def smooth_sensitivity_study(prior, sample_size, epsilon, delta, percentage):
Bayesian_Model = BayesInferwithDirPrior(prior, sample_size, epsilon, delta)
observation = [sample_size * i for i in percentage]
Bayesian_Model._set_observation([sample_size * i for i in percentage])
Bayesian_Model._set_candidate_scores()
Bayesian_Model._set_LS_Candidates()
xstick = [
str((r._minus(Bayesian_Model._prior))._alphas)
for r in Bayesian_Model._candidates
]
beta = 0.00000001
y = [
Bayesian_Model._LS_Candidates[r] *
math.exp(-beta * Hamming_Distance(observation, r._alphas))
for r in Bayesian_Model._candidates
]
scatter_plot(
y, xstick, 'Smooth Sensitivity Study w.r.t. x :' +
str([sample_size * i for i in percentage]) + r'; $\epsilon = $' +
str(epsilon) + r'; $\delta:$' + str(delta),
r"$\Delta_l(H(BI(x'),-))e^{- \gamma *d(x,x')}$")
def smooth_sensitivity_study2(prior, sample_size, epsilon, delta, percentage):
Bayesian_Model = BayesInferwithDirPrior(prior, sample_size, epsilon, delta)
observation = [sample_size * i for i in percentage]
Bayesian_Model._set_observation([sample_size * i for i in percentage])
Bayesian_Model._set_candidate_scores()
Bayesian_Model._set_LS_Candidates()
x = [(c._minus(Bayesian_Model._prior)) for c in Bayesian_Model._candidates]
beta = 0.001 # math.log(1 - epsilon / (2.0 * math.log(delta / (2.0 * (sample_size)))))
max_value_list = []
xstick = [
str((r._minus(Bayesian_Model._prior))._alphas)
for r in Bayesian_Model._candidates
]
for t in x:
Bayesian_Model._set_observation(t._alphas)
# Bayesian_Model._set_candidate_scores()
# Bayesian_Model._set_LS_Candidates()
max_value = 0.0
max_y = ''
# max_y_list = []
for r in Bayesian_Model._candidates:
temp = Bayesian_Model._LS_Candidates[r] * math.exp(
-beta * Hamming_Distance(t._alphas, r._alphas))
if max_value < temp:
max_y = r._alphas
max_value = temp
# for r in Bayesian_Model._candidates:
# temp = Bayesian_Model._LS_Candidates[r] * math.exp(- beta * Hamming_Distance(observation, r._alphas))
# if max_value == temp:
# max_y_list.append(str(r._alphas))
max_value_list.append(max_value)
print "when data set x = " + str(
t._alphas) + ", smooth sensitivity: S(" + str(
t._alphas) + ") = " + str(max_value)
scatter_plot(
y,
xstick,
'Smooth Sensitivity Given Size' + str(sample_size),
)
def ss_ls_component_study(prior, sample_size, epsilon, delta, percentage):
Bayesian_Model = BayesInferwithDirPrior(prior, sample_size, epsilon, delta)
observation = [sample_size * i for i in percentage]
Bayesian_Model._set_observation([sample_size * i for i in percentage])
Bayesian_Model._set_candidate_scores()
Bayesian_Model._set_LS_Candidates()
xstick = [str(c._alphas) for c in Bayesian_Model._candidates]
beta = math.log(1 - epsilon / (2.0 * math.log(delta / (2.0 *
(sample_size)))))
y = [
1.0 / ((1 - Bayesian_Model._LS_Candidates[r]**2))
for r in Bayesian_Model._candidates
]
print abs(y[2] - y[1])
scatter_plot(y, xstick, "", r"$\frac{1}{LS((\alpha, 100 - \alpha))}$")
return