Can we assume they are the same shape?
In [202]: a=sparse.csr_matrix([[0,1],[1,0]])
In [203]: b=sparse.csr_matrix([[0,1],[1,1]])
In [204]: (a!=b).nnz==0
Out[204]: False
This checks the sparsity of the inequality array.
It will give you an efficiency warning if you try a==b
(at least the 1st time you use it). That’s because it has to test all those zeros. It can’t take much advantage of the sparsity.
You need a relatively recent version to use logical operators like this. Were you trying to use x.__eq__(y)
in some if
expression, or did you get error from just that expression?
In general you probably want to check several parameters first. Same shape
, same nnz
, same dtype
. You need to be careful with floats.
For dense arrays np.allclose
is a good way of testing equality. And if the sparse arrays aren’t too large, that might be good as well
np.allclose(a.A, b.A)
allclose
uses all(less_equal(abs(x-y), atol + rtol * abs(y)))
. You can use a-b
, but I suspect that this too will give an efficiecy warning.