如何使用scipy编辑稀疏矩阵中的单元格? [英] How do you edit cells in a sparse matrix using scipy?

问题描述

我正在尝试处理稀疏矩阵中的某些数据.创建一个后，如何在其中添加/更改/更新值?这似乎很基础，但是我在稀疏矩阵类的文档中或在Web上找不到它.我想我缺少了一些关键的东西.

I'm trying to manipulate some data in a sparse matrix. Once I've created one, how do I add / alter / update values in it? This seems very basic, but I can't find it in the documentation for the sparse matrix classes, or on the web. I think I'm missing something crucial.

这是我尝试失败的尝试，与使用普通数组一样.

This is my failed attempt to do so the same way I would a normal array.

>>> from scipy.sparse import bsr_matrix
>>> A = bsr_matrix((10,10))
>>> A[5][7] = 6

Traceback (most recent call last):
  File "<pyshell#11>", line 1, in <module>
    A[5][7] = 6
  File "C:\Python27\lib\site-packages\scipy\sparse\bsr.py", line 296, in __getitem__
    raise NotImplementedError
NotImplementedError

推荐答案

有几种稀疏矩阵格式.有些更适合索引.实现它的一个是lil_matrix.

There several Sparse matrix formats. Some are better suited to indexing. One that has implemented it is lil_matrix.

Al = A.tolil()
Al[5,7] = 6  # the normal 2d matrix indexing notation
print Al
print Al.A # aka Al.todense()
A1 = Al.tobsr()  # if it must be in bsr format

每种格式的文档都建议了它的优缺点.但是它没有一个清晰的列表，列出了哪些操作定义了哪些操作.

The documentation for each format suggests what it is good at, and where it is bad. But it does not have a neat list of which ones have which operations defined.

Advantages of the LIL format
  supports flexible slicing
  changes to the matrix sparsity structure are efficient
  ...
Intended Usage
  LIL is a convenient format for constructing sparse matrices
  ...

dok_matrix还实现了索引编制.

coo_matrix的基础数据结构很容易理解.它本质上是coo_matrix((data, (i, j)), [shape=(M, N)])定义的参数.要创建相同的矩阵，您可以使用:

The underlying data structure for coo_matrix is easy to understand. It is essentially the parameters for coo_matrix((data, (i, j)), [shape=(M, N)]) definition. To create the same matrix you could use:

sparse.coo_matrix(([6],([5],[7])), shape=(10,10))

如果分配的任务更多，请构建更大的data，i，j列表(或一维数组)，并在完成后构造稀疏矩阵.

If you have more assignments, build larger data, i, j lists (or 1d arrays), and when complete construct the sparse matrix.

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