def __init__(self,
allow_identical_outcomes=False,
outcome_warning_threshold=0.99,
n_outcomes_cutoff=None):
super(FiniteOutcomeModel, self).__init__(
outcome_warning_threshold=outcome_warning_threshold,
allow_identical_outcomes=allow_identical_outcomes)
self._n_outcomes_cutoff = n_outcomes_cutoff
if self.is_n_outcomes_constant:
# predefine if we can
self._domain = IntegerDomain(min=0, max=self.n_outcomes(None) - 1)
def domain(self, expparams):
"""
Returns a list of ``Domain``s, one for each input expparam.
:param numpy.ndarray expparams: Array of experimental parameters. This
array must be of dtype agreeing with the ``expparams_dtype``
property, or, in the case where ``n_outcomes_constant`` is ``True``,
``None`` should be a valid input.
:rtype: list of ``Domain``
"""
return [
IntegerDomain(min=0, max=n_o - 1)
for n_o in self.n_outcomes(expparams)
]
def domain(self, expparams):
"""
Returns a list of :class:`Domain` objects, one for each input expparam.
:param numpy.ndarray expparams: Array of experimental parameters. This
array must be of dtype agreeing with the ``expparams_dtype``
property, or, in the case where ``n_outcomes_constant`` is ``True``,
``None`` should be a valid input.
:rtype: list of ``Domain``
"""
# As a convenience to most users, we define domain for them. If a
# fancier domain is desired, this method can easily be overridden.
if self.is_n_outcomes_constant:
return self._domain if expparams is None else [
self._domain for ep in expparams
]
else:
return [
IntegerDomain(min=0, max=n_o - 1)
for n_o in self.n_outcomes(expparams)
]