def _predict_non_logged_density(self, Fmu, Fvar, Y):
with params_as_tensors_for(self.invlink):
gh_x, gh_w = hermgauss(self.num_gauss_hermite_points)
p = self.invlink.prob_is_largest(Y, Fmu, Fvar, gh_x, gh_w)
den = p * (1. - self.invlink.epsilon) + (1. - p) * (
self.invlink._eps_K1)
return den
def variational_expectations(self, Fmu, Fvar, Y):
with params_as_tensors_for(self.invlink):
gh_x, gh_w = hermgauss(self.num_gauss_hermite_points)
Fvar = Fvar + self.a
p = self.invlink.prob_is_largest(Y, Fmu, Fvar, gh_x, gh_w)
ve = p * tf.log(1. - self.invlink.epsilon) + (1. - p) * tf.log(
self.invlink._eps_K1)
return ve
def _predict_non_logged_density(self, Fmu, Fvar, Y):
if isinstance(self.invlink, RobustMax):
with params_as_tensors_for(self.invlink):
gh_x, gh_w = hermgauss(self.num_gauss_hermite_points)
p = self.invlink.prob_is_largest(Y, Fmu, Fvar, gh_x, gh_w)
den = p * (1. - self.invlink.epsilon) + (1. - p) * (self.invlink._eps_K1)
return den
else:
raise NotImplementedError
def variational_expectations(self, Fmu, Fvar, Y):
if isinstance(self.invlink, RobustMax):
with params_as_tensors_for(self.invlink):
gh_x, gh_w = hermgauss(self.num_gauss_hermite_points)
p = self.invlink.prob_is_largest(Y, Fmu, Fvar, gh_x, gh_w)
ve = p * tf.log(1. - self.invlink.epsilon) + (1. - p) * tf.log(self.invlink._eps_K1)
return ve
else:
raise NotImplementedError
