File size: 3,148 Bytes
29b620f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | """Test rapide et léger du système MLE."""
import sys
sys.path.insert(0, '.')
import numpy as np
import time
from mle.mle_system import MLESystem
from mle.memory import VECTOR_SIZE
np.random.seed(42)
print("="*60)
print("MLE QUICK TEST")
print("="*60)
mle = MLESystem(memory_capacity=500, online_learning=True, temperature=0.5)
# Phase 1: Crée des vecteurs reliés (concepts)
print("\n--- Creating related concepts ---")
n_concepts = 3
n_variants = 3
concepts = []
for c in range(n_concepts):
base_active = np.random.choice(VECTOR_SIZE, size=200, replace=False)
base = np.zeros(VECTOR_SIZE, dtype=np.uint8)
base[base_active] = 1
variants = []
for v in range(n_variants):
variant = base.copy()
# Ajoute du bruit
to_flip = np.random.choice(VECTOR_SIZE, size=30, replace=False)
variant[to_flip] = 1 - variant[to_flip]
variants.append(variant)
concepts.append((base, variants))
# Traite les variants
for variant in variants:
t0 = time.time()
result = mle.process(variant)
t1 = time.time()
if result.energy_trajectory:
print(f" Concept {c}, variant: energy={result.energy_trajectory[-1]:.0f}, "
f"conv={result.converged}, time={(t1-t0)*1000:.0f}ms")
print(f"\nMemory after concepts: {mle.memory.size}")
# Phase 2: Test généralisation avec requêtes bruitées
print("\n--- Testing generalization (noisy queries) ---")
for c, (base, _) in enumerate(concepts):
query = base.copy()
# Plus de bruit
to_flip = np.random.choice(VECTOR_SIZE, size=80, replace=False)
query[to_flip] = 1 - query[to_flip]
result = mle.process(query)
if result.energy_trajectory:
print(f" Query concept {c}: energy={result.energy_trajectory[-1]:.0f}, "
f"conv={result.converged}")
# Phase 3: Test stabilité
print("\n--- Testing stability (continuous stream) ---")
energies = []
for i in range(20):
c = i % n_concepts
_, variants = concepts[c]
vec = variants[i % len(variants)]
result = mle.process(vec)
if result.energy_trajectory:
energies.append(result.energy_trajectory[-1])
if i % 5 == 0:
print(f" [{i:2d}] memory={mle.memory.size}, n_assoc={len(mle.energy.associations)}")
if len(energies) >= 10:
early = np.mean(energies[:5])
late = np.mean(energies[-5:])
print(f"\n Early energy: {early:.0f}")
print(f" Late energy: {late:.0f}")
if late < early * 0.95:
print(" ✓ System is LEARNING")
else:
print(" ~ System is STABLE")
# Phase 4: Test binding
print("\n--- Testing binding ---")
a = np.zeros(VECTOR_SIZE, dtype=np.uint8)
a[np.random.choice(VECTOR_SIZE, size=200, replace=False)] = 1
b = np.zeros(VECTOR_SIZE, dtype=np.uint8)
b[np.random.choice(VECTOR_SIZE, size=200, replace=False)] = 1
bound = mle.binder.bind(a, b)
recovered = mle.binder.unbind(bound, a)
sim = np.mean(recovered == b)
print(f" Binding similarity: {sim:.3f}")
# Résumé
print("\n" + "="*60)
print("SUMMARY")
print("="*60)
mle.print_summary()
print("\n✓ Quick test complete!") |