Question

给定一个任意的numpy数组（ndarray），是否有函数或简短的方法将其转换为scipy.sparse矩阵？

我想要的东西就像：

A = numpy.array([0,1,0],[0,0,0],[1,0,0])
S = to_sparse(A, type="csr_matrix")

Answer 1

我经常做类似

的事情

>>> import numpy, scipy.sparse
>>> A = numpy.array([[0,1,0],[0,0,0],[1,0,0]])
>>> Asp = scipy.sparse.csr_matrix(A)
>>> Asp
<3x3 sparse matrix of type '<type 'numpy.int64'>'
    with 2 stored elements in Compressed Sparse Row format>

Answer 2

帮助中有一个非常有用且相关的例子！

import scipy.sparse as sp
help(sp)

这给出了：

Example 2
---------

Construct a matrix in COO format:

>>> from scipy import sparse
>>> from numpy import array
>>> I = array([0,3,1,0])
>>> J = array([0,3,1,2])
>>> V = array([4,5,7,9])
>>> A = sparse.coo_matrix((V,(I,J)),shape=(4,4))

值得注意的是各种构造函数（再次来自帮助）：

    1. csc_matrix: Compressed Sparse Column format
    2. csr_matrix: Compressed Sparse Row format
    3. bsr_matrix: Block Sparse Row format
    4. lil_matrix: List of Lists format
    5. dok_matrix: Dictionary of Keys format
    6. coo_matrix: COOrdinate format (aka IJV, triplet format)
    7. dia_matrix: DIAgonal format

To construct a matrix efficiently, use either lil_matrix (recommended) or
dok_matrix. The lil_matrix class supports basic slicing and fancy
indexing with a similar syntax to NumPy arrays.

你的例子很简单：

S = sp.csr_matrix(A)

Answer 3

请参考以下答案：https://stackoverflow.com/a/65017153/9979257

在这个答案中，我解释了如何将二维NumPy矩阵转换为CSR或CSC格式。

numpy数组到scipy.sparse矩阵

3 个答案: