pyapproxmc 4.1.22

Bindings to ApproxMC, an approximate model counter

pyapproxmc: bindings to the ApproxMC model counter

This directory provides Python bindings to ApproxMC on the C++ level, i.e. when importing pyapproxmc, the ApproxMC counter becomes part of the Python process itself.

Installing

pip install pyapproxmc

Compiling

If you don't want to use the pip package, you can compile it:

apt-get install python-dev
cd python
git clone https://github.com/msoos/cryptominisat
git clone https://github.com/meelgroup/arjun
cd ..
python -m build

You will then find the files under "dist/".

Usage

import pyapproxmc
c = pyapproxmc.Counter()
c.add_clause([1,2,3])
c.add_clause([3,20])
count = c.count()
print("Approximate count is: %d*2**%d" % (count[0], count[1]))

The above will print that Approximate count is: 88*2**13. Since the largest variable in the clauses was 20, the system contained 220 (i.e. 1048576) potential models. However, some of these models were prohibited by the two clauses, and so only approximately 88*213 (i.e. 720896) models remained.

If you want to count over a projection set, you need to call count(projection_set), for example:

import pyapproxmc
c = pyapproxmc.Counter()
c.add_clause([1,2,3])
c.add_clause([3,20])
count = c.count(range(1,10))
print("Approximate count is: %d*2**%d" % (count[0], count[1]))

This now prints Approximate count is: 56*2**3, which corresponds to the approximate count of models, projected over variables 1..10.

Counter Object

You can give the following arguments to Counter:

• seed -- sets the random seed
• verbosity -- sets the verbosity of the system (default = 0)
• epsilon -- Tolerance parameter, i.e. sets how approximate the returned count is. Default = 0.8
• delta -- Confidence parameter, i.e. sets how probabilistically correct the returned count is. Default = 0.20