def generate_wcnfs(path, models_and_contexts):
for i, elem in enumerate(models_and_contexts):
model = elem["model"]
contexts = elem["contexts"]
wcnf = WCNF()
for weight_clause in model:
if weight_clause[0] == None:
wcnf.append(tuple(weight_clause[1]))
else:
wcnf.append(tuple(weight_clause[1]), weight=weight_clause[0])
wcnf.to_file(path + f"_{i}.wcnf")
for j, context in enumerate(contexts):
wcnf_context = wcnf.copy()
# print(context)
for literals in context:
wcnf_context.append((literals,))
wcnf_context.to_file(path + f"_{i}_context_{j}.wcnf")
mcsls, to_enum, improved, solver, verbose, files = parse_options()
if files:
# input WCNF formula with multiple observations
wcnf = WCNF(from_file=files[0], comment_lead=['c', 'o'])
# multiple observations
mobs = list(filter(lambda x: x[0] == 'o', wcnf.comments))
# list of lists of individual diagnoses
diags = [[] for o in mobs]
# going through all observations
for i, o in enumerate(mobs):
# creating a copy of the formula
wcnf_o = wcnf.copy()
for l in o.split()[1:-1]:
wcnf_o.append([int(l)])
if verbose:
print('c observation', i + 1)
# enumerating individual diagnoses
with mcsls(wcnf_o, use_cld=True, solver_name=solver) as oracle:
for j, mcs in enumerate(oracle.enumerate()):
if verbose > 1:
print('c I:', ' '.join([str(cl_id) for cl_id in mcs]),
'0')
if to_enum and j + 1 == to_enum:
break
