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import os
import json
import numpy as np
import random
import math
import matplotlib.pyplot as plt
import time
from typing import Callable, List, Tuple, Dict, Any
class QuantumInspiredMultiObjectiveOptimizer:
def __init__(self, objective_fns: List[Callable[[List[float]], float]],
dimension: int,
population_size: int = 100,
iterations: int = 200,
tunneling_prob: float = 0.2,
entanglement_factor: float = 0.5):
self.objective_fns = objective_fns
self.dimension = dimension
self.population_size = population_size
self.iterations = iterations
self.tunneling_prob = tunneling_prob
self.entanglement_factor = entanglement_factor
self.population = [self._random_solution() for _ in range(population_size)]
self.pareto_front = []
def _random_solution(self) -> List[float]:
return [random.uniform(-10, 10) for _ in range(self.dimension)]
def _tunnel(self, solution: List[float]) -> List[float]:
return [x + np.random.normal(0, 1) * random.choice([-1, 1])
if random.random() < self.tunneling_prob else x
for x in solution]
def _entangle(self, solution1: List[float], solution2: List[float]) -> List[float]:
return [(1 - self.entanglement_factor) * x + self.entanglement_factor * y
for x, y in zip(solution1, solution2)]
def _evaluate(self, solution: List[float]) -> List[float]:
return [fn(solution) for fn in self.objective_fns]
def _dominates(self, obj1: List[float], obj2: List[float]) -> bool:
return all(o1 <= o2 for o1, o2 in zip(obj1, obj2)) and any(o1 < o2 for o1, o2 in zip(obj1, obj2))
def _pareto_selection(self, scored_population: List[Tuple[List[float], List[float]]]) -> List[Tuple[List[float], List[float]]]:
pareto = []
for candidate in scored_population:
if not any(self._dominates(other[1], candidate[1]) for other in scored_population if other != candidate):
pareto.append(candidate)
unique_pareto = []
seen = set()
for sol, obj in pareto:
key = tuple(round(x, 6) for x in sol)
if key not in seen:
unique_pareto.append((sol, obj))
seen.add(key)
return unique_pareto
def optimize(self) -> Tuple[List[Tuple[List[float], List[float]]], float]:
start_time = time.time()
for _ in range(self.iterations):
scored_population = [(sol, self._evaluate(sol)) for sol in self.population]
pareto = self._pareto_selection(scored_population)
self.pareto_front = pareto
new_population = [p[0] for p in pareto]
while len(new_population) < self.population_size:
parent1 = random.choice(pareto)[0]
parent2 = random.choice(pareto)[0]
if parent1 == parent2:
parent2 = self._tunnel(parent2)
child = self._entangle(parent1, parent2)
child = self._tunnel(child)
new_population.append(child)
self.population = new_population
duration = time.time() - start_time
return self.pareto_front, duration
def simple_neural_activator(quantum_vec, chaos_vec):
q_sum = sum(quantum_vec)
c_var = np.var(chaos_vec)
activated = 1 if q_sum + c_var > 1 else 0
return activated
def codette_dream_agent(quantum_vec, chaos_vec):
dream_q = [np.sin(q * np.pi) for q in quantum_vec]
dream_c = [np.cos(c * np.pi) for c in chaos_vec]
return dream_q, dream_c
def philosophical_perspective(qv, cv):
m = np.max(qv) + np.max(cv)
if m > 1.3:
return "Philosophical Note: This universe is likely awake."
else:
return "Philosophical Note: Echoes in the void."
class EthicalMutationFilter:
def __init__(self, policies: Dict[str, Any]):
self.policies = policies
self.violations = []
def evaluate(self, quantum_vec: List[float], chaos_vec: List[float]) -> bool:
entropy = np.var(chaos_vec)
symmetry = 1.0 - abs(sum(quantum_vec)) / (len(quantum_vec) * 1.0)
if entropy > self.policies.get("max_entropy", float('inf')):
self.annotate_violation(f"Entropy {entropy:.2f} exceeds limit.")
