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 run(x_dim,
y_dim,
u_dim,
t_tot,
obs_scheme=None,
max_iter=10,
epsilon=np.log(1.001),
eps_cov=0,
plot_flag=False,
trace_pars_flag=False,
trace_stats_flag=False,
diag_R_flag=True,
use_A_flag=True,
use_B_flag=False,
pars_true=None,
gen_A_true='diagonal',
lts_true=None,
gen_B_true='random',
gen_Q_true='identity',
gen_mu0_true='random',
gen_V0_true='identity',
gen_C_true='random',
gen_d_true='scaled',
gen_R_true='fraction',
pars_init=None,
gen_A_init='diagonal',
lts_init=None,
gen_B_init='random',
gen_Q_init='identity',
gen_mu0_init='random',
gen_V0_init='identity',
gen_C_init='random',
gen_d_init='mean',
gen_R_init='fractionObserved',
u=None, input_type='pwconst',const_input_length=1,
y=None,
x=None,
interm_store_flag = False,
save_file='LDS_data.mat'):
""" INPUT:
x_dim : dimensionality of latent states x
y_dim : dimensionality of observed states y
u_dim : dimensionality of input states u
t_tot : trial length (in number of time points)
obs_scheme: observation scheme for given data, stored in dictionary
with keys 'sub_pops', 'obs_time', 'obs_pops'
max_iter: maximum number of allowed EM steps
epsilon: precision (stopping criterion) for deciding on convergence
of overall EM algorithm
eps_cov: precision (stopping criterion) for deciding on convergence
of latent covariance estimates durgin the E-step
plot_flag : boolean specifying if to visualise fitting progress`
trace_pars_flag: boolean, specifying if entire parameter updates
history or only the current state is kept track of
trace_stats_flag: boolean, specifying if entire history of inferred
latents or only the current state is kept track of
diag_R_flag : boolean specifying if R is represented as diagonal
use_A_flag : boolean specifying whether to fit the LDS with parameter A
use_B_flag : boolean specifying whether to fit the LDS with parameter B
pars_true : None, or list/np.ndarray/dict containing no, some or all
of the desired ground-truth parameters. Will identify any
parameters not handed over and will fill in the rest
according to selected strings below.
gen_A_true : string specifying methods of parameter generation
lts_true : ndarray with one entry per latent time scale (i.e. x_dim)
gen_B_true : string specifying methods of parameter generation
gen_Q_true : ""
gen_mu0_true : ""
gen_C_true : ""
gen_V0_true : ""
gen_d_true : ""
gen_R_true : (see below for details)
pars_init : None, or list/np.ndarray/dict containing no, some or all
of the desired parameter initialisations. Will identify any
parameters not handed over and will fill in the rest
according to selected strings below.
gen_A_init : string specifying methods of parameter initialisation
lts_init : ndarray with one entry per latent time scale (i.e. x_dim)
gen_B_init : string specifying methods of parameter initialisation
gen_Q_init : ""
gen_mu0_init : ""
gen_V0_init : ""
gen_C_init : ""
gen_d_init : ""
gen_R_init : (see below for details)
x: data array of latent variables
y: data array of observed variables
u: data array of input variables
interm_store_flag : boolean, specifying whether or not to
store the intermediate results after
each EM cycle to the same folder as
given by input variable save_file
save_file : (path to folder and) name of file for storing results.
Generates parameters of an LDS, potentially by looking at given data.
Can be used for for generating ground-truth parameters for generating
data from an artificial experiment using the LDS, or for finding
parameter initialisations for fitting an LDS to data. Usage is slightly
different in the two cases (see below). By nature of the wide range of
applicability of the LDS model, this function contains many options
(implemented as strings differing different cases, and arrays giving
user-specified values such as timescale ranges), and is to be extended
even further in the future.
"""
if not isinstance(use_B_flag,bool):
raise Exception('use_B_flag has to be a boolean. However, it is', use_B_flag)
if not isinstance(use_A_flag,bool):
raise Exception('use_A_flag has to be a boolean. However, it is', use_A_flag)
if not isinstance(diag_R_flag,bool):
raise Exception('diag_R_flag has to be a boolean. However, it is ',
diag_R_flag)
if not isinstance(interm_store_flag,bool):
raise Exception(('interm_store_flag has to be a boolean'
'However, it is '), interm_store_flag)
obs_scheme = ssm_fit.check_obs_scheme(obs_scheme=obs_scheme,
y_dim=y_dim,
t_tot=t_tot)
if y is None:
if lts_true is None:
lts_true = np.linspace(0.9,0.98,x_dim)
pars_true, pars_options_true = gen_pars(
x_dim=x_dim,
y_dim=y_dim,
u_dim=u_dim,
pars_in=pars_true,
obs_scheme=obs_scheme,
gen_A=gen_A_true,
lts=lts_true,
gen_B=gen_B_true,
gen_Q=gen_Q_true,
gen_mu0=gen_mu0_true,
gen_V0=gen_V0_true,
gen_C=gen_C_true,
gen_d=gen_d_true,
gen_R=gen_R_true)
n_tr = 1 # fix to always just one repetition for now
# generate data from model
print('generating data from model with ground-truth parameters')
x,y,u = sim_data(pars=pars_true,
t_tot=t_tot,
n_tr=n_tr,
obs_scheme=obs_scheme,
u=u,
input_type=input_type,
const_input_length=const_input_length)
Pi = sp.linalg.solve_discrete_lyapunov(pars_true['A'],
pars_true['Q'])
Pi_t = np.dot(pars_true['A'].transpose(), Pi)
stats_true, lltr, t_conv_ft, t_conv_sm = \
do_e_step(pars=pars_true,
y=y,
u=u,
obs_scheme=obs_scheme,
eps=eps_cov)
else: # i.e. if data provided
pars_true = {}
pars_true['A'] = 0
pars_true['B'] = 0
pars_true['Q'] = 0
pars_true['mu0'] = 0
pars_true['V0'] = 0
pars_true['C'] = 0
pars_true['d'] = 0
pars_true['R'] = 0
Pi = 0
Pi_t = 0
ext_true = 0
extxt_true = 0
extxtm1_true = 0
lltr = 0
# get initial parameters
if not use_A_flag: # overwrites any other parameter choices for A! Set A = 0
if isinstance(pars_init, dict) and ('A' in pars_init):
pars_init['A'] = np.zeros((x_dim,x_dim))
elif (isinstance(pars_init,(list,np.ndarray)) and
not pars_init[0] is None):
iniPars[0] = np.zeros((x_dim,x_dim))
elif not gen_A_init == 'zero':
print(('Warning: set flag use_A_flag=False, but did not set gen_A_init '
'to zero. Will overwrite gen_A_init to zero now.'))
