def propose(self):
tau = 1./(self.adaptive_scale_factor * self.proposal_sd)**2
time = pymc.rnormal(self.stochastic.value.time, tau)
n = pymc.rnormal(len(self.stochastic.value), tau)
if n <= 0:
n = 0
times = [rand.random() for _ in range(n)]
total = float(sum(times))
times = [item*time/total for item in times]
events = [_Survival._event(time=item, censored=False) for item in times]
events = numpy.array(events)
self.stochastic.value = _Survival._multiEvent(events)
def propose(self):
tau = 1./(self.adaptive_scale_factor * self.proposal_sd)**2
time = pymc.rnormal(self.stochastic.value.time, tau)
censored = rand.random() > 0.5
self.stochastic.value = _Survival._event(time, censored)