mathdatech/Python
1
0
Fork 0
mirror of https://github.com/TheAlgorithms/Python.git synced 2025-02-07 10:00:55 +00:00
Code Issues Packages Projects Releases Wiki Activity

removed test_probability

Browse Source
This commit is contained in:
Isidro Arias 2023-04-07 17:23:01 +02:00
parent 8b1bec0dc6
commit 28e66913b0
1 changed files with 0 additions and 40 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

40
data_structures/hashing/bloom_filter.py
View File

@ -50,8 +50,6 @@ True
0.140625
"""
from hashlib import md5, sha256
from random import choices
from string import ascii_lowercase
HASH_FUNCTIONS = (sha256, md5)
@ -95,41 +93,3 @@ class Bloom:
n_ones = bin(self.bitarray).count("1")
k = len(HASH_FUNCTIONS)
return (n_ones / self.size) ** k
def random_string(size: int) -> str:
return "".join(choices(ascii_lowercase + " ", k=size))
def test_probability(filter_bits: int = 64, added_elements: int = 20) -> None:
b = Bloom(size=filter_bits)
k = len(HASH_FUNCTIONS)
estimated_error_rate_beforehand = (
1 - (1 - 1 / filter_bits) ** (k * added_elements)
) ** k
not_added = {random_string(10) for i in range(1000)}
for _ in range(added_elements):
b.add(not_added.pop())
n_ones = bin(b.bitarray).count("1")
estimated_error_rate = (n_ones / filter_bits) ** k
errors = 0
for string in not_added:
if b.exists(string):
errors += 1
error_rate = errors / len(not_added)
print(f"error_rate = {errors}/{len(not_added)} = {error_rate}")
print(f"{estimated_error_rate=}")
print(f"{estimated_error_rate_beforehand=}")
assert (
abs(estimated_error_rate - error_rate) <= 0.05
) # 5% absolute margin calculated experiementally
if __name__ == "__main__":
test_probability()