Q_cb = sum(theta_CB,3).*QCB_step;
时间: 2024-04-21 18:24:19 浏览: 14
这行代码在MATLAB中计算了一个名为 `Q_cb` 的矩阵。它通过将 `theta_CB` 在第三个维度上求和,并与 `QCB_step` 相乘来得到结果。
`theta_CB` 是一个三维矩阵,而 `sum(theta_CB,3)` 则是将 `theta_CB` 在第三个维度上进行求和,得到一个二维矩阵。
然后,通过使用 `.*` 运算符,将这个二维矩阵与 `QCB_step` 逐元素相乘,得到最终的结果矩阵 `Q_cb`。
请注意,这段代码假设 `theta_CB` 和 `QCB_step` 的维度是兼容的,以便进行元素级别的运算。如果你还有其他问题,请随时提问。
相关问题
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);
}
prob_theta = np.squeeze(prob_fit.theta_) prob_theta = prob_theta.reshape(-1, 1) coef_mat = np.column_stack((prob_theta, logit_fit.coef_[0], linear_fit.coef_[0]))
这段代码的作用是将三个模型的系数矩阵按列合并成一个矩阵`coef_mat`。其中,`prob_fit.theta_`是`GaussianNB`模型的系数矩阵,`logit_fit.coef_`是`LogisticRegression`模型的系数矩阵,`linear_fit.coef_`是`LinearRegression`模型的系数矩阵。
具体来说,`prob_fit.theta_`是一个形状为`(1, n)`的矩阵,其中`n`是特征的数量;`logit_fit.coef_`是一个形状为`(1, n)`的矩阵;`linear_fit.coef_`是一个形状为`(1, m)`的矩阵,其中`m`是特征的数量。为了将它们按列合并成一个矩阵,我们需要先将`prob_fit.theta_`转换成形状为`(n, 1)`的矩阵,然后再使用`np.column_stack`函数进行列合并。
具体的代码如下所示:
```python
prob_theta = np.squeeze(prob_fit.theta_)
prob_theta = prob_theta.reshape(-1, 1)
coef_mat = np.column_stack((prob_theta, logit_fit.coef_[0], linear_fit.coef_[0]))
```
这里使用了`np.squeeze`函数将`prob_fit.theta_`的维度从`(1, n)`压缩成`(n,)`,然后使用`reshape`函数将其转换成`(n, 1)`的矩阵。最后,使用`np.column_stack`函数将三个矩阵按列合并成一个矩阵`coef_mat`。