def execute(self, model: FeatureModel) -> 'FMCountLeafs':
number = 0
for feat in model.get_features():
if len(feat.get_relations()) == 0:
number += 1
self.number_of_leafs = number
return self
def execute(self, model: FeatureModel) -> 'FMAverageBranchingFactor':
features = model.get_features()
childrens = 0
for feat in features:
for relation in feat.get_relations():
childrens += len(relation.children)
self.average_branching_factor = round(childrens / len(features))
return self
def average_branching_factor(feature_model: FeatureModel,
precision: int = 2) -> float:
nof_branches = 0
nof_children = 0
for feature in feature_model.get_features():
if feature.get_relations():
nof_branches += 1
nof_children += sum(
len(r.children) for r in feature.get_relations())
return round(nof_children / nof_branches, precision)
def get_false_optional_features(sat_model: PySATModel, feature_model: FeatureModel) -> list[Any]:
real_optional_features = [f for f in feature_model.get_features()
if not f.is_root() and not f.is_mandatory()]
result = []
solver = Glucose3()
for clause in sat_model.get_all_clauses():
solver.add_clause(clause)
for feature in real_optional_features:
variable = sat_model.variables.get(feature.name)
parent_variable = sat_model.variables.get(feature.get_parent().name)
assert variable is not None
satisfiable = solver.solve(assumptions=[parent_variable, -variable])
if not satisfiable:
result.append(feature.name)
solver.delete()
return result
