Python 在numpy中共轭转置运算符“.H”
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Conjugate transpose operator ".H" in numpy
提问by benpro
It is very convenient in numpy to use the .Tattribute to get a transposed version of an ndarray. However, there is no similar way to get the conjugate transpose. Numpy's matrix class has the .Hoperator, but not ndarray. Because I like readable code, and because I'm too lazy to always write .conj().T, I would like the .Hproperty to always be available to me. How can I add this feature? Is it possible to add it so that it is brainlessly available every time numpy is imported?
在 numpy 中使用该.T属性来获取ndarray. 但是，没有类似的方法来获得共轭转置。Numpy 的矩阵类有.H操作符，但没有 ndarray。因为我喜欢可读的代码，而且因为我懒得一直写.conj().T，所以我希望该.H属性始终可供我使用。如何添加此功能？是否可以添加它，以便每次导入 numpy 时都可以无脑地使用它？
(A similar question could by asked about the .Iinverse operator.)
（一个类似的问题可以问到.I逆运算符。）
采纳答案by benpro
In general, the difficulty in this problem is that Numpy is a C-extension, which cannot be monkey patched...or can it? The forbiddenfruitmodule allows one to do this, although it feels a little like playing with knives.
一般来说，这个问题的难点在于 Numpy 是一个 C 扩展，它不能被猴子补丁......或者可以吗？该forbiddenfruit模块允许一个做到这一点，虽然感觉有点像用刀子玩。
So here is what I've done:
所以这就是我所做的：
Install the very simple forbiddenfruitpackage
Determine the user customization directory:
import site print site.getusersitepackages()In that directory, edit
usercustomize.pyto include the following:from forbiddenfruit import curse from numpy import ndarray from numpy.linalg import inv curse(ndarray,'H',property(fget=lambda A: A.conj().T)) curse(ndarray,'I',property(fget=lambda A: inv(A)))Test it:
python -c python -c "import numpy as np; A = np.array([[1,1j]]); print A; print A.H"Results in:
[[ 1.+0.j 0.+1.j]] [[ 1.-0.j] [ 0.-1.j]]
安装非常简单的forbiddenfruit包
确定用户自定义目录：
import site print site.getusersitepackages()在该目录中，编辑
usercustomize.py以包含以下内容：from forbiddenfruit import curse from numpy import ndarray from numpy.linalg import inv curse(ndarray,'H',property(fget=lambda A: A.conj().T)) curse(ndarray,'I',property(fget=lambda A: inv(A)))测试一下：
python -c python -c "import numpy as np; A = np.array([[1,1j]]); print A; print A.H"结果是：
[[ 1.+0.j 0.+1.j]] [[ 1.-0.j] [ 0.-1.j]]
回答by Saullo G. P. Castro
You can subclass the ndarrayobject like:
您可以将ndarray对象子类化，例如：
from numpy import ndarray
class myarray(ndarray):
@property
def H(self):
return self.conj().T
such that:
使得：
a = np.random.random((3, 3)).view(myarray)
a.H
will give you the desired behavior.
会给你想要的行为。
