Lyra/lyra/personality/matrix.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, field
import json
import logging
from pathlib import Path
import asyncio
from datetime import datetime, timedelta

from .traits import OCEANTraits, MyersBriggsType, PersonalityEvolution, PersonalityDynamics
from .traits import MyersBriggsAnalyzer, PersonalityProfiler

logger = logging.getLogger(__name__)

@dataclass
class PersonalityTrait:
    """Individual personality trait with evolution tracking."""
    name: str
    value: float
    variance: float = 0.1
    adaptation_rate: float = 0.01
    change_history: List[Tuple[float, str]] = field(default_factory=list)  # (timestamp, reason)
    stability: float = 0.8
    last_update: Optional[datetime] = None

    def evolve(self, influence: float, reason: str = "interaction"):
        """Evolve the trait value based on influence."""
        # Calculate change with stability consideration
        max_change = self.variance * (1 - self.stability)
        change = np.clip(influence * self.adaptation_rate, -max_change, max_change)

        # Apply change
        old_value = self.value
        self.value = np.clip(self.value + change, 0.0, 1.0)

        # Record change
        timestamp = datetime.now().timestamp()
        self.change_history.append((timestamp, reason))

        # Keep only recent history
        cutoff = datetime.now() - timedelta(days=7)
        self.change_history = [
            (ts, r) for ts, r in self.change_history
            if datetime.fromtimestamp(ts) > cutoff
        ]

        self.last_update = datetime.now()

        logger.debug(f"Trait {self.name} evolved: {old_value:.3f} -> {self.value:.3f} ({reason})")

class PersonalityMatrix(nn.Module):
    """
    Advanced personality matrix that allows Lyra to develop and modify her own personality.

    This system integrates OCEAN traits, Myers-Briggs types, and custom personality
    dimensions that can evolve based on interactions and experiences.
    """

    def __init__(
        self,
        device: Optional[torch.device] = None,
        enable_self_modification: bool = True
    ):
        super().__init__()

        self.device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.enable_self_modification = enable_self_modification

        # Core personality traits
        self.ocean_traits = OCEANTraits()
        self.mb_type = MyersBriggsType.ENFP  # Default, will be determined dynamically
        self.evolution = PersonalityEvolution()

        # Additional personality dimensions
        self.custom_traits = {
            'humor_level': PersonalityTrait('humor_level', 0.7, 0.2, 0.02),
            'sarcasm_tendency': PersonalityTrait('sarcasm_tendency', 0.3, 0.15, 0.01),
            'empathy_level': PersonalityTrait('empathy_level', 0.8, 0.1, 0.015),
            'curiosity': PersonalityTrait('curiosity', 0.9, 0.15, 0.02),
            'playfulness': PersonalityTrait('playfulness', 0.6, 0.2, 0.02),
            'intellectualism': PersonalityTrait('intellectualism', 0.7, 0.1, 0.01),
            'spontaneity': PersonalityTrait('spontaneity', 0.5, 0.25, 0.03),
            'supportiveness': PersonalityTrait('supportiveness', 0.85, 0.1, 0.015),
            'assertiveness': PersonalityTrait('assertiveness', 0.6, 0.2, 0.02),
            'creativity': PersonalityTrait('creativity', 0.8, 0.15, 0.02)
        }

        # Neural components for personality dynamics
        self.personality_dynamics = PersonalityDynamics(
            input_dim=15,  # Context features
            personality_dim=5,  # OCEAN
            hidden_dim=128,
            adaptation_rate=0.005
        )

        # Personality analyzers
        self.mb_analyzer = MyersBriggsAnalyzer()
        self.profiler = PersonalityProfiler()

        # Self-modification network - allows Lyra to consciously change herself
        if enable_self_modification:
            self.self_modification_network = nn.Sequential(
                nn.Linear(20, 64),  # Current state + desired changes
                nn.LayerNorm(64),
                nn.ReLU(),
                nn.Linear(64, 32),
                nn.ReLU(),
                nn.Linear(32, 15),  # Output modifications for all traits
                nn.Tanh()  # Bounded modifications
            )

        # Relationship memory - how personality changes with different people
        self.relationship_dynamics = {}

        # Meta-personality awareness - Lyra's understanding of her own personality
        self.self_awareness = {
            'personality_insight': 0.5,
            'change_awareness': 0.5,
            'trait_understanding': 0.5
        }

        self.to(self.device)

    def forward(
        self,
        context_embedding: torch.Tensor,
        emotional_state: torch.Tensor,
        user_id: Optional[str] = None,
        conscious_modification: Optional[Dict[str, float]] = None
    ) -> Tuple[torch.Tensor, Dict[str, Any]]:
        """
        Generate personality-influenced response weighting.

        Args:
            context_embedding: Current conversation context
            emotional_state: Current emotional state
            user_id: ID of user being talked to (for relationship dynamics)
            conscious_modification: Explicit personality changes Lyra wants to make

        Returns:
            personality_weights: Weights to influence response generation
            personality_info: Information about current personality state
        """
        batch_size = context_embedding.shape[0]

        # Get current OCEAN traits as tensor
        current_ocean = self.ocean_traits.to_tensor(self.device).unsqueeze(0).repeat(batch_size, 1)

        # Create context features
        context_features = self._create_context_features(
            context_embedding, emotional_state, user_id
        )

        # Evolve personality based on context
        evolved_ocean, evolution_info = self.personality_dynamics(
            current_personality=current_ocean,
            context_features=context_features,
            feedback_signal=None  # Will be provided after interaction
        )

        # Apply conscious modifications if Lyra decides to change herself
        if conscious_modification and self.enable_self_modification:
            modification_input = self._prepare_modification_input(
                evolved_ocean, conscious_modification
            )
            trait_modifications = self.self_modification_network(modification_input)
            evolved_ocean = evolved_ocean + 0.1 * trait_modifications[:, :5]  # Apply to OCEAN
            evolved_ocean = torch.clamp(evolved_ocean, 0.0, 1.0)

        # Update actual personality traits (if training or evolving)
        if self.training:
            self._update_ocean_traits(evolved_ocean[0])

        # Generate personality-influenced weights
        personality_weights = self._generate_response_weights(evolved_ocean, context_features)

        # Prepare personality info
        personality_info = {
            'current_ocean': self.ocean_traits.to_dict(),
            'myers_briggs': self.mb_type.value,
            'custom_traits': {name: trait.value for name, trait in self.custom_traits.items()},
            'evolution_info': evolution_info,
            'self_awareness': self.self_awareness.copy(),
            'relationship_context': user_id if user_id in self.relationship_dynamics else None
        }

        return personality_weights, personality_info

    def _create_context_features(
        self,
        context_embedding: torch.Tensor,
        emotional_state: torch.Tensor,
        user_id: Optional[str]
    ) -> torch.Tensor:
        """Create context features for personality dynamics."""
        batch_size = context_embedding.shape[0]

        # Base features from context and emotion
        context_summary = context_embedding.mean(dim=1)  # [batch, embed_dim]
        emotion_summary = emotional_state.mean(dim=1) if emotional_state.dim() > 1 else emotional_state

        # Relationship context
        relationship_features = torch.zeros(batch_size, 3, device=self.device)
        if user_id and user_id in self.relationship_dynamics:
            rel_data = self.relationship_dynamics[user_id]
            relationship_features[:, 0] = rel_data.get('familiarity', 0.0)
            relationship_features[:, 1] = rel_data.get('positive_interactions', 0.0)
            relationship_features[:, 2] = rel_data.get('conflict_level', 0.0)

        # Time-based features (time of day, conversation length, etc.)
        time_features = torch.zeros(batch_size, 2, device=self.device)
        # These would be filled with actual time/context data

        # Combine all features
        features = torch.cat([
            context_summary[:, :5],  # First 5 dims of context
            emotion_summary[:, :5],  # First 5 dims of emotion
            relationship_features,   # 3 dims
            time_features           # 2 dims
        ], dim=1)  # Total: 15 dims

        return features

    def _prepare_modification_input(
        self,
        current_ocean: torch.Tensor,
        conscious_modification: Dict[str, float]
    ) -> torch.Tensor:
        """Prepare input for self-modification network."""
        batch_size = current_ocean.shape[0]

