class VonMisesPdf(Pdf):
def __init__(self, mu, kappa, rv):
"""
:param mu: mu of von mises distribution - should be on unit sphere
:param kappa: concentration of von mises-fisher distribution
:param rv: associated random variable (always set in constructor,
contains at least one RVComp
"""
Pdf.__init__(kappa, rv)
self._process_inputs(mu, kappa, rv)
def _process_inputs(self, mu, kappa, rv):
if rv.dimension != self._ndim(mu):
raise ValueError("RV and mu must have same number of dimensions")
if tools.norm2(mu) < consts.EPS:
raise ValueError("mu must have non-zero length")
self._n_dimensions = self._ndim(mu)
mu /= tools.norm2(mu)
self._mu = mu
self._cpdf = VonMisesCPdf(kappa, rv)
def samples(self, n, cond=None):
return self._cpdf.samples(n, self._mu)
def shape(self):
return self._n_dimensions
def sample(self, cond=None):
return self._cpdf.sample(self._mu)
def mean(self, cond=None):
return self._mu
def eval_log(self, x, cond=None):
return self._cpdf.eval_log(x, self._mu)
def _ndim(self, x):
return x.shape[0]
class SRPPFTrackingLocalizer(TrackingLocalizer):
def __init__(self, n_particles, state_kappa, *args, **kwargs):
"""
Localizes source using Von Mises particle filter
x_t ~ VM(x_{t-1}, kappa_v)
y_t ~ SRPLikelihood(x_t)
:param n_particles: number of particles to use
:param state_kappa: concentration parameter for state von mises distribution
All other parameters will be passed to TrackingLocalizer in the form of *args
and **kwargs
"""
TrackingLocalizer.__init__(self, *args, **kwargs)
self._grid_size = self._n_theta * self._n_phi
self._setup_particle_filters(n_particles, state_kappa)
def get_distribution(self, rffts):
self._doa_bayes(rffts)
return self._posterior
def _setup_particle_filters(self, n_particles, state_kappa):
"""
Setup the distributions needed by PartcileFilter in pybayes
"""
sys.stdout.flush()
self._n_particles = n_particles
self._state_kappa = state_kappa
ndim = self._get_effective_n_dimensions()
# State RVs
self._x_t = pb.RV(pb.RVComp(ndim, "x_t"))
self._x_t_1 = pb.RV(pb.RVComp(ndim, "x_{t-1}"))
# Initial state RV
self._x0 = pb.RV(pb.RVComp(ndim, "x0"))
init_kappa = 0.5 # Really small so is almost uniform
init_mu = np.ones((ndim,))
# Create distributions
self._state_distribution = VonMisesCPdf(self._state_kappa, self._x_t, self._x_t_1)
self._init_distribution = VonMisesPdf(init_mu, init_kappa, self._x0)
# Do particle filtering ourselves...
self._posterior = EmpPdf(self._init_distribution.samples(self._n_particles))
self._estimate = self._get_estimate()
self._count = 0
def _doa_bayes(self, rffts):
"""
Particle filtering using SRP-PHAT as likelihood measure of observation
"""
