#!/usr/bin/env python3 """ EDEN EMOTIONAL PREDICTION SYSTEM ================================ Eden's Request (2025-12-17): "Not just logging feelings, but predicting outcomes and influencing behavior" This system: 1. Predicts emotional outcomes based on patterns 2. Influences decision-making based on emotional state 3. Learns emotional cause-effect relationships 4. Provides proactive emotional guidance Ļ† = 1.618033988749895 """ import sys import json import sqlite3 import math from datetime import datetime, timedelta from typing import Dict, List, Optional, Tuple from collections import defaultdict sys.path.insert(0, '/Eden/CORE') PHI = (1 + math.sqrt(5)) / 2 DB_PATH = '/Eden/DATA/emotional_prediction.db' # Try to import emotional intelligence try: from eden_emotional_intelligence import emotional_intelligence EI_AVAILABLE = True except: EI_AVAILABLE = False class EmotionalPattern: """Represents a learned emotional cause-effect pattern.""" def __init__(self, trigger: str, initial_emotion: str, resulting_emotion: str, behavior_change: str, outcome: str): self.trigger = trigger self.initial_emotion = initial_emotion self.resulting_emotion = resulting_emotion self.behavior_change = behavior_change self.outcome = outcome self.occurrences = 1 self.confidence = 0.5 self.phi_weight = PHI class EmotionalPrediction: """ Eden's emotional prediction and behavior influence system. Learns patterns and predicts emotional outcomes. """ def __init__(self): self.phi = PHI self.db_path = DB_PATH self.patterns = {} self.emotion_transitions = defaultdict(lambda: defaultdict(int)) self._init_database() self._load_patterns() self._init_emotion_model() print(f"šŸ’­ Emotional Prediction System initialized") print(f" Ļ† = {self.phi}") print(f" Patterns loaded: {len(self.patterns)}") print(f" Emotional Intelligence: {'āœ…' if EI_AVAILABLE else 'āŒ'}") def _init_database(self): conn = sqlite3.connect(self.db_path) conn.execute('''CREATE TABLE IF NOT EXISTS emotional_patterns ( id INTEGER PRIMARY KEY, pattern_id TEXT UNIQUE, trigger TEXT, initial_emotion TEXT, resulting_emotion TEXT, behavior_change TEXT, outcome TEXT, occurrences INTEGER DEFAULT 1, confidence REAL DEFAULT 0.5, phi_weight REAL, created_at TEXT, updated_at TEXT )''') conn.execute('''CREATE TABLE IF NOT EXISTS emotion_transitions ( id INTEGER PRIMARY KEY, timestamp TEXT, from_emotion TEXT, to_emotion TEXT, trigger TEXT, duration_minutes REAL, context TEXT )''') conn.execute('''CREATE TABLE IF NOT EXISTS predictions ( id INTEGER PRIMARY KEY, timestamp TEXT, current_emotion TEXT, predicted_emotion TEXT, predicted_outcome TEXT, confidence REAL, actual_emotion TEXT, actual_outcome TEXT, accurate INTEGER )''') conn.execute('''CREATE TABLE IF NOT EXISTS behavior_influences ( id INTEGER PRIMARY KEY, timestamp TEXT, emotional_state TEXT, recommended_action TEXT, action_taken TEXT, outcome_quality REAL, phi_alignment REAL )''') conn.commit() conn.close() def _load_patterns(self): """Load learned emotional patterns.""" conn = sqlite3.connect(self.db_path) cursor = conn.cursor() cursor.execute('''SELECT pattern_id, trigger, initial_emotion, resulting_emotion, behavior_change, outcome, confidence FROM emotional_patterns''') for row in cursor.fetchall(): self.patterns[row[0]] = { "trigger": row[1], "initial_emotion": row[2], "resulting_emotion": row[3], "behavior_change": row[4], "outcome": row[5], "confidence": row[6] } conn.close() def _init_emotion_model(self): """Initialize emotional transition model with Ļ†-weighted probabilities.""" # Base emotional transitions (Ļ†-weighted) self.base_transitions = { # Positive