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Using WAPS for custom application #1
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I would suggest that you try and compile to d-DNNF independently using D4/Dsharp and see if that stalls as well. Getting joint probability distribution given enough samples seems to be alright. Although, I think that you can only get probabilistic approximate guarantees given a finite number of samples. For realizing the polarized variables, considering that our approach uses a d-DNNF of the original formula, I would instead suggest that you rather modify sharpSAT/Dsharp directly instead of getting samples via WAPS. Exact conditioned model counting can give you an exact estimate of the polarity. A sampler would be useful if it does not uses entire search space exploration. |
Thank you very much for your suggestion. On an other note, I have a following cnf file from which I want to sample using the corresponding weight files. I see that WAPS doesn't move forward. Is it our problem instance that is making it hard for WAPS or something else? Any help would be great. Thanks again. |
Can you please post the output of |
cachesize Max: 2084718 kbytes END preprocessing ... The following is what I get from WAPS: Seperating weights from Input cnf After this it just continues to stay indefinitely. |
How much time did you wait before killing the process? It should be annotating the d-DNNF in this time. |
I waited for about 2-3 hours I think. In the past, I have let WAPS run for more than 24 hours for this instance. |
Hi,
We want to use WAPS to sample for one of our applications. Currently, we used WAPS and it stalls indefinitely. I have a couple of questions. It would be very helpful if you (or your team members) can guide us.
If F is a CNF formula with variables X = {x_1, x_2,…, x_n} and S is the set of satisfying assignments of F. Our understanding is that WAPS makes it possible to specify any subset V of X, so that the samples returned will take the form of strings of length n such as the following: 11*1000011**1
where the variables with * are the variables not in our set of interest V. Given enough samples of the above type we can compute the joint probability distribution of the variables in V (induced by selecting uniformly from S).
In our case we don’t really care about the joint distribution of the variables in the set of interest V. The only thing we care about is whether some of the variables in V are “highly polarized.” This means that, in principle, our set V (of variables of interest) could even be as small as a single variable. That is, we run the sampler with V consisting of a single variable x, get 1000 samples (in each of which the only thing we learn is the value of x), and if x takes the same value in, say, 950 of the samples or more, we record x.
Also, we believe that asking the sampler for only one variable at a time, may make its life much easier. Is it possible to do this? If so, can we modify WAPS to achieve this?
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