# resample -- do it here so that the weights will be available after one run
# of inference.
self._posterior.resample()
for i in range(self._posterior.particles.shape[0]):
# generate new ith particle:
self._posterior.particles[i] = self._state_distribution.sample(self._posterior.particles[i])
# Get SRP likelihood
particles_3d = self._to_3d_particles(self._posterior.particles).T
# Get likelihoods
srp = self._get_srp_likelihood(rffts, particles_3d)
srp -= np.min(srp)
srp /= np.sum(srp) + consts.EPS
self._posterior.weights *= srp
# assure that weights are normalised
self._posterior.normalise_weights()
return True
def _get_effective_n_dimensions(self):
if self._n_phi == 1:
return 2
return self._n_dimensions
def _to_3d_particles(self, mat):
"""
Change matrix so that instead of each column being in 2d, each column is
in 3d. This equates to adding a zero to each column vector
"""
if self._get_effective_n_dimensions() == 3:
return mat
return np.hstack((np.asarray(mat), np.zeros((mat.shape[0], 1))))
def _get_estimate(self):
w = np.asarray(self._posterior.weights)
parts = np.asarray(self._posterior.particles)
return w.dot(parts)
class VonMisesTrackingLocalizer(TrackingLocalizer):
def __init__(self, n_particles, state_kappa, observation_kappa, outlier_prob=0,
*args, **kwargs):
"""
Localizes source using Von Mises particle filter
x_t ~ VM(x_{t-1}, kappa_v)
y_t ~ VM(x_t, kappa_w)
:param n_particles: number of particles to use
:param state_kappa: concentration parameter for state von mises distribution
:param observation_kappa: concentration parameter for observation von mises
distribution
:param outlier_prob: Probability that a given observation comes from a
background uniform outlier von mises distribution. If
this is omitted, it will be set to 0, and the normal
particle filtering algorithm will be used
All other parameters will be passed to TrackingLocalizer in the form of *args
and **kwargs
"""
TrackingLocalizer.__init__(self, *args, **kwargs)
self._grid_size = self._n_theta * self._n_phi
#self._process_search_space(search_space)
self._setup_particle_filters(n_particles, state_kappa, observation_kappa, outlier_prob)
#def _process_search_space(self, search_space):
# self._search_space = search_space
# self._planes = self._search_space.get_planes()
# self._tracking_plane = self._planes[0]
def get_distribution(self, rffts):
d, energy = self.get_distribution_real(rffts, 'gcc')
maxind = np.argmax(d)
obs = self._directions[:, maxind]
obs = np.asarray(obs, dtype=float) # port audio uses 32, pybayes uses 64
if self._use_outlier_distribution():
self._weighted_bayes(obs)
else:
self._bayes(obs)
#self._particle_filter.bayes(obs)
#self._posterior = self._particle_filter.posterior()
return self._posterior
def _setup_particle_filters(self, n_particles, state_kappa,
observation_kappa, outlier_prob):
"""
Setup the distributions needed by PartcileFilter in pybayes
"""
sys.stdout.flush()
self._n_particles = n_particles
self._state_kappa = state_kappa
self._obs_kappa = observation_kappa
ndim = self._get_effective_n_dimensions()
# State RVs
self._x_t = pb.RV(pb.RVComp(ndim, 'x_t'))
self._x_t_1 = pb.RV(pb.RVComp(ndim, 'x_{t-1}'))
# Observation RV
self._y_t = pb.RV(pb.RVComp(ndim, 'y_t'))
# Initial state RV
self._x0 = pb.RV(pb.RVComp(ndim, 'x0'))
init_kappa = .5 # Really small so is almost uniform
init_mu = np.ones((ndim,))
# Create distributions
self._state_distribution = \
VonMisesCPdf(self._state_kappa, self._x_t, self._x_t_1)
self._obs_distribution = \
VonMisesCPdf(self._obs_kappa, self._y_t, self._x_t)
self._init_distribution = \
VonMisesPdf(init_mu, init_kappa, self._x0)
# Setup distribution for outliers
if outlier_prob < 0 or outlier_prob > 1:
raise ValueError("Outlier probability must be between 0 and 1")
self._outlier_rv = pb.RV(pb.RVComp(ndim, 'outlier'))
self._outlier_mu = np.hstack((np.array([0, -1.]), np.zeros((ndim-2,))))
self._outlier_kappa = .001
self._outlier_prob = outlier_prob # Probability of generation from background pdf
self._outlier_distribution = \
VonMisesPdf(self._outlier_mu, self._outlier_kappa, self._outlier_rv)
# Do particle filtering ourselves...
self._posterior = EmpPdf(self._init_distribution.samples(self._n_particles))
self._estimate = self._get_estimate()
self._count = 0
# Create a set of weights for tracking the distribution p(c_t|x_t,y_{1:t})
self._class_weights = np.array([.5, .5])
# Matrix used to store weights for each particle for each class in order
# to calculate class posterior weights and state posterior weights
self._joint_weights = np.ones((2, self._n_particles,))
#self._particle_filter = pb.ParticleFilter(self._n_particles,
# self._init_distribution,
# self._state_distribution,
# self._obs_distribution)
#self._posterior = self._particle_filter.posterior()
def _bayes(self, yt):
"""
Take care of particle filtering ourselves, otherwise we don't have easy access
to the weights of particles
"""
