def dispersed_init(k, params):
#initial = random_init(len(beliefs))
#initial = initial_using_actual(actual, len(beliefs))
state = {}
wp_apart = [[.1, .9], [.5, .5], [.9, .1]]
sm_apart = [np.array([[.5, .4], [.5, .6]]), np.array([[.9, .8], [.1, .2]]),
np.array([[.1, .05], [.9, .95]])]
worlds_apart = [0, 1, 0]
state['wp'] = wp_apart[k]
state['sm'] = sm_apart[k]
state['world'] = worlds_apart[k]
state['signals'] = fwd.sample_signals(state['world'], state['sm'],
params['num_responses'])
state['meta_noise'] = params['meta_noise']
if params['fixed_noise']:
state['own_noise'] = params['own_noise']
else:
state['own_noise'] = .25
return state
def wp_expt(sm=np.array([[.7, .4], [.3, .6]]), wp=np.array([.1, .9]),
change_wp=True):
params = setup_params.init_params_demo1()
params['binary_beliefs'] = False
indiv = fwd.generate_individual(params)
grid = 100
diff = 0
res0 = np.zeros((grid-grid*diff, grid-grid*diff))
res1 = np.zeros((grid-grid*diff, grid-grid*diff))
bm0 = np.zeros((grid-grid*diff, grid-grid*diff))
bm1 = np.zeros((grid-grid*diff, grid-grid*diff))
mm0 = np.zeros((grid-grid*diff, grid-grid*diff))
mm1 = np.zeros((grid-grid*diff, grid-grid*diff))
mm0avg = np.zeros((grid-grid*diff, grid-grid*diff))
#hacky since grid needn't be nice num.:
for k, gen in enumerate(np.arange(diff * grid, grid)):
actual = {}
gen_s0 = gen / grid + .01
if change_wp:
gen_wp = np.array([gen_s0, 1 - gen_s0])
actual['wp'] = gen_wp
actual['sm'] = sm
else:
actual['wp'] = wp
gen_sm = np.array([[gen_s0, gen_s0 - diff],
[1 - gen_s0, 1 - gen_s0 + diff]])
actual['sm'] = gen_sm
actual['world'] = 0
actual['signals'] = fwd.sample_signals(actual['world'], actual['sm'],
params['num_responses'])
beliefs, meta = fwd.generate_data(actual, indiv, params)
for j, ans in enumerate(np.arange(grid*diff, grid)):
ans_s0 = ans / grid + .01
if change_wp:
ans_wp = np.array([ans_s0, 1 - ans_s0])
ans_sm = actual['sm']
else:
ans_wp = actual['wp']
ans_sm = np.array([[ans_s0, ans_s0 - diff],
[1 - ans_s0, 1 - ans_s0 + diff]])
ll = np.array([aggregation.reports_ll_marg(beliefs, meta, w,
ans_sm, ans_wp,
indiv['own_noise'],
indiv['meta_noise'],
params)
for w in [0, 1]])
ll += aggregation.log_prob_world(actual['world'], ans_wp)
bel = fwd.calc_belief_matrix(ans_sm, ans_wp)
bm0[k, j] = bel[0,0]
bm1[k, j] = bel[0,1]
met = fwd.calc_meta_matrix(ans_sm, ans_wp)
mm0[k, j] = met[0,0]
mm1[k, j] = met[0,1]
if bel[0, 0] < .5:
mm0avg[k,j] = 0
elif bel[0, 1] > .5:
mm0avg[k,j] = 1
else:
mm0avg[k,j] = met[0,0]
if bel[0, 1] > .5:
non_bij = 1
elif bel[0,0] < .5:
non_bij = -1
else:
non_bij = 0
res0[k, j] = ll[0]
res1[k, j] = ll[1]
plt.imshow(res0, cmap=cm.Greys_r)
plt.imshow(res1, cmap=cm.Greys_r)
1/0
return res