def beta_proposal(current, var):
alpha_beta_fwd = jmutils.beta_shape(current, var)
proposed = beta.rvs(*alpha_beta_fwd)
fwd_prob = beta.pdf(proposed, *alpha_beta_fwd)
alpha_beta_back = jmutils.beta_shape(proposed, var)
back_prob = beta.pdf(current, *alpha_beta_back)
log_back_fwd = math.log(back_prob / fwd_prob)
return proposed, log_back_fwd
def make_noisy_probs(exact, noise_type, noise):
"""Noisify probabilities
Args: exact - 2D np.array, rows are options, cols are ppl
model_params: dict
Outputs: 2d np.aray, rows are options, cols are ppl
"""
if noise_type == "noiseless":
return exact
elif noise_type == "binomial":
num_hypothetical_trials = noise
num_successes = nr.binomial(num_hypothetical_trials, exact[0])
noisy0 = num_successes / num_hypothetical_trials
return np.array([noisy0, 1 - noisy0])
elif noise_type == "beta":
alpha_beta = jmutils.beta_shape(exact[0], noise)
noisy0 = nr.beta(*alpha_beta)
return np.array([noisy0, 1 - noisy0])
elif noise_type == "truncnorm":
scale = noise
noisy0 = truncnorm.rvs(-exact[0] / scale, (1 - exact[0]) / scale,
loc=exact[0], scale=scale)
return np.array([noisy0, 1 - noisy0])
elif noise_type == "log_odds":
lo = np.log(exact[0] / exact[1])
noisy_lo = nr.normal(lo, noise)
given_1 = 1 / (math.exp(noisy_lo) + 1)
return np.array([1 - given_1, given_1])
else:
print("Error: meta noise_type not specified correctly")
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 prob_noisy_vec(given, bayes, noise_type, noise):
if noise_type == "truncnorm":
return tn_pdf_01(given, bayes, noise)
elif noise_type == "beta":
if hasattr(noise, "__len__"):
#return np.array([beta.pdf(g, *jmutils.beta_shape(bayes, n)) for g, n in zip(given, noise)])
return np.array([beta.pdf(g, *mode_beta(bayes, n)) for g, n in zip(given, noise)])
else:
return beta.pdf(given, *jmutils.beta_shape(bayes, noise))
else:
print("Unknown noise type")
sys.exit("prob_noisy_vec: Unknown noise type")
