def clean_chain(self, scenario: Scenario,
chain_ensemble_state: cdict) -> cdict:
chain_ensemble_state.temperature = chain_ensemble_state.temperature[:,
0]
scenario.temperature = float(chain_ensemble_state.temperature[-1])
chain_ensemble_state.ess = chain_ensemble_state.ess[:, 0]
return chain_ensemble_state
def startup(self, scenario: Scenario, n: int, initial_state: cdict,
initial_extra: cdict, **kwargs) -> Tuple[cdict, cdict]:
if not hasattr(scenario, 'prior_sample'):
raise TypeError(
f'Likelihood tempering requires scenario {scenario.name} to have prior_sample implemented'
)
initial_state, initial_extra = super().startup(scenario, n,
initial_state,
initial_extra, **kwargs)
random_keys = random.split(initial_extra.random_key, 2 * n + 1)
initial_extra.random_key = random_keys[-1]
initial_state.prior_potential = vmap(scenario.prior_potential)(
initial_state.value, random_keys[:n])
initial_state.likelihood_potential = vmap(
scenario.likelihood_potential)(initial_state.value,
random_keys[n:(2 * n)])
initial_state.potential = initial_state.prior_potential
initial_state.temperature = jnp.zeros(n)
initial_state.log_weight = jnp.zeros(n)
initial_state.ess = jnp.zeros(n) + n
scenario.temperature = 0.
return initial_state, initial_extra
def adapt(self, previous_ensemble_state: cdict, previous_extra: cdict,
new_ensemble_state: cdict,
new_extra: cdict) -> Tuple[cdict, cdict]:
n = new_ensemble_state.value.shape[0]
next_temperature = self.next_temperature(new_ensemble_state, new_extra)
new_ensemble_state.temperature = jnp.ones(n) * next_temperature
new_ensemble_state.log_weight = previous_ensemble_state.log_weight \
+ self.log_weight(previous_ensemble_state, previous_extra,
new_ensemble_state, new_extra)
new_ensemble_state.ess = jnp.ones(n) * jnp.exp(
log_ess_log_weight(new_ensemble_state.log_weight))
return new_ensemble_state, new_extra
