I am trying to parse some real data into a .mat object to be loaded in my matlab script.
I am getting this error:
TypeError: 'coo_matrix' object does not support item assignment
I found coo_matrix. However, I am not able to assign values to it.
data.txt
10 45
11 12
4 1
I would like to get a sparse matrix of size 100x100. And to assign 1's to
Mat(10, 45) = 1
Mat(11, 12) = 1
Mat(4, 1) = 1
CODE
import numpy as np
from scipy.sparse import coo_matrix
def pdata(pathToFile):
M = coo_matrix(100, 100)
with open(pathToFile) as f:
for line in f:
s = line.split()
x, y = [int(v) for v in s]
M[x, y] = 1
return M
if __name__ == "__main__":
M = pdata('small.txt')
Any suggestions please ?
解决方案
Constructing this matrix with coo_matrix, using the (data, (rows, cols))` parameter format:
In [2]: from scipy import sparse
In [3]: from scipy import io
In [4]: data=np.array([[10,45],[11,12],[4,1]])
In [5]: data
Out[5]:
array([[10, 45],
[11, 12],
[ 4, 1]])
In [6]: rows = data[:,0]
In [7]: cols = data[:,1]
In [8]: data = np.ones(rows.shape, dtype=int)
In [9]: M = sparse.coo_matrix((data, (rows, cols)), shape=(100,100))
In [10]: M
Out[10]:
<100x100 sparse matrix of type ''
with 3 stored elements in COOrdinate format>
In [11]: print(M)
(10, 45) 1
(11, 12) 1
(4, 1) 1
If you save it to a .mat file for use in MATLAB, it will save it in csc format (having converted it from the coo):
In [13]: io.savemat('test.mat',{'M':M})
In [14]: d = io.loadmat('test.mat')
In [15]: d
Out[15]:
{'M': <100x100 sparse matrix of type ''
with 3 stored elements in Compressed Sparse Column format>,
'__globals__': [],
'__header__': b'MATLAB 5.0 MAT-file Platform: posix, Created on: Mon Aug 7 08:45:12 2017',
'__version__': '1.0'}
coo format does not implement item assignment. csr and csc do implement it, but will complain. But they are the normal formats for calculation. lil and dok are the best formats for iterative assignment.
