Python creates empty sparse matrix
I am trying to parse some real data into an object .mat
that will be loaded into my matlab script.
I am getting this error:
TypeError: object 'coo_matrix' does not support element assignment
I found coo_matrix . However, I cannot assign values to it.
data.txt
10 45
11 12
4 1
I would like to get a 100x100 sparse matrix . And assign 1 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?
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Plotting this matrix using coo_matrix
using the parameters format (data, (rows, cols)):
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 '<class 'numpy.int32'>'
with 3 stored elements in COOrdinate format>
In [11]: print(M)
(10, 45) 1
(11, 12) 1
(4, 1) 1
If you save it in a .mat file for use in MATLAB, it will save it in the format csc
(convert it from 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 '<class 'numpy.int32'>'
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
the format does not implement the element's purpose. csr
and csc
do it, but they will complain. But they are normal formats for calculation. lil
and dok
are the best formats for iterative assignment.
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Use a sparse format that supports efficient indexing like dok_matrix
It is an efficient framework for building sparse matrices incrementally.
...
Allows efficient O (1) access to individual elements. Duplicates are not allowed. Can be efficiently converted to a constructed coo_matrix.
The last sentence can be generalized to: can be effectively converted to all other common formats if needed.
from scipy.sparse import dok_matrix
M = dok_matrix((100, 100)) # extra brackets needed as mentioned in comments
# thanks Daniel!
M[0,3] = 5
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