def getNoisyAnalysis(self,noise,err): exact=self.getAnalysis(); m=len(exact); for i in range(0,m): pert=lap(scale=noise); while pert>err and err>0: pert=lap(scale=noise); exact[i]=exact[i]+pert; return exact;
def getTrueValue(self):
exact = self.analyser.getAnalysis()
m = len(exact)
for i in range(0, m):
pert = lap(scale=self.noise)
while abs(pert) > self.err and self.err > 0:
pert = lap(scale=self.noise)
exact[i] = exact[i] + pert
self.truth = [i for i in exact]
return [i for i in exact]
def lapLM(data, eps, m):
lapv = lap(scale=(m / eps), size=len(data))
loc_data = data + lapv
loc_mean = np.mean(loc_data)
return loc_mean
def lapLM(data, eps, m):
lapv = lap(scale=(m / eps), size=len(data))
loc_data = data + lapv
loc_mean = np.mean(loc_data)
# loc_mean = truncate(loc_mean, 0.0, 1.0)
return loc_mean
def tcm(data, eps, m):
tcm_mean = np.mean(data) + lap(scale=m / (len(data) * eps))
# tcm_mean = truncate(tcm_mean, 0.0, 1.0)
return tcm_mean