def generic_demo(params):
print("Num mh steps: ", params['num_mh_samples'])
fwd_results = fwd.generate_synthetic_multiple(params)
individual, actual, beliefs, meta = fwd_results
if params['own_noise_type'] == "noiseless":
params['own_noise_type'] = "truncnorm"
params['own_noise_default'] = 0.1
print(datetime.datetime.now())
inf_results = do_inference(params, beliefs, meta)
if len(inf_results) == 4:
states, indiv_states, accept, internals = inf_results
return (individual, actual, beliefs, meta, states,
indiv_states, params, accept, internals)
else:
states, indiv_states, accept = inf_results
return (individual, actual, beliefs, meta, states,
indiv_states, params, accept)
print(datetime.datetime.now())
def accuracy_vs_expertise():
expertise = np.arange(-1, 1, .01)
params['expertise_default'] = expertise
params['num_responses'] = len(expertise)
params['own_noise_hierarchy'] = "individual"
#for own_noise in np.arange(0.01, .5, .05):
for own_noise in range(10):
#params['own_noise_default'] = own_noise
correct = np.zeros((num_trials, params['num_responses']))
for n in range(num_trials):
if n % 100 == 0:
print(n)
fwd_results = fwd.generate_synthetic_multiple(params)
individual, actual, beliefs, meta = fwd_results
nominal = np.array([(np.array(beliefs[0][1,k]) > .5).astype(int)
for k in range(beliefs[0].shape[1])])
correct[n,:] = np.where(nominal == actual[0]['world'], 1, 0)
accuracy = np.sum(correct, axis=0)
#plt.plot(expertise, accuracy)
print(own_noise, pearsonr(accuracy, individual['expertise']))
1/0