Comments (6)
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Hi Chris,
Thank you very much for your quick response - I will try it (we have previously used 1e-6 instead of zero, and it only worked so-so). Do you mind sharing what approaches you use to represent trajectories when passing them to ripser.py?
Thank you very much - best,
Wolfgang
from ripser.py.
from ripser.py.
Hi both,
I have encountered a similar issue with the missing entries in a sparse matrix being interpreted as inf instead of a zero.
I generated a distance matrix that has a large zero block:
The dense matrix produces the correct results at the cost of storage and probably additional computation.
I'd like to add myself as another future user of the feature to optionally to treat the undefined elements of a sparse matrix as zero.
Best,
Vladimir
from ripser.py.
@wxmerkt a bit late to the party here but, in my experience, things work out as I think you would like them to in ripser.py
if you explicitly store zeros in your sparse matrices (this can be done in a number of scipy
sparse formats, though not in all). I can show you an example if you like (perhaps this got solved in the meantime?).
When making pyflagser
(docs), we had to face some similar conundrums concerning the expected format of sparse matrices. In the end, we settled for a design choice which is explained in the function flagser_weighted
-- analogous to ripser
(it computes the same persistence diagrams when directed=False
is passed!). In brief, you can pass sparse adjacency matrices with explicitly stored zeros and they are treated as zero filtration parameters, not as absent edges. The absent edges, as @ctralie pointed out is the case also in ripser.py
, are the non-stored entries in the sparse matrix (the "sparse zeros", if you will). But again, I think ripser.py
does the same thing!
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