feat: add BloomFilter data structure

[chenmiaoming]

Description

This PR implements issue #7 by adding a BloomFilter data structure for space-efficient probabilistic membership checks.

Changes included

• Added BloomFilter with configurable number of hash functions.
• Added support for custom primary and secondary hash algorithms.
• Added with_capacity(expected_items, target_fpr) factory for parameter tuning.
• Added insert, contains, clear, false_positive_rate, hash_functions, size, and bit_count APIs.
• Added BloomFilter unit tests and wired them into the existing CMake test target.
• Updated documentation navigation and user-facing docs.
• Added a dedicated Bloom Filter documentation page.

Validation performed

cmake -S . -B build -DLOON_BUILD_TESTS=ON
cmake --build build
ctest --test-dir build --output-on-failure

Result

• 42 tests passed, 0 failed.

Related Issue

Closes #7

Type of Change

• Bug fix (non-breaking change that fixes an issue)
• New feature (non-breaking change that adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Documentation update
• Performance improvement
• Code refactoring (no functional changes)

Checklist

• My code follows the project's coding standards
• I have run make check-format and fixed any issues
• I have added tests that prove my fix/feature works
• All new and existing tests pass (make build)
• I have updated documentation if needed
• My changes generate no new warnings

Performance Impact

N/A (no dedicated benchmark or performance regression measurements were added in this PR).

Additional Notes

• Bloom filters guarantee no false negatives and allow false positives by design.
• The false positive rate is estimated from inserted item count, bit size, and hash function count.
• Existing warnings observed during build are in test/test_ring_buffer.cpp and were not introduced by this PR.
• The estimation follows the standard approximation:

$$ FPR \approx (1 - e^{-kn/m})^k $$

where:

• $n$ is inserted item count,
• $m$ is bit count,
• $k$ is hash function count.