def alt_init(params, beliefs):
alt0, alt1, worked0, worked1 = sm_wp_alternatives(beliefs)
alt = [alt0, alt1]
init = []
alt_options = params['alt_options']
for k in range(params['num_qs']):
if k in worked0 and k in worked1:
state = {}
option = alt_options[k]
state['world'] = option
state['sm'] = alt[option][k][0]
state['wp'] = alt[option][k][1]
on = fwd.specify_own_noise(params['own_noise_hierarchy'],
params['own_noise_default'],
params['num_responses'])
state['own_noise'] = on
mn = fwd.specify_meta_noise(params['meta_noise_hierarchy'],
params['meta_noise_type'],
params['meta_noise_default'],
params['num_responses'])
state['meta_noise'] = mn
state['expertise'] = np.zeros(params['num_responses'])
init.append(state)
else:
init.append(aggregation_par.random_init_question(params))
return init
def random_init_question(params):
"""Sample random state to initialise mcmc chain.
Args: num-responses - num people answering questions.
Outputs: dict
"""
state = {}
state['wp'], state['sm'] = fwd.sample_wp_sm(params['prior_wp_sm'])
#state['world'] = fwd.sample_world(state['wp'])
state['world'] = random.randint(0, 1)
#state['signals'] = fwd.sample_signals(state['world'], state['sm'],
# params['num_responses'])
state['own_noise'] = fwd.specify_own_noise(params['own_noise_hierarchy'],
params['own_noise_default'],
params['num_responses'])
state['meta_noise'] = fwd.specify_meta_noise(params['meta_noise_hierarchy'],
params['meta_noise_type'],
params['meta_noise_default'],
params['num_responses'])
state['expertise'] = np.zeros(params['num_responses'])
return state