def test_mean():
means = rand.random(100)
min_val = means.min()
max_val = means.max()
boots = bootstrap.mean(means, 200)
assert boots.shape == (200,)
assert np.all(boots >= min_val)
assert np.all(boots <= max_val)
perc_boots = bootstrap.mean(means, 200, [2.5, 97.5])
assert perc_boots.shape == (2,)
assert np.all(perc_boots >= min_val)
assert np.all(perc_boots <= max_val)
def main(args):
data = json.load(args.input)
if args.regret is not None:
game, serial = gameio.read_base_game(json.load(args.regret[0]))
mix = serial.from_prof_json(json.load(args.regret[1]))
devs = load_devs(game, serial, data)
expect = game.role_reduce(devs * mix)
result = bootstrap.sample_regret(game, expect, devs, args.num_bootstraps, args.percentiles)
if args.mean:
mdevs = devs.mean(0)
mexpect = game.role_reduce(mdevs * mix, keepdims=True)
mean = np.max(mdevs - mexpect)
elif args.dev_surplus is not None:
game, serial = gameio.read_base_game(json.load(args.dev_surplus[0]))
mix = serial.from_prof_json(json.load(args.dev_surplus[1]))
devs = load_devs(game, serial, data)
surpluses = np.sum(devs * mix * game.role_repeat(game.num_players), 1)
result = bootstrap.mean(surpluses, args.num_bootstraps, args.percentiles)
if args.mean:
mean = surpluses.mean()
else:
data = np.asarray(data, float)
result = bootstrap.mean(data, args.num_bootstraps, args.percentiles)
if args.mean:
mean = data.mean()
if args.percentiles is None:
args.percentiles = np.linspace(0, 100, args.num_bootstraps)
jresult = {str(p).rstrip("0").rstrip("."): v.item() for p, v in zip(args.percentiles, result)}
if args.mean:
jresult["mean"] = mean
json.dump(jresult, args.output)
args.output.write("\n")