# resample -- do it here so that the weights will be available after one run
# of inference.
self._posterior.resample()
for i in range(self._posterior.particles.shape[0]):
# generate new ith particle:
self._posterior.particles[i] = \
self._state_distribution.sample(self._posterior.particles[i])
# recompute ith weight:
self._posterior.weights[i] *= \
np.exp(self._obs_distribution.eval_log(yt, self._posterior.particles[i]))
# assure that weights are normalised
self._posterior.normalise_weights()
return True
def _weighted_bayes(self, yt):
"""
Do particle filtering using a spike and slab method. That is, assume we
have a background near-uniform distribution eating up all the outlier
data. Then weight the particles using this assumption
"""
self._count += 1
if self._count % 1 == 0:
self._weighted_resample()
#self._posterior.resample()
class_weight_sum = np.zeros((2,))
for i in range(self._posterior.particles.shape[0]):
# generate new ith particle:
self._posterior.particles[i] = \
self._state_distribution.sample(self._posterior.particles[i])
# recompute ith weight:
# Get likelihoods of classes p(y_t|c_t=j,x_t) for each class c_j
state_ll = self._obs_distribution.eval_log(yt, self._posterior.particles[i])
outlier_ll = self._outlier_distribution.eval_log(yt)
# calculate p(c_t=j|x_t,y_{1:t-1})
state_class_prob = np.log(1. - self._outlier_prob) + np.log(self._class_weights[0])
outlier_class_prob = np.log(self._outlier_prob) + np.log(self._class_weights[1])
# Class weights
self._joint_weights[0, i] = np.exp(state_ll + state_class_prob)
self._joint_weights[1, i] = np.exp(outlier_ll + outlier_class_prob)
class_weight_sum += self._joint_weights[:, i]
self._posterior.weights[i] *= np.sum(self._joint_weights[:, i])
# Now find p(c_j|x_t) = p(x_t|c_j)p(c_j) / p(x_t) for each class c_j
# First compute p(x_t, c_j) = p(x_t|c_j)p(c_j)
#state_particle_log_prob = \
# self._obs_distribution.eval_log(self._posterior.particles[i], \
# self._estimate) + np.log(1 - self._outlier_prob)
#outlier_particle_log_prob = \
# self._outlir_distribution.eval_log(self._posterior.particles[i]) \
# + np.log(self._outlier_prob)
## Now compute p(x_t) = \sum_j p(x_t, c_j)
#total_particle_log_prob = np.log(np.exp(state_particle_log_prob) + \
# np.exp(outlier_particle_log_prob))
## Finally compute the actual posteriors p(c_j|x_t)
#state_class_post = state_particle_log_prob - total_particle_log_prob
#outlier_class_post = outlier_particle_log_prob - total_particle_log_prob
## Now compute total likelihood
## p(y_t|x_t) = sum_j p(y_t,c_j|x_t) = sum_j p(y_t|c_j,x_t)p(c_j|x_t)
##total_likelihood = np.exp(state_ll + state_class_post) + \
## np.exp(outlier_ll + outlier_class_post)
#total_likelihood = state_ll + np.log(1 - self._outlier_prob) + \
# outlier_ll + np.log(self._outlier_prob)
## We want to calculate p(x_t,c_state|y_t) = p(x_t|c_0,y_t)p(c_0|y_t)
## we have p(x_t|c_0,y_t) from above. Now calculate p(c_0|y_t)
## p(c_0|y_t) \propto p(y_t|c_0)p(c_0)
#state_data_joint = self._obs_distribution.eval_log(yt, self._estimate) \
# + np.log(1 - self._outlier_prob)
#outlier_data_joint = self._outlier_distribution.eval_log(yt) + \
# np.log(self._outlier_prob)
#total_data_lhood = np.log(np.exp(state_data_joint) + np.exp(outlier_data_joint))
#state_class_data_post = state_data_joint - total_data_lhood
#outlier_class_data_post = outlier_data_joint - total_data_lhood
#self._posterior.weights[i] *= np.exp(state_ll + total_data_lhood)
#print "state: %f, outlier: %f, update: %f" %(eta_state, eta_outlier, weight_update)
# assure that weights are normalised
total_sum = np.sum(class_weight_sum)
self._joint_weights /= (total_sum + consts.EPS)
self._class_weights = class_weight_sum / (total_sum + consts.EPS)
self._posterior.normalise_weights()
self._estimate = self._get_estimate()
def _get_effective_n_dimensions(self):
if self._n_phi == 1:
return 2
return self._n_dimensions
def _get_estimate(self):
w = np.asarray(self._posterior.weights)
parts = np.asarray(self._posterior.particles)
return w.dot(parts)
def get_joint_weights(self):
return self._joint_weights.copy()
def get_class_weights(self):
return self._class_weights
def _use_outlier_distribution(self):
return self._outlier_prob > 0
def _weighted_resample(self):
resample_idxs = self._posterior.get_resample_indices()
