Enhance documentation for BeliefState, BayesianBeliefUpdater, and MetaModelGenerator classes

[leonvanbokhorst]

Summary by Sourcery

Enhance documentation for the BeliefState and BayesianBeliefUpdater classes in bayes_updating.py, and the MetaModelGenerator class in maml_model_agnostic_meta_learning.py, providing detailed explanations of their components, algorithms, and design principles.

Documentation:

• Expand documentation for the BeliefState class in bayes_updating.py, detailing its components and functionality.
• Enhance documentation for the BayesianBeliefUpdater class, explaining its algorithm and design principles.
• Add comprehensive documentation for the MetaModelGenerator class in maml_model_agnostic_meta_learning.py, describing its architecture and training process.

[sourcery-ai]

Reviewer's Guide by Sourcery

This PR enhances documentation for two key components: the Bayesian belief updating system and the Model-Agnostic Meta-Learning (MAML) implementation. The changes focus on adding detailed docstrings that explain the architecture, algorithms, and implementation details of both systems.

Updated class diagram for BeliefState and BayesianBeliefUpdater

classDiagram
    class BeliefState {
        +np.ndarray belief_vector
        +float confidence
        +List~Tuple~np.ndarray, float~~ prior_states
        +List~str~ themes
    }
    note for BeliefState "Represents a belief about a topic using vector embeddings."

    class BayesianBeliefUpdater {
        +__init__(llm: LanguageModel)
    }
    note for BayesianBeliefUpdater "Implements dynamic belief updating using Bayesian inference and LLM embeddings."

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Updated class diagram for MetaModelGenerator

classDiagram
    class MetaModelGenerator {
        +__init__(input_size: int, hidden_sizes: List~int~, output_size: int, inner_lr: float=0.05, meta_lr: float=0.003)
        +float inner_lr
        +optim.SGD meta_optimizer
        +optim.lr_scheduler scheduler
    }
    note for MetaModelGenerator "Meta-learning model implementing MAML algorithm for few-shot learning."

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File-Level Changes

Change	Details	Files
Enhanced documentation for the BeliefState class with detailed component descriptions	Added comprehensive explanation of semantic understanding and epistemic uncertainty concepts Documented belief vector structure and normalization Detailed confidence value interpretation and behavior Added explanation of prior states tracking and purpose Documented themes list functionality and future use	src/bayes_updating.py
Expanded documentation for BayesianBeliefUpdater with detailed algorithm explanation	Added detailed core algorithm section covering belief representation and update mechanism Documented design principles including conservative updates and uncertainty handling Added explanation of memory effects and state tracking Included comprehensive breakdown of the update process stages	src/bayes_updating.py
Added comprehensive documentation for the MetaModelGenerator MAML implementation	Documented architecture features including skip connections and weight initialization Added detailed explanation of inner loop (task adaptation) process Described outer loop (meta-update) optimization process Added comprehensive argument and attribute documentation	pocs/maml_model_agnostic_meta_learning.py

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[sourcery-ai]

Hey @leonvanbokhorst - I've reviewed your changes and they look great!

Here's what I looked at during the review

• 🟡 General issues: 1 issue found
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟢 Documentation: all looks good

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[sourcery-ai]

issue: Docstring specifies default values for inner_lr and meta_lr that aren't reflected in the init signature

Either update the init signature to include these defaults or adjust the docstring to match the actual implementation.