def _apply_dynamics(self, weight):
prior = util.prior_weight()
adjusted_variance = weight.variance * prior.variance / \
((1.0 - self._config.epsilon) * prior.variance +
self._config.epsilon * weight.variance)
adjusted_mean = adjusted_variance * (
(1.0 - self._config.epsilon) * weight.mean / weight.variance +
self._config.epsilon * prior.mean / prior.variance)
adjusted = pb.Gaussian(mean=adjusted_mean, variance=adjusted_variance)
logger.debug("Adjusting weight %s to %s", util.pp(weight),
util.pp(adjusted))
return adjusted
def _apply_dynamics(self, weight):
prior = util.prior_weight()
adjusted_variance = weight.variance * prior.variance / \
((1.0 - self._config.epsilon) * prior.variance +
self._config.epsilon * weight.variance)
adjusted_mean = adjusted_variance * (
(1.0 - self._config.epsilon) * weight.mean / weight.variance +
self._config.epsilon * prior.mean / prior.variance)
adjusted = pb.Gaussian(mean=adjusted_mean, variance=adjusted_variance)
logger.debug("Adjusting weight %s to %s",
util.pp(weight), util.pp(adjusted))
return adjusted
def test_dynamics_neutral_for_prior(self):
p = self._create_predictor(epsilon=0.05)
initial = util.prior_weight()
adjusted = p._apply_dynamics(initial)
self.assertAlmostEqual(initial.mean, adjusted.mean)
self.assertAlmostEqual(initial.variance, adjusted.variance)
def _get_weight(self, feature):
return self._weights.get(util.serialize_feature(feature),
util.prior_weight())
def _get_weight(self, feature):
return self._weights.get(
util.serialize_feature(feature),
util.prior_weight())
