IS_CLUSTER INSTALL=false
时间: 2023-10-23 20:12:10 浏览: 34
这是一个设置环境变量的命令,将 IS_CLUSTER 的值设置为 false,INSTALL 的值设置为 false。
IS_CLUSTER 是一个用于判断当前系统是否处于集群环境的变量,如果设置为 true,系统会以集群模式运行,否则会以单机模式运行。INSTALL 是一个用于指定是否需要安装软件的变量,如果设置为 true,系统会执行安装脚本,否则不会执行。
通过设置环境变量,可以方便地控制系统的行为。如果您有其他问题或需要更详细的解释,请告诉我。
相关问题
转matlab: n_points_total = numpy.int(noisy_sensor_measured_total.shape[1]/(n_obs_in_sensor_array + 1)) intrinsic_process_total_reshaped = numpy.reshape(intrinsic_process_total, [dim_intrinsic, n_points_total, n_obs_in_sensor_array + 1], order='C') noisy_sensor_measured_total_reshaped = numpy.reshape(noisy_sensor_measured_total, [dim_measurement, n_points_total, n_obs_in_sensor_array + 1], order='C') intrinsic_process_base_total = intrinsic_process_total_reshaped[:, :, 0] intrinsic_process_step_total = intrinsic_process_total_reshaped[:, :, 1:] noisy_sensor_base_total = noisy_sensor_measured_total_reshaped[:, :, 0] n_obs_used_in_each_cluster = min(n_obs_used_in_cluster, n_obs_in_sensor_array) obs_used_in_each_cluster_indexes = numpy.random.choice(n_obs_in_sensor_array, size=n_obs_used_in_each_cluster, replace=False) sensor_array_matrix = sensor_array_matrix_dense[:, obs_used_in_each_cluster_indexes] noisy_sensor_step_total = noisy_sensor_measured_total_reshaped[:, :, 1:]
在Matlab中,您可以将以下Python代码转换为对应的Matlab代码:
```matlab
n_points_total = int(size(noisy_sensor_measured_total, 2) / (n_obs_in_sensor_array + 1));
intrinsic_process_total_reshaped = reshape(intrinsic_process_total, [dim_intrinsic, n_points_total, n_obs_in_sensor_array + 1]);
noisy_sensor_measured_total_reshaped = reshape(noisy_sensor_measured_total, [dim_measurement, n_points_total, n_obs_in_sensor_array + 1]);
intrinsic_process_base_total = intrinsic_process_total_reshaped(:, :, 1);
intrinsic_process_step_total = intrinsic_process_total_reshaped(:, :, 2:end);
noisy_sensor_base_total = noisy_sensor_measured_total_reshaped(:, :, 1);
n_obs_used_in_each_cluster = min(n_obs_used_in_cluster, n_obs_in_sensor_array);
obs_used_in_each_cluster_indexes = datasample(1:n_obs_in_sensor_array, n_obs_used_in_each_cluster, 'Replace', false);
sensor_array_matrix = sensor_array_matrix_dense(:, obs_used_in_each_cluster_indexes);
noisy_sensor_step_total = noisy_sensor_measured_total_reshaped(:, :, 2:end);
```
请注意,上述代码中的 `datasample` 函数用于在 `1:n_obs_in_sensor_array` 中随机选择 `n_obs_used_in_each_cluster` 个索引,并且不允许重复。如果您的Matlab版本不支持 `datasample` 函数,可以使用以下代码替代:
```matlab
obs_used_in_each_cluster_indexes = randsample(1:n_obs_in_sensor_array, n_obs_used_in_each_cluster, false);
```
这里使用了 `randsample` 函数来实现相同的功能。
df_window_list = [] cluster_id_list = [] new_frame_cluster_list = [] linkage_matrix_list = [] max_cluster_id = 0 frame_cluster = 1 max_frame = max(df['frame']) # Gather the frames indexes in temporal windows for the whole video frames_window = self.frames_window if window is None else window frames_to_visit = np.arange(frames_window, max_frame, frames_window) frames_to_visit = np.append(frames_to_visit, max_frame)
这段代码首先定义了四个空列表df_window_list、cluster_id_list、new_frame_cluster_list、linkage_matrix_list,然后初始化了一些变量max_cluster_id、frame_cluster、max_frame和frames_window。其中max_frame是数据框df中的最大帧数,frames_window是一个整数,表示要处理的数据的窗口大小,window是一个可选参数,如果给定,则使用window的值,否则使用self.frames_window。接下来,使用numpy库的arange函数生成一个数组frames_to_visit,表示要处理的帧的索引,这些帧的索引按照frames_window的大小间隔。最后,将最大帧数max_frame添加到frames_to_visit数组中。
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