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 _fit(self,
model: AbstractPriorModel,
analysis,
log_likelihood_cap=None):
if self.fit_fast:
result = self._fit_fast(model=model, analysis=analysis)
return result
if model.prior_count == 0:
raise AssertionError(
"There are no priors associated with the model!")
if model.prior_count != len(model.unique_prior_paths):
raise AssertionError(
"Prior count doesn't match number of unique prior paths")
index = 0
unit_vector = model.prior_count * [0.5]
while True:
try:
instance = model.instance_from_unit_vector(unit_vector)
fit = analysis.log_likelihood_function(instance)
break
except exc.FitException as e:
unit_vector[index] += 0.1
if unit_vector[index] >= 1:
raise e
index = (index + 1) % model.prior_count
samples = MockSamples(
samples=samples_with_log_likelihood_list(self.sample_multiplier *
fit),
model=model,
gaussian_tuples=[
(prior.mean,
prior.width if math.isfinite(prior.width) else 1.0)
for prior in sorted(model.priors, key=lambda prior: prior.id)
],
)
self.paths.save_samples(samples)
return MockResult(
model=model,
samples=samples,
)
