import numpy as np
import scipy.stats as stats
from popsynth.auxiliary_sampler import AuxiliarySampler, AuxiliaryParameter
[docs]class DeltaAuxSampler(AuxiliarySampler):
_auxiliary_sampler_name = "DeltaAuxSampler"
xp = AuxiliaryParameter(default=0)
sigma = AuxiliaryParameter(default=1, vmin=0)
[docs] def __init__(self, name: str, observed: bool = True):
"""
A delta-function sampler for which the true value
is fixed at ``xp``. Assumes property is observed by default,
in which case the observed value is sampled from
the true value with some normally-distributed error, ``sigma``.
:param name: Name of the property
:type name: str
:param observed: `True` if the property is observed,
`False` if it is latent. Defaults to `True`
:type observed: bool
:param xp: Value at which delta function is located
:type xp: :class:`AuxiliaryParameter`
:param sigma: Standard deviation of normal distribution
from which observed values are sampled, if ``observed``
is `True`
:type sigma: :class:`AuxiliaryParameter`
"""
super(DeltaAuxSampler, self).__init__(name=name, observed=observed)
[docs] def true_sampler(self, size: int):
self._true_values = np.repeat(self.xp, repeats=size)
[docs] def observation_sampler(self, size: int):
if self._is_observed:
self._obs_values = stats.norm.rvs(loc=self._true_values,
scale=self.sigma,
size=size)
else:
self._obs_values = self._true_values