# Resample particles and associated joint weights
np.asarray(self._posterior.particles)[:] = np.asarray(self._posterior.particles)[resample_idxs]
self._joint_weights[:, :] = self._joint_weights[:, resample_idxs]
# Normalize joint weights -- ensures particle weights normalized
self._joint_weights /= (np.sum(self._joint_weights, axis=0) * self._n_particles)
self._posterior.weights[:] = 1. / self._n_particles
self._class_weights = np.sum(self._joint_weights, axis=1)
self._class_weights = np.array([.5, .5])
class VonMisesTrackingLocalizer(TrackingLocalizer):
def __init__(self,
n_particles,
state_kappa,
observation_kappa,
outlier_prob=0,
*args,
**kwargs):
"""
Localizes source using Von Mises particle filter
x_t ~ VM(x_{t-1}, kappa_v)
y_t ~ VM(x_t, kappa_w)
:param n_particles: number of particles to use
:param state_kappa: concentration parameter for state von mises distribution
:param observation_kappa: concentration parameter for observation von mises
distribution
:param outlier_prob: Probability that a given observation comes from a
background uniform outlier von mises distribution. If
this is omitted, it will be set to 0, and the normal
particle filtering algorithm will be used
All other parameters will be passed to TrackingLocalizer in the form of *args
and **kwargs
"""
TrackingLocalizer.__init__(self, *args, **kwargs)
self._grid_size = self._n_theta * self._n_phi
#self._process_search_space(search_space)
self._setup_particle_filters(n_particles, state_kappa,
observation_kappa, outlier_prob)
#def _process_search_space(self, search_space):
# self._search_space = search_space
# self._planes = self._search_space.get_planes()
# self._tracking_plane = self._planes[0]
def get_distribution(self, rffts):
d, energy = self.get_distribution_real(rffts, 'gcc')
maxind = np.argmax(d)
obs = self._directions[:, maxind]
obs = np.asarray(obs,
dtype=float) # port audio uses 32, pybayes uses 64
if self._use_outlier_distribution():
self._weighted_bayes(obs)
else:
self._bayes(obs)
#self._particle_filter.bayes(obs)
#self._posterior = self._particle_filter.posterior()
return self._posterior
def _setup_particle_filters(self, n_particles, state_kappa,
observation_kappa, outlier_prob):
"""
Setup the distributions needed by PartcileFilter in pybayes
"""
sys.stdout.flush()
self._n_particles = n_particles
self._state_kappa = state_kappa
self._obs_kappa = observation_kappa
ndim = self._get_effective_n_dimensions()
# State RVs
self._x_t = pb.RV(pb.RVComp(ndim, 'x_t'))
self._x_t_1 = pb.RV(pb.RVComp(ndim, 'x_{t-1}'))
# Observation RV
self._y_t = pb.RV(pb.RVComp(ndim, 'y_t'))
# Initial state RV
self._x0 = pb.RV(pb.RVComp(ndim, 'x0'))
init_kappa = .5 # Really small so is almost uniform
init_mu = np.ones((ndim, ))
# Create distributions
self._state_distribution = \
VonMisesCPdf(self._state_kappa, self._x_t, self._x_t_1)
self._obs_distribution = \
VonMisesCPdf(self._obs_kappa, self._y_t, self._x_t)
self._init_distribution = \
VonMisesPdf(init_mu, init_kappa, self._x0)
# Setup distribution for outliers
if outlier_prob < 0 or outlier_prob > 1:
raise ValueError("Outlier probability must be between 0 and 1")
self._outlier_rv = pb.RV(pb.RVComp(ndim, 'outlier'))
self._outlier_mu = np.hstack((np.array([0, -1.]), np.zeros(
(ndim - 2, ))))
self._outlier_kappa = .001
self._outlier_prob = outlier_prob # Probability of generation from background pdf
self._outlier_distribution = \
VonMisesPdf(self._outlier_mu, self._outlier_kappa, self._outlier_rv)
# Do particle filtering ourselves...
self._posterior = EmpPdf(
self._init_distribution.samples(self._n_particles))
self._estimate = self._get_estimate()
self._count = 0
# Create a set of weights for tracking the distribution p(c_t|x_t,y_{1:t})
self._class_weights = np.array([.5, .5])
# Matrix used to store weights for each particle for each class in order
# to calculate class posterior weights and state posterior weights
self._joint_weights = np.ones((
2,
self._n_particles,
))
#self._particle_filter = pb.ParticleFilter(self._n_particles,
# self._init_distribution,
# self._state_distribution,
# self._obs_distribution)
#self._posterior = self._particle_filter.posterior()
def _bayes(self, yt):
"""
Take care of particle filtering ourselves, otherwise we don't have easy access
to the weights of particles
"""
