def discrete_outcomes(self,
n_max: int = None,
astype: Type['Outcome'] = dict) -> List['Outcome']:
"""
A discrete set of outcomes that spans the outcome space
Args:
n_max: The maximum number of outcomes to return. If None, all outcomes will be returned for discrete issues
and *100* if any of the issues was continuous
astype: A type to cast the resulting outcomes to.
Returns:
List[Outcome]: List of `n` or less outcomes
"""
if self.outcomes is not None:
return self.outcomes
if self.__discrete_outcomes is None:
if all(issue.is_discrete() for issue in self.issues):
self.__discrete_outcomes = Issue.sample(
issues=self.issues,
n_outcomes=n_max,
astype=astype,
with_replacement=False,
fail_if_not_enough=False)
else:
self.__discrete_outcomes = Issue.sample(
issues=self.issues,
n_outcomes=n_max if n_max is not None else 100,
astype=astype,
with_replacement=False,
fail_if_not_enough=False)
return self.__discrete_outcomes
def random_outcomes(
self, n: int = 1, astype: Type[Outcome] = None
) -> List["Outcome"]:
"""Returns random offers.
Args:
n: Number of outcomes to generate
astype: The type to use for the generated outcomes
Returns:
A list of outcomes (in the type specified using `astype`) of at most n outcomes.
Remarks:
- If the number of outcomes `n` cannot be satisfied, a smaller number will be returned
- Sampling is done without replacement (i.e. returned outcomes are unique).
"""
if astype is None:
astype = self.ami.outcome_type
if self.ami.issues is None or len(self.ami.issues) == 0:
raise ValueError("I do not have any issues to generate offers from")
return Issue.sample(
issues=self.issues,
n_outcomes=n,
astype=astype,
with_replacement=False,
fail_if_not_enough=False,
)
def equiprobable_thresholds(cls,
n: int,
ufun: "UtilityFunction",
issues: List[Issue],
n_samples: int = 1000) -> List[float]:
"""
Generates thresholds for the n given levels where levels are equally likely approximately
Args:
n: Number of scale levels (one side)
ufun: The utility function to use
issues: The issues to generate the thresholds for
n_samples: The number of samples to use during the process
"""
samples = list(
Issue.sample(issues,
n_samples,
with_replacement=False,
fail_if_not_enough=False))
n_samples = len(samples)
diffs = []
for i, first in enumerate(samples):
n_diffs = min(10, n_samples - i - 1)
for second in sample(samples[i + 1:], k=n_diffs):
diffs.append(abs(ufun.compare_real(first, second)))
diffs = np.array(diffs)
hist, edges = np.histogram(diffs, bins=n + 1)
return edges[1:-1].tolist()
def random_outcomes(self,
n: int = 1,
astype: Type[Outcome] = dict) -> List['Outcome']:
"""Returns random offers"""
if self.info.issues is None or len(self.info.issues) == 0:
raise ValueError(
'I do not have any issues to generate offers from')
return Issue.sample(issues=self.issues,
n_outcomes=n,
astype=astype,
with_replacement=False,
fail_if_not_enough=False)
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]
