为什么索引该Numpy矩阵会导致错误? [英] Why does indexing this Numpy matrix cause an error?

问题描述

我正在使用:

• Python 3.5.4
• Numpy 1.16.2

给出代码:

import numpy as np
num = 3
a = np.asmatrix(np.eye(num, num))
b = np.asmatrix(range(0, num))
print(a[b].transpose())

我得到结果:

[[1.00000000e+000 0.00000000e+000 0.00000000e+000]
 [1.77658241e-307 0.00000000e+000 0.00000000e+000]
 [3.47328271e-310 0.00000000e+000 0.00000000e+000]]

但是通过将b的定义更改为np.asarray(...)，或者通过执行第二次索引以获得列表中的第一项(print(a[b][0].transpose())，我得到了预期的结果:

But by either changing the definition of b to np.asarray(...), or by performing a second indexing to get the first item in the list (print(a[b][0].transpose()) I get the expected result:

[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]

这是怎么回事?在没有根本原因的线索之前，我还没有遇到过此错误.如果它是Python的基础知识(我仍然是它的新手)，我很想学习它，这样我就不会花很多时间来调试它了.预先非常感谢.

What is going on? I've not experienced this bug before don't have a clue to the underlying cause. If its something fundamental to Python (I'm quite new to it still) I'd quite like to learn about it so that I don't sink quite as much time into debugging it as I have this time. Many thanks in advance.

推荐答案

NumPy matrix对象试图确保它们始终是2D的，但是它们并没有完全完成它.

NumPy matrix objects try to ensure they're always 2D, but they don't quite do a complete job of it.

这样做的时候

b = np.asmatrix(range(0, num))

range(0, num)是一维序列，但是asmatrix创建单行2D矩阵而不是1D对象. asarray会产生一维数组.

range(0, num) is a 1D sequence, but asmatrix creates a single-row 2D matrix instead of a 1D object. asarray would produce a 1D array.

这样做的时候

a[b]

这是一个精美的索引操作，可生成一个 3D 矩阵. numpy.matrix对象永远都不应该是3D的，下面的transpose不知道如何处理它，从而产生荒谬的结果.特别是，由于舍入误差，输出中的奇数几乎为0不会不；它们是由于结果矩阵的无意义的步幅引起的，这些步幅导致未对齐的内存访问，并尝试将单独的float的片段读取为单个float:

this is a fancy indexing operation that produces a 3D matrix. numpy.matrix objects are never supposed to be 3D, and the following transpose doesn't know how to handle that, producing nonsensical results. Particularly, the strange, almost-0 numbers in the output are not due to rounding error; they're due to the nonsensical strides of the resulting matrix, which cause unaligned memory access and try to read fragments of separate floats as a single float:

In [13]: a[b].transpose().strides
Out[13]: (1, 24)

在第一个维度上跨度为1字节.

That's a 1-byte stride in the first dimension.

这里的重点:不要使用numpy.matrix.

The key takeaway here: don't use numpy.matrix.

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