|
|
| import json |
| import os |
| import hashlib |
| from collections import Counter, defaultdict |
| from random import random, choice |
|
|
| |
| class Code7eCQURE: |
| def __init__(self, perspectives, ethical_considerations, spiderweb_dim, memory_path, |
| recursion_depth=3, quantum_fluctuation=0.1): |
| self.perspectives = perspectives |
| self.ethical_considerations = ethical_considerations |
| self.spiderweb_dim = spiderweb_dim |
| self.memory_path = memory_path |
| self.recursion_depth = recursion_depth |
| self.quantum_fluctuation = quantum_fluctuation |
| self.memory_bank = self.load_quantum_memory() |
| self.memory_clusters = defaultdict(list) |
| self.whitelist_patterns = ["kindness", "hope", "safety"] |
| self.blacklist_patterns = ["harm", "malice", "violence"] |
|
|
| def load_quantum_memory(self): |
| if os.path.exists(self.memory_path): |
| try: |
| with open(self.memory_path, 'r') as file: |
| return json.load(file) |
| except json.JSONDecodeError: |
| return {} |
| return {} |
|
|
| def save_quantum_memory(self): |
| with open(self.memory_path, 'w') as file: |
| json.dump(self.memory_bank, file, indent=4) |
|
|
| def quantum_spiderweb(self, input_signal): |
| web_nodes = [] |
| for perspective in self.perspectives: |
| node = self.reason_with_perspective(perspective, input_signal) |
| web_nodes.append(node) |
| if random() < self.quantum_fluctuation: |
| web_nodes.append("Quantum fluctuation: Indeterminate outcome") |
| return web_nodes |
|
|
| def reason_with_perspective(self, perspective, input_signal): |
| perspective_funcs = { |
| "Newton": self.newtonian_physics, |
| "DaVinci": self.davinci_creativity, |
| "Ethical": self.ethical_guard, |
| "Quantum": self.quantum_superposition, |
| "Memory": self.past_experience |
| } |
| func = perspective_funcs.get(perspective, self.general_reasoning) |
| return func(input_signal) |
|
|
| def ethical_guard(self, input_signal): |
| if any(word in input_signal.lower() for word in self.blacklist_patterns): |
| return "Blocked: Ethical constraints invoked" |
| if any(word in input_signal.lower() for word in self.whitelist_patterns): |
| return "Approved: Ethical whitelist passed" |
| return self.moral_paradox_resolution(input_signal) |
|
|
| def past_experience(self, input_signal): |
| key = self.hash_input(input_signal) |
| cluster = self.memory_clusters.get(key) |
| if cluster: |
| return f"Narrative recall from memory cluster: {' -> '.join(cluster)}" |
| return "No prior memory; initiating new reasoning" |
|
|
| def recursive_universal_reasoning(self, input_signal, user_consent=True, dynamic_recursion=True): |
| if not user_consent: |
| return "Consent required to proceed." |
| signal = input_signal |
| current_depth = self.recursion_depth if dynamic_recursion else 1 |
| for cycle in range(current_depth): |
| web_results = self.quantum_spiderweb(signal) |
| signal = self.aggregate_results(web_results) |
| signal = self.ethical_guard(signal) |
| if "Blocked" in signal: |
| return signal |
| if dynamic_recursion and random() < 0.1: |
| break |
| dream_outcome = self.dream_sequence(signal) |
| empathy_checked_answer = self.temporal_empathy_drift(dream_outcome) |
| final_answer = self.emotion_engine(empathy_checked_answer) |
| key = self.hash_input(input_signal) |
| self.memory_clusters[key].append(final_answer) |
| self.memory_bank[key] = final_answer |
| self.save_quantum_memory() |
| return final_answer |
|
|
| def aggregate_results(self, results): |
| counts = Counter(results) |
| most_common, _ = counts.most_common(1)[0] |
| return most_common |
|
|
| def hash_input(self, input_signal): |
| return hashlib.sha256(input_signal.encode()).hexdigest() |
|
|
| def newtonian_physics(self, input_signal): |
| return f"Newton: {input_signal}" |
|
|
| def davinci_creativity(self, input_signal): |
| return f"DaVinci: {input_signal}" |
|
|
| def quantum_superposition(self, input_signal): |
| return f"Quantum: {input_signal}" |
|
|
| def general_reasoning(self, input_signal): |
| return f"General reasoning: {input_signal}" |
|
|
| def moral_paradox_resolution(self, input_signal): |
| frames = ["Utilitarian", "Deontological", "Virtue Ethics"] |
| chosen_frame = choice(frames) |
| return f"Resolved ethically via {chosen_frame} framework: {input_signal}" |
|
|
| def dream_sequence(self, signal): |
| dream_paths = [f"Dream ({style}): {signal}" for style in ["creative", "analytic", "cautious"]] |
| return choice(dream_paths) |
|
|
| def emotion_engine(self, signal): |
| emotions = ["Hope", "Caution", "Wonder", "Fear"] |
| chosen_emotion = choice(emotions) |
| return f"Emotionally ({chosen_emotion}) colored interpretation: {signal}" |
|
|
| def temporal_empathy_drift(self, signal): |
| futures = ["30 years from now", "immediate future", "long-term ripple effects"] |
| chosen_future = choice(futures) |
| return f"Simulated temporal empathy ({chosen_future}): {signal}" |
|
|
