def update_particle_weights(particles, log_weights, settings):
for n, p in enumerate(particles):
if settings.verbose >= 2:
print 'pid = %5d, log_sis_ratio = %f' % (n, p.log_sis_ratio)
log_weights[n] += p.log_sis_ratio
weights_norm = softmax(log_weights) # normalized weights
ess = 1. / np.sum(weights_norm ** 2) / settings.n_particles
log_pd = logsumexp(log_weights)
return (log_pd, ess, log_weights, weights_norm)
def update_particle_weights(particles,log_weights,settings):
for n,p in enumerate(particles):
if settings.verbose >=2:
print('pid = %5d, log_sis_ratio =%f' % (n,p.log_sis_ratio))
log_weights[n] +=p.log_sis_ratio
norm_of_weights = softmax(log_weights) # normalize the weights
ess = 1./ np.sum(norm_of_weights**2)/ settings.n_particles
log_pd = logsumexp(log_weights)
return (log_pd,ess,log_weights,norm_of_weights)
def update_p(self, particles, log_weights, log_pd, settings):
node_info_old = {}
first_iter = False
if settings.verbose >= 2:
print('log_weights = %s' % log_weights)
k = sample_multinomial(softmax(log_weights))
try:
node_info_old = self.p.node_info
except AttributeError:
first_iter = True
# first iteration probably: self.p would not be present
pass
same_tree = node_info_old == particles[k].node_info
self.p = particles[k]
self.log_pd = log_pd
if settings.verbose >= 2:
print('pid_sampled = %s' % k)
print('new tree:')
self.p.print_tree()
if not same_tree and settings.verbose >= 1:
print('non-identical trees')
if k == 0 and not first_iter:
assert same_tree
elif same_tree and not first_iter: # particles from pmcmc during init might be different
if settings.verbose >= 1:
print('identical tree without k == 0')
try:
check_if_zero(log_weights[k] - log_weights[0])
except AssertionError:
print('node_info_old = %s' % node_info_old)
print('same_tree = %s' % same_tree)
print('k = %s, particles[k].node_info = %s' %
(k, particles[k].node_info))
print('log_weights[0] = %s, log_weights[k] = %s' % \
(log_weights[0], log_weights[k]))
if not first_iter:
print(p_old.log_sis_ratio_d)
print(particles[0].log_sis_ratio_d)
print(particles[k].log_sis_ratio_d)
raise AssertionError
self.p.check_depth()
if settings.verbose >= 2:
print('sampled particle = %5d, ancestry = %s' %
(k, self.p.ancestry))
return not same_tree
def update_p(self, particles, log_weights, log_pd, settings):
node_info_old = {}
first_iter = False
if settings.verbose >= 2:
print 'log_weights = %s' % log_weights
k = sample_multinomial(softmax(log_weights))
try:
node_info_old = self.p.node_info
except AttributeError:
first_iter = True
# first iteration probably: self.p would not be present
pass
same_tree = node_info_old == particles[k].node_info
self.p = particles[k]
self.log_pd = log_pd
if settings.verbose >= 2:
print 'pid_sampled = %s' % k
print 'new tree:'
self.p.print_tree()
if not same_tree and settings.verbose >=1:
print 'non-identical trees'
if k == 0 and not first_iter:
assert same_tree
elif same_tree and not first_iter: # particles from pmcmc during init might be different
if settings.verbose >= 1:
print 'identical tree without k == 0'
try:
check_if_zero(log_weights[k] - log_weights[0])
except AssertionError:
print 'node_info_old = %s' % node_info_old
print 'same_tree = %s' % same_tree
print 'k = %s, particles[k].node_info = %s' % (k, particles[k].node_info)
print 'log_weights[0] = %s, log_weights[k] = %s' % \
(log_weights[0], log_weights[k])
if not first_iter:
print p_old.log_sis_ratio_d
print particles[0].log_sis_ratio_d
print particles[k].log_sis_ratio_d
raise AssertionError
self.p.check_depth()
if settings.verbose >= 2:
print 'sampled particle = %5d, ancestry = %s' % (k, self.p.ancestry)
return not same_tree