def testing():
responses = [1, 1, 0]
meta_true = [0.7, 0.3, 0.4]
bts_easy = calc_easy_bts_binary(responses, meta_true)
correct_surprise = [0.4201811, 0.4201811, -0.4081212]
correct_accuracy = [-0.002593396, -0.285026016, -0.144621528]
assert jmutils.bounded(sum(bts_easy['bts']), -0.000001, 0.000001)
assert all([jmutils.fuzzy_equals(c, a, 0.0001) for c, a in zip(correct_surprise, bts_easy['surprise'])])
assert all([jmutils.fuzzy_equals(c, a, 0.0001) for c, a in zip(correct_accuracy, bts_easy['accuracy'])])
print('done testing')
def prob_noisy(given, bayes, noise_type, noise):
"""Prob of saying given when rational bayesian would say bayes
Args: given: in [0,1], what respondent said
bayes: in [0, 1]
model_params: dict, incl keys with noise info
Returns: prob in [0, 1]
Different types of noise models: beta, binomial, noiseless.
"""
if noise_type == "noiseless":
tolerance = 1e-04
if jmutils.fuzzy_equals(given, bayes, tolerance):
return 1
else:
return 0
elif noise_type == "binomial":
num_trials = noise
num_successes = int(given * num_trials)
#return binom.pmf(num_successes, num_trials, bayes)
return my_binom_pmf(num_successes, num_trials, bayes)
elif noise_type == "beta":
print("Optimize the beta stuff almost certainly!!!")
alpha_beta = jmutils.beta_shape(bayes, noise)
return beta.pdf(given, *alpha_beta)
elif noise_type == "truncnorm":
scale = noise
#return truncnorm.pdf(given, -bayes / scale, (1 - bayes) / scale,
# loc=bayes, scale=scale)
return mytruncpdf_01bounds(given, bayes, noise)
else:
print("Error: meta noise_type not specified correctly")
sys.exit()
def test_prob_responses_signals():
responses = np.array([0,0,1,1])
signals = np.array([0,1,1,0])
noise = .9
tolerance = 1e-04
assert fuzzy_equals(agg.prob_responses_signals(responses, signals, noise),
0.008100000000000, tolerance)
def test_prob_meta_noisy():
given = .5
bayes = .45
model_params = {'noise_meta': 'noiseless'}
assert agg.prob_meta_noisy(given, bayes, model_params) == 0
model_params = {'noise_meta': 'binomial', 'binomial_num_trials': 100}
tolerance = 1e-04
assert fuzzy_equals(agg.prob_meta_noisy(given, bayes, model_params),
0.048151971498, tolerance)