# CEREBRUM - Changelog

All notable changes to the CEREBRUM module will be documented in this file.

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [0.1.0] - 2026-01-05

### Added
- Initial implementation of CEREBRUM module
- Case-based reasoning with Case, CaseBase, and CaseRetriever classes
- Bayesian inference with BayesianNetwork and InferenceEngine
- Active inference with ActiveInferenceAgent based on free energy principle
- Model transformation with AdaptationTransformer and LearningTransformer
- Visualization tools for networks, cases, and inference results
- Core CerebrumEngine orchestrator
- Comprehensive documentation (README, AGENTS, SPEC, API, Usage Examples)
- MCP tool specifications
- Unit and integration test structure

### Features
- Case similarity computation (Euclidean, cosine)
- Multiple Bayesian inference methods (variable elimination, MCMC)
- Case weighting strategies (distance, frequency, hybrid)
- Model adaptation and learning from feedback
- Integration with other codomyrmex modules

### Dependencies
- numpy: Numerical computations
- scipy: Statistical functions
- networkx: Graph structures
- scikit-learn: Similarity metrics (optional)
- matplotlib: Visualization (optional)



## Navigation Links

- **Parent**: [Project Overview](../README.md)
- **Module Index**: [All Agents](../../AGENTS.md)
- **Documentation**: [Reference Guides](../../../docs/README.md)
- **Home**: [Root README](../../../README.md)

## Detailed Architecture and Implementation



### Design Principles
1.  **Strict Modularity**: Each component is isolated and communicates via well-defined APIs.
2.  **Performance Optimization**: Implementation leverages lazy loading and intelligent caching to minimize resource overhead.
3.  **Error Resilience**: Robust exception handling ensures system stability even under unexpected conditions.
4.  **Extensibility**: The architecture is designed to accommodate future enhancements without breaking existing contracts.

### Technical Implementation
The codebase utilizes modern Python features (version 3.10+) to provide a clean, type-safe API. Interaction patterns are documented in the corresponding `AGENTS.md` and `SPEC.md` files, ensuring that both human developers and automated agents can effectively utilize these capabilities.

