如何将numpy.matrix或数组转换为scipy稀疏矩阵

3 回答

• 0

  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
  

  回复于 2024-04-28T22:14:45+08:00
• 19

  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
  

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

  回复于 2024-04-28T22:14:45+08:00
• 84

  至于逆，函数是 inv(A) ，但我赢了't recommend using it, since for huge matrices it is very computationally costly and unstable. Instead, you should use an approximation to the inverse, or if you want to solve Ax = b you don' t真的需要A-1 .

  回复于 2024-04-28T22:14:45+08:00