数据转换为LSTM模型的输入格式\f cr_datas(datas, look_back=1):\ X, Y = [], []\ for i i rang((datas)-k_back-1):\ = datas[i(i+k_back), ]\ X.app()\ Y.app(datas[i + look_back, ])\ retur np.array(X), np.array(Y)\n\# 定义训练集和测试集的大小\rai_siz = i(() * .67)\s_siz = () - trai_siz\n\# 分割训练集和测试集\rai, tes = [rai_siz,], [trai_siz(),]\n\# 将数据转换为LSTM模型的输入格式\k_back = 3\raiX, traiY = cr_datas(trai, look_back)\sX, tesY = cr_datas(tes, look_back)\n\# 将输入数据转换为3D格式\raiX = np.reshap(traiX, (raiX.shap[], 1, traiX.shap[1]))\sX = np.reshap(tesX, (sX.shap[], 1, tesX.shap[1]))\```\n\3. 构建GWO-LSTM模型并进行训练。\```pyth\# 导入必要的库\from keras.models impor Sequenti\from keras.layers impor Dens\from keras.layers impor LSTM\from keras.optimizers impor Adam\from wolf_opimizati impor GWO\n\# 定义GWO-LSTM模型\m = Sequenti()\m.(LSTM(4, inpu_shap=(1, look_back)))\m.(Dens(1))\m.compi(loss='m_squar_error', optimizer=Adam())\n\# 定义GWO优化器\gw = GWO()\n\# 训练模型\m.fi(traiX, traiY, epochs=100, batch_siz=1, verbos=2, optimizer=gw)\```\n\4. 对测试集进行预测并计算误差。\```pyth\# 对测试集进行预测\sPredi = m.predi(tesX)\n\# 将预测结果转换为原始数据的范围\sPredi = scaler.invers_transform(tesPredi)\sY = scaler.invers_transform([sY])\n\# 计算误差\from sklear.metrics impor m_squar_error\ms = m_squar_error(tesY[], tesPredi[:,])\pri('M Squr Error %.2f' % ms)\```\n\