稀疏矩阵的逐元素方次幂 [英] Element-wise power of scipy.sparse matrix

问题描述

如何将scipy.sparse矩阵提升为幂(逐元素)?根据其手册 ，执行此操作，但在稀疏矩阵上失败:

How do I raise a scipy.sparse matrix to a power, element-wise? numpy.power should, according to its manual, do this, but it fails on sparse matrices:

>>> X
<1353x32100 sparse matrix of type '<type 'numpy.float64'>'
        with 144875 stored elements in Compressed Sparse Row format>

>>> np.power(X, 2)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File ".../scipy/sparse/base.py", line 347, in __pow__
    raise TypeError('matrix is not square')
TypeError: matrix is not square

与X**2相同的问题.转换为密集数组是可行的，但会浪费宝贵的时间.

Same problem with X**2. Converting to a dense array works, but wastes precious seconds.

我遇到了与np.multiply相同的问题，我使用稀疏矩阵的multiply方法解决了该问题，但似乎没有pow方法.

I've had the same problem with np.multiply, which I solved using the sparse matrix's multiply method, but there seems to be no pow method.

推荐答案

这有点底层，但是对于元素级操作，您可以直接使用基础数据数组:

This is a little low-level, but for element-wise operations you can work with the underlying data array directly:

>>> import scipy.sparse
>>> X = scipy.sparse.rand(1000,1000, density=0.003)
>>> X = scipy.sparse.csr_matrix(X)
>>> Y = X.copy()
>>> Y.data **= 3
>>> 
>>> abs((X.toarray()**3-Y.toarray())).max()
0.0

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