def lwis_update(self, prior):
"""
clustering:
pairwise greedy merging - compare means, weights & variances
salmond's method and runnals' method (better)
"""
prior_mean = np.asarray(prior.means[0])
prior_var = np.asarray(prior.covariances[0])
# Importance distribution
q = GaussianMixture(1, prior_mean, prior_var)
# Importance sampling correction
w = np.zeros(num_samples) # Importance weights
x = q.rvs(size=num_samples) # Sampled points
x = np.asarray(x)
if hasattr(likelihood, 'subclasses'):
measurement_class = likelihood.subclasses[measurement]
else:
measurement_class = likelihood.classes[measurement]
for i in range(num_samples):
w[i] = prior.pdf(x[i]) \
* measurement_class.probability(state=x[i])\
/ q.pdf(x[i])
w /= np.sum(w) # Normalize weights
mu_hat = np.zeros_like(np.asarray(mu_VB))
for i in range(num_samples):
x_i = np.asarray(x[i])
mu_hat = mu_hat + x_i.dot(w[i])
var_hat = np.zeros_like(np.asarray(var_VB))
for i in range(num_samples):
x_i = np.asarray(x[i])
var_hat = var_hat + w[i] * np.outer(x_i, x_i)
var_hat -= np.outer(mu_hat, mu_hat)
if mu_hat.size == 1 and mu_hat.ndim > 0:
mu_lwis = mu_hat[0]
else:
mu_lwis = mu_hat
if var_hat.size == 1:
var_lwis = var_hat[0][0]
else:
var_lwis = var_hat
logging.debug(
'LWIS update found mean of {} and variance of {}.'.format(
mu_lwis, var_lwis))
return mu_lwis, var_lwis, log_c_hat
def update(self,
measurement,
likelihood,
prior,
use_LWIS=False,
poly=None,
num_std=1):
"""VB update using Gaussian mixtures and multimodal softmax.
This uses Variational Bayes with Importance Sampling (VBIS) for
each mixand-softmax pair available.
"""
# If we have a polygon, update only the mixands intersecting with it
if poly is None:
update_intersections_only = False
else:
update_intersections_only = True
h = 0
relevant_subclasses = likelihood.classes[measurement].subclasses
num_relevant_subclasses = len(relevant_subclasses)
# Use intersecting priors only
if update_intersections_only:
other_priors = prior.copy()
weights = []
means = []
covariances = []
mixand_ids = []
ellipses = prior.std_ellipses(num_std)
any_intersection = False
for i, ellipse in enumerate(ellipses):
try:
has_intersection = poly.intersects(ellipse)
except ValueError:
logging.warn('Null geometry error! Defaulting to true.')
has_intersection = True
if has_intersection:
# Get parameters for intersecting priors
mixand_ids.append(i)
weights.append(prior.weights[i])
means.append(prior.means[i])
covariances.append(prior.covariances[i])
any_intersection = True
if not any_intersection:
logging.debug('No intersection with any ellipse.')
mu_hat = other_priors.means
var_hat = other_priors.covariances
beta_hat = other_priors.weights
return mu_hat, var_hat, beta_hat
# Remove these from the other priors
other_priors.weights = \
np.delete(other_priors.weights, mixand_ids, axis=0)
other_priors.means = \
np.delete(other_priors.means, mixand_ids, axis=0)
other_priors.covariances = \
np.delete(other_priors.covariances, mixand_ids, axis=0)
# Retain total weight of intersection weights for renormalization
max_intersecion_weight = sum(weights)
# Create new prior
prior = GaussianMixture(weights, means, covariances)
logging.debug(
'Using only mixands {} for VBIS fusion. Total weight {}'.
