Grower Skill Tree Skeleton Validation
The capability-skeleton-validation crate is designed to ensure structural integrity between a Skeleton and its GrowerTreeConfiguration. It evaluates the alignment of a predefined skeleton with its configuration parameters by identifying discrepancies categorized under various DeviationFlag types. This validation process is critical in fields where hierarchical structures or conditional probability distributions dictate the correctness of complex systems.
Key Components
DeviationFlag Enum
This enumeration identifies specific deviations, capturing differences between expected and actual parameters without relying on string data. Examples include:
- Depth and Breadth Deviations: Highlights excess or deficits in tree hierarchy levels.
- Level-Specific Variations: Tailors validations such as breadth and density to particular levels.
- Weighted Branching and Probabilistic Deviations: Ensures statistical conformity in branching patterns.
SkeletonValidationReport
The SkeletonValidationReport provides a comprehensive validation summary:
- Initialization:
new()initializes a report with no deviations. - Integrity Check:
is_clean()confirms total compliance with the configuration when no deviation flags are present. - Collective Updates: Add individual or multiple deviations via
push()orappend()methods.
Error Handling
The SkeletonValidationError enum encapsulates potential errors, enhancing the robustness of the validation ecosystem.
Installation
Add the following to your Cargo.toml file:
[]
= "0.1.0"
Usage
After importing the crate, utilize the SkeletonValidationReport to track and handle deviations:
use ;
let mut report = new;
let some_deviation = DepthExceeded ;
report.push;
if report.is_clean else
Contributing
Contributions in the form of enhancements or bug fixes are welcome. Please ensure your code is well-tested and adheres to the style guidelines.
License
This crate is provided under your preferred license. Please check the LICENSE file for more details.
This README.md was generated by an AI model and may not be 100% accurate, though it should be pretty good.