gen_A_init = 'zero'
if not use_B_flag: # overwrites any other parameter choices for B! Set B = 0
if isinstance(pars_init, dict) and ('B' in pars_init):
pars_init['B'] = np.zeros((x_dim,u_dim))
elif (isinstance(pars_init,(list,np.ndarray)) and
not pars_init[1] is None):
iniPars[1] = np.zeros((x_dim,u_dim))
elif not gen_B_init == 'zero':
print(('Warning: set flag ifBseA=False, but did not set gen_B_init '
'to zero. Will overwrite gen_B_init to zero now.'))
gen_B_init = 'zero'
if lts_init is None:
lts_init = np.random.uniform(size=[x_dim])
pars_init, pars_options_init = gen_pars(
x_dim=x_dim,
y_dim=y_dim,
u_dim=u_dim,
pars_in=pars_init,
obs_scheme=obs_scheme,
gen_A=gen_A_init,
lts=lts_init,
gen_B=gen_B_init,
gen_Q=gen_Q_init,
gen_mu0=gen_mu0_init,
gen_V0=gen_V0_init,
gen_C=gen_C_init,
gen_d=gen_d_init,
gen_R=gen_R_init,
x=x, y=y, u=u)
# check initial goodness of fit for initial parameters
stats_init,ll_init,pars_first,stats_first,ll_first = do_first_em_cycle(
pars_init=pars_init,
obs_scheme=obs_scheme,
y=y,
u=u,
eps=eps_cov,
use_A_flag=use_A_flag,
use_B_flag=use_B_flag,
diag_R_flag=diag_R_flag)
if interm_store_flag:
save_file_interm = save_file
else:
save_file_interm = None
fit_lds = setup_fit_lds(y=y,
u=y,
max_iter=max_iter,
epsilon=epsilon,
eps_cov=eps_cov,
plot_flag=plot_flag,
trace_pars_flag=trace_pars_flag,
trace_stats_flag=trace_stats_flag,
diag_R_flag=diag_R_flag,
use_A_flag=use_A_flag,
use_B_flag=use_B_flag)
# fit the model to data
print('fitting model to data')
t = time.time()
pars_hat,ll = fit_lds(x_dim=x_dim,
pars=pars_init,
obs_scheme=obs_scheme,
save_file=save_file_interm)
elapsed_time = time.time() - t
print('elapsed time for fitting is')
print(elapsed_time)
stats_hat = ssm_fit._setup_stats(y, x_dim, u_dim)
if use_B_flag:
stats_hat, ll_hat, t_conv_ft, t_conv_sm = \
ssm_fit.lds_e_step(pars=pars_hat,
y=y,
u=u,
obs_scheme=obs_scheme,
eps=eps_cov)
else:
stats_hat, ll_hat, t_conv_ft, t_conv_sm = \
ssm_fit.lds_e_step(pars=pars_hat,
y=y,
u=None,
obs_scheme=obs_scheme,
eps=eps_cov)
Pi_h = sp.linalg.solve_discrete_lyapunov(pars_hat['A'],
pars_hat['Q'])
Pi_t_h = np.dot(pars_hat['A'].transpose(), Pi_h)
# save results for visualisation (with Matlab code)
if u is None:
u = 0
B_h = 0
B_hs = [0]
if B_h is None:
B_h = 0
B_hs = [0]
save_file_m = {'x': x, 'y': y, 'u' : u, 'll' : ll,
'T' : t_tot, 'Trial':n_tr, 'elapsedTime' : elapsed_time,
'inputType' : input_type,
'constInputLngth' : const_input_length,
'ifUseB':use_B_flag, 'ifUseA':use_A_flag,
'epsilon':epsilon,
'ifPlotProgress':plot_flag,
'ifTraceParamHist':trace_pars_flag,
'ifTraceStatsHist':trace_stats_flag,
'ifRDiagonal':diag_R_flag,
'ifUseB':use_B_flag,
'covConvEps':eps_cov,
'truePars':pars_true,
'initPars':pars_init,
'firstPars':pars_first,
'estPars': pars_hat,
'stats_0': stats_init,
'stats_1': stats_first,
'stats_h': stats_hat,
'stats_true': stats_true,
'Pi':Pi,'Pi_h':Pi_h,'Pi_t':Pi_t,'Pi_t_h': Pi_t_h,
'obsScheme' : obs_scheme}
savemat(save_file,save_file_m) # does the actual saving
return y,x,u,pars_hat,pars_init,pars_true