我一直在尝试将 python scipy 稀疏矩阵除以其行的向量和。这是我的代码
sparse_mat = bsr_matrix((l_data, (l_row, l_col)), dtype=float)
sparse_mat = sparse_mat / (sparse_mat.sum(axis = 1)[:,None])
然而，无论我如何尝试，它都会抛出错误
sparse_mat = sparse_mat / (sparse_mat.sum(axis = 1)[:,None])
File "/usr/lib/python2.7/dist-packages/scipy/sparse/base.py", line 381, in __div__
return self.__truediv__(other)
File "/usr/lib/python2.7/dist-packages/scipy/sparse/compressed.py", line 427, in __truediv__
raise NotImplementedError
NotImplementedError
有人知道我哪里出错了吗？
请您参考如下方法:
您可以通过从行总和的倒数创建一个稀疏对角矩阵，然后将其与您的矩阵相乘来规避这个问题。在产品中，对角矩阵向左，您的矩阵向右。
例子:
>>> a
array([[0, 9, 0, 0, 1, 0],
[2, 0, 5, 0, 0, 9],
[0, 2, 0, 0, 0, 0],
[2, 0, 0, 0, 0, 0],
[0, 9, 5, 3, 0, 7],
[1, 0, 0, 8, 9, 0]])
>>> b = sparse.bsr_matrix(a)
>>>
>>> c = sparse.diags(1/b.sum(axis=1).A.ravel())
>>> # on older scipy versions the offsets parameter (default 0)
... # is a required argument, thus
... # c = sparse.diags(1/b.sum(axis=1).A.ravel(), 0)
...
>>> a/a.sum(axis=1, keepdims=True)
array([[ 0. , 0.9 , 0. , 0. , 0.1 , 0. ],
[ 0.125 , 0. , 0.3125 , 0. , 0. , 0.5625 ],
[ 0. , 1. , 0. , 0. , 0. , 0. ],
[ 1. , 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0.375 , 0.20833333, 0.125 , 0. , 0.29166667],
[ 0.05555556, 0. , 0. , 0.44444444, 0.5 , 0. ]])
>>> (c @ b).todense() # on Python < 3.5 replace c @ b with c.dot(b)
matrix([[ 0. , 0.9 , 0. , 0. , 0.1 , 0. ],
[ 0.125 , 0. , 0.3125 , 0. , 0. , 0.5625 ],
[ 0. , 1. , 0. , 0. , 0. , 0. ],
[ 1. , 0. , 0. , 0. , 0. , 0. ],
[ 0. , 0.375 , 0.20833333, 0.125 , 0. , 0.29166667],
[ 0.05555556, 0. , 0. , 0.44444444, 0.5 , 0. ]])
