learn_triggers.py

#!/usr/bin/env python3
"""
🧠 RECURSIVE OCEAN - LEARN TRIGGERS
==================================

This shows all the ways RECURSIVE OCEAN can learn and improve itself:
"""

class OceanLearnTriggers:
    """All the ways RECURSIVE OCEAN can learn and improve."""
    
    def __init__(self):
        self.triggers = {
            # AUTOMATIC TRIGGERS
            "automatic": {
                "every_game": "Learns from every game played",
                "every_move": "Analyzes every move for patterns",
                "every_position": "Evaluates every position for improvements",
                "every_loss": "Analyzes losses to improve strategies",
                "every_win": "Analyzes wins to reinforce good strategies",
                "time_based": "Learns every 5 minutes automatically",
                "performance_drop": "Learns when performance decreases",
                "new_patterns": "Learns when new patterns are detected"
            },
            
            # MANUAL TRIGGERS
            "manual": {
                "button_click": "User clicks 'Trigger Improvement' button",
                "api_call": "API call to /api/recursive_improve",
                "command_line": "Command line trigger",
                "webhook": "External webhook trigger",
                "scheduled": "Scheduled learning sessions"
            },
            
            # DATA TRIGGERS
            "data": {
                "new_training_data": "When new chess games are added",
                "new_evaluations": "When new position evaluations are added",
                "new_strategies": "When new strategies are discovered",
                "pattern_recognition": "When new patterns are identified",
                "grandmaster_games": "When analyzing grandmaster games"
            },
            
            # PERFORMANCE TRIGGERS
            "performance": {
                "rating_drop": "When chess rating drops",
                "loss_streak": "When losing multiple games",
                "weak_moves": "When making weak moves",
                "time_pressure": "When under time pressure",
                "endgame_weakness": "When endgame performance is poor"
            },
            
            # EVOLUTION TRIGGERS
            "evolution": {
                "generation_complete": "When a generation is complete",
                "mutation_ready": "When ready for genetic mutations",
                "crossover_ready": "When ready for strategy crossover",
                "selection_ready": "When ready for strategy selection",
                "specialization_ready": "When ready for specialization"
            }
        }
    
    def show_all_triggers(self):
        """Show all learning triggers."""
        print("🧠 RECURSIVE OCEAN - ALL LEARN TRIGGERS")
        print("=" * 50)
        
        for category, triggers in self.triggers.items():
            print(f"\n📂 {category.upper()} TRIGGERS:")
            for trigger, description in triggers.items():
                print(f"  🔥 {trigger}: {description}")
    
    def get_trigger_examples(self):
        """Get specific examples of how triggers work."""
        examples = {
            "automatic_learning": {
                "description": "OCEAN learns automatically every 5 minutes",
                "code": """
# Automatic learning trigger
if time.time() - last_learning_time > 300:  # 5 minutes
    ocean.trigger_recursive_improvement("automatic")
    last_learning_time = time.time()
                """,
                "result": "OCEAN analyzes recent games and improves strategies"
            },
            
            "manual_learning": {
                "description": "User manually triggers learning",
                "code": """
# Manual learning trigger
POST /api/recursive_improve
{
    "type": "strategy",
    "depth": 3,
    "focus": "endgame"
}
                """,
                "result": "OCEAN focuses on endgame strategy improvement"
            },
            
            "data_learning": {
                "description": "Learning from new training data",
                "code": """
# Data learning trigger
if new_chess_games_available():
    ocean.load_new_training_data()
    ocean.trigger_recursive_improvement("data")
                """,
                "result": "OCEAN learns from new chess games"
            },
            
            "performance_learning": {
                "description": "Learning from performance issues",
                "code": """
# Performance learning trigger
if chess_rating_dropped():
    ocean.analyze_recent_losses()
    ocean.trigger_recursive_improvement("performance")
                """,
                "result": "OCEAN improves weak areas"
            }
        }
        
        return examples
    
    def show_learning_process(self):
        """Show how the learning process works."""
        print("\n🧬 RECURSIVE OCEAN - LEARNING PROCESS")
        print("=" * 40)
        
        steps = [
            "1. 🔍 TRIGGER DETECTED - Learning event occurs",
            "2. 📊 DATA COLLECTION - Gather relevant data",
            "3. 🧠 ANALYSIS - Analyze patterns and weaknesses",
            "4. 🧬 EVOLUTION - Generate improvements",
            "5. 🧪 TESTING - Test new strategies",
            "6. 📈 EVALUATION - Evaluate improvements",
            "7. 🔄 INTEGRATION - Integrate successful changes",
            "8. 📚 MEMORY - Store new knowledge",
            "9. 🚀 DEPLOYMENT - Deploy improved version",
            "10. 📊 MONITORING - Monitor new performance"
        ]
        
        for step in steps:
            print(f"  {step}")
    
    def show_learning_types(self):
        """Show different types of learning."""
        print("\n🎓 RECURSIVE OCEAN - LEARNING TYPES")
        print("=" * 35)
        
        learning_types = {
            "supervised": "Learns from labeled training data (chess games with results)",
            "unsupervised": "Discovers patterns in unlabeled data",
            "reinforcement": "Learns from rewards and punishments (wins/losses)",
            "transfer": "Applies knowledge from one domain to another",
            "meta": "Learns how to learn better",
            "recursive": "Learns by analyzing its own learning process",
            "evolutionary": "Learns through genetic algorithms and mutations",
            "neural": "Learns through neural network training"
        }
        
        for learning_type, description in learning_types.items():
            print(f"  🧠 {learning_type}: {description}")
    
    def show_improvement_areas(self):
        """Show what areas OCEAN can improve."""
        print("\n🎯 RECURSIVE OCEAN - IMPROVEMENT AREAS")
        print("=" * 40)
        
        areas = {
            "position_evaluation": "How well it evaluates chess positions",
            "move_generation": "How it generates candidate moves",
            "search_depth": "How deep it searches the game tree",
            "opening_knowledge": "Knowledge of chess openings",
            "middlegame_strategy": "Middlegame strategic understanding",
            "endgame_technique": "Endgame technical skills",
            "tactical_vision": "Ability to see tactical patterns",
            "time_management": "How it manages time in games",
            "pattern_recognition": "Recognition of chess patterns",
            "strategy_planning": "Long-term strategic planning"
        }
        
        for area, description in areas.items():
            print(f"  🎯 {area}: {description}")


def main():
    """Main entry point."""
    triggers = OceanLearnTriggers()
    
    # Show all triggers
    triggers.show_all_triggers()
    
    # Show learning process
    triggers.show_learning_process()
    
    # Show learning types
    triggers.show_learning_types()
    
    # Show improvement areas
    triggers.show_improvement_areas()
    
    # Show examples
    print("\n💡 LEARNING TRIGGER EXAMPLES")
    print("=" * 30)
    
    examples = triggers.get_trigger_examples()
    for example_name, example_data in examples.items():
        print(f"\n🔥 {example_name.upper()}:")
        print(f"  Description: {example_data['description']}")
        print(f"  Code: {example_data['code']}")
        print(f"  Result: {example_data['result']}")


if __name__ == "__main__":
    main()