def to_child(self):
x = self.parent / self.message_to_parent
# x = self.parent
m, v = x.mean_and_variance()
integral = sigmoid_integrals(m, v, [0])[0]
proba = tf.where(self.child.proba() == 1., integral, 1. - integral)
self.message_to_child = BernoulliArray.from_array(proba)
def to_result(self, x):
# compute incoming message
from_x = x / self.message_to_x
# add noise
from_x = GaussianArray.from_array(
mean=from_x.mean(),
variance=from_x.log_var() + self.variance.value()
)
# compute probability
proba = 1. - tfp.distributions.Normal(*from_x.mean_and_variance()).cdf(0.0)
return BernoulliArray.from_array(proba)
def to_child(self):
# only used for prediction, does not affect VMP
x = self.parent / self.message_to_parent
proba = 1. - tfp.distributions.Normal(*x.mean_and_stddev()).cdf(0.0)
self.message_to_child = BernoulliArray.from_array(proba)
def to_result(self, x):
x = x / self.message_to_x
# TODO: should that be sqrt?
proba = 1. - tfp.distributions.Normal(*x.mean_and_variance()).cdf(0.0)
return BernoulliArray.from_array(proba)