Validates triadic color systems using predictive loops and active inference to ensure compositional coherence.
The Triadic Color Validation skill provides a robust framework for training and evaluating 3-stream color systems through self-validation loops. By implementing active inference principles, it allows developers to predict expected color outputs as efference copies and compare them against real-world observations. This skill is essential for maintaining GF(3) naturality and mathematical consistency in topological structures, providing detailed metrics on accuracy and surprise to ensure your models meet high-precision thresholds.
Key Features
01Predictive color output estimation via efference copy
022 GitHub stars
03Support for ternary generators like splitmix and xoroshiro
04Automated accuracy and surprise metric calculation
05JSON-based logging for comprehensive validation audits
06Active inference integration for advanced self-modeling
Use Cases
01Validating GF(3) naturality within topological AI structures
02Testing mathematical consistency in triadic data streams
03Evaluating active inference model performance in color space