format(mixand_ids, max_intersecion_weight))
# Parameters for all new mixands
K = num_relevant_subclasses * prior.weights.size
mu_hat = np.zeros((K, prior.means.shape[1]))
var_hat = np.zeros(
(K, prior.covariances.shape[1], prior.covariances.shape[2]))
log_beta_hat = np.zeros(K) # Weight estimates
for u, mixand_weight in enumerate(prior.weights):
mix_sm_corr = 0
# Check to see if the mixand is completely contained within
# the softmax class (i.e. doesn't need an update)
mixand = GaussianMixture(1, prior.means[u], prior.covariances[u])
mixand_samples = mixand.rvs(self.num_mixand_samples)
p_hat_ru_samples = likelihood.classes[measurement].probability(
state=mixand_samples)
mix_sm_corr = np.sum(p_hat_ru_samples) / self.num_mixand_samples
if mix_sm_corr > self.mix_sm_corr_thresh:
logging.debug(
'Mixand {}\'s correspondence with {} was {},'
'above the threshold of {}, so VBIS was skipped.'.format(
u, measurement, mix_sm_corr, self.mix_sm_corr_thresh))
# Append the prior's parameters to the mixand parameter lists
mu_hat[h, :] = prior.means[u]
var_hat[h, :] = prior.covariances[u]
log_beta_hat[h] = np.log(mixand_weight)
h += 1
continue
# Otherwise complete the full VBIS update
ordered_subclasses = iter(sorted(relevant_subclasses.iteritems()))
for label, subclass in ordered_subclasses:
# Compute \hat{P}_s(r|u)
mixand_samples = mixand.rvs(self.num_mixand_samples)
p_hat_ru_samples = subclass.probability(state=mixand_samples)
p_hat_ru_sampled = np.sum(
p_hat_ru_samples) / self.num_mixand_samples
mu_vbis, var_vbis, log_c_hat = \
self.vbis_update(label, subclass.softmax_collection,
mixand, use_LWIS=use_LWIS)
# Compute log odds of r given u
if np.isnan(log_c_hat): # from LWIS update
log_p_hat_ru = np.log(p_hat_ru_sampled)
else:
log_p_hat_ru = np.max(
(log_c_hat, np.log(p_hat_ru_sampled)))
# Find log of P(u,r|D_k) \approxequal \hat{B}_{ur}
log_beta_vbis = np.log(mixand_weight) + log_p_hat_ru
# Symmetrize var_vbis
var_vbis = 0.5 * (var_vbis.T + var_vbis)
# Update estimate values
log_beta_hat[h] = log_beta_vbis
mu_hat[h, :] = mu_vbis
var_hat[h, :] = var_vbis
h += 1
# Renormalize and truncate (based on weight threshold)
log_beta_hat = log_beta_hat - np.max(log_beta_hat)
unnormalized_beta_hats = np.exp(log_beta_hat)
beta_hat = np.exp(log_beta_hat) / np.sum(np.exp(log_beta_hat))
# Reattach untouched prior values
if update_intersections_only:
beta_hat = unnormalized_beta_hats * max_intersecion_weight
beta_hat = np.hstack((other_priors.weights, beta_hat))
mu_hat = np.vstack((other_priors.means, mu_hat))
var_hat = np.concatenate((other_priors.covariances, var_hat))
# Shrink mu, var and beta if necessary
h += other_priors.weights.size
beta_hat = beta_hat[:h]
mu_hat = mu_hat[:h]
var_hat = var_hat[:h]
beta_hat /= beta_hat.sum()
else:
# Shrink mu, var and beta if necessary
beta_hat = beta_hat[:h]
mu_hat = mu_hat[:h]
var_hat = var_hat[:h]
# Threshold based on weights
mu_hat = mu_hat[beta_hat > self.weight_threshold, :]
var_hat = var_hat[beta_hat > self.weight_threshold, :]
beta_hat = beta_hat[beta_hat > self.weight_threshold]
# Check if covariances are positive semidefinite
for i, var in enumerate(var_hat):
try:
assert np.all(np.linalg.det(var) > 0)
except AssertionError, e:
logging.warn('Following variance is not positive '
'semidefinite: \n{}'.format(var))
var_hat[i] = np.eye(var.shape[0]) * 10**-3
def vbis_update(self,
measurement,
likelihood,
prior,
init_mean=0,
init_var=1,
init_alpha=0.5,
init_xi=1,
num_samples=None,
use_LWIS=False):
"""VB update with importance sampling for Gaussian and Softmax.
"""
if num_samples is None:
num_samples = self.num_importance_samples
if use_LWIS:
q_mu = np.asarray(prior.means[0])
log_c_hat = np.nan
else:
# Use VB update
q_mu, var_VB, log_c_hat = self.vb_update(measurement, likelihood,
prior, init_mean,
init_var, init_alpha,
init_xi)
q_var = np.asarray(prior.covariances[0])
# Importance distribution
q = GaussianMixture(1, q_mu, q_var)
# Importance sampling correction
w = np.zeros(num_samples) # Importance weights
x = q.rvs(size=num_samples) # Sampled points
x = np.asarray(x)
if hasattr(likelihood, 'subclasses'):
measurement_class = likelihood.subclasses[measurement]
else:
measurement_class = likelihood.classes[measurement]
# Compute parameters using samples
w = prior.pdf(x) * measurement_class.probability(state=x) / q.pdf(x)
w /= np.sum(w) # Normalize weights
mu_hat = np.sum(x.T * w, axis=-1)
# <>TODO: optimize this
var_hat = np.zeros_like(np.asarray(q_var))
for i in range(num_samples):
x_i = np.asarray(x[i])
var_hat = var_hat + w[i] * np.outer(x_i, x_i)
var_hat -= np.outer(mu_hat, mu_hat)
# Ensure properly formatted output
if mu_hat.size == 1 and mu_hat.ndim > 0:
mu_post_vbis = mu_hat[0]
else:
mu_post_vbis = mu_hat
if var_hat.size == 1:
var_post_vbis = var_hat[0][0]
else:
var_post_vbis = var_hat
logging.debug(
'VBIS update found mean of {} and variance of {}.'.format(
mu_post_vbis, var_post_vbis))
return mu_post_vbis, var_post_vbis, log_c_hat