如何遍历此n维数据集? [英] How to iterate over this n-dimensional dataset?

问题描述

我有一个dataset，它有4个维度(目前...)，我需要对其进行迭代.

I have a dataset which has 4 dimensions (for now...) and I need to iterate over it.

要访问dataset中的值，请执行以下操作:

To access a value in the dataset, I do this:

value = dataset[i,j,k,l]

现在，我可以获取dataset的shape:

shape = [4,5,2,6]

shape中的值表示尺寸的长度.

The values in shape represent the length of the dimension.

在给定维数的情况下，如何遍历数据集中的所有元素?这是一个示例:

How, given the number of dimensions, can I iterate over all the elements in my dataset? Here is an example:

for i in range(shape[0]):
    for j in range(shape[1]):
        for k in range(shape[2]):
            for l in range(shape[3]):
                print('BOOM')
                value = dataset[i,j,k,l]

将来，shape可能会更改.因此，例如，shape可能包含10个元素，而不是当前的4个.

In the future, the shape may change. So for example, shape may have 10 elements rather than the current 4.

使用Python 3可以做到这一点吗?

Is there a nice and clean way to do this with Python 3?

推荐答案

您可以使用 itertools.product 遍历笛卡尔积 ¹ 某些值(在本例中为索引):

You could use itertools.product to iterate over the cartesian product ¹ of some values (in this case the indices):

import itertools
shape = [4,5,2,6]
for idx in itertools.product(*[range(s) for s in shape]):
    value = dataset[idx]
    print(idx, value)
    # i would be "idx[0]", j "idx[1]" and so on...

---

但是，如果要迭代的是一个numpy数组，则可以易于使用

---

However if it's a numpy array you want to iterate over, it could be easier to use np.ndenumerate:

import numpy as np

arr = np.random.random([4,5,2,6])
for idx, value in np.ndenumerate(arr):
    print(idx, value)
    # i would be "idx[0]", j "idx[1]" and so on...

---

¹ 您要求澄清itertools.product(*[range(s) for s in shape])的实际作用.因此，我将对其进行详细说明.

---

¹ You asked for clarification what itertools.product(*[range(s) for s in shape]) actually does. So I'll explain it in more details.

例如，您有此循环:

for i in range(10):
    for j in range(8):
        # do whatever

这也可以使用product编写为:

for i, j in itertools.product(range(10), range(8)):
#                                        ^^^^^^^^---- the inner for loop
#                             ^^^^^^^^^-------------- the outer for loop
    # do whatever

这意味着product只是减少独立 for循环数量的便捷方法.

That means product is just a handy way of reducing the number of independant for-loops.

如果要将可变数量的for-循环转换为product，则基本上需要两个步骤:

If you want to convert a variable number of for-loops to a product you essentially need two steps:

# Create the "values" each for-loop iterates over
loopover = [range(s) for s in shape]

# Unpack the list using "*" operator because "product" needs them as 
# different positional arguments:
prod = itertools.product(*loopover)

for idx in prod:
     i_0, i_1, ..., i_n = idx   # index is a tuple that can be unpacked if you know the number of values.
                                # The "..." has to be replaced with the variables in real code!
     # do whatever

等同于:

for i_1 in range(shape[0]):
    for i_2 in range(shape[1]):
        ... # more loops
            for i_n in range(shape[n]):  # n is the length of the "shape" object
                # do whatever

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