def replace(self, worst):
"""
Replace the worst particle.
"""
# Threshold
threshold = self.distances[worst]
# Copy a survivor
if self.num_particles > 1:
while True:
copy = rng.randint(self.num_particles)
if copy != worst:
break
self.particles[worst] = deepcopy(self.particles[copy])
# Do mcmc
for i in range(0, self.mcmc_steps):
# Generate proposal
proposal = deepcopy(self.particles[worst])
logA = proposal.perturb()
proposal_distance = distance(self.reference_particle, proposal)
if logA > 0.0:
logA = 0.0
# Acceptance
if proposal_distance < threshold and rng.rand() <= np.exp(logA):
self.particles[worst] = proposal
self.distances[worst] = proposal_distance
def compute_distances(self):
"""
Compute the distance from the reference particle to each
comparison particle.
"""
self.distances = [ distance(self.reference_particle, p)
for p in self.particles ]
self.distances = np.array(self.distances)
