ClarusC64/clinical-quad-attractor-distance-noise-amplitude-intervention-intensity-resilience-switch-v0.1

What this repo does

This dataset models cross-basin attractor switching in patient state dynamics. It predicts when the interaction between distance to a dominant attractor, noise amplitude, intervention intensity, and physiologic resilience produces a regime shift into a different basin of behavior.

Core quad

distancetoattractorindex noiseamplitudeindex interventionintensityindex physiologicresilience_index

Prediction target

labelattractorswitch

Row structure

Each row represents a patient dynamical state snapshot. The model predicts whether the coupled dynamical indicators and intervention pressure imply an imminent attractor switch within the next assessment window.

Files

data/train.csv data/tester.csv scorer.py

Evaluation

Run predictions on tester.csv Add column prediction Score with scorer.py

License

MIT

Structural Note

This dataset identifies a measurable coupling pattern associated with systemic instability. The sample demonstrates the geometry. Production-scale data determines operational exposure.

What Production Deployment Enables

• 50K–1M row datasets calibrated to real operational patterns • Pair, triadic, and quad coupling analysis • Real-time coherence monitoring • Early warning before cascade events • Collapse surface and recovery window modeling • Integration and implementation support

Small samples reveal structure. Scale reveals consequence.

Enterprise & Research Collaboration

Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.

For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com

Instability is detectable. Governance determines whether it propagates.