Zentrube Formula for Entropy Drift and Symbolic Alignment
Open Research Initiative
Zentrube formula is a revolutionary, time-aware entropy signal—rising as rupture forms and falling as recovery returns—bounded, comparable, and ready to test.
Check the White Papers & Code
Read the White Paper (v1.8) →
https://github.com/OMPSHUNYAYA/Entropy-to-Zentrube/releases/tag/v1.8
Browse the GitHub Repository → https://github.com/OMPSHUNYAYA/Entropy-to-Zentrube
Classical Entropy vs Zentrube — Six Proof Points
| Classical entropy | Distribution snapshot, Unbounded, Not time-aware. |
| Zentrube | Time-aware via exp(−λt), Compact via log(Var+1), Bounded & Comparable. |
- 🌪 Hurricanes: 20–30% earlier drift vs category thresholds
- ❤️ ECG: 15–25% earlier anomaly visibility with fewer false positives
- 🔐 Cybersecurity: earlier DoS onset with clear rupture/recovery polarity
- 📈 Insurance: ~20–30% tail moderation via entropy-tempered valuation
- 📡 Telecom: 150–200 ms earlier jitter anticipation
- ❄ Snowfall: drift flagged 7–14 days before major accumulations
All results are observation-only and reproducible from public datasets; see the v1.8 white paper and repo.
What the Zentrube Formula Is (10 Seconds)
Zentrube reframes entropy as readiness—a compact, interpretable number that moves with the system:
- Time-aware: earlier values fade via exp(−λt)
- Bounded & comparable: log(Var + 1) keeps scale in check
- Cross-domain: plug in any signal, compare windows cleanly
Canonical formula (plain text)
Zentrubeₜ = log(Var(x₀:ₜ) + 1) × exp(−λt)
Weighted (multi-input) variation
Zentrubeᵤ = log( Σᵢ[ wᵢ × Var(xᵢ₀:ᵤ) ] + 1 ) × exp(−λu)
Try It Yourself
import numpy as np, math
def zentrube(x, lam=0.02):
# Zentrubeₜ = log(Var(x₀:ₜ) + 1) × exp(−λt)
a = np.asarray(x, float)
a = a[np.isfinite(a)]
t = a.size
return 0.0 if t == 0 else math.log(np.var(a) + 1.0) * math.exp(-lam * t)
print(zentrube([1,2,3,4,5,6], lam=0.02)) # ≈ 1.2109
How to Read the Formula
- Rising value → instability forming (rupture)
- Falling value → stabilization (recovery)
- exp(−λt) gives a practical memory horizon (≈ 1/λ)
Explore further
- Browse the code and examples: https://github.com/OMPSHUNYAYA/Entropy-to-Zentrube
- Read the detailed methods (v1.8): https://github.com/OMPSHUNYAYA/Entropy-to-Zentrube/releases/tag/v1.8
- Explore symbolic Q&A → real-world answers:
Symbolic Q & A
The Zentrube formula is part of the Shunyaya Framework — an open research effort redefining entropy, drift, and alignment across science, AI, medicine, and climate.
Scope & license
Observation-only research; not an operational prediction system.
© The Authors of Shunyaya Framework and Zentrube Formula
— CC BY-NC 4.0.
Explore New Canonical Entropy Formula
ZEOZO-Core
https://github.com/OMPSHUNYAYA/ZEOZO/blob/main/GETTING_STARTED.md
y = m x + c, redefined
SHUNYAYA 