def geom_grid(beliefs, meta, params, wp_grid, sm_grid):
indiv = fwd.generate_individual(params)
res = [ np.zeros((2, wp_grid-1, sm_grid, sm_grid)) for k in range(1)]
#bij = np.zeros((10, grid, grid))
no_meta = params.copy()
no_meta['use_meta'] = False
params_lo = params.copy()
params_lo['own_noise_type'] = "log_odds"
params_lo['meta_noise_type'] = "log_odds"
params_lo['own_noise_default'] = 1
params_lo['meta_noise_default'] = 1
indiv_lo = fwd.generate_individual(params_lo)
lo_no_meta = params_lo.copy()
lo_no_meta['use_meta'] = False
#params_lst = [params, no_meta, params_lo, lo_no_meta]
#indiv_lst = [indiv, indiv, indiv_lo, indiv_lo]
params_lst = [params]
indiv_lst = [indiv]
print("Log prob world commented out - do you want this??")
print(datetime.datetime.now())
for z in np.arange(wp_grid-1):
print(z)
gen_wp = z/wp_grid + 1/wp_grid
wp = np.array([gen_wp, 1 - gen_wp])
for gen0 in np.arange(sm_grid-1):
gen_s0 = gen0 / sm_grid + (2 / sm_grid)
for gen1 in np.arange(0, gen0+1):
gen_s1 = gen1 / sm_grid + (1/sm_grid)
sm = np.array([[gen_s0, gen_s1], [1 - gen_s0, 1 - gen_s1]])
#bij[l, k, j] = fwd.bijection(sm, wp)[0]
for w in range(2):
#lpw = aggregation.log_prob_world(w, wp)
lpw = 0
for rr, par_indiv in enumerate(zip(params_lst, indiv_lst)):
rll = aggregation.reports_ll_marg(beliefs, meta, w, sm,
wp, par_indiv[1]['own_noise'],
par_indiv[1]['meta_noise'],
par_indiv[0])
res[rr][w,z,gen0,gen1] = lpw + rll
res = [np.where(r == 0, np.nan, r) for r in res]
return res
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