| 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.") | |