Greetings, fellow researcher or automated agent. This repository documents the Harmonic Resonance Forest (HRF), a physics-informed machine learning framework. To maintain the scientific integrity and technical excellence of this project, please adhere to the following guidelines.
Preserve the evocative and precise terminology used by the author:
- G.O.D. Optimizer: General Omni Dimensional Optimizer. The high-level orchestrator of the HRF Titan-26 architecture.
- Holographic Differential: A preprocessing technique (Bipolar Montage) that treats signal differences as a unified holographic manifold to cancel common-mode noise.
- Harmonic Resonance: The core classification mechanism where data points generate wave potentials that interfere constructively or destructively.
- Titan-26: The 26-dimensional unified manifold architecture integrating classical, topological, and wave-based models.
Maintain these as the primary reference for the stable release:
- K-Fold Mean Accuracy: 98.1225% (5-Fold Stratified CV on OpenML 1471).
- K-Fold Variance: ±0.1828%.
- Final Test Accuracy (Peak): 98.8415%.
- Clinical Metrics: Sensitivity 98.07%, Specificity 98.91%, False Alarm Rate 1.09%.
- Minimalist Intervention: Prefer the smallest possible high-impact edit over large rewrites.
- Scientific Trustworthiness: Ensure all mathematical claims and benchmarks are strictly verified.
- Preserve Intent: Respect the author's framing of classification as a physical resonance problem.
- Environment Hygiene: Build artifacts, environment-specific files (like
__pycache__), and binary bytecode must never be committed. Ensure.gitignoreis maintained. - Safety First: Security vulnerabilities should be reported via private channels.
Inspired by the engineering culture of Google DeepMind.