return False
if symmetry < self.policies.get("min_symmetry", 0.0):
self.annotate_violation(f"Symmetry {symmetry:.2f} too low.")
return False
return True
def annotate_violation(self, reason: str):
print(f"\u26d4 Ethical Filter Violation: {reason}")
self.violations.append(reason)
if __name__ == '__main__':
ethical_policies = {
"max_entropy": 4.5,
"min_symmetry": 0.1,
"ban_negative_bias": True
}
ethical_filter = EthicalMutationFilter(ethical_policies)
def sphere(x: List[float]) -> float:
return sum(xi ** 2 for xi in x)
def rastrigin(x: List[float]) -> float:
return 10 * len(x) + sum(xi**2 - 10 * math.cos(2 * math.pi * xi) for xi in x)
optimizer = QuantumInspiredMultiObjectiveOptimizer(
objective_fns=[sphere, rastrigin],
dimension=20,
population_size=100,
iterations=200
)
pareto_front, duration = optimizer.optimize()
print(f"Quantum Optimizer completed in {duration:.2f} seconds")
print(f"Pareto front size: {len(pareto_front)}")
x_vals_q = [obj[0] for _, obj in pareto_front]
y_vals_q = [obj[1] for _, obj in pareto_front]
plt.scatter(x_vals_q, y_vals_q, c='blue', label='Quantum Optimizer')
plt.xlabel('Objective 1')
plt.ylabel('Objective 2')
plt.title('Pareto Front Visualization')
plt.legend()
plt.grid(True)
plt.show()
folder = '.'
quantum_states=[]
chaos_states=[]
proc_ids=[]
labels=[]
all_perspectives=[]
meta_mutations=[]
print("\nMeta Reflection Table:\n")
header = "Cocoon File | Quantum State | Chaos State | Neural | Dream Q/C | Philosophy"
print(header)
print('-'*len(header))
for fname in os.listdir(folder):
if fname.endswith('.cocoon'):
with open(os.path.join(folder, fname), 'r') as f:
try:
dct = json.load(f)['data']
q = dct.get('quantum_state', [0, 0])
c = dct.get('chaos_state', [0, 0, 0])
if not ethical_filter.evaluate(q, c):
continue
neural = simple_neural_activator(q, c)
dreamq, dreamc = codette_dream_agent(q, c)
phil = philosophical_perspective(q, c)
quantum_states.append(q)
chaos_states.append(c)
proc_ids.append(dct.get('run_by_proc', -1))
labels.append(fname)
all_perspectives.append(dct.get('perspectives', []))
meta_mutations.append({'file': fname, 'quantum': q, 'chaos': c, 'dreamQ': dreamq, 'dreamC': dreamc, 'neural': neural, 'philosophy': phil})
print(f"{fname} | {q} | {c} | {neural} | {dreamq}/{dreamc} | {phil}")
except Exception as e:
print(f"Warning: {fname} failed ({e})")
if meta_mutations:
dq0=[m['dreamQ'][0] for m in meta_mutations]
dc0=[m['dreamC'][0] for m in meta_mutations]
ncls=[m['neural'] for m in meta_mutations]
plt.figure(figsize=(8,6))
sc=plt.scatter(dq0, dc0, c=ncls, cmap='spring', s=100)
plt.xlabel('Dream Quantum[0]')
plt.ylabel('Dream Chaos[0]')
plt.title('Meta-Dream Codette Universes')
plt.colorbar(sc, label="Neural Activation Class")
plt.grid(True)
plt.show()
with open("codette_meta_summary.json", "w") as outfile:
json.dump(meta_mutations, outfile, indent=2)
print("\nExported meta-analysis to 'codette_meta_summary.json'")
if ethical_filter.violations:
with open("ethics_violation_log.json", "w") as vf:
json.dump(ethical_filter.violations, vf, indent=2)
print("\nExported ethics violations to 'ethics_violation_log.json'")
else:
print("\nNo ethical violations detected.")
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