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,
))
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 _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)