# resample -- do it here so that the weights will be available after one run
# of inference.
self._posterior.resample()
for i in range(self._posterior.particles.shape[0]):
# generate new ith particle:
self._posterior.particles[i] = \
self._state_distribution.sample(self._posterior.particles[i])
# recompute ith weight:
self._posterior.weights[i] *= \
np.exp(self._obs_distribution.eval_log(yt, self._posterior.particles[i]))
# assure that weights are normalised
self._posterior.normalise_weights()
return True
def _weighted_bayes(self, yt):
"""
Do particle filtering using a spike and slab method. That is, assume we
have a background near-uniform distribution eating up all the outlier
data. Then weight the particles using this assumption
"""
self._count += 1
if self._count % 1 == 0:
self._weighted_resample()
#self._posterior.resample()
class_weight_sum = np.zeros((2, ))
for i in range(self._posterior.particles.shape[0]):
# generate new ith particle:
self._posterior.particles[i] = \
self._state_distribution.sample(self._posterior.particles[i])
# recompute ith weight:
# Get likelihoods of classes p(y_t|c_t=j,x_t) for each class c_j
state_ll = self._obs_distribution.eval_log(
yt, self._posterior.particles[i])
outlier_ll = self._outlier_distribution.eval_log(yt)
# calculate p(c_t=j|x_t,y_{1:t-1})
state_class_prob = np.log(1. - self._outlier_prob) + np.log(
self._class_weights[0])
outlier_class_prob = np.log(self._outlier_prob) + np.log(
self._class_weights[1])
# Class weights
self._joint_weights[0, i] = np.exp(state_ll + state_class_prob)
self._joint_weights[1, i] = np.exp(outlier_ll + outlier_class_prob)
class_weight_sum += self._joint_weights[:, i]
self._posterior.weights[i] *= np.sum(self._joint_weights[:, i])
# Now find p(c_j|x_t) = p(x_t|c_j)p(c_j) / p(x_t) for each class c_j
# First compute p(x_t, c_j) = p(x_t|c_j)p(c_j)
#state_particle_log_prob = \
# self._obs_distribution.eval_log(self._posterior.particles[i], \
# self._estimate) + np.log(1 - self._outlier_prob)
#outlier_particle_log_prob = \
# self._outlir_distribution.eval_log(self._posterior.particles[i]) \
# + np.log(self._outlier_prob)
## Now compute p(x_t) = \sum_j p(x_t, c_j)
#total_particle_log_prob = np.log(np.exp(state_particle_log_prob) + \
# np.exp(outlier_particle_log_prob))
## Finally compute the actual posteriors p(c_j|x_t)
#state_class_post = state_particle_log_prob - total_particle_log_prob
#outlier_class_post = outlier_particle_log_prob - total_particle_log_prob
## Now compute total likelihood
## p(y_t|x_t) = sum_j p(y_t,c_j|x_t) = sum_j p(y_t|c_j,x_t)p(c_j|x_t)
##total_likelihood = np.exp(state_ll + state_class_post) + \
## np.exp(outlier_ll + outlier_class_post)
#total_likelihood = state_ll + np.log(1 - self._outlier_prob) + \
# outlier_ll + np.log(self._outlier_prob)
## We want to calculate p(x_t,c_state|y_t) = p(x_t|c_0,y_t)p(c_0|y_t)
## we have p(x_t|c_0,y_t) from above. Now calculate p(c_0|y_t)
## p(c_0|y_t) \propto p(y_t|c_0)p(c_0)
#state_data_joint = self._obs_distribution.eval_log(yt, self._estimate) \
# + np.log(1 - self._outlier_prob)
#outlier_data_joint = self._outlier_distribution.eval_log(yt) + \
# np.log(self._outlier_prob)
#total_data_lhood = np.log(np.exp(state_data_joint) + np.exp(outlier_data_joint))
#state_class_data_post = state_data_joint - total_data_lhood
#outlier_class_data_post = outlier_data_joint - total_data_lhood
#self._posterior.weights[i] *= np.exp(state_ll + total_data_lhood)
#print "state: %f, outlier: %f, update: %f" %(eta_state, eta_outlier, weight_update)
# assure that weights are normalised
total_sum = np.sum(class_weight_sum)
self._joint_weights /= (total_sum + consts.EPS)
self._class_weights = class_weight_sum / (total_sum + consts.EPS)
self._posterior.normalise_weights()
self._estimate = self._get_estimate()
