# Generate bars data
my_data = model.generate_data(params_gt, N // comm.size)
# Configure DataLogger
print_list = ('T', 'Q', 'pi', 'sigma', 'N', 'MAE')
store_list = ('*')
dlog.set_handler(print_list, TextPrinter)
dlog.set_handler(print_list, StoreToTxt, output_path + '/terminal.txt')
dlog.set_handler(store_list, StoreToH5, output_path + '/result.h5')
model_params = model.standard_init(my_data)
if 'anneal' in params:
anneal = params.get('anneal')
else:
# Choose annealing schedule
anneal = LinearAnnealing(50)
anneal['T'] = [(0, 2.), (.7, 1.)]
anneal['Ncut_factor'] = [(0, 0.), (2. / 3, 1.)]
anneal['anneal_prior'] = False
# Create and start EM annealing
em = EM(model=model, anneal=anneal)
em.data = my_data
em.lparams = model_params
em.run()
dlog.close()
pprint("Done")
# Choose annealing schedule
from prosper.em.annealing import LinearAnnealing
anneal = LinearAnnealing(50) # decrease
anneal['T'] = [(0, 5.), (.8, 1.)]
anneal['Ncut_factor'] = [(0, 0.), (0.5, 0.), (0.6, 1.)]
# anneal['Ncut_factor'] = [(0,0.),(0.7,1.)]
# anneal['Ncut_factor'] = [(0,0.),(0.7,1.)]
anneal['W_noise'] = [(0, np.std(ts) / 2.), (0.7, 0.)]
# anneal['pi_noise'] = [(0,0.),(0.2,0.1),(0.7,0.)]
anneal['anneal_prior'] = False
assert train_labels.shape[0] == ts.shape[0]
my_data = {'y': ts, 'l': train_labels}
model_params = model.standard_init(my_data)
pp("model defined", 0)
em = EM(model=model, anneal=anneal)
em.data = my_data
em.lparams = model_params
pp("em defined", 0)
em.run(verbose=True)
pp("em finished", 0)
my_test_data = {'y': ts_test}
res = model.inference(anneal, em.lparams, my_test_data)
sparse_codes = res['s'][:, 0, :] # should be Number of samples x H
dlog.close()
model_params = em.lparams
if resume:
res_file = output_path + 'results.h5'
with tb.open_file(res_file) as fh: