def _beta_vlb(self):
return np.log(self.beta).sum() + self.gamma*np.log(1-cumsum(self.beta)).sum() \
- 3*np.log(1-cumsum(self.beta,strict=True)).sum()
def _grad_log_p_beta(beta, alpha):
# NOTE: switched argument name gamma <-> alpha
return -(alpha-1)*rcumsum(1./(1-cumsum(beta))) \
+ 2*rcumsum(1./(1-cumsum(beta,strict=True)),strict=True)
def feas(pt):
return (pt > 0.).all() and pt.sum() < 1. and not np.isinf(
1. / (1 - cumsum(pt))).any()
def _beta_vlb(self):
return np.log(self.beta).sum() + self.gamma*np.log(1-cumsum(self.beta)).sum() \
- 3*np.log(1-cumsum(self.beta,strict=True)).sum()
def _grad_log_p_beta(beta,alpha):
# NOTE: switched argument name gamma <-> alpha
return -(alpha-1)*rcumsum(1./(1-cumsum(beta))) \
+ 2*rcumsum(1./(1-cumsum(beta,strict=True)),strict=True)
def feas(pt):
return (pt>0.).all() and pt.sum() < 1. and not np.isinf(1./(1-cumsum(pt))).any()