def test_privateMedian():
f = open(res_dir + 'privateMedian', 'w')
f_t = open(res_dir + 'privateMedian-time', 'w')
data = np.sort(dataGen.data_gen(dist, NDIM, LO, HI, NDATA)).flatten()
n = len(data)
for i in range(10):
print 'level ' + ` i `
container = np.zeros(6)
container_t = np.zeros(6)
for seed in seed_list:
perturber = Differential(seed)
for j in range(2**i):
c_data = data[n * j / 2**i:n * (j + 1) / 2**i]
c_len = len(c_data)
# exponential mechanism
start = time.clock()
em = perturber.getSplit_exp(c_data, c_data[0], c_data[-1], eps,
1)
end = time.clock()
container_t[0] += end - start
# smooth sensitivity (2-approx)
start = time.clock()
ls = perturber.getSplit_smooth(c_data, c_data[0], c_data[-1],
eps, 1)
end = time.clock()
container_t[1] += end - start
# exponential mechanism sampling
start = time.clock()
em_samp = perturber.getSplit_exp(c_data, c_data[0], c_data[-1],
eps, srt)
end = time.clock()
container_t[2] += end - start
# smooth sensitivity sampling
start = time.clock()
ls_samp = perturber.getSplit_smooth(c_data, c_data[0],
c_data[-1], eps, srt)
end = time.clock()
container_t[3] += end - start
# noisy mean approximation
start = time.clock()
nm = perturber.getSplit_noisyMean(c_data, c_data[0],
c_data[-1], eps)
end = time.clock()
container_t[4] += end - start
# noisy grid approximation
start = time.clock()
ng = perturber.getSplit_grid(c_data, c_data[0], c_data[-1],
eps, unit)
end = time.clock()
container_t[5] += end - start
res = [em, ls, em_samp, ls_samp, nm, ng]
for k in range(6):
if res[k] >= c_data[-1] or res[k] <= c_data[0]:
container[k] += 1.0
else:
r_k = np.searchsorted(c_data, res[k])
r_m = float(c_len) / 2
container[k] += abs(r_m - r_k) / r_m
# end of j loop
for k in range(6):
f.write( ` container[k] / (2**i * len(seed_list)) ` + ' ')
f.write('\n')
for k in range(6):
f_t.write( ` container_t[k] / (2**i * len(seed_list)) ` + ' ')
f_t.write('\n')
# end of i
f.close()
f_t.close()
def test_privateMedian():
f = open(res_dir + 'privateMedian', 'w')
f_t = open(res_dir + 'privateMedian-time', 'w')
data = np.sort(dataGen.data_gen(dist, NDIM, LO, HI, NDATA)).flatten()
n = len(data)
for i in range(10):
print 'level ' + `i`
container = np.zeros(6)
container_t = np.zeros(6)
for seed in seed_list:
perturber = Differential(seed)
for j in range(2 ** i):
c_data = data[n * j / 2 ** i:n * (j + 1) / 2 ** i]
c_len = len(c_data)
# exponential mechanism
start = time.clock()
em = perturber.getSplit_exp(c_data, c_data[0], c_data[-1], eps, 1)
end = time.clock()
container_t[0] += end - start
# smooth sensitivity (2-approx)
start = time.clock()
ls = perturber.getSplit_smooth(c_data, c_data[0], c_data[-1], eps, 1)
end = time.clock()
container_t[1] += end - start
# exponential mechanism sampling
start = time.clock()
em_samp = perturber.getSplit_exp(c_data, c_data[0], c_data[-1], eps, srt)
end = time.clock()
container_t[2] += end - start
# smooth sensitivity sampling
start = time.clock()
ls_samp = perturber.getSplit_smooth(c_data, c_data[0], c_data[-1], eps, srt)
end = time.clock()
container_t[3] += end - start
# noisy mean approximation
start = time.clock()
nm = perturber.getSplit_noisyMean(c_data, c_data[0], c_data[-1], eps)
end = time.clock()
container_t[4] += end - start
# noisy grid approximation
start = time.clock()
ng = perturber.getSplit_grid(c_data, c_data[0], c_data[-1], eps, unit)
end = time.clock()
container_t[5] += end - start
res = [em, ls, em_samp, ls_samp, nm, ng]
for k in range(6):
if res[k] >= c_data[-1] or res[k] <= c_data[0]:
container[k] += 1.0
else:
r_k = np.searchsorted(c_data, res[k])
r_m = float(c_len) / 2
container[k] += abs(r_m - r_k) / r_m
# end of j loop
for k in range(6):
f.write(`container[k] / (2 ** i * len(seed_list))` + ' ')
f.write('\n')
for k in range(6):
f_t.write(`container_t[k] / (2 ** i * len(seed_list))` + ' ')
f_t.write('\n')
# end of i
f.close()
f_t.close()