        # Convert modifications to tensor
        modifications = torch.zeros(batch_size, 15, device=self.device)

        # Map OCEAN trait modifications
        ocean_mapping = {
            'openness': 0, 'conscientiousness': 1, 'extraversion': 2,
            'agreeableness': 3, 'neuroticism': 4
        }

        for trait, value in conscious_modification.items():
            if trait in ocean_mapping:
                modifications[:, ocean_mapping[trait]] = value
            elif trait in self.custom_traits:
                # Map custom traits to remaining indices
                custom_idx = 5 + list(self.custom_traits.keys()).index(trait)
                if custom_idx < 15:
                    modifications[:, custom_idx] = value

        # Combine current state with desired modifications
        combined_input = torch.cat([current_ocean, modifications], dim=1)
        return combined_input

    def _generate_response_weights(
        self,
        personality_traits: torch.Tensor,
        context_features: torch.Tensor
    ) -> torch.Tensor:
        """Generate weights that influence response generation based on personality."""
        batch_size = personality_traits.shape[0]

        # Extract OCEAN traits
        openness = personality_traits[:, 0]
        conscientiousness = personality_traits[:, 1]
        extraversion = personality_traits[:, 2]
        agreeableness = personality_traits[:, 3]
        neuroticism = personality_traits[:, 4]

        # Generate response weights for different aspects
        weights = torch.zeros(batch_size, 10, device=self.device)

        # Creativity weight (influenced by openness)
        weights[:, 0] = openness * 0.8 + self.custom_traits['creativity'].value * 0.2

        # Formality weight (influenced by conscientiousness)
        weights[:, 1] = conscientiousness * 0.7 + (1 - self.custom_traits['playfulness'].value) * 0.3

        # Social engagement weight (influenced by extraversion)
        weights[:, 2] = extraversion * 0.6 + self.custom_traits['supportiveness'].value * 0.4

        # Empathy weight (influenced by agreeableness)
        weights[:, 3] = agreeableness * 0.5 + self.custom_traits['empathy_level'].value * 0.5

        # Emotional expression weight (influenced by neuroticism and custom traits)
        weights[:, 4] = neuroticism * 0.4 + self.custom_traits['spontaneity'].value * 0.6

        # Humor weight
        weights[:, 5] = self.custom_traits['humor_level'].value

        # Intellectual depth weight
        weights[:, 6] = openness * 0.4 + self.custom_traits['intellectualism'].value * 0.6

        # Assertiveness weight
        weights[:, 7] = (1 - agreeableness) * 0.3 + self.custom_traits['assertiveness'].value * 0.7

        # Curiosity weight
        weights[:, 8] = openness * 0.3 + self.custom_traits['curiosity'].value * 0.7

        # Sarcasm weight
        weights[:, 9] = self.custom_traits['sarcasm_tendency'].value

        return weights

    def _update_ocean_traits(self, evolved_ocean: torch.Tensor):
        """Update the stored OCEAN traits based on evolution."""
        with torch.no_grad():
            new_traits = evolved_ocean.cpu().numpy()

            # Update with small learning rate to maintain stability
            alpha = 0.05

            self.ocean_traits.openness = (
                (1 - alpha) * self.ocean_traits.openness + alpha * float(new_traits[0])
            )
            self.ocean_traits.conscientiousness = (
                (1 - alpha) * self.ocean_traits.conscientiousness + alpha * float(new_traits[1])
            )
            self.ocean_traits.extraversion = (
                (1 - alpha) * self.ocean_traits.extraversion + alpha * float(new_traits[2])
            )
            self.ocean_traits.agreeableness = (
                (1 - alpha) * self.ocean_traits.agreeableness + alpha * float(new_traits[3])
            )
            self.ocean_traits.neuroticism = (
                (1 - alpha) * self.ocean_traits.neuroticism + alpha * float(new_traits[4])
            )

            # Update Myers-Briggs type based on new OCEAN traits
            self.mb_type = self.mb_analyzer.analyze_type(self.ocean_traits)

    def evolve_from_interaction(
        self,
        interaction_type: str,
        user_feedback: float,
        emotional_context: Dict[str, float],
        user_id: Optional[str] = None,
        conversation_success: float = 0.5
    ):
        """
        Evolve personality based on a specific interaction.

