我有兩個(gè)csr_matrix，uniFeature和biFeature。我想要一個(gè)新的矩陣Feature = [uniFeature, biFeature]。但是，如果我以這種方式直接連接它們，則會(huì)出現(xiàn)一個(gè)錯(cuò)誤，指出矩陣Feature是一個(gè)列表。如何實(shí)現(xiàn)矩陣級(jí)聯(lián)并仍然獲得相同類型的矩陣，即a csr_matrix？如果我在連接后執(zhí)行此操作，將不起作用：Feature = csr_matrix(Feature) 它給出了錯(cuò)誤：Traceback (most recent call last): File "yelpfilter.py", line 91, in <module> Feature = csr_matrix(Feature) File "c:\python27\lib\site-packages\scipy\sparse\compressed.py", line 66, in __init__ self._set_self( self.__class__(coo_matrix(arg1, dtype=dtype)) ) File "c:\python27\lib\site-packages\scipy\sparse\coo.py", line 185, in __init__ self.row, self.col = M.nonzero()TypeError: __nonzero__ should return bool or int, returned numpy.bool_
1 回答
holdtom
TA貢獻(xiàn)1805條經(jīng)驗(yàn) 獲得超10個(gè)贊
該scipy.sparse模塊包括功能hstack和vstack。
例如：
In [44]: import scipy.sparse as sp
In [45]: c1 = sp.csr_matrix([[0,0,1,0],
...: [2,0,0,0],
...: [0,0,0,0]])
In [46]: c2 = sp.csr_matrix([[0,3,4,0],
...: [0,0,0,5],
...: [6,7,0,8]])
In [47]: h = sp.hstack((c1, c2), format='csr')
In [48]: h
Out[48]:
<3x8 sparse matrix of type '<type 'numpy.int64'>'
with 8 stored elements in Compressed Sparse Row format>
In [49]: h.A
Out[49]:
array([[0, 0, 1, 0, 0, 3, 4, 0],
[2, 0, 0, 0, 0, 0, 0, 5],
[0, 0, 0, 0, 6, 7, 0, 8]])
In [50]: v = sp.vstack((c1, c2), format='csr')
In [51]: v
Out[51]:
<6x4 sparse matrix of type '<type 'numpy.int64'>'
with 8 stored elements in Compressed Sparse Row format>
In [52]: v.A
Out[52]:
array([[0, 0, 1, 0],
[2, 0, 0, 0],
[0, 0, 0, 0],
[0, 3, 4, 0],
[0, 0, 0, 5],
[6, 7, 0, 8]])
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