问题描述
您有一个原始的稀疏矩阵X:
You have an original sparse matrix X:
>>print type(X)
>>print X.todense()
<class 'scipy.sparse.csr.csr_matrix'>
[[1,4,3]
[3,4,1]
[2,1,1]
[3,6,3]]
您有第二个稀疏矩阵Z，它是从X的某些行派生而来的(例如，将值加倍，以便我们可以看到两个矩阵之间的差异).在pseudo-code:
You have a second sparse matrix Z, which is derived from some rows of X (say the values are doubled so we can see the difference between the two matrices). In pseudo-code:
>>Z = X[[0,2,3]]
>>print Z.todense()
[[1,4,3]
[2,1,1]
[3,6,3]]
>>Z = Z*2
>>print Z.todense()
[[2, 8, 6]
[4, 2, 2]
[6, 12,6]]
使用来自X的原始索引来检索Z中的行的最佳方法是什么.例如，以伪代码:
What's the best way of retrieving the rows in Z using the ORIGINAL indices from X. So for instance, in pseudo-code:
>>print Z[[0,3]]
[[2,8,6] #0 from Z, and what would be row **0** from X)
[6,12,6]] #2 from Z, but what would be row **3** from X)
也就是说，如何使用引用原始矩阵X中原始行位置的索引从Z中检索行?为此，您无论如何都不能修改X(您不能向矩阵X添加索引列)，但是没有其他限制.
That is, how can you retrieve rows from Z, using indices that refer to the original rows position in the original matrix X? To do this, you can't modify X in anyway (you can't add an index column to the matrix X), but there are no other limits.
推荐答案
如果原始索引位于数组i中，并且i中的值按升序排列(如您的示例)，则可以使用numpy.searchsorted(i，[0，3])在Z中找到与原始X中的索引[0，3]对应的索引.这是IPython会话中的一个演示:
If you have the original indices in an array i, and the values in i are in increasing order (as in your example), you can use numpy.searchsorted(i, [0, 3]) to find the indices in Z that correspond to indices [0, 3] in the original X. Here's a demonstration in an IPython session:
In [39]: X = csr_matrix([[1,4,3],[3,4,1],[2,1,1],[3,6,3]])
In [40]: X.todense()
Out[40]:
matrix([[1, 4, 3],
[3, 4, 1],
[2, 1, 1],
[3, 6, 3]])
In [41]: i = array([0, 2, 3])
In [42]: Z = 2 * X[i]
In [43]: Z.todense()
Out[43]:
matrix([[ 2, 8, 6],
[ 4, 2, 2],
[ 6, 12, 6]])
In [44]: Zsub = Z[searchsorted(i, [0, 3])]
In [45]: Zsub.todense()
Out[45]:
matrix([[ 2, 8, 6],
[ 6, 12, 6]])
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