def fit(
self,
model: abstract.AbstractPriorModel,
analysis: abstract_search.Analysis
):
best_likelihood = float("-inf")
best_instance = None
likelihoods = list()
for list_ in make_lists(
no_dimensions=model.prior_count,
step_size=self.step_size
):
instance = model.instance_from_unit_vector(
list_
)
likelihood = analysis.log_likelihood_function(
instance
)
likelihoods.append(likelihood)
if likelihood > best_likelihood:
best_likelihood = likelihood
best_instance = instance
return Result(
samples=MockSamples(
max_log_likelihood_instance=best_instance,
log_likelihood_list=likelihoods,
gaussian_tuples=None
),
model=model
)
def _lists(self) -> List[List[float]]:
"""
A list of hypercube vectors, used to instantiate
the perturbation_model and create the individual
perturbations.
"""
return make_lists(self.perturbation_model.prior_count,
step_size=self.step_size)