多个数组的Python apply_along_axis [英] Python apply_along_axis of multiple arrays

问题描述

如果我有一个函数f(x)，它使用一个1d数组作为参数并生成一个1d数组作为输出，则可以使用numpy.apply_along_axis 将该函数应用于二维数组X的每一行，该数组的行是f的有效参数.

If I have a function, f(x) which takes a single 1d-array as argument and produces a 1d-array as output, I can use numpy.apply_along_axis to apply the function, to each row of a 2d-array X whose rows are valid arguments for f.

现在，我想使用带有两个参数的函数来做类似的事情. 例如.我有一个函数f(x，y)，它将两个1d数组作为参数，我也有两个2d数组X，Y都具有n行.我想将f应用于每对行，从而产生一个具有n行的数组.

Now I want to do the analogous thing with a function that takes two arguments. E.g. I have a function f(x, y) which takes tow 1d-arrays as arguments and I also have two 2d-arrays X, Y both with n rows. I want to apply f to each pair of rows, producing an array which has again n rows.

如何有效地做到这一点?

How to accomplish this in an efficient way?

我也对变体感兴趣，其中f接受更多参数或涉及更高维数组:

I am also interested in variants, where f takes more arguments or higher dimensional arrays are involved:

例如，f可以采用3个形状为(2,2)的数组x，y，z； (3，); (5，) 并产生形状(4,4)的结果.

For example f could take 3 arrays x, y, z of shape (2,2); (3,); (5,) and produce a result of shape (4,4).

我的X，Y，Z形状为(50、100、2、2)； (50，100，3); (50，100，5) 并希望得到形状为(50，100，4，4)的结果

I have X, Y, Z of shapes (50, 100, 2, 2); (50, 100, 3); (50, 100, 5) and want a result of shape (50, 100, 4, 4)

推荐答案

查看numpy.apply_along_axis的代码，我发现它只是在其他维度上进行迭代，将函数应用于每个行".还有一些额外的代码可以使尺寸约为2.但是对于2d X，它可以归结为:

Looking at the code for numpy.apply_along_axis I see that it just iterates over the other dimensions, applying your function to each 'row'. There's extra code that allows for dimensions about 2. But for 2d X it boils down to:

result = np.empty_like(X)
for i, x in enumerate(X):
    result[i] = func1d(x)

还有一些代码可以推断result应该具有的形状.例如，如果func1d是np.sum，则result将是1d，而不是输入的2d.

There's also code to deduce what shape the result should have. For example if func1d is np.sum, then result will be 1d, not 2d like the input.

因此，此功能没有特殊的效率".对多个输入的扩展可以是普通的Python zip:

So there's not special 'efficiency' in this function. An extension to multiple inputs could be a normal Python zip:

 result = np.empty_like(X)
 for i,(x,y) in enumerate(zip(X,Y)):
     result[i] = func1d(x,y)

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np.ndindex是用于生成索引的便捷工具.值得看一下它的代码.它使用通用的numpy迭代器np.nditer，该迭代器请参见: http: //docs.scipy.org/doc/numpy/reference/arrays.nditer.html

---

np.ndindex is handy tool for generating indices. It's worth looking at its code. It uses the general purpose numpy iterator, np.nditer, which see: http://docs.scipy.org/doc/numpy/reference/arrays.nditer.html

例如，f可以采用3个形状为(2,2)的数组x，y，z； (3，); (5，)并产生形状(4,4)的结果.

For example f could take 3 arrays x, y, z of shape (2,2); (3,); (5,) and produce a result of shape (4,4).

我的X，Y，Z形状为(50、100、2、2)； (50，100，3); (50，100，5)并想要形状的结果(50，100，4，4)

I have X, Y, Z of shapes (50, 100, 2, 2); (50, 100, 3); (50, 100, 5) and want a result of shape (50, 100, 4, 4)

for i,j in np.ndindex(50,100):
    result[i,j,:,:] = f(X[i,j,:,:], Y[i,j,:,:], Z[i,j,:,:])

:"不是必需的，但请明确说明我们正在对其中2个维度进行索引，并对其余维度进行切片.如果要在第1维和第3维上进行迭代，然后在第2维上切片，则需要使用它们.

The ':' aren't necessary, but make it clear that we are indexing on 2 of the dimensions, and slicing the rest. They would be required if you wanted to iterate on the 1 and 3rd dimensions, and slice the 2nd.

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