echo [res]='restore database isa_test from disk =''d:\dbbackup\'+'isa'+'_'+convert(varchar(8),getdate(),112)+'.bak''' >>%RECOVERYFILE% echo + ' with move ''ISA_Data'' to ''D:\DATA\isa_test.mdf'',move''ISA_Log'' to ''D:\DATA\isa_test.ldf'';'

这是一个用于恢复数据库的SQL语句，它将从指定路径下的备份文件中还原名为isa_test的数据库，并将数据文件和日志文件移动到指定目录。其中，%RECOVERYFILE%是一个变量，表示恢复命令将被写入的文件路径。具体来说，这个语句会将如下恢复命令写入%RECOVERYFILE%文件：

restore database isa_test from disk ='d:\dbbackup\isa_20210609.bak' with move 'ISA_Data' to 'D:\DATA\isa_test.mdf',move'ISA_Log' to 'D:\DATA\isa_test.ldf';

其中，备份文件的路径为d:\dbbackup\isa_20210609.bak，这个备份文件是以“isa_日期.bak”的格式命名的，日期部分使用了getdate()函数获取当前日期，并转换为yyyyMMdd的格式。数据文件的新路径为D:\DATA\isa_test.mdf，日志文件的新路径为D:\DATA\isa_test.ldf。

相关问题

import numpy as np import matplotlib.pyplot as plt import pickle as pkl import pandas as pd import tensorflow.keras from tensorflow.keras.models import Sequential, Model, load_model from tensorflow.keras.layers import LSTM, GRU, Dense, RepeatVector, TimeDistributed, Input, BatchNormalization, \ multiply, concatenate, Flatten, Activation, dot from sklearn.metrics import mean_squared_error,mean_absolute_error from tensorflow.keras.optimizers import Adam from tensorflow.python.keras.utils.vis_utils import plot_model from tensorflow.keras.callbacks import EarlyStopping from keras.callbacks import ReduceLROnPlateau df = pd.read_csv('lorenz.csv') signal = df['signal'].values.reshape(-1, 1) x_train_max = 128 signal_normalize = np.divide(signal, x_train_max) def truncate(x, train_len=100): in_, out_, lbl = [], [], [] for i in range(len(x) - train_len): in_.append(x[i:(i + train_len)].tolist()) out_.append(x[i + train_len]) lbl.append(i) return np.array(in_), np.array(out_), np.array(lbl) X_in, X_out, lbl = truncate(signal_normalize, train_len=50) X_input_train = X_in[np.where(lbl <= 9500)] X_output_train = X_out[np.where(lbl <= 9500)] X_input_test = X_in[np.where(lbl > 9500)] X_output_test = X_out[np.where(lbl > 9500)] # Load model model = load_model("model_forecasting_seq2seq_lstm_lorenz.h5") opt = Adam(lr=1e-5, clipnorm=1) model.compile(loss='mean_squared_error', optimizer=opt, metrics=['mae']) #plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True) # Train model early_stop = EarlyStopping(monitor='val_loss', patience=20, verbose=1, mode='min', restore_best_weights=True) #reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=9, verbose=1, mode='min', min_lr=1e-5) #history = model.fit(X_train, y_train, epochs=500, batch_size=128, validation_data=(X_test, y_test),callbacks=[early_stop]) #model.save("lstm_model_lorenz.h5") # 对测试集进行预测 train_pred = model.predict(X_input_train[:, :, :]) * x_train_max test_pred = model.predict(X_input_test[:, :, :]) * x_train_max train_true = X_output_train[:, :] * x_train_max test_true = X_output_test[:, :] * x_train_max # 计算预测指标 ith_timestep = 10 # Specify the number of recursive prediction steps # List to store the predicted steps pred_len =2 predicted_steps = [] for i in range(X_output_test.shape[0]-pred_len+1): YPred =[],temdata = X_input_test[i,:] for j in range(pred_len): Ypred.append (model.predict(temdata)) temdata = [X_input_test[i,j+1:-1],YPred] # Convert the predicted steps into numpy array predicted_steps = np.array(predicted_steps) # Plot the predicted steps #plt.plot(X_output_test[0:ith_timestep], label='True') plt.plot(predicted_steps, label='Predicted') plt.legend() plt.show()

