def _compute_prevalence(self, category=None):
"""Return prevalence, P( category )."""
beta_prior_count = self.beta - 1.
if category is None:
# initialize at the *mode* of the distribution
return normalize(beta_prior_count)
else:
return normalize(beta_prior_count + category.sum(0))
def _compute_prevalence(self, category=None):
"""Return prevalence, P( category )."""
beta_prior_count = self.beta - 1.
if category is None:
# initialize at the *mode* of the distribution
return normalize(beta_prior_count)
else:
return normalize(beta_prior_count + category.sum(0))
def _mle_em_step(self, annotations, init_accuracy=0.6):
# True if annotations is missing
missing_mask_nclasses = self._missing_mask(annotations)
# prevalence is P( category )
prevalence = np.empty((self.nclasses, ))
prevalence.fill(1. / float(self.nclasses))
accuracy = self._initial_accuracy(init_accuracy)
while True:
# Expectation step (E-step)
# compute marginal likelihood P(category[i] | model, data)
log_likelihood, unnorm_category = (self._log_likelihood_core(
annotations, prevalence, accuracy, missing_mask_nclasses))
# category is P(category[i] = k | model, data)
category = unnorm_category / unnorm_category.sum(1)[:, None]
# return here with E[cat|prev,acc] and LL(prev,acc;y)
yield (log_likelihood, prevalence, category, accuracy)
# Maximization step (M-step)
# update parameters to maximize likelihood
prevalence = normalize(category.sum(0))
accuracy = self._compute_accuracy(category,
annotations,
use_prior=False)
def _mle_em_step(self, annotations, init_accuracy=0.6):
# True if annotations is missing
missing_mask_nclasses = self._missing_mask(annotations)
# prevalence is P( category )
prevalence = np.empty((self.nclasses,))
prevalence.fill(1. / float(self.nclasses))
accuracy = self._initial_accuracy(init_accuracy)
while True:
# Expectation step (E-step)
# compute marginal likelihood P(category[i] | model, data)
log_likelihood, unnorm_category = (
self._log_likelihood_core(annotations,
prevalence,
accuracy,
missing_mask_nclasses)
)
# category is P(category[i] = k | model, data)
category = unnorm_category / unnorm_category.sum(1)[:,None]
# return here with E[cat|prev,acc] and LL(prev,acc;y)
yield (log_likelihood, prevalence, category, accuracy)
# Maximization step (M-step)
# update parameters to maximize likelihood
prevalence = normalize(category.sum(0))
accuracy = self._compute_accuracy(category, annotations,
use_prior=False)
