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)]