def _unorm(self, u: UtilityFunction, mn, mx):
if not isinstance(u, LinearUtilityAggregationFunction) and not isinstance(
u, LinearUtilityFunction
):
return normalize(u, outcomes=Issue.enumerate(issues, max_n_outcomes=1000))
# _, mx = self._urange(u, issues)
if mx < 0:
return None
u.weights = {k: _ / mx for k, _ in u.weights.items()}
return u
def random(
cls,
issues: List["Issue"],
reserved_value=(0.0, 1.0),
normalized=True,
max_n_outcomes: int = 10000,
):
outcomes = (Issue.enumerate(issues)
if Issue.num_outcomes(issues) <= max_n_outcomes else
Issue.sample(issues,
max_n_outcomes,
with_replacement=False,
fail_if_not_enough=False))
return UtilityFunction.generate_random(1, outcomes)[0]
def test_reading_writing_linear_ufun(tmp_path):
from negmas.utilities import LinearUtilityAggregationFunction, UtilityFunction
from negmas.outcomes import Issue
base_folder = pkg_resources.resource_filename(
"negmas", resource_name="tests/data/Laptop")
_, agent_info, issues = load_genius_domain_from_folder(
base_folder,
keep_issue_names=True,
keep_value_names=True,
)
ufuns = [_["ufun"] for _ in agent_info]
for ufun in ufuns:
assert isinstance(ufun, LinearUtilityAggregationFunction)
dst = tmp_path / "tmp.xml"
print(dst)
UtilityFunction.to_genius(ufun, issues=issues, file_name=dst)
ufun2, _ = UtilityFunction.from_genius(dst)
assert isinstance(ufun2, LinearUtilityAggregationFunction)
for outcome in Issue.enumerate(issues):
assert abs(ufun2(outcome) - ufun(outcome)) < 1e-3