如何迭代这个n维数据集？

我有一个dataset它有 4 个维度（目前......），我需要迭代它。

要访问中的值dataset，我这样做：

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

现在，我可以获得shape为了dataset:

shape = [4,5,2,6]

中的值shape表示尺寸的长度。

给定维度数，如何迭代数据集中的所有元素？这是一个例子：

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]

未来，shape可能会改变。例如，shape可能有 10 个元素，而不是当前的 4 个。

有没有一种好的、干净的方法用 Python 3 来做到这一点？

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，如果您想要迭代它是一个 numpy 数组，那么它could更容易使用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 You asked for clarification what itertools.product(*[range(s) for s in shape]) actually does. So I'll explain it in more details.

例如你有这个循环：

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

这也可以写成使用product as:

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只是减少数量的便捷方法独立的for 循环。

如果你想转换可变数量的for-循环到aproduct你基本上需要两个步骤：

# 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

这相当于：

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