for _ in xrange(Nmax)]
emission_distns = [
Regression(
A=np.eye(D),sigma=0.05*np.eye(D),
nu_0=5.,S_0=np.eye(P),M_0=np.eye(P),K_0=10.*np.eye(P))
for _ in xrange(Nmax)]
init_dynamics_distns = [
Gaussian(nu_0=3,sigma_0=3.*np.eye(P),mu_0=np.zeros(P),kappa_0=0.01)
for _ in xrange(Nmax)]
model = WeakLimitStickyHDPHMMSLDS(
dynamics_distns=dynamics_distns,
emission_distns=emission_distns,
init_dynamics_distns=init_dynamics_distns,
kappa=100.,alpha=3.,gamma=3.,init_state_distn='uniform')
model.add_data(data)
model.resample_states()
##################
# run sampling #
##################
from matplotlib.transforms import Bbox
import matplotlib.gridspec as gridspec
n_show = 50
nu_0=2*P,S_0=2*P*np.eye(P),M_0=np.zeros((P,P)),K_0=np.eye(P))
for _ in xrange(Nmax)]
emission_distns = [
Regression(
A=np.eye(D),sigma=0.1*np.eye(D), # TODO remove special case
nu_0=5,S_0=np.eye(D),M_0=np.zeros((D,P)),K_0=np.eye(P))
for _ in xrange(Nmax)]
init_dynamics_distns = [
Gaussian(nu_0=5,sigma_0=np.eye(P),mu_0=np.zeros(P),kappa_0=1.)
for _ in xrange(Nmax)]
model = WeakLimitStickyHDPHMMSLDS(
dynamics_distns=dynamics_distns,
emission_distns=emission_distns,
init_dynamics_distns=init_dynamics_distns,
kappa=50.,alpha=5.,gamma=5.,init_state_concentration=1.)
##################
# run sampling #
##################
def resample():
model.resample_model()
return model.stateseqs[0].copy()
model.add_data(data)
samples = [resample() for _ in progprint_xrange(1000)]
