Scipy稀疏矩阵中的行除法 [英] Row Division in Scipy Sparse Matrix

问题描述

我想将稀疏矩阵的行除以数组中给出的标量.

I want to divide a sparse matrix's rows by scalars given in an array.

例如:我有一个csr_matrix C:

For example : I have a csr_matrix C :

C = [[2,4,6], [5,10,15]]
D = [2,5]

我希望C除后的结果是:

I want the result of C after division to be :

result = [[1, 2, 3], [1, 2, 3]]

我已经使用了用于numpy数组的方法进行了尝试:

I have tried this using the method that we use for numpy arrays :

result = C / D[:,None]

但这似乎真的很慢.在稀疏矩阵中如何有效地做到这一点?

But this seems really slow. How to do this efficiently in sparse matrices ?

推荐答案

方法1

这是使用indexing-

from scipy.sparse import csr_matrix

r,c = C.nonzero()
rD_sp = csr_matrix(((1.0/D)[r], (r,c)), shape=(C.shape))
out = C.multiply(rD_sp)

输出是一个稀疏矩阵，与C / D[:,None]的输出相反，它创建一个完整的矩阵.因此，该方法节省了内存.

The output is a sparse matrix as well as opposed to the output from C / D[:,None] that creates a full matrix. As such, the proposed approach saves on memory.

使用np.repeat代替索引进行复制可能会提高性能-

Possible performance boost with replication using np.repeat instead of indexing -

val = np.repeat(1.0/D, C.getnnz(axis=1))
rD_sp = csr_matrix((val, (r,c)), shape=(C.shape))

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方法2

另一种方法可能涉及稀疏矩阵的data方法，该方法为我们提供了对in-place结果的稀疏矩阵的扁平视图，并且避免使用nonzero，就像这样-

Another approach could involve data method of the sparse matrix that gives us a flattened view into the sparse matrix for in-place results and also avoid the use of nonzero, like so -

val = np.repeat(D, C.getnnz(axis=1))
C.data /= val

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