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.
:rtype: list of ``Domain``
"""
return [
MultinomialDomain(n_elements=self.n_sides, n_meas=ep['n_meas'])
for ep in expparams
]
def __init__(self, underlying_model):
super(MultinomialModel, self).__init__(underlying_model)
if isinstance(underlying_model.expparams_dtype, str):
# We default to calling the original experiment parameters "x".
self._expparams_scalar = True
self._expparams_dtype = [('x', underlying_model.expparams_dtype),
('n_meas', 'uint')]
else:
self._expparams_scalar = False
self._expparams_dtype = underlying_model.expparams_dtype + [
('n_meas', 'uint')
]
# Demand that the underlying model always has the same number of outcomes
# This assumption could in principle be generalized, but not worth the effort now.
assert (self.underlying_model.is_n_outcomes_constant)
self._underlying_domain = self.underlying_model.domain(None)
self._n_sides = self._underlying_domain.n_members
# Useful for getting the right type, etc.
self._example_domain = MultinomialDomain(n_elements=self.n_sides,
n_meas=3)
