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- 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
2.2 KiB
2.2 KiB
| id | title | status | priority | created | last_updated | owner | dependencies | tags | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| implement-lfm-kernel-core | Implement core LFM kernel functionality | Proposed | High | 2025-08-15 | 2025-08-15 | Neil Lawrence |
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Implement core LFM kernel functionality
Description
Implement the core LFM kernel class with basic functionality including kernel computation, parameter handling, and gradient computation.
Background
- Design phase completed with modern LFM kernel architecture
- Need to implement the core kernel computation methods
- Should follow the mathematical foundations from the papers and MATLAB implementation
Implementation Tasks
- Create
GPy.kern.LFMclass inheriting from appropriate base class - Implement parameter handling for mass, damper, spring, sensitivity, delay
- Implement
K()method for kernel matrix computation - Implement
Kdiag()method for diagonal computation - Add parameter constraints and transformations
- Implement basic gradient computation
- Add support for different base kernels for latent functions
Core Methods to Implement
__init__()- Parameter initialization and validationK(X, X2=None)- Kernel matrix computationKdiag(X)- Diagonal computationupdate_gradients_full()- Gradient computationupdate_gradients_diag()- Diagonal gradient computationparameters_changed()- Parameter update handling
Acceptance Criteria
- Core LFM kernel class implemented and functional
- Basic kernel computation working correctly
- Parameter handling and constraints implemented
- Gradient computation implemented
- Unit tests passing for core functionality
- Integration with GPy's parameterization system
Implementation Notes
- Follow the mathematical structure from the MATLAB implementation
- Use GPy's parameterization system for constraints
- Implement efficient computation methods
- Ensure proper handling of edge cases and numerical stability
- Add comprehensive docstrings and documentation
Related
- CIP: 0001 (LFM kernel implementation)
- Backlog: design-modern-lfm-kernel
- Papers: Álvarez et al. (2009, 2012)