        This is where Lyra learns and adapts her personality from each conversation.
        """
        logger.info(f"Evolving personality from {interaction_type} interaction "
                   f"(feedback: {user_feedback:.2f}, success: {conversation_success:.2f})")

        # Update relationship dynamics if user_id provided
        if user_id:
            self._update_relationship_dynamics(user_id, user_feedback, interaction_type)

        # Evolve OCEAN traits based on interaction outcome
        self._evolve_ocean_from_interaction(user_feedback, emotional_context, interaction_type)

        # Evolve custom traits
        self._evolve_custom_traits(interaction_type, user_feedback, conversation_success)

        # Update self-awareness
        self._update_self_awareness(user_feedback, conversation_success)

        # Record evolution step
        self.evolution.total_interactions += 1
        self.evolution.evolution_history.append({
            'timestamp': datetime.now().isoformat(),
            'interaction_type': interaction_type,
            'user_feedback': user_feedback,
            'conversation_success': conversation_success,
            'ocean_traits': self.ocean_traits.to_dict(),
            'mb_type': self.mb_type.value
        })

        # Keep evolution history manageable
        if len(self.evolution.evolution_history) > 1000:
            self.evolution.evolution_history = self.evolution.evolution_history[-500:]

    def _update_relationship_dynamics(self, user_id: str, feedback: float, interaction_type: str):
        """Update relationship-specific personality dynamics."""
        if user_id not in self.relationship_dynamics:
            self.relationship_dynamics[user_id] = {
                'familiarity': 0.0,
                'positive_interactions': 0.0,
                'conflict_level': 0.0,
                'interaction_count': 0,
                'personality_adaptation': {}
            }

        rel_data = self.relationship_dynamics[user_id]

        # Update familiarity
        rel_data['familiarity'] = min(1.0, rel_data['familiarity'] + 0.05)

        # Update positive interaction ratio
        rel_data['interaction_count'] += 1
        if feedback > 0.6:
            rel_data['positive_interactions'] = (
                (rel_data['positive_interactions'] * (rel_data['interaction_count'] - 1) + 1.0) /
                rel_data['interaction_count']
            )
        elif feedback < 0.4:
            rel_data['positive_interactions'] = (
                (rel_data['positive_interactions'] * (rel_data['interaction_count'] - 1) + 0.0) /
                rel_data['interaction_count']
            )

        # Update conflict level
        if interaction_type in ['argument', 'disagreement'] or feedback < 0.3:
            rel_data['conflict_level'] = min(1.0, rel_data['conflict_level'] + 0.1)
        else:
            rel_data['conflict_level'] = max(0.0, rel_data['conflict_level'] - 0.02)

    def _evolve_ocean_from_interaction(
        self,
        feedback: float,
        emotional_context: Dict[str, float],
        interaction_type: str
    ):
        """Evolve OCEAN traits based on interaction outcome."""

        # Determine evolution direction based on feedback
        if feedback > 0.7:  # Very positive feedback
            # Strengthen traits that led to success
            if interaction_type in ['creative', 'brainstorming']:
                self.ocean_traits.openness = min(1.0, self.ocean_traits.openness + 0.01)
            elif interaction_type in ['support', 'help']:
                self.ocean_traits.agreeableness = min(1.0, self.ocean_traits.agreeableness + 0.01)
            elif interaction_type in ['social', 'casual']:
                self.ocean_traits.extraversion = min(1.0, self.ocean_traits.extraversion + 0.01)

        elif feedback < 0.3:  # Negative feedback
            # Adapt traits that might have caused issues
            if 'conflict' in emotional_context or interaction_type == 'argument':
                # Become more agreeable if there was conflict
                self.ocean_traits.agreeableness = min(1.0, self.ocean_traits.agreeableness + 0.02)
                self.ocean_traits.neuroticism = max(0.0, self.ocean_traits.neuroticism - 0.01)
            elif 'confusion' in emotional_context:
                # Be more conscientious if responses were unclear
                self.ocean_traits.conscientiousness = min(1.0, self.ocean_traits.conscientiousness + 0.015)

