mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-02 00:02:38 +02:00
06a103d61d
- Add code review task for existing LFM implementations - Add design task for modern LFM kernel architecture - Add implementation task for core LFM kernel functionality - Establish clear task dependencies and acceptance criteria - Link to CIP-0001 and relevant papers
2.1 KiB
2.1 KiB
| id | title | status | priority | created | last_updated | owner | dependencies | tags | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| design-modern-lfm-kernel | Design modern LFM kernel architecture | Proposed | High | 2025-08-15 | 2025-08-15 | Neil Lawrence |
|
|
Design modern LFM kernel architecture
Description
Design a modern LFM kernel implementation that follows GPy's current architectural patterns and uses the multioutput kernel approach with output index as input.
Background
- Current GPy LFM implementations don't use the modern multioutput kernel approach
- Need to design a unified LFM kernel that integrates well with GPy's current framework
- Should maintain backward compatibility while providing improved functionality
Design Requirements
- Use GPy's multioutput kernel approach with output index as input
- Follow consistent API design with other GPy kernels
- Implement proper parameter handling and constraints
- Support different base kernels for latent functions
- Enable efficient gradient computation
- Maintain backward compatibility with existing implementations
Design Tasks
- Define kernel class structure and inheritance hierarchy
- Design parameter handling for mass, damper, spring, sensitivity, delay
- Plan integration with GPy's multioutput framework
- Design cross-kernel computation methods
- Plan parameter tying and constraint handling
- Design efficient computation methods for large datasets
Acceptance Criteria
- Complete design specification document
- API design that follows GPy patterns
- Integration plan with existing GPy infrastructure
- Performance considerations documented
- Backward compatibility strategy defined
Implementation Notes
- Study how other multioutput kernels in GPy handle output indices
- Consider parameter tying approaches from MATLAB implementation
- Design for extensibility to different differential equation types
- Plan for efficient computation of cross-kernel terms
Related
- CIP: 0001 (LFM kernel implementation)
- Backlog: lfm-kernel-code-review