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rappor_lasso2.py
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rappor_lasso2.py
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import random
import math
import numpy as np
from sklearn.linear_model import Lasso
class BloomFilter:
def __init__(self):
self.arr = [0] * 18
def hash1(self, num):
hash_value = 0
if num == 0:
return 0
while True:
if num > 0:
k = num % 10
hash_value = hash_value * 1311 + k
num = num // 10
else:
return hash_value
def hash2(self, num):
hash_value = 0
if num == 0:
return 0
while True:
if num > 0:
k = num % 10
hash_value = hash_value * 3677 + k
num = num // 10
else:
return hash_value
def hash3(self, num):
hash_value = 0
if num == 0:
return 0
while True:
if num > 0:
k = num % 10
hash_value = hash_value * 4423 + k
num = num // 10
else:
return hash_value
def hash4(self, num):
hash_value = 0
if num == 0:
return 0
while True:
if num > 0:
k = num % 10
hash_value = hash_value * 4871 + k
num = num // 10
else:
return hash_value
def in_4_hash(self, num):
self.arr[self.hash1(num) % 18] = 1
self.arr[self.hash2(num) % 18] = 1
self.arr[self.hash3(num) % 18] = 1
self.arr[self.hash4(num) % 18] = 1
def test(self, num):
if self.arr[self.hash1(num) % 18] != 1:
return False
if self.arr[self.hash2(num) % 18] != 1:
return False
if self.arr[self.hash3(num) % 18] != 1:
return False
if self.arr[self.hash4(num) % 18] != 1:
return False
return True
def answer(self):
result = []
for i in range(100):
if self.test(i):
result.append(i)
return result
def hash11(self, num):
temp = [0] * 4
num = num * num
if num < 26:
return num + 2
for i in range(4):
k = num % 10
temp[i] = k
num = num // 23
return temp[2] * 10 + temp[1]
def hash12(self, num):
temp = [0] * 4
num = num * num
if num < 26:
return num + 1
for i in range(4):
k = num % 10
temp[i] = k
num = num // 11
return temp[2] * 10 + temp[1]
def hash13(self, num):
temp = [0] * 4
num = num * num
if num < 26:
return num + 5
for i in range(4):
k = num % 10
temp[i] = k
num = num // 13
return temp[2] * 10 + temp[1]
def hash14(self, num):
temp = [0] * 4
num = num * num
if num < 26:
return num + 9
for i in range(4):
k = num % 10
temp[i] = k
num = num // 17
return temp[2] * 10 + temp[1]
def in_4_hash1(self, num):
self.arr[self.hash11(num) % 18] = 1
self.arr[self.hash12(num) % 18] = 1
self.arr[self.hash13(num) % 18] = 1
self.arr[self.hash14(num) % 18] = 1
def test0(self, num):
if self.arr[self.hash11(num) % 18] != 1:
return False
if self.arr[self.hash12(num) % 18] != 1:
return False
if self.arr[self.hash13(num) % 18] != 1:
return False
if self.arr[self.hash14(num) % 18] != 1:
return False
return True
def test01(self, num):
if self.arr[self.hash11(num) % 18] <= 0:
return 0
min_val = self.arr[self.hash11(num) % 18]
if self.arr[self.hash12(num) % 18] <= 0:
return 0
min_val = min(min_val, self.arr[self.hash12(num) % 18])
if self.arr[self.hash13(num) % 18] <= 0:
return 0
min_val = min(min_val, self.arr[self.hash13(num) % 18])
if self.arr[self.hash14(num) % 18] <= 0:
return 0
min_val = min(min_val, self.arr[self.hash14(num) % 18])
return min_val
def random_permanent(f):
a = random.randint(0, 9999)
F = int(f * 10000)
if a < F // 2:
return 1
if a > F:
return -1
return 0
def random_instant(p, q, a):
s = random.randint(0, 9999)
if a == 1:
return 1 if s < q * 10000 else 0
if a == 0:
return 1 if s < p * 10000 else 0
def test(f, p, q, num1, num2, c, arr_all): # 将 c 和 arr_all 作为参数传递
A = BloomFilter()
A.in_4_hash1(num1)
A.in_4_hash1(num2)
for k in range(18):
s = random_permanent(f)
if s == 0:
A.arr[k] = 0
if s == 1:
A.arr[k] = 1
s = [0] * 18
for k in range(18):
if A.arr[k] == 0:
s[k] = random_instant(p, q, A.arr[k])
A.arr[k] = s[k]
for k in range(18):
if A.arr[k] == 1:
c[k] += 1
arr_all[num1] += 1
arr_all[num2] += 1
def calculate_epsilon(f):
return -math.log(1/f - 1)
def improved_counting():
N = 10000
random.seed()
num_interests = 100
epsilon = float(input("请输入扰动的参数: ")) # 输入扰动的参数 epsilon
f = 1.0 / (1.0 + math.exp(epsilon))
p = float(input("请输入p: ")) # 输入p
q = float(input("请输入q: ")) # 输入q
c = [0] * 18 # 初始化 c 列表
arr_all = [0] * num_interests # 初始化 arr_all 列表
for i in range(5000):
test(f, p, q, 10, 11, c, arr_all)
for i in range(1000):
test(f, p, q, 12, 13, c, arr_all)
for i in range(4000):
test(f, p, q, 14, 15, c, arr_all)
# 计算 tij
t = []
for tij, ni in zip(c, arr_all):
tij_estimate = (tij - (p + 0.5 * f * q - 0.5 * f * p) * ni) / ((1 - f) * (q - p))
t.append(tij_estimate)
# 输出计算后的结果
for i, tij_estimate in enumerate(t):
print(f"t{i+1} = {tij_estimate}")
# 使用 Lasso 回归改进计数
X = create_design_matrix(c, num_interests)
# 构建响应变量 Y
Y = np.array(t)
# 初始化 Lasso 回归模型
lasso = Lasso(alpha=0.1) # 可根据数据集调整 alpha 值
# 拟合模型
lasso.fit(X, Y)
# 预测 tij 值
t = np.round(lasso.predict(X)).astype(int)
# 使用改进后的计数更新 BloomFilter
T = BloomFilter()
T.arr = t
arr_all_2 = [T.test01(i) for i in range(num_interests)]
# 输出改进后的兴趣数组
for i, count in enumerate(arr_all_2):
print(f"新兴趣{i+1}的个数为:{count}")
def create_design_matrix(c, m):
X = np.zeros((len(c), m))
for j in range(m):
for i, tij in enumerate(c):
X[i][j] = tij
return X
if __name__ == "__main__":
improved_counting()