如何将numpy.matrix或数组转换为scipy稀疏矩阵 [英] How to transform numpy.matrix or array to scipy sparse matrix

问题描述

对于SciPy稀疏矩阵，可以使用todense()或toarray()转换为NumPy矩阵或数组.进行逆运算的功能是什么?

For SciPy sparse matrix, one can use todense() or toarray() to transform to NumPy matrix or array. What are the functions to do the inverse?

我搜索了，但不知道应该正确选择哪些关键字.

I searched, but got no idea what keywords should be the right hit.

推荐答案

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.

You can pass a numpy array or matrix as an argument when initializing a sparse matrix. For a CSR matrix, for example, you can do the following.

>>> 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

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