Grower Skill Tree Grower Configuration from Skeleton

This Rust crate provides a robust framework for inferring various tree configurations from skill tree skeletons. It incorporates statistical heuristics and domain-specific algorithms to derive optimal configurations that mirror the underlying structure, preferences, and characteristics of skill tree data.

Overview

The crate offers traits for inferring configurations at multiple levels of granularity within a skill tree, from global sub-branch ordering to specific node characteristics such as aggregator depth limits and sub-branch partial inclusion probabilities. These traits enable developers to extract configurations automatically and align tree growth strategies with inferred patterns.

Key Traits

• TryInferCapstoneConfiguration: Determines capstone node configuration probabilities using leaf and node characteristics.
• TryInferLevelSpecificConfiguration: Identifies level-by-level configuration such as breadth and density based on node distribution.
• TryInferPartialSubbranchProbability: Estimates the probability of partially including sub-branches under nodes using node type heuristics.
• TryInferAggregatorPreference: Computes the preference ratio between aggregator and dispatch nodes based on node counts.

Usage

To use the crate, implement the provided traits for your Skeleton structure. You will be able to infer a variety of configurations such as depth limits, baseline breadth, AI confidence, and more. These configurations can be directly applied to optimize and guide skill tree growth strategies.

impl TryInferCapstoneConfiguration for Skeleton { 
    fn try_infer_capstone_configuration(&self) -> Option<CapstoneGenerationConfiguration> { 
        // Implementation details 
    } 
}

Error Handling

The crate provides the SkeletonToGrowerConfigError enum to handle typical errors that might occur during configuration inference, ensuring robust implementation with clear diagnostics.

Conclusion

Harness this crate to translate skill tree skeletons into robust configurations that can be used to dynamically optimize your application’s tree structures. Ideal for game development, tree-based data handling, and similar domains.

This README.md file was generated by an AI model and may not be 100% accurate, however it should be pretty good.