def meanfieldupdate_p(self, stepsize=1.0):
"""
Update p given the network parameters and the current variational
parameters of the weight distributions.
:return:
"""
logit_p = self.network.expected_log_p(
) - self.network.expected_log_notp()
logit_p += self.network.kappa * self.network.expected_log_v(
) - gammaln(self.network.kappa)
logit_p += gammaln(
self.mf_kappa_1) - self.mf_kappa_1 * np.log(self.mf_v_1)
logit_p += gammaln(self.kappa_0) - self.kappa_0 * np.log(self.nu_0)
logit_p += self.mf_kappa_0 * np.log(self.mf_v_0) - gammaln(
self.mf_kappa_0)
p_hat = logistic(logit_p)
self.mf_p = (1.0 - stepsize) * self.mf_p + stepsize * p_hat
def meanfieldupdate_p(self, stepsize=1.0):
"""
Update p given the network parameters and the current variational
parameters of the weight distributions.
:return:
"""
logit_p = self.network.expected_log_p() - self.network.expected_log_notp()
logit_p += self.network.kappa * self.network.expected_log_v() - gammaln(self.network.kappa)
logit_p += gammaln(self.mf_kappa_1) - self.mf_kappa_1 * np.log(self.mf_v_1)
logit_p += gammaln(self.kappa_0) - self.kappa_0 * np.log(self.nu_0)
logit_p += self.mf_kappa_0 * np.log(self.mf_v_0) - gammaln(self.mf_kappa_0)
# p_hat = logistic(logit_p)
# self.mf_p = (1.0 - stepsize) * self.mf_p + stepsize * p_hat
logit_p_hat = (1-stepsize) * logit(self.mf_p) + \
stepsize * logit_p
# self.mf_p = logistic(logit_p_hat)
self.mf_p = np.clip(logistic(logit_p_hat), 1e-8, 1-1e-8)