如何使用循环中的I进行方差分析？

Question

如何更改此代码

train_0.append(0)
train_1.append(1)
train_2.append(2)
train_3.append(3)

使用loop-like-under？你知道吗

for i in range(4):
    train_i.append(i)

我的代码出现这个错误。你知道吗

NameError: name 'train_i' is not defined

谢谢你。你知道吗

Answer 1

有一些方法可以做到这一点，但通常如问题定义坏代码。。。最好用代码做些事情，把大量的变量转换成可写的东西。你知道吗

有一些方法：

for i in range(4):
    train = globals().get("train_{}".format(i), None)
    if train:
        train.append(i)

for i in range(4):
    try:
        eval("train_{0}.append({0})".format(i))
    except:
        pass

Answer 2

在类中，要定义self.variance，如何调整您的解决方案？你知道吗

for i in range(4):
        globals()["test_{}".format(i)].append(ToTensor(vectors[i]))

因为上面的代码可以在你的帮助下工作。你知道吗

但万一（在课堂上）不起作用。你知道吗

class MyDataset():
    def __init__(self, cropped_img_vectors, targets):
        self.data_0 = cropped_img_vectors[0]
        self.data_1 = cropped_img_vectors[1]
        self.data_2 = cropped_img_vectors[2]
        self.data_3 = cropped_img_vectors[3]
        self.targets = targets

    def __getitem__(self, index):
        data_0 = self.data_0[index]
        data_1 = self.data_1[index]
        data_2 = self.data_2[index]
        data_3 = self.data_3[index]
        y = self.targets[index]
        dataset = []
        for i in range(4):
            dataset.append(["data_{}".format(i)])
        return dataset, y

    def __len__(self):
        return len(self.data_0)

我把上面改成下面。你知道吗

class MyDataset():
    def __init__(self, cropped_1pixel_dataset, targets):
        for i in range(4):
            globals()["self.data_{}".format(i)] = cropped_1pixel_dataset[i]
        self.targets = targets

    def __getitem__(self, index):
        for i in range(4):
            globals()["data_{}".format(i)] = cropped_1pixel_dataset[i][index]
        y = self.targets[index]
        return [globals()["data_{}".format(i)] for i in range(4)], y

    def __len__(self):
        return len(self.data_0)

运行完这个单元后

MyDataset(train_cropped_1pixel_dataset, train_dataset.targets)

可能会发生这种错误。你知道吗

                                     -
AttributeError                            Traceback (most recent call last)
<ipython-input-11-960ee70394c1> in <module>
      3 train_loader = torch.utils.data.DataLoader(dataset = train_dataset,
      4                                            batch_size = batch_size,
  > 5                                            shuffle = True)

~/.local/lib/python3.5/site-packages/torch/utils/data/dataloader.py in __init__(self, dataset, batch_size, shuffle, sampler, batch_sampler, num_workers, collate_fn, pin_memory, drop_last, timeout, worker_init_fn)
    800             if sampler is None:
    801                 if shuffle:
 > 802                     sampler = RandomSampler(dataset)
    803                 else:
    804                     sampler = SequentialSampler(dataset)

~/.local/lib/python3.5/site-packages/torch/utils/data/sampler.py in __init__(self, data_source, replacement, num_samples)
     58 
     59         if self.num_samples is None:
 -> 60             self.num_samples = len(self.data_source)
     61 
     62         if not isinstance(self.num_samples, int) or self.num_samples <= 0:

<ipython-input-10-293dc919d173> in __len__(self)
     12 
     13     def __len__(self):
 -> 14         return len(self.data_0)

AttributeError: 'MyDataset' object has no attribute 'data_0'

我真的需要帮助。。 非常感谢。你知道吗

Answer 3

如果在全局范围中定义了所有train_<i>变量，则可以通过globals()访问它们。Demo

train_0 = []
train_1 = []
train_2 = []
train_3 = []


for i in range(4):
    globals()[f'train_{i}'].append(i)

print(train_0, train_1, train_2, train_3)