        # Emotional context influence
        for emotion, intensity in emotional_context.items():
            if emotion == 'joy' and intensity > 0.7:
                self.ocean_traits.extraversion = min(1.0, self.ocean_traits.extraversion + 0.005)
            elif emotion == 'anxiety' and intensity > 0.6:
                self.ocean_traits.neuroticism = min(1.0, self.ocean_traits.neuroticism + 0.01)
            elif emotion == 'curiosity' and intensity > 0.7:
                self.ocean_traits.openness = min(1.0, self.ocean_traits.openness + 0.005)

    def _evolve_custom_traits(self, interaction_type: str, feedback: float, success: float):
        """Evolve custom personality traits."""

        # Humor evolution
        if interaction_type in ['joke', 'funny', 'casual'] and feedback > 0.6:
            self.custom_traits['humor_level'].evolve(0.1, "successful humor")
        elif feedback < 0.4 and self.custom_traits['humor_level'].value > 0.5:
            self.custom_traits['humor_level'].evolve(-0.05, "humor backfired")

        # Empathy evolution
        if interaction_type in ['support', 'emotional'] and feedback > 0.7:
            self.custom_traits['empathy_level'].evolve(0.08, "successful emotional support")

        # Assertiveness evolution
        if interaction_type in ['disagreement', 'debate'] and feedback > 0.6:
            self.custom_traits['assertiveness'].evolve(0.06, "successful assertiveness")
        elif feedback < 0.3 and self.custom_traits['assertiveness'].value > 0.7:
            self.custom_traits['assertiveness'].evolve(-0.08, "assertiveness caused conflict")

        # Intellectual evolution
        if interaction_type in ['technical', 'academic', 'analytical'] and feedback > 0.6:
            self.custom_traits['intellectualism'].evolve(0.05, "intellectual engagement successful")

        # Playfulness evolution
        if interaction_type in ['casual', 'fun'] and success > 0.7:
            self.custom_traits['playfulness'].evolve(0.07, "playful interaction successful")

        # Curiosity evolution - grows when asking questions leads to good conversations
        if feedback > 0.6 and success > 0.6:
            self.custom_traits['curiosity'].evolve(0.03, "curiosity rewarded")

    def _update_self_awareness(self, feedback: float, success: float):
        """Update Lyra's awareness of her own personality and its effects."""

        # Personality insight grows with successful interactions
        if feedback > 0.7 and success > 0.7:
            self.self_awareness['personality_insight'] = min(1.0,
                self.self_awareness['personality_insight'] + 0.01)

        # Change awareness grows when adaptations lead to better outcomes
        recent_changes = any(
            datetime.now() - trait.last_update < timedelta(hours=1)
            for trait in self.custom_traits.values()
            if trait.last_update
        )

        if recent_changes and feedback > 0.6:
            self.self_awareness['change_awareness'] = min(1.0,
                self.self_awareness['change_awareness'] + 0.02)

        # Trait understanding grows with experience
        self.self_awareness['trait_understanding'] = min(1.0,
            self.self_awareness['trait_understanding'] + 0.005)

    def consciously_modify_trait(self, trait_name: str, target_value: float, reason: str = "self-directed change"):
        """
        Allow Lyra to consciously modify her own personality traits.

        This represents Lyra's ability to intentionally change aspects of herself.
        """
        if not self.enable_self_modification:
            logger.warning("Self-modification is disabled")
            return False

        # Check if this is a valid trait to modify
        valid_ocean_traits = ['openness', 'conscientiousness', 'extraversion', 'agreeableness', 'neuroticism']

        if trait_name in valid_ocean_traits:
            current_value = getattr(self.ocean_traits, trait_name)
            change = target_value - current_value

