def done(self, smc):
res = super().done(smc)
if res:
assert np.allclose(self.pf_debug_access.logLt, self.logging.compact_particle_history.logLT)
# noinspection PyTypeChecker
self.logging.resampling_mode.append(None)
self.logging.inner_ESS_ratio.append(ut.ESS_ratio(smc.wgts))
return res
def M(self, t: int, xp: np.ndarray,
w: rs.Weights) -> typing.Tuple[np.ndarray, rs.Weights]:
resampling_needed = ut.ESS_ratio(w) < self.ESSrmin
self.logging.punctual_logging[
t] = GenericParticleFilterPunctualLogging()
self.logging.punctual_logging[t].ESS_ratio = ut.ESS_ratio(w)
self.logging.punctual_logging[t].ESS = w.ESS
self.logging.punctual_logging[t].resampled = resampling_needed
if resampling_needed:
ancestors = rs.resampling(self.resampling_mode, w.W)
xp = xp[ancestors]
w = rs.Weights(lw=np.zeros(len(xp)))
self.logging.compact_particle_history.add(lw=w.lw,
ancestor=ancestors,
last_particles=xp)
x = self.fk_model.M(t, xp)
return x, w
def done(self, smc: 'SMCNew') -> bool:
res = smc.t > self.fk_model.T
if res:
assert np.allclose(smc.logLt,
self.logging.compact_particle_history.logLT)
self.logging.punctual_logging[
smc.t] = GenericParticleFilterPunctualLogging()
self.logging.punctual_logging[smc.t].ESS_ratio = ut.ESS_ratio(
smc.wgts)
self.logging.punctual_logging[smc.t].ESS = smc.wgts.ESS
return res
def done(self, smc):
res = super().done(smc)
if res:
assert np.allclose(smc.logLt, self.logging.weight_cumulator.logLT)
assert np.allclose(smc.logLt, sum([hist.logLT for hist in self.histories_between_two_outer_resampling]))
self.logging.inner_ESS_ratio.append(self._inner_ess_ratio(smc.wgts))
self.logging.outer_ESS_ratio.append(ut.ESS_ratio(MultipleViewWeights(flattened_lw=smc.wgts.lw, M=self.M_resample, M1=self.M1).outer_weights))
self.logging.resampling_mode.append(-1)
self.logging.chain_length.append(0)
self.logging.thinning.append(0)
self.logging.done = True
return res
def need_rejuvenate(self, t, xp, w: rs.Weights) -> bool:
inner_ess_ratio = self._inner_ess_ratio(w)
self.logging.inner_ESS_ratio.append(inner_ess_ratio)
self.logging.outer_ESS_ratio.append(ut.ESS_ratio(MultipleViewWeights(flattened_lw=w.lw, M=self.M_resample, M1=self.M1).outer_weights))
res = inner_ess_ratio < self.ESSrmin_inner
if not res:
self.logging.resampling_mode.append(0)
self.logging.weight_cumulator.update_M(new_weights=w, normalize=False)
self.logging.chain_length.append(0)
self.logging.thinning.append(0)
self.last_resampling_mode = 0
return res
def outer_resampling_needed(self) -> bool:
outer_ess_ratio = ut.ESS_ratio(self.w.outer_weights)
return outer_ess_ratio < self.ESSrmin_outer
def need_rejuvenate(self, t:int, xp, w: rs.Weights) -> bool:
self.logging.inner_ESS_ratio.append(ut.ESS_ratio(w))
res = w.ESS < len(xp) * self.ESSrmin
self.logging.resampling_mode.append(res)
return res
def ESS_ratio(self, t: int) -> typing.Union[float, None]:
if t >= len(self._logG):
return None
return ut.ESS_ratio(rs.Weights(lw=self._logG[t]))
