10

没问题：

>>> t = np.array([[1,1,1,1,1],[2,2,2,2,2],[3,3,3,3,3],[4,4,4,4,4],[5,5,5,5,5]])
>>> x = np.arange(5).reshape((-1,1)); y = np.arange(5)
>>> print (t[[x]],t[[y]])

大问题：

>>> s = scipy.sparse.csr_matrix(t)
>>> print (s[[x]].toarray(),s[[y]].toarray())
Traceback (most recent call last):
  File "<pyshell#22>", line 1, in <module>
:               :
:               :
ValueError: data, indices, and indptr should be rank 1

s.toarray()[[x]]效果很好，但是由于我的数组太大，因此破坏了我使用稀疏矩阵的全部目的。我已经检查了与一些稀疏矩阵相关联的属性和方法，以获取任何引用高级索引的内容，但没有骰子。有任何想法吗？

4

1 回答 1

18

稀疏矩阵的索引支持非常有限，可用的索引取决于矩阵的格式。

例如：

>>> a = scipy.sparse.rand(100,100,format='coo')
>>> a[2:5, 6:8]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: 'coo_matrix' object has no attribute '__getitem__'

但

>>> a = scipy.sparse.rand(100,100,format='csc')
>>> a[2:5, 6:8]
<3x2 sparse matrix of type '<type 'numpy.float64'>'
    with 0 stored elements in Compressed Sparse Column format>

虽然

>>> a[2:5:2, 6:8:3]
Traceback (most recent call last):
...
ValueError: slicing with step != 1 not supported

还有

>>> a = scipy.sparse.rand(100,100,format='dok')
>>> a[2:5:2, 6:8:3]
Traceback (most recent call last):
...
NotImplementedError: fancy indexing supported over one axis only
>>> a[2:5:2,1]
<3x1 sparse matrix of type '<type 'numpy.float64'>'
    with 0 stored elements in Dictionary Of Keys format>

乃至

>>> a = scipy.sparse.rand(100,100,format='lil')
>>> a[2:5:2,1]
<2x1 sparse matrix of type '<type 'numpy.int32'>'
    with 0 stored elements in LInked List format>
C:\Python27\lib\site-packages\scipy\sparse\lil.py:230: SparseEfficiencyWarning: Indexing into a lil_matrix with multiple indices is slow. Pre-converting to CSC or CSR beforehand is more efficient.
  SparseEfficiencyWarning)
>>> a[2:5:2, 6:8:3]
<2x1 sparse matrix of type '<type 'numpy.int32'>'
    with 0 stored elements in LInked List format>
于 2013-01-24T00:57:36.697 回答