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Alexa Amundson
2026-02-03 19:49:01 -06:00
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59
codex/mirror/capability_optimizer.py
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"""
Capability Optimizer for Mirror Engine.
This script performs a simple random search over mirror engine parameters to maximise capability
defined as the harmonic mean of reach and stability. It leverages the run_mirror_engine function
from mirror_engine.py and summarises results.
"""
import numpy as np
import random
from mirror_engine import run_mirror_engine
def evaluate_params(params):
history = run_mirror_engine(iterations=params.get('iterations', 20),
target=params.get('target', 0.5),
threshold=params.get('threshold', 0.1),
step_init=params.get('step_init', 1.0),
min_step=params.get('min_step', 0.01),
max_step=params.get('max_step', 10.0))
# compute reach: fraction of aggregated values within reach_threshold of target
aggregates = np.array([rec['aggregate'] for rec in history], dtype=float)
step_sizes = np.array([rec['step_size'] for rec in history], dtype=float)
target = params.get('target', 0.5)
reach_threshold = params.get('reach_threshold', 0.1)
reach = float(np.mean(np.abs(aggregates - target) <= reach_threshold))
# compute stability: inverse of normalised step variance (lower variance implies stability)
step_std = float(np.std(step_sizes))
stability = 1.0 / (1.0 + step_std)
capability = 0.0
if (reach + stability) > 0:
capability = 2.0 * reach * stability / (reach + stability)
return {'reach': reach, 'stability': stability, 'capability': capability, 'params': params}
def random_search(num_samples=10):
"""Perform random search over parameter space to find configurations with high capability."""
results = []
for _ in range(int(num_samples)):
params = {
'iterations': random.randint(10, 30),
'target': random.uniform(0.1, 0.9),
'threshold': random.uniform(0.05, 0.2),
'step_init': random.uniform(0.1, 5.0),
'min_step': 0.01,
'max_step': 10.0,
'reach_threshold': random.uniform(0.05, 0.2)
}
res = evaluate_params(params)
results.append(res)
results_sorted = sorted(results, key=lambda x: x['capability'], reverse=True)
return results_sorted
if __name__ == "__main__":
search_results = random_search(20)
best = search_results[0] if search_results else None
if best:
print(f"Best capability: {best['capability']:.3f}")
print(f"Parameters: {best['params']}")
print(f"Reach: {best['reach']:.3f}, Stability: {best['stability']:.3f}")
else:
print("No results")