import json, numpy as np
from experiment import simulate, theoretical_bias

def one(n, mode, seed):
    return simulate(n,8,.028,mode,seed,rounds=6000,burn=1500)
for n in (8,64):
    print('N',n,'theory',theoretical_bias(.028,n,8))
    for mode in ('vanilla','output_correction','gradient_correction'):
        z=[one(n,mode,9000+i) for i in range(5)]
        # output correction's relevant bias is mean_reported_x; other modes use mean_x.
        vals=np.array([r['mean_reported_x'] for r in z])
        print(mode,'mean',vals.mean(),'abs(mean)',abs(vals.mean()),'seed_abs_mean',np.abs(vals).mean(),'sd',vals.std())