3x+1 :
import numpy as np
import torch
import torch.nn as nn
from datetime import datetime
import matplotlib.pyplot as plt
import warnings
import dask.array as da
from dask.distributed import Client
import logging
import hashlib
import json
from flask import Flask, request, jsonify
import jwt
from functools import wraps
import os
# Set up logging
logging.basicConfig(filename='sws_log.txt', level=logging.INFO)
# Simulated user consent database (replace with real system in production)
CONSENT_DB = {} # Format: {user_id: {'consented': bool, 'timestamp': datetime}}
# Ethical Oversight Module
class EthicalOversight:
def __init__(self, consent_required=True):
self.consent_required = consent_required
self.consent_log = []
self.amplitude_threshold = None
def set_dynamic_threshold(self, amplitudes):
self.amplitude_threshold = np.mean(amplitudes) + 3 * np.std(amplitudes)
logging.info(f"Dynamic amplitude threshold set to {self.amplitude_threshold:.2f}")
def check_nudging_risk(self, amplitudes, source):
if self.amplitude_threshold is None:
self.set_dynamic_threshold(amplitudes)
if np.max(amplitudes) > self.amplitude_threshold:
warnings.warn(f"High amplitude in {source} (max: {np.max(amplitudes):.0f}); potential nudging risk")
logging.warning(f"Nudging risk in {source}: max amplitude {np.max(amplitudes):.0f}")
return False
return True
def log_consent(self, user_id, consented):
self.consent_log.append((user_id, consented, datetime.now()))
logging.info(f"User {user_id} consent: {consented}")
CONSENT_DB[user_id] = {'consented': consented, 'timestamp': datetime.now()}
class BehaviorPredictor(nn.Module):
def __init__(self):
super(BehaviorPredictor, self).__init__()
self.fc1 = nn.Linear(20, 64)
self.fc2 = nn.Linear(64, 32)
self.fc3 = nn.Linear(32, 1)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.fc3(x)
return x
class GrokArchitecture:
def __init__(self):
self.conversation_histor
2025-07-27 17:05:30