- Create automated build script with version extraction and Aliyun registry push
- Add comprehensive HNSW implementation guide with step-by-step instructions
- Update Dockerfile to use musl target and enc binary for deployment
- Include performance optimization strategies and debugging tips
- Add HNSW search algorithm support with perform_hnsw_search function
- Implement optimal parameters (M=8, L=3, P=0.6) from systematic testing
- Replace broken search_layer with comprehensive TODO framework for manual implementation
- Add generic type support for both FheInt32 and FheUint12 operations
- Include debug mode, performance analysis, and implementation guide
- Restructure code into separate modules: data, algorithms, logging, cache
- Add efficient caching system for keys and encrypted distances
- Implement three sorting algorithms: selection, bitonic, heap
- Add comprehensive logging with timestamps and progress tracking
- Configure musl target for static compilation
- Support command-line algorithm selection and cache control
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit adds a complete FHE-based K-nearest neighbors implementation using TFHE:
Key Features:
- Encrypts training data and query vectors using FheInt32 and FheUint8
- Implements encrypted Euclidean distance calculation with 100x scaling for precision
- Uses bitonic sorting with encrypted conditional swaps for secure k-selection
- Includes comprehensive progress tracking and timing for long-running operations
- Memory optimizations: pre-allocated vectors and reused encrypted constants
Algorithm Implementation:
- Encrypted distance computation with homomorphic arithmetic operations
- Bitonic sort algorithm adapted for encrypted data structures
- Secure index tracking with encrypted FheUint8 values
- Select API usage for conditional swaps maintaining data privacy
Performance:
- Handles 100 training points with 10 dimensions in ~98 minutes on consumer hardware
- Includes detailed progress bars and time estimation
- Results validated against plain-text implementation (8/10 match rate)
Documentation:
- Comprehensive function documentation for all core algorithms
- Time complexity analysis and performance benchmarking notes
- Clear separation between client-side encryption/decryption and server-side computation
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add plain KNN implementation with JSONL data processing
- Create Docker deployment setup with python:3.13-slim base
- Add comprehensive OJ-style testing system with accuracy validation
- Update README with detailed scoring mechanism explanation
- Add run.sh script following competition manual requirements
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add manual.md documentation
- Include train.jsonl dataset for FHE-KNN training
- Update README.md with project details
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Add Cargo.toml with TFHE, CSV, and Serde dependencies
- Add .gitignore for Rust target directory
- Include Iris dataset for machine learning experiments
- Add plain KNN implementation binary
- Update LICENSE to MIT and improve README
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
