def reverse_cumulative_obs_potentials(self,tblock):
t = self.segmentstarts[tblock] + self.segmentlens[tblock]
possible_durations = rcumsum(self.segmentlens[:tblock+1])[-TRUNC if TRUNC is not None else None:]
return np.hstack([hmm.reverse_cumulative_obs_potentials(self.aBls[state][:t],self,t)\
[-possible_durations][:,na]
# [possible_durations -1][:,na]
for state, hmm in enumerate(self.model.HMMs)])
def mf_reverse_cumulative_obs_potentials(self,tblock):
return rcumsum(self.mf_aBl[:tblock+1])\
[-self.trunc if self.trunc is not None else None:]
def reverse_dur_survival_potentials(self,tblock):
# NOTE: untested, unused
max_dur = rcumsum(self.segmentlens[:tblock+1])\
[-self.trunc if self.trunc is not None else None:][0]
return self.aDsl[max_dur -1]
def mf_reverse_cumulative_obs_potentials(self,t):
start = 0 if self.trunc is None else max(0,t-self.trunc+1)
return rcumsum(self.mf_aBl[start:t+1])
def expected_log_sf(self,x):
x = np.atleast_1d(x).astype('int32')
assert x.ndim == 1
inf = max(2*x.max(),2*1000) # approximately infinity, we hope
return rcumsum(self.expected_log_pmf(np.arange(1,inf)),strict=True)[x]
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 _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 mf_reverse_dur_potentials(self,tblock):
possible_durations = rcumsum(self.segmentlens[:tblock+1])\
[-self.trunc if self.trunc is not None else None:]
return self.mf_aDl[possible_durations -1]
def reverse_dur_survival_potentials(self,tblock):
# NOTE: untested, unused
max_dur = rcumsum(self.segmentlens[:tblock+1])\
[-self.trunc if self.trunc is not None else None:][0]
return self.aDsl[max_dur -1]
def mf_reverse_cumulative_obs_potentials(self,tblock):
return rcumsum(self.mf_aBl[:tblock+1])\
[-self.trunc if self.trunc is not None else None:]
def mf_reverse_cumulative_obs_potentials(self,t):
start = 0 if self.trunc is None else max(0,t-self.trunc+1)
return rcumsum(self.mf_aBl[start:t+1])
def reverse_dur_survival_potentials(self,tblock):
max_dur = rcumsum(self.segmentlens[:tblock+1])[-TRUNC if TRUNC is not None else None:][0]
return self.aDsl[max_dur -1]
def reverse_dur_potentials(self,tblock):
possible_durations = rcumsum(self.segmentlens[:tblock+1])[-TRUNC if TRUNC is not None else None:]
return self.aDl[possible_durations -1]
def mf_reverse_dur_potentials(self,tblock):
possible_durations = rcumsum(self.segmentlens[:tblock+1])\
[-self.trunc if self.trunc is not None else None:]
return self.mf_aDl[possible_durations -1]
def mf_reverse_dur_survival_potentials(self,tblock):
max_dur = rcumsum(self.segmentlens[:tblock+1])\
[-self.trunc if self.trunc is not None else None:][0]
return self.mf_aDsl[max_dur -1]
def mf_reverse_dur_survival_potentials(self,tblock):
max_dur = rcumsum(self.segmentlens[:tblock+1])\
[-self.trunc if self.trunc is not None else None:][0]
return self.mf_aDsl[max_dur -1]
def expected_log_sf(self, x):
x = np.atleast_1d(x).astype('int32')
assert x.ndim == 1
inf = max(2 * x.max(), 2 * 1000) # approximately infinity, we hope
return rcumsum(self.expected_log_pmf(np.arange(1, inf)),
strict=True)[x]