使用两个索引在Numpy中进行逻辑索引，如MATLAB中所示 [英] Logical indexing in Numpy with two indices as in MATLAB

问题描述

如何使用Numpy复制在MATLAB中完成的索引？

How do I replicate this indexing done in MATLAB with Numpy?

X=magic(5);
M=[0,0,1,2,1];
X(M==0,M==2)

返回：

ans =
  8
 14

我发现在Numpy中这样做是不正确的，因为它不会给我相同的结果..

I've found that doing this in Numpy is not correct, since it does not give me the same results..

X = np.matrix([[17, 24,  1,  8, 15],
        [23,  5,  7, 14, 16],
        [ 4,  6, 13, 20, 22],
        [10, 12, 19, 21,  3],
        [11, 18, 25,  2,  9]])

M=array([0,0,1,2,1])
X.take([M==0]).take([M==2], axis=1)

因为我得到：

 matrix([[24, 24, 24, 24, 24]])

在numpy中使用两个索引进行逻辑索引的正确方法是什么？

What is the correct way to logically index with two indices in numpy?

推荐答案

一般来说，有两种方法可以解释 X [a，b] 当a和b都是数组（matlab中的向量），内部样式索引或外部样式索引时。

In general there are two ways to interpret X[a, b] when both a and b are arrays (vectors in matlab), "inner-style" indexing or "outer-style" indexing.

matlab chos的设计者e外部式索引和numpy的设计者选择了内部式索引。要在numpy中进行外部风格索引，可以使用：

The designers of matlab chose "outer-style" indexing and the designers of numpy chose inner-style indexing. To do "outer-style" indexing in numpy one can use:

X[np.ix_(a, b)]
# This is roughly equal to matlab's
X(a, b)

completness你可以在matlab中做内部式索引：

for completness you can do "inner-style" indexing in matlab by doing:

X(sub2ind(size(X), a, b))
# This is roughly equal to numpy's
X[a, b]

简而言之，尝试 X [np.ix_（M == 0，M == 1）] 。

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