def _get_effective_n_dimensions(self):
if self._n_phi == 1:
return 2
return self._n_dimensions
def _get_estimate(self):
w = np.asarray(self._posterior.weights)
parts = np.asarray(self._posterior.particles)
return w.dot(parts)
def get_joint_weights(self):
return self._joint_weights.copy()
def get_class_weights(self):
return self._class_weights
def _use_outlier_distribution(self):
return self._outlier_prob > 0
def _weighted_resample(self):
resample_idxs = self._posterior.get_resample_indices()
# Resample particles and associated joint weights
np.asarray(self._posterior.particles)[:] = np.asarray(
self._posterior.particles)[resample_idxs]
self._joint_weights[:, :] = self._joint_weights[:, resample_idxs]
# Normalize joint weights -- ensures particle weights normalized
self._joint_weights /= (np.sum(self._joint_weights, axis=0) *
self._n_particles)
self._posterior.weights[:] = 1. / self._n_particles
self._class_weights = np.sum(self._joint_weights, axis=1)
self._class_weights = np.array([.5, .5])
class SRPPFTrackingLocalizer(TrackingLocalizer):
def __init__(self, n_particles, state_kappa, *args, **kwargs):
"""
Localizes source using Von Mises particle filter
x_t ~ VM(x_{t-1}, kappa_v)
y_t ~ SRPLikelihood(x_t)
:param n_particles: number of particles to use
:param state_kappa: concentration parameter for state von mises distribution
All other parameters will be passed to TrackingLocalizer in the form of *args
and **kwargs
"""
TrackingLocalizer.__init__(self, *args, **kwargs)
self._grid_size = self._n_theta * self._n_phi
self._setup_particle_filters(n_particles, state_kappa)
def get_distribution(self, rffts):
self._doa_bayes(rffts)
return self._posterior
def _setup_particle_filters(self, n_particles, state_kappa):
"""
Setup the distributions needed by PartcileFilter in pybayes
"""
sys.stdout.flush()
self._n_particles = n_particles
self._state_kappa = state_kappa
ndim = self._get_effective_n_dimensions()
# State RVs
self._x_t = pb.RV(pb.RVComp(ndim, 'x_t'))
self._x_t_1 = pb.RV(pb.RVComp(ndim, 'x_{t-1}'))
# Initial state RV
self._x0 = pb.RV(pb.RVComp(ndim, 'x0'))
init_kappa = .5 # Really small so is almost uniform
init_mu = np.ones((ndim, ))
# Create distributions
self._state_distribution = \
VonMisesCPdf(self._state_kappa, self._x_t, self._x_t_1)
self._init_distribution = \
VonMisesPdf(init_mu, init_kappa, self._x0)
# Do particle filtering ourselves...
self._posterior = EmpPdf(
self._init_distribution.samples(self._n_particles))
self._estimate = self._get_estimate()
self._count = 0
def _doa_bayes(self, rffts):
"""
Particle filtering using SRP-PHAT as likelihood measure of observation
"""
# resample -- do it here so that the weights will be available after one run
# of inference.
self._posterior.resample()
for i in range(self._posterior.particles.shape[0]):
# generate new ith particle:
self._posterior.particles[i] = \
self._state_distribution.sample(self._posterior.particles[i])
# Get SRP likelihood
particles_3d = self._to_3d_particles(self._posterior.particles).T
# Get likelihoods
srp = self._get_srp_likelihood(rffts, particles_3d)
srp -= np.min(srp)
srp /= (np.sum(srp) + consts.EPS)
self._posterior.weights *= srp
# assure that weights are normalised
self._posterior.normalise_weights()
return True
def _get_effective_n_dimensions(self):
if self._n_phi == 1:
return 2
return self._n_dimensions
def _to_3d_particles(self, mat):
"""
Change matrix so that instead of each column being in 2d, each column is
in 3d. This equates to adding a zero to each column vector
"""
if self._get_effective_n_dimensions() == 3:
return mat
return np.hstack((np.asarray(mat), np.zeros((mat.shape[0], 1))))
def _get_estimate(self):
w = np.asarray(self._posterior.weights)
parts = np.asarray(self._posterior.particles)
return w.dot(parts)