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如何将稀疏矩阵中第一列的行和其他列的零相加,使原始矩阵的维数相同?

sparse-matrix scipy python

Bilal · 技术社区 · 6 年前

我有一个稀疏矩阵B,我想通过求第一列中所有行的和,然后将第一列除以'2',并使其他列为零,从B得到稀疏矩阵a。

from numpy import array
from scipy import csr_matrix

row = array([0,0,1,2,2,2])
col = array([0,2,2,0,1,2])
data = array([1,2,3,4,5,6])

B = csr_matrix( (data,(row,col)), shape=(3,3) )

A = B.copy()

A = A.sum(axis=1)/2
# A shape becomes 1 x 3 instead of 3 x 3 here!

0 回复 | 直到 6 年前

hpaulj 6 年前

我认为这可以通过几种方式来实现。你的孩子很好。

In [275]: from scipy.sparse import csr_matrix 
     ...:  
     ...: row = np.array([0,0,1,2,2,2]) 
     ...: col = np.array([0,2,2,0,1,2]) 
     ...: data = np.array([1,2,3,4,5,6.])    # make float 
     ...:  
     ...: B = csr_matrix( (data,(row,col)), shape=(3,3) )                                              
In [276]: A = B.copy()                                                                                 
In [277]: A                                                                                            
Out[277]: 
<3x3 sparse matrix of type '<class 'numpy.float64'>'
    with 6 stored elements in Compressed Sparse Row format>

任务是:

In [278]: A[:,0]  = A.sum(axis=1)/2                                                                    
/usr/local/lib/python3.6/dist-packages/scipy/sparse/_index.py:126: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
  self._set_arrayXarray(i, j, x)
In [279]: A[:,1:] = 0                                                                                  
/usr/local/lib/python3.6/dist-packages/scipy/sparse/_index.py:126: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
  self._set_arrayXarray(i, j, x)
In [280]: A                                                                                            
Out[280]: 
<3x3 sparse matrix of type '<class 'numpy.float64'>'
    with 9 stored elements in Compressed Sparse Row format>

In [283]: A.eliminate_zeros()                                                                          
In [284]: A                                                                                            
Out[284]: 
<3x3 sparse matrix of type '<class 'numpy.float64'>'
    with 3 stored elements in Compressed Sparse Row format>
In [285]: A.A                                                                                          
Out[285]: 
array([[1.5, 0. , 0. ],
       [1.5, 0. , 0. ],
       [7.5, 0. , 0. ]])

或者我们从零开始 A

In [286]: A = csr_matrix(np.zeros(B.shape))   # may be better method                                                         
In [287]: A[:,0]  = B.sum(axis=1)/2                                                                    
/usr/local/lib/python3.6/dist-packages/scipy/sparse/_index.py:126: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
  self._set_arrayXarray(i, j, x)
In [288]: A                                                                                            
Out[288]: 
<3x3 sparse matrix of type '<class 'numpy.float64'>'
    with 3 stored elements in Compressed Sparse Row format>

或者,可以使用列和矩阵来构造 A 直接使用与用于定义 B :

In [289]: A1  = B.sum(axis=1)/2                                                                        
In [290]: A1                                                                                           
Out[290]: 
matrix([[1.5],
        [1.5],
        [7.5]])
In [296]: row = np.arange(3)                                                                           
In [297]: col = np.zeros(3,int)                                                                        
In [298]: data = A1.A1                                                                                 
In [299]: A = csr_matrix((data, (row, col)), shape=(3,3))                                              
In [301]: A                                                                                            
Out[301]: 
<3x3 sparse matrix of type '<class 'numpy.float64'>'
    with 3 stored elements in Compressed Sparse Row format>
In [302]: A.A                                                                                          
Out[302]: 
array([[1.5, 0. , 0. ],
       [1.5, 0. , 0. ],
       [7.5, 0. , 0. ]])

sparse.hstack 看起来不错,不过在被子里, hstack 正在构建 row,col,data 从 coo 格式,并制作一个新的 coo_matrix . 虽然它是可靠的,但并不是特别精简。

Bilal 6 年前

from numpy import array
from scipy import csr_matrix

row = array([0,0,1,2,2,2])
col = array([0,2,2,0,1,2])
data = array([1,2,3,4,5,6])

B = csr_matrix( (data,(row,col)), shape=(3,3) )

B_C = B.copy()
column1 = B_C.sum(axis=1)/2
#------------------------------------------------
columns = csr_matrix((B.shape[0],B.shape[1]-1))
A = sparse.hstack((column1 , columns ))
#------------------------------------------------