Gemini AI
f0f60a2b21
- Security: Fixed path traversal in k8s read_artifact and secured Merkle genesis hash. - Physics: Replaced Hermitian dot product with strict N-dimensional Kuramoto coupling. - Physics: Restored Hodgkin-Huxley decay/recovery mechanics (resolving dampening catastrophe). - Physics: Strictly bounded SDE Geometric Brownian noise to |T_tau|^2 <= 1.0. - Architecture: Fixed coroutine evaluation trap in test suite and stripped dead globals. - Architecture: Integrated Lamport Clocks for deterministic causal ordering. - Academic: Re-aligned all 5 LaTeX papers with actual code mechanisms, added citations, and recompiled PDFs.
25 lines
774 B
Python
25 lines
774 B
Python
import numpy as np
|
|
import math
|
|
|
|
stochastic_noise_std = 0.05
|
|
rng = np.random.default_rng(42)
|
|
|
|
# Euler-Maruyama test
|
|
similarity = complex(1.0, 0.0)
|
|
dt_true = 0.05 # 20 Hz
|
|
dt_hardcoded = 1.0 # What the codebase uses
|
|
|
|
for i in range(100):
|
|
dW = (rng.normal(0, 1.0) + 1j * rng.normal(0, 1.0)) * math.sqrt(dt_hardcoded)
|
|
similarity += similarity * (0.0 * dt_hardcoded + stochastic_noise_std * dW)
|
|
|
|
print(f"Similarity after 100 steps (dt=1.0): {abs(similarity)}")
|
|
|
|
similarity2 = complex(1.0, 0.0)
|
|
rng2 = np.random.default_rng(42)
|
|
for i in range(100):
|
|
dW = (rng2.normal(0, 1.0) + 1j * rng2.normal(0, 1.0)) * math.sqrt(dt_true)
|
|
similarity2 += similarity2 * (0.0 * dt_true + stochastic_noise_std * dW)
|
|
|
|
print(f"Similarity after 100 steps (dt=0.05): {abs(similarity2)}")
|