如何将numpy.matrix或数组转换为scipy稀疏矩阵

Question

对于SciPy稀疏矩阵,可以使用todense()或toarray()转换为NumPy矩阵或数组.反向的功能是什么？

我搜索过,但不知道哪些关键字应该是正确的.

Answer 1

初始化稀疏矩阵时,可以将numpy数组或矩阵作为参数传递.例如,对于CSR矩阵,您可以执行以下操作.

>>> import numpy as np
>>> from scipy import sparse
>>> A = np.array([[1,2,0],[0,0,3],[1,0,4]])
>>> B = np.matrix([[1,2,0],[0,0,3],[1,0,4]])

>>> A
array([[1, 2, 0],
       [0, 0, 3],
       [1, 0, 4]])

>>> sA = sparse.csr_matrix(A)   # Here's the initialization of the sparse matrix.
>>> sB = sparse.csr_matrix(B)

>>> sA
<3x3 sparse matrix of type '<type 'numpy.int32'>'
        with 5 stored elements in Compressed Sparse Row format>

>>> print sA
  (0, 0)        1
  (0, 1)        2
  (1, 2)        3
  (2, 0)        1
  (2, 2)        4

Answer 2

scipy中有几个稀疏矩阵类.

bsr_matrix(arg1 [,shape,dtype,copy,blocksize])块稀疏行矩阵
coo_matrix(arg1 [,shape,dtype,copy])COOrdinate格式的稀疏矩阵.
csc_matrix(arg1 [,shape,dtype,copy])压缩稀疏列矩阵
csr_matrix(arg1 [,shape,dtype,copy])压缩稀疏行矩阵
dia_matrix(arg1 [,shape,dtype,copy])带DIAgonal存储的稀疏矩阵
dok_matrix (arg1 [,shape,dtype,copy])基于密钥的字典稀疏矩阵.
lil_matrix(arg1 [,shape,dtype,copy])基于行的链表稀疏矩阵

他们中的任何一个都可以进行转换.

import numpy as np
from scipy import sparse
a=np.array([[1,0,1],[0,0,1]])
b=sparse.csr_matrix(a)
print(b)

(0, 0)  1
(0, 2)  1
(1, 2)  1

请参阅http://docs.scipy.org/doc/scipy/reference/sparse.html#usage-information.