def propagate_particle_smoother_bs(ssm_scenario: StateSpaceModel,
particle_filter: ParticleFilter,
particles: cdict,
y_new: jnp.ndarray,
t_new: float,
random_key: jnp.ndarray,
lag: int,
ess_threshold: float,
maximum_rejections: int,
init_bound_param: float,
bound_inflation: float) -> cdict:
n = particles.value.shape[1]
if not hasattr(particles, 'num_transition_evals'):
particles.num_transition_evals = jnp.array(0)
if not hasattr(particles, 'marginal_filter'):
particles.marginal_filter = cdict(value=particles.value,
log_weight=particles.log_weight,
y=particles.y,
t=particles.t,
ess=particles.ess)
split_keys = random.split(random_key, 4)
out_particles = particles
# Propagate marginal filter particles
out_particles.marginal_filter = propagate_particle_filter(ssm_scenario, particle_filter, particles.marginal_filter,
y_new, t_new, split_keys[1], ess_threshold, False)
out_particles.y = jnp.append(out_particles.y, y_new[jnp.newaxis], axis=0)
out_particles.t = jnp.append(out_particles.t, t_new)
out_particles.log_weight = jnp.zeros(n)
out_particles.ess = out_particles.marginal_filter.ess[-1]
len_t = len(out_particles.t)
stitch_ind_min_1 = len_t - lag - 1
stitch_ind = len_t - lag
def back_sim_only(marginal_filter):
backward_sim = backward_simulation(ssm_scenario,
marginal_filter,
split_keys[2],
n,
maximum_rejections,
init_bound_param,
bound_inflation)
return backward_sim.value, backward_sim.num_transition_evals.sum()
def back_sim_and_stitch(marginal_filter):
backward_sim = backward_simulation(ssm_scenario,
marginal_filter[stitch_ind_min_1:],
split_keys[2],
n,
maximum_rejections,
init_bound_param,
bound_inflation)
vals, stitch_nte = fixed_lag_stitching(ssm_scenario,
out_particles.value[:(stitch_ind_min_1 + 1)],
out_particles.t[stitch_ind_min_1],
backward_sim.value,
jnp.zeros(n),
out_particles.t[stitch_ind],
random_key,
maximum_rejections,
init_bound_param,
bound_inflation)
return vals, stitch_nte + backward_sim.num_transition_evals.sum()
if stitch_ind_min_1 >= 0:
out_particles.value, num_transition_evals = back_sim_and_stitch(out_particles.marginal_filter)
else:
out_particles.value, num_transition_evals = back_sim_only(out_particles.marginal_filter)
out_particles.num_transition_evals = jnp.append(out_particles.num_transition_evals, num_transition_evals)
# out_particles.value = cond(stitch_ind_min_1 >= 0,
# back_sim_and_stitch,
# back_sim_only,
# out_particles.marginal_filter)
return out_particles
def propagate_particle_smoother_pf(ssm_scenario: StateSpaceModel,
particle_filter: ParticleFilter,
particles: cdict,
y_new: jnp.ndarray,
t_new: float,
random_key: jnp.ndarray,
lag: int,
maximum_rejections: int,
init_bound_param: float,
bound_inflation: float) -> cdict:
if not hasattr(particles, 'num_transition_evals'):
particles.num_transition_evals = jnp.array(0)
n = particles.value.shape[1]
# Check particles are unweighted
out_particles = cond(ess_log_weight(jnp.atleast_2d(particles.log_weight)[-1]) < (n - 1e-3),
lambda p: resample_particles(p, random_key, True),
lambda p: p.copy(),
particles)
out_particles.log_weight = jnp.zeros(n)
x_previous = out_particles.value[-1]
t_previous = out_particles.t[-1]
split_keys = random.split(random_key, len(x_previous))
x_new, out_particles.log_weight = particle_filter.propose_and_intermediate_weight_vectorised(ssm_scenario,
x_previous, t_previous,
y_new, t_new,
split_keys)
out_particles.value = jnp.append(out_particles.value, x_new[jnp.newaxis], axis=0)
out_particles.y = jnp.append(out_particles.y, y_new[jnp.newaxis], axis=0)
out_particles.t = jnp.append(out_particles.t, t_new)
out_particles.ess = ess_log_weight(out_particles.log_weight)
len_t = len(out_particles.t)
stitch_ind_min_1 = len_t - lag - 1
stitch_ind = len_t - lag
# out_particles.value = cond(stitch_ind_min_1 >= 0,
# lambda vals: fixed_lag_stitching(ssm_scenario,
# vals[:(stitch_ind_min_1 + 1)],
# out_particles.t[stitch_ind_min_1],
# vals[stitch_ind_min_1:],
# out_particles.log_weight,
# out_particles.t[stitch_ind],
# random_key,
# maximum_rejections,
# init_bound_param,
# bound_inflation),
# lambda vals: vals,
# out_particles.value)
num_transition_evals = 0
if stitch_ind_min_1 >= 0:
out_particles.value, num_transition_evals = fixed_lag_stitching(ssm_scenario,
out_particles.value[:(stitch_ind_min_1 + 1)],
out_particles.t[stitch_ind_min_1],
out_particles.value[stitch_ind_min_1:],
out_particles.log_weight,
out_particles.t[stitch_ind],
random_key,
maximum_rejections,
init_bound_param,
bound_inflation)
out_particles.num_transition_evals = jnp.append(out_particles.num_transition_evals, num_transition_evals)
out_particles.log_weight = jnp.where(stitch_ind_min_1 >= 0, jnp.zeros(n), out_particles.log_weight)
return out_particles
