def do_first_em_cycle(pars_init, obs_scheme, y, u=None, eps=1e-30,
use_A_flag=True,use_B_flag=False,diag_R_flag=True):
""" INPUT:
pars: collection of initial parameters for LDS
obs_scheme: observation scheme for given data, stored in dictionary
with keys 'sub_pops', 'obs_time', 'obs_pops'
y: data array of observed variables
u: data array of input variables
eps: precision (stopping criterion) for deciding on convergence
of latent covariance estimates durgin the E-step
This function serves to quickly get the results of one EM-cycle. It is
mostly intended to generate results that can quickly be compared with
other EM implementations or different parameter initialisation methods.
"""
y_dim = y.shape[0]
u_dim = ssm_fit._get_u_dim(u)
t_tot = y.shape[1]
pars_init = dict(pars_init)
ssm_fit.check_pars(pars=pars_init,
y_dim=y_dim,
u_dim=u_dim)
obs_scheme = dict(obs_scheme)
ssm_fit.check_obs_scheme(obs_scheme=obs_scheme,
y_dim=y_dim,
t_tot=t_tot)
# do one E-step
stats_init, ll_init, t_conv_ft, t_conv_sm = \
ssm_fit.lds_e_step(pars=pars_init,
y=y,
u=u,
obs_scheme=obs_scheme,
eps=eps)
# do one M-step
pars_first = ssm_fit.lds_m_step(stats=stats_init,
y=y,
u=u,
obs_scheme=obs_scheme,
use_A_flag=use_A_flag,
use_B_flag=use_B_flag,
diag_R_flag=diag_R_flag)
# do another E-step
stats_first, ll_first, t_conv_ft, t_conv_sm = \
ssm_fit.lds_e_step(pars=pars_first,
y=y,
u=u,
obs_scheme=obs_scheme,
eps=eps)
return stats_init, ll_init, pars_first, stats_first, ll_first
def do_e_step(pars, y, u=None, obs_scheme=None, eps=1e-30):
""" INPUT:
pars : (list of) LDS parameters
y: data array of observed variables
u: data array of input variables
obs_scheme: observation scheme for given data, stored in dictionary
with keys 'sub_pops', 'obs_time', 'obs_pops'
eps: precision (stopping criterion) for deciding on convergence
of latent covariance estimates durgin the E-step
Wrapper function to quickly compute a single E-step with given data y
and LDS parameters pars. This function mostly exists to deal with the
difference between having no input u and not using it (i.e. parameter
B = 0).
"""
if (u is None and
not (pars['B'] is None or pars['B'] == [] or
(isinstance(pars['B'],np.ndarray) and pars['B'].size==0) or
(isinstance(pars['B'],np.ndarray) and pars['B'].size>0
and np.max(abs(pars['B']))==0) or
(isinstance(pars['B'],(float,numbers.Integral))
and pars['B']==0))):
print(('Warning: Parameter B is initialised, but input u = None. '
'Algorithm will ignore input for the LDS, outcomes may be '
'not as expected.'))
y_dim = y.shape[0]
if isinstance(u, np.ndarray):
u_dim = u.shape[0]
else:
u_dim = 0
if obs_scheme is None:
t_tot = y.shape[1]
print('creating default observation scheme: Full population observed')
obs_scheme = {'sub_pops': [list(range(y_dim))], # creates default case
'obs_time': [t_tot], # of fully observed
'obs_pops': [0]} # population
ssm_fit.check_pars(pars=pars,
y_dim=y_dim,
u_dim=u_dim)
stats, lltr, t_conv_ft, t_conv_sm = \
ssm_fit.lds_e_step(pars=pars,
y=y,
u=u,
obs_scheme=obs_scheme,
eps=eps)
return stats, lltr, t_conv_ft, t_conv_sm
