def generate_synthetic_data(nobservations):
# Create model.
true_model = testsystems.force_spectroscopy_model()
nstates = true_model.nstates
# Generate synthetic data.
print "Generating synthetic data..."
[O, S] = true_model.generate_synthetic_observation_trajectories(ntrajectories=1, length=nobservations)
# DEBUG
print "synthetic observation trajectories:"
print O
print "Total state visits, min_state, max_state:"
print testsystems.total_state_visits(nstates, S)
return [true_model, O, S]
def run(nstates, nsamples):
# Create model.
true_model = testsystems.force_spectroscopy_model()
nstates = true_model.nstates
tau = 0.001 # time interval per observation
# Generate synthetic data.
print "Generating synthetic data..."
[O, S] = true_model.generate_synthetic_observation_trajectories(
ntrajectories=1, length=50000)
# DEBUG
print "synthetic observation trajectories:"
print O
print "Total state visits, min_state, max_state:"
print testsystems.total_state_visits(nstates, S)
# Generate MLHMM.
print "Generating MLHMM..."
estimator = bhmm.MLHMM(O, nstates)
print "Initial guess:"
print str(estimator.hmm.output_model)
print estimator.hmm.transition_matrix
print estimator.hmm.stationary_distribution
# Plot initial guess.
s_t = None
o_t = O[0]
plots.plot_state_assignments(
estimator.hmm,
s_t,
o_t,
time_units='s',
obs_label='force / pN',
tau=tau,
pdf_filename='synthetic-three-state-model-guess-nstates' +
str(nstates) + '.pdf')
print "Fitting HMM..."
mle = estimator.fit()
# Plot.
s_t = mle.hidden_state_trajectories[0]
import numpy as np
o_t = O[0]
plots.plot_state_assignments(
mle,
s_t,
o_t,
time_units='s',
obs_label='force / pN',
tau=tau,
pdf_filename='synthetic-three-state-model-mlhmm-nstates' +
str(nstates) + '.pdf')
# Initialize BHMM with MLHMM model.
print "Sampling models from BHMM..."
sampler = bhmm.BHMM(O, nstates, initial_model=mle)
bhmm_models = sampler.sample(nsamples=nsamples,
save_hidden_state_trajectory=False)
# Generate a sample saving a hidden state trajectory.
final_models = sampler.sample(nsamples=1,
save_hidden_state_trajectory=True)
# Plot final BHMM sample.
model = final_models[0]
s_t = model.hidden_state_trajectories[0]
o_t = O[0]
plots.plot_state_assignments(
model,
s_t,
o_t,
time_units='s',
obs_label='force / pN',
tau=tau,
pdf_filename='synthetic-three-state-model-bhmm-nstates' +
str(nstates) + '.pdf')
# write latex table with sample statistics
conf = 0.95
sampled_hmm = bhmm.SampledGaussianHMM(mle, bhmm_models)
generate_latex_table(
sampled_hmm,
conf=conf,
dt=1,
time_unit='step',
caption=
'Bayesian HMM parameter estimates for synthetic three-state model.',
outfile='synthetic-three-state-model-bhmm-statistics.tex')
def run(nstates, nsamples):
# Create model.
true_model = testsystems.force_spectroscopy_model()
nstates = true_model.nstates
tau = 0.001 # time interval per observation
# Generate synthetic data.
print "Generating synthetic data..."
[O, S] = true_model.generate_synthetic_observation_trajectories(ntrajectories=1, length=50000)
# DEBUG
print "synthetic observation trajectories:"
print O
print "Total state visits, min_state, max_state:"
print testsystems.total_state_visits(nstates, S)
# Generate MLHMM.
print "Generating MLHMM..."
estimator = bhmm.MLHMM(O, nstates)
print "Initial guess:"
print str(estimator.hmm.output_model)
print estimator.hmm.transition_matrix
print estimator.hmm.stationary_distribution
# Plot initial guess.
s_t = None
o_t = O[0]
plots.plot_state_assignments(estimator.hmm, s_t, o_t, time_units='s', obs_label='force / pN', tau=tau,
pdf_filename='synthetic-three-state-model-guess-nstates'+str(nstates)+'.pdf')
print "Fitting HMM..."
mle = estimator.fit()
# Plot.
s_t = mle.hidden_state_trajectories[0]
import numpy as np
o_t = O[0]
plots.plot_state_assignments(mle, s_t, o_t, time_units='s', obs_label='force / pN', tau=tau,
pdf_filename='synthetic-three-state-model-mlhmm-nstates'+str(nstates)+'.pdf')
# Initialize BHMM with MLHMM model.
print "Sampling models from BHMM..."
sampler = bhmm.BHMM(O, nstates, initial_model=mle)
bhmm_models = sampler.sample(nsamples=nsamples, save_hidden_state_trajectory=False)
# Generate a sample saving a hidden state trajectory.
final_models = sampler.sample(nsamples=1, save_hidden_state_trajectory=True)
# Plot final BHMM sample.
model = final_models[0]
s_t = model.hidden_state_trajectories[0]
o_t = O[0]
plots.plot_state_assignments(model, s_t, o_t, time_units='s', obs_label='force / pN', tau=tau,
pdf_filename='synthetic-three-state-model-bhmm-nstates'+str(nstates)+'.pdf')
# write latex table with sample statistics
conf = 0.95
sampled_hmm = bhmm.SampledGaussianHMM(mle, bhmm_models)
generate_latex_table(sampled_hmm, conf=conf, dt=1, time_unit='step',
caption='Bayesian HMM parameter estimates for synthetic three-state model.',
outfile='synthetic-three-state-model-bhmm-statistics.tex')