numpy的多个切片布尔 [英] numpy multiple slicing booleans

问题描述

我无法在numpy数组中编辑值

I'm having trouble editing values in a numpy array

import numpy as np
foo = np.ones(10,10,2)

foo[row_criteria, col_criteria, 0] += 5
foo[row_criteria,:,0][:,col_criteria] += 5

row_criteria和col_criteria是布尔数组(1D).在第一种情况下，我得到

row_criteria and col_criteria are boolean arrays (1D). In the first case I get a

形状不匹配:无法将对象广播为单个形状"错误

"shape mismatch: objects cannot be broadcast to a single shape" error

在第二种情况下，根本不会应用+ = 5.

In the second case, += 5 doesn't get applied at all. When I do

foo[row_criteria,:,0][:,col_criteria] + 5

我得到了修改后的返回值，但是就地修改该值似乎不起作用...

I get a modified return value but modifying the value in place doesn't seem to work...

有人可以解释如何解决此问题吗?谢谢！

Can someone explain how to fix this? Thanks!

推荐答案

您要:

foo[np.ix_(row_criteria, col_criteria, [0])] += 5

要了解其工作原理，请举以下示例:

To understand how this works take this example:

import numpy as np
A = np.arange(25).reshape([5, 5])
print A[[0, 2, 4], [0, 2, 4]]
# [0, 12, 24]

# The above example gives the the elements A[0, 0], A[2, 2], A[4, 4]
# But what if I want the "outer product?" ie for [[0, 2, 4], [1, 3]] i want
# A[0, 1], A[0, 3], A[2, 1], A[2, 3], A[4, 1], A[4, 3]
print A[np.ix_([0, 2, 4], [1, 3])]
# [[ 1  3]
#  [11 13]
#  [21 23]]

同样的事情也适用于布尔索引.同样，np.ix_并没有做任何真正令人惊奇的事情，它只是重塑了其参数，以便可以相互广播:

The same thing works with boolean indexing. Also np.ix_ doesn't do anything really amazing, it just reshapes it's arguments so they can be broadcast against each other:

i, j = np.ix_([0, 2, 4], [1, 3])
print i.shape
# (3, 1)
print j.shape
# (1, 2)

这篇关于numpy的多个切片布尔的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！