Implement a Bloom Filter Data Structure

[Akshat-Shu]

Fixes #666

Bloom Filter data structure for efficient probabilistic membership testing. The implementation is based on a fixed-size bit array and can support multiple hash functions from hashlib. This Bloom Filter allows users to insert elements and check for membership with a low false positive rate.

Changes Implemented

• BitArray Class: Implements a compact bit array with efficient bit manipulation.
• BloomFilter Class:
  • Supports insertion of elements.
  • Provides a probabilistic membership check.
  • Uses multiple hash functions (sha256, md5, etc.) for uniform bit distribution.
• Dynamic Calculation of Parameters: Computes the optimal bit array size and number of hash functions based on expected capacity and false positive rate.
• Serialization of keys to support multiple data types
• Unit Tests: Tests basic insertion and membership queries as well as BitArray functioning

[codecov]

Codecov Report

Attention: Patch coverage is 91.00000% with 9 lines in your changes missing coverage. Please review.

Project coverage is 97.204%. Comparing base (f4c1677) to head (9bb7346).
Report is 4 commits behind head on main.

Files with missing lines	Patch %	Lines
...ucts/miscellaneous_data_structures/bloom_filter.py	90.816%	9 Missing ⚠️

Additional details and impacted files

@@              Coverage Diff              @@
##              main      #667       +/-   ##
=============================================
- Coverage   97.345%   97.204%   -0.142%     
=============================================
  Files           36        37        +1     
  Lines         4445      4650      +205     
=============================================
+ Hits          4327      4520      +193     
- Misses         118       130       +12     

Files with missing lines	Coverage Δ
...astructs/miscellaneous_data_structures/__init__.py	100.000% <100.000%> (ø)
...ucts/miscellaneous_data_structures/bloom_filter.py	90.816% <90.816%> (ø)

... and 4 files with indirect coverage changes

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