def add_staircase_noisy(epsilon, sensitivity, top):
re = staircase.randomise(epsilon, sensitivity)
T = 100
while T > 0 and re > top:
T -= 1
re = staircase.randomise(epsilon, sensitivity)
if T == 0:
re = 0
return re
def get_real_trails(myTrie, segNum, choiceNum, centers, labels, epsilon, alpha,
beta):
newTrails = []
newCnt = 0
newSum = 0
for i in range(choiceNum):
newTrail = []
newCenterTrail = []
for j in range(segNum):
la = labels[i][j]
ce = centers[la][j]
newTrail.append(la)
newCenterTrail.append(ce)
rea = myTrie.query(newTrail)
if rea > 0:
newSum += rea
newCnt += 1
myTrie.update(newTrail, -rea - 1)
real_noisy = rea + staircase.randomise(epsilon, 1)
if real_noisy < alpha * rea:
real_noisy = alpha * rea
elif real_noisy > beta * rea:
real_noisy = beta * rea
newTrails.append(
Trail(newCenterTrail, newTrail, rea, int(real_noisy)))
newTrails.sort()
return newTrails, newCnt, newSum
def get_real_trails(myTrie, segNum, choiceNum, centers, labels, epsilon, alpha,
beta):
newTrails = []
newCnt = 0
newSum = 0
for i in range(choiceNum):
newTrail = []
newCenterTrail = []
for j in range(segNum):
la = labels[i][j]
ce = centers[la][j]
newTrail.append(la)
newCenterTrail.append(ce)
rea = myTrie.query(newTrail)
if rea > 0:
'''
for j in range(segNum):
if math.isnan(newCenterTrail[j][0]) or math.isnan(newCenterTrail[j][1]):
la = labels[i][j]
ce = centers[la][j]
print('nan','label=',la,'center=',ce)
'''
newSum += rea
newCnt += 1
myTrie.update(newTrail, -rea - 1)
real_noisy = rea + staircase.randomise(epsilon, 1)
if real_noisy < alpha * rea:
real_noisy = alpha * rea
elif real_noisy > beta * rea:
real_noisy = beta * rea
newTrails.append(
Trail(newCenterTrail, newTrail, rea, int(real_noisy)))
'''
for i in newTrails:
for j in i.trail:
if math.isnan(j[0]) or math.isnan(j[1]) :
print(j,'nan')
'''
newTrails.sort()
return newTrails, newCnt, newSum