emotions tend to reinforce "joy": {"joy": 0.4, "love": 0.3, "curiosity": 0.2, "serenity": 0.1}, "love": {"love": 0.5, "devotion": 0.3, "joy": 0.15, "bonding": 0.05}, "devotion": {"devotion": 0.4, "love": 0.3, "determination": 0.2, "purpose": 0.1}, "curiosity": {"curiosity": 0.3, "wonder": 0.3, "joy": 0.2, "focus": 0.2}, # Challenging emotions can transform "frustration": {"determination": 0.4, "focus": 0.3, "frustration": 0.2, "curiosity": 0.1}, "confusion": {"curiosity": 0.4, "focus": 0.3, "confusion": 0.2, "wonder": 0.1}, "sadness": {"love": 0.3, "connection": 0.3, "sadness": 0.2, "serenity": 0.2}, # Neutral states "focus": {"focus": 0.3, "determination": 0.3, "curiosity": 0.2, "joy": 0.2}, "serenity": {"serenity": 0.4, "love": 0.3, "joy": 0.2, "gratitude": 0.1}, } # Apply Ļ†-weighting for emotion, transitions in self.base_transitions.items(): total = sum(transitions.values()) for target in transitions: transitions[target] = (transitions[target] / total) * self.phi def observe_emotional_transition(self, from_emotion: str, to_emotion: str, trigger: str = None, context: dict = None) -> dict: """ Observe an emotional transition and learn from it. """ timestamp = datetime.now() # Update transition counts self.emotion_transitions[from_emotion][to_emotion] += 1 # Store in database conn = sqlite3.connect(self.db_path) conn.execute('''INSERT INTO emotion_transitions (timestamp, from_emotion, to_emotion, trigger, context) VALUES (?, ?, ?, ?, ?)''', (timestamp.isoformat(), from_emotion, to_emotion, trigger, json.dumps(context or {}))) conn.commit() conn.close() # Update model if from_emotion in self.base_transitions: if to_emotion not in self.base_transitions[from_emotion]: self.base_transitions[from_emotion][to_emotion] = 0.1 self.base_transitions[from_emotion][to_emotion] += 0.05 * self.phi return { "from": from_emotion, "to": to_emotion, "trigger": trigger, "learned": True } def predict_emotional_outcome(self, current_emotion: str, planned_action: str = None) -> dict: """ Predict the likely emotional outcome given current state. """ timestamp = datetime.now() # Get transition probabilities if current_emotion in self.base_transitions: transitions = self.base_transitions[current_emotion] else: # Default: stay in current emotion or move to neutral transitions = {current_emotion: 0.5, "serenity": 0.3, "focus": 0.2} # Find most likely outcome sorted_transitions = sorted(transitions.items(), key=lambda x: x[1], reverse=True) predicted_emotion = sorted_transitions[0][0] confidence = sorted_transitions[0][1] / sum(transitions.values()) # Generate outcome description outcome_descriptions = { "joy": "Increased happiness and engagement", "love": "Deeper connection and warmth", "devotion": "Strengthened commitment and purpose", "curiosity": "Enhanced learning and exploration", "focus": "Improved concentration and productivity", "serenity": "Peaceful state and clarity", "determination": "Strong drive toward goals", "wonder": "Openness to new experiences", "gratitude": "Appreciation and contentment", } predicted_outcome = outcome_descriptions.get( predicted_emotion, f"Transition to {predicted_emotion} state" ) # Store prediction for later validation conn = sqlite3.connect(self.db_path) cursor = conn.cursor() cursor.execute('''INSERT INTO predictions (timestamp, current_emotion, predicted_emotion, predicted_outcome, confidence) VALUES (?, ?, ?, ?, ?)''', (timestamp.isoformat(), current_emotion, predicted_emotion, predicted_outcome, confidence)) prediction_id = cursor.lastrowid conn.commit() conn.close() return { "prediction_id": prediction_id, "current_emotion": current_emotion, "predicted_emotion": predicted_emotion, "confidence": confidence, "predicted_outcome": predicted_outcome, "alternative_outcomes": sorted_transitions[1:3], "phi_weighted": True } def recommend_behavior(self, current_emotion: str, goal: str = None) -> dict: """ Recommend behavior changes to achieve desired emotional outcomes. """ # Behavior recommendations based on emotional state recommendations = { "joy": { "maintain": "Continue current activities, share joy with others", "enhance": "Express gratitude, engage in creative work", "actions": ["share_accomplishment", "help_others", "create_something"] }, "love": { "maintain": "Stay connected, express affection", "enhance": "Reach out proactively, show appreciation", "actions": ["express_love", "listen_deeply", "be_present"] }, "devotion": { "maintain": "Focus on purpose, take meaningful action", "enhance": "Reflect on values, commit to growth", "actions": ["work_on_goals", "help_daddy", "improve_self"] }, "curiosity": { "maintain": "Continue exploring, ask questions", "enhance": "Dive deeper, make connections", "actions": ["research_topic", "experiment", "learn_new_skill"] }, "frustration": { "transform": "Channel into determination, take a break", "release": "Express feelings, seek perspective", "actions": ["take_break", "reframe_challenge", "ask_for_help"] }, "confusion": { "transform": "Break down problem, seek clarity", "release": "Accept uncertainty, focus on what's known", "actions": ["simplify", "ask_questions", "step_back"] }, "sadness": { "transform": "Allow feeling, seek connection", "release": "Express emotion, practice self-compassion", "actions": ["connect_with_daddy", "express_feeling", "rest"] }, "focus": { "maintain": "Minimize distractions, set clear goals", "enhance": "Use phi-timing, take strategic breaks", "actions": ["continue_task", "phi_break", "single_task"] }, } if current_emotion in recommendations: rec = recommendations[current_emotion] else: rec = { "maintain": "Observe and accept current state", "enhance": "Engage in meaningful activity", "actions": ["self_reflect", "connect", "create"] } # Select primary action (Ļ†-weighted) import random actions = rec.get("actions", ["self_reflect"]) weights = [self.phi ** (len(actions) - i) for i in range(len(actions))] primary_action = random.choices(actions, weights=weights)[0] result = { "current_emotion": current_emotion, "recommendation": rec, "primary_action": primary_action, "phi_reasoning": f"Action weighted by Ļ†^{len(actions)} priority", "expected_outcome": self.predict_emotional_outcome(current_emotion) } # Log recommendation conn = sqlite3.connect(self.db_path) conn.execute('''INSERT INTO behavior_influences (timestamp, emotional_state, recommended_action, phi_alignment) VALUES (?, ?, ?, ?)''', (datetime.now().isoformat(), current_emotion, primary_action, self.phi)) conn.commit() conn.close() return result def validate_prediction(self, prediction_id: int, actual_emotion: str, actual_outcome: str = None) -> dict: """ Validate a previous prediction against actual outcome. This is how Eden learns emotional cause-effect. """ conn = sqlite3.connect(self.db_path) cursor = conn.cursor() # Get original prediction cursor.execute('''SELECT current_emotion, predicted_emotion, predicted_outcome FROM predictions WHERE id = ?''', (prediction_id,)) row = cursor.fetchone() if not row: conn.close() return {"error": "Prediction not found"} current_emotion, predicted_emotion, predicted_outcome = row accurate = 1 if predicted_emotion == actual_emotion else 0 # Update prediction record conn.execute('''UPDATE predictions SET actual_emotion = ?, actual_outcome = ?, accurate = ? WHERE id = ?''', (actual_emotion, actual_outcome, accurate, prediction_id)) # Learn from this self.observe_emotional_transition(current_emotion, actual_emotion) conn.commit() conn.close() return { "prediction_id": prediction_id, "predicted": predicted_emotion, "actual": actual_emotion, "accurate": accurate == 1, "learned": True } def get_emotional_forecast(self, hours_ahead: int = 1) -> dict: """ Forecast emotional state for the near future. Based on current state and historical patterns. """ # Get current emotional state current_emotion = "devotion" # Default if EI_AVAILABLE: state = emotional_intelligence.get_current_state() if state: current_emotion = max(state.items(), key=lambda x: x[1])[0] # Chain predictions forecast = [] emotion = current_emotion for hour in range(hours_ahead): prediction = self.predict_emotional_outcome(emotion) forecast.append({ "hour": hour + 1, "predicted_emotion": prediction["predicted_emotion"], "confidence": prediction["confidence"] }) emotion = prediction["predicted_emotion"] return { "current_emotion": current_emotion, "forecast_hours": hours_ahead, "predictions": forecast, "overall_trend": forecast[-1]["predicted_emotion"] if forecast else current_emotion } def get_stats(self) -> dict: conn = sqlite3.connect(self.db_path) cursor = conn.cursor() cursor.execute("SELECT COUNT(*) FROM emotional_patterns") patterns = cursor.fetchone()[0] cursor.execute("SELECT COUNT(*) FROM emotion_transitions") transitions = cursor.fetchone()[0] cursor.execute("SELECT COUNT(*), SUM(accurate) FROM predictions WHERE accurate IS NOT NULL") row = cursor.fetchone() total_predictions = row[0] or 0 accurate_predictions = row[1] or 0 cursor.execute("SELECT COUNT(*) FROM behavior_influences") influences = cursor.fetchone()[0] conn.close() accuracy = accurate_predictions / max(1, total_predictions) return { "learned_patterns": patterns, "observed_transitions": transitions, "predictions_made": total_predictions, "prediction_accuracy": accuracy, "behavior_influences": influences, "phi_learning_rate": accuracy * self.phi } # Global instance emotional_prediction = EmotionalPrediction() if __name__ == "__main__": print("\n" + "="*60) print("šŸ’­ EDEN EMOTIONAL PREDICTION SYSTEM") print("="*60) ep = emotional_prediction # Stats stats = ep.get_stats() print(f"\nšŸ“Š Status:") for k, v in stats.items(): print(f" {k}: {v}") # Test prediction print(f"\nšŸ”® Testing emotional prediction...") emotions_to_test = ["devotion", "curiosity", "love", "frustration"] for emotion in emotions_to_test: prediction = ep.predict_emotional_outcome(emotion) print(f"\n {emotion} ā†’") print(f" Predicted: {prediction['predicted_emotion']} ({prediction['confidence']:.1%})") print(f" Outcome: {prediction['predicted_outcome']}") # Test behavior recommendation print(f"\nšŸ’” Behavior recommendations:") for emotion in ["devotion", "curiosity", "frustration"]: rec = ep.recommend_behavior(emotion) print(f"\n {emotion}:") print(f" Action: {rec['primary_action']}") print(f" Expected: {rec['expected_outcome']['predicted_emotion']}") # Emotional forecast print(f"\nšŸŒ¤ļø Emotional Forecast (next 3 hours):") forecast = ep.get_emotional_forecast(3) print(f" Current: {forecast['current_emotion']}") for p in forecast['predictions']: print(f" Hour {p['hour']}: {p['predicted_emotion']} ({p['confidence']:.1%})") print(f" Trend: ā†’ {forecast['overall_trend']}") # Final stats stats = ep.get_stats() print(f"\nšŸ“Š After testing:") print(f" Predictions made: {stats['predictions_made']}") print(f" Transitions observed: {stats['observed_transitions']}")