from pybasicbayes.util.text import progprint_xrange
from pylds.models import DefaultLDS
npr.seed(0)
# Set parameters
D_obs = 1
D_latent = 2
D_input = 0
T = 2000
# Simulate from one LDS
truemodel = DefaultLDS(D_obs, D_latent, D_input)
inputs = np.random.randn(T, D_input)
data, stateseq = truemodel.generate(T, inputs=inputs)
# Fit with another LDS
model = DefaultLDS(D_obs, D_latent, D_input)
model.add_data(data, inputs=inputs)
# Initialize with a few iterations of Gibbs
for _ in progprint_xrange(10):
model.resample_model()
# Run EM
def update(model):
model.EM_step()
return model.log_likelihood()
from pybasicbayes.distributions import Regression, DiagonalRegression
from pybasicbayes.util.text import progprint_xrange
from pylds.models import DefaultLDS, LDS
npr.seed(0)
# Model parameters
D_obs = 4
D_latent = 4
T = 1000
# Simulate from an LDS
truemodel = DefaultLDS(D_obs, D_latent)
data, stateseq = truemodel.generate(T)
# Mask off a chunk of data
mask = np.ones_like(data, dtype=bool)
chunksz = 100
for i, offset in enumerate(range(0, T, chunksz)):
j = i % (D_obs + 1)
if j < D_obs:
mask[offset:min(offset + chunksz, T), j] = False
if j == D_obs:
mask[offset:min(offset + chunksz, T), :] = False
# Fit with another LDS
model = LDS(dynamics_distn=Regression(nu_0=D_latent + 3,
S_0=D_latent * np.eye(D_latent),
M_0=np.zeros((D_latent, D_latent)),