这段代码看起来是一个用于时间序列预测的深度学习模型。该模型使用了序列到序列 LSTM (Seq2Seq LSTM) 模型进行预测，使用了 EarlyStopping 回调函数来避免过度拟合，并使用 Adam 优化器来进行模型优化。

具体来说，该代码读取了一个名为 'lorenz.csv' 的数据文件，将其中的信号列读取出来并进行了归一化处理。然后，使用 truncate 函数将信号序列切割成训练集和测试集，将其输入到 Seq2Seq LSTM 模型中进行训练。训练完成后，对测试集进行预测并计算预测指标，最后使用 matplotlib 库将预测结果可视化。

如果需要更详细的解释或修改建议，请提出具体问题或要求。

给下列程序添加注释： void DWAPlannerROS::reconfigureCB(DWAPlannerConfig &config, uint32_t level) { if (setup_ && config.restore_defaults) { config = default_config_; config.restore_defaults = false; } if ( ! setup_) { default_config_ = config; setup_ = true; } // update generic local planner params base_local_planner::LocalPlannerLimits limits; limits.max_vel_trans = config.max_vel_trans; limits.min_vel_trans = config.min_vel_trans; limits.max_vel_x = config.max_vel_x; limits.min_vel_x = config.min_vel_x; limits.max_vel_y = config.max_vel_y; limits.min_vel_y = config.min_vel_y; limits.max_vel_theta = config.max_vel_theta; limits.min_vel_theta = config.min_vel_theta; limits.acc_lim_x = config.acc_lim_x; limits.acc_lim_y = config.acc_lim_y; limits.acc_lim_theta = config.acc_lim_theta; limits.acc_lim_trans = config.acc_lim_trans; limits.xy_goal_tolerance = config.xy_goal_tolerance; limits.yaw_goal_tolerance = config.yaw_goal_tolerance; limits.prune_plan = config.prune_plan; limits.trans_stopped_vel = config.trans_stopped_vel; limits.theta_stopped_vel = config.theta_stopped_vel; planner_util_.reconfigureCB(limits, config.restore_defaults); // update dwa specific configuration dp_->reconfigure(config); }

/**
* @brief Callback function for dynamic reconfiguration of DWA planner parameters
*
* @param config Reference to the configuration object that stores the updated parameters
* @param level The level of reconfiguration, unused in this function
*/
void DWAPlannerROS::reconfigureCB(DWAPlannerConfig &config, uint32_t level) {

// If the setup has been completed and restore_defaults flag is set, restore default configuration
if (setup_ && config.restore_defaults) {
config = default_config_;
config.restore_defaults = false;
}

// If setup has not been completed, store default configuration and set the setup flag to true
if ( ! setup_) {
default_config_ = config;
setup_ = true;
}

// Update generic local planner parameters
base_local_planner::LocalPlannerLimits limits;
limits.max_vel_trans = config.max_vel_trans;
limits.min_vel_trans = config.min_vel_trans;
limits.max_vel_x = config.max_vel_x;
limits.min_vel_x = config.min_vel_x;
limits.max_vel_y = config.max_vel_y;
limits.min_vel_y = config.min_vel_y;
limits.max_vel_theta = config.max_vel_theta;
limits.min_vel_theta = config.min_vel_theta;
limits.acc_lim_x = config.acc_lim_x;
limits.acc_lim_y = config.acc_lim_y;
limits.acc_lim_theta = config.acc_lim_theta;
limits.acc_lim_trans = config.acc_lim_trans;
limits.xy_goal_tolerance = config.xy_goal_tolerance;
limits.yaw_goal_tolerance = config.yaw_goal_tolerance;
limits.prune_plan = config.prune_plan;
limits.trans_stopped_vel = config.trans_stopped_vel;
limits.theta_stopped_vel = config.theta_stopped_vel;

// Call reconfigureCB function of the planner_util_ object with updated limits and restore_defaults flag
planner_util_.reconfigureCB(limits, config.restore_defaults);

// Call reconfigure function of the dp_ object with updated configuration
dp_->reconfigure(config);
}