从numpy中的单个块中创建块矩阵的更好方法? [英] Better way to create block matrices out of individual blocks in numpy?

问题描述

考虑代码

M=5;N=3;
A11=np.random.rand(M,M);
A12=np.random.rand(M,N);
A21=np.random.rand(N,M);
A22=np.random.rand(N,N);

我是numpy的新手，正在学习它.我想以以下方式创建一个块矩阵

I am new to numpy and learning it. I want to create a block matrix in the following manner

RowBlock1=np.concatenate((A11,A12),axis=1)
RowBlock2=np.concatenate((A21,A22),axis=1)
Block=np.concatenate((RowBlock1,RowBlock2),axis=0)

有更简单的方法吗?例如:在matlab中，我会做

Is there a more easy way to do it? For eg:, in matlab I would do

Block=[[A11,A12];[A21,A22]]

并完成它.我知道这仅保留给数组.

and will be done with it.I understand that this is reserved only for arrays.

推荐答案

从NumPy 1.13开始，存在

As of NumPy 1.13, there's numpy.block:

Block = numpy.block([[A11, A12], [A21, A22]])

对于以前的版本，有 bmat :

For previous versions, there's bmat:

Block = numpy.bmat([[A11, A12], [A21, A22]])

numpy.bmat创建一个矩阵，而不是一个数组.这通常是一件坏事.如果需要数组，可以在结果上调用asarray，或使用

numpy.bmat creates a matrix, rather than an array. This is usually a bad thing. You can call asarray on the result if you want an array, or use the A attribute:

Block = numpy.bmat([[A11, A12], [A21, A22]]).A

bmat还会弄乱堆栈帧，让您做到这一点:

bmat also does some messing around with stack frames to let you do this:

Block = numpy.bmat('A11,A12; A21,A22')

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