Create components/ethics.py

components/ethics.py

@@ -0,0 +1,220 @@
+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.")