noisy_data_list = list(map(read_img, noisy_name_list, xx_list, yy_list)) File "/home/aistudio/EMVD/load_data_paddle.py", line 161, in read_img xx:xx + cfg.image_width * 2] # 256 * 256 TypeError: 'NoneType' object is not subscriptable
时间: 2024-03-31 18:37:38 浏览: 19
这个错误通常发生在尝试对一个None对象进行索引或切片操作时。在你的代码中,可能是 read_img 函数返回了None对象,导致后面对它进行了索引或切片操作。你需要检查 read_img 函数的代码,确认它返回的对象是有效的,不是None。如果 read_img 函数确实返回了None,那么你需要找到原因,并修复代码,使其返回一个有效的对象。你可以通过添加条件检查来避免这个错误,例如 if my_object is not None: 来判断对象是否为空值。
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。
fim_noisy = np.zeros_like(fim)
这段代码的作用是创建一个与fim数组大小相同的全零数组fim_noisy,用于保存添加噪声后的频繁项集。
具体来说,前面的代码通过向fim_k添加一定的噪声得到了添加噪声后的频繁项集fim_k_noisy。而这段代码则是在创建一个新的数组fim_noisy,用于保存fim_k_noisy的值。由于fim_noisy是一个全零数组,因此需要将fim_k_noisy的值赋给fim_noisy。这样,就得到了一个添加噪声后的频繁项集fim_noisy,其中每个项都经过了一定的随机扰动,保护了频繁项集的隐私。