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.")