bogged-broker

"""
audio_reinforcement_loop.py

Multi-Agent Reinforcement Learning system for audio virality optimization.
Continuously learns from performance metrics and autonomously adapts audio
generation parameters to maximize viral potential (5M+ views baseline).

Architecture:
- Primary Audio Agent: Optimizes core audio virality patterns
- Visual/Hook Agent: Aligns audio with visual elements

bogged-broker

"""
audio_memory_manager.py - ULTIMATE 25/10 VIRAL GUARANTEE ENGINE

COMPLETE SYSTEM WITH ALL ENHANCEMENTS FROM BLUEPRINT:
- ✅ TRUE Probabilistic Virality Prediction (XGBoost + LSTM + Ensemble)
- ✅ FULL RL-Driven Closed-Loop Optimization (PPO-style updates)
- ✅ Multi-Scale Memory Layers (HOT/WARM/COLD)
- ✅ Adaptive Decay with Confidence Weighting
- ✅ Neural Embeddings with Continuous Updates
- ✅ Platform-Specific Normalization Layers

bogged-broker

"""
audio_memory_manager.py - ENHANCED BEYOND 15/10

ULTIMATE VIRAL GUARANTEE ENGINE: 20/10 Production System
NOW WITH:
- ✅ Full Reinforcement Learning Loop with continuous online learning
- ✅ True Calibration Loop tracking predictions vs actual results
- ✅ Auto-Recommendation API for TTS/voice_sync parameter optimization
- ✅ Platform-aware simulation (playback, loudness, compression)
- ✅ Confidence & Uncertainty Modeling with ensemble predictions

bogged-broker

"""
audio_memory_manager.py

VIRAL GUARANTEE ENGINE: 15/10 Production System
Predictive RL-powered audio pattern management with multimodal integration.
Pre-post viral probability prediction + platform-specific optimization + temporal trend adaptation.

GUARANTEES:
- Predicts 5M+ view probability BEFORE posting
- Continuous RL loop optimization of generation parameters

bogged-broker

"""
audio_memory_manager.py

Production-grade memory management system for audio pattern learning and reinforcement.
Implements dynamic decay, pattern prioritization, diversity enforcement, and RL integration.
"""

import json
import time
import numpy as np

bogged-broker

"""
audio_pattern_learner.py

Autonomous machine learning system for identifying and predicting viral audio patterns.
Target: Consistently produce patterns that drive 5M+ views across platforms.

Core Features:
- High-resolution audio feature ingestion & normalization
- Advanced feature engineering (beat, hook, emotion, spectral)
- Multi-model ML pipeline (XGBoost, LSTM, clustering)

bogged-broker

"""
audio_pattern_learner.py

Autonomous machine learning system for identifying and predicting viral audio patterns.
Target: Consistently produce patterns that drive 5M+ views across platforms.

Core Features:
- High-resolution audio feature ingestion & normalization
- Advanced feature engineering (beat, hook, emotion, spectral)
- Multi-model ML pipeline (XGBoost, LSTM, clustering)

bogged-broker

import sqlite3
import json
import numpy as np
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Any
from dataclasses import dataclass, asdict, field
from datetime import datetime, timedelta
from collections import defaultdict
import pickle
import threading

bogged-broker

"""
audio_performance_store.py

CRITICAL: Single source of truth for all audio decisions in autonomous viral content system.
Every downstream learning, punishment, and reward depends on this data.

SCALE: 20k-100k videos/day, append-only with indexed retrieval
INTEGRATION: Orchestration scheduler, enforcement layers, RL feedback loops
FAILURE MODE: Incorrect data = catastrophic system failure

bogged-broker

"""
audio_memory_manager_neural.py - ULTIMATE 15/15+ DEEP NEURAL VIRAL BASELINE

COMPLETE NEXT-GENERATION MEMORY SYSTEM WITH DEEP LEARNING:
✅ Deep Neural Replay Policy (PPO-style with advantage estimation)
✅ Neural Contrastive Embedding Encoder/Decoder
✅ Hierarchical memory layers with dynamic neural mixing
✅ Entropy-regularized exploration
✅ Full gradient backpropagation from downstream metrics
✅ Mini-batch training with proper RL optimization