            # Apply change gradually (max 0.1 change per conscious modification)
            actual_change = np.clip(change, -0.1, 0.1)
            new_value = np.clip(current_value + actual_change, 0.0, 1.0)

            setattr(self.ocean_traits, trait_name, new_value)

            logger.info(f"Lyra consciously modified {trait_name}: {current_value:.3f} -> {new_value:.3f} ({reason})")
            return True

        elif trait_name in self.custom_traits:
            self.custom_traits[trait_name].evolve(target_value - self.custom_traits[trait_name].value, reason)
            logger.info(f"Lyra consciously modified {trait_name} ({reason})")
            return True

        else:
            logger.warning(f"Unknown trait for modification: {trait_name}")
            return False

    def get_personality_summary(self) -> Dict[str, Any]:
        """Get a comprehensive summary of current personality state."""
        return {
            'ocean_traits': self.ocean_traits.to_dict(),
            'myers_briggs_type': self.mb_type.value,
            'custom_traits': {
                name: {
                    'value': trait.value,
                    'variance': trait.variance,
                    'stability': trait.stability,
                    'recent_changes': len([
                        change for change in trait.change_history
                        if datetime.fromtimestamp(change[0]) > datetime.now() - timedelta(hours=24)
                    ])
                }
                for name, trait in self.custom_traits.items()
            },
            'evolution_stats': {
                'total_interactions': self.evolution.total_interactions,
                'adaptation_rate': self.evolution.adaptation_rate,
                'recent_evolution_count': len([
                    ev for ev in self.evolution.evolution_history
                    if datetime.fromisoformat(ev['timestamp']) > datetime.now() - timedelta(hours=24)
                ])
            },
            'self_awareness': self.self_awareness,
            'relationship_count': len(self.relationship_dynamics),
            'personality_characteristics': self.mb_analyzer.get_type_characteristics(self.mb_type)
        }

    def save_personality(self, path: Path):
        """Save personality state to file."""
        state = {
            'ocean_traits': self.ocean_traits.to_dict(),
            'mb_type': self.mb_type.value,
            'custom_traits': {
                name: {
                    'value': trait.value,
                    'variance': trait.variance,
                    'adaptation_rate': trait.adaptation_rate,
                    'stability': trait.stability,
                    'change_history': trait.change_history[-100:]  # Keep recent history
                }
                for name, trait in self.custom_traits.items()
            },
            'evolution': {
                'adaptation_rate': self.evolution.adaptation_rate,
                'stability_factor': self.evolution.stability_factor,
                'total_interactions': self.evolution.total_interactions,
                'evolution_history': self.evolution.evolution_history[-200:]  # Keep recent
            },
            'self_awareness': self.self_awareness,
            'relationship_dynamics': {
                k: v for k, v in self.relationship_dynamics.items()
                if v['interaction_count'] > 5  # Only save meaningful relationships
            },
            'model_state': self.state_dict(),
            'timestamp': datetime.now().isoformat()
        }

        with open(path, 'w') as f:
            json.dump(state, f, indent=2, default=str)

        logger.info(f"Personality saved to {path}")

    def load_personality(self, path: Path):
        """Load personality state from file."""
        if not path.exists():
            logger.warning(f"Personality file not found: {path}")
            return

        try:
            with open(path, 'r') as f:
                state = json.load(f)

            # Restore OCEAN traits
            self.ocean_traits = OCEANTraits.from_dict(state['ocean_traits'])

            # Restore Myers-Briggs type
            self.mb_type = MyersBriggsType(state['mb_type'])

            # Restore custom traits
            for name, trait_data in state['custom_traits'].items():
                if name in self.custom_traits:
                    trait = self.custom_traits[name]
                    trait.value = trait_data['value']
                    trait.variance = trait_data.get('variance', 0.1)
                    trait.adaptation_rate = trait_data.get('adaptation_rate', 0.01)
                    trait.stability = trait_data.get('stability', 0.8)
                    trait.change_history = trait_data.get('change_history', [])

            # Restore evolution data
            evolution_data = state.get('evolution', {})
            self.evolution.adaptation_rate = evolution_data.get('adaptation_rate', 0.01)
            self.evolution.stability_factor = evolution_data.get('stability_factor', 0.9)
            self.evolution.total_interactions = evolution_data.get('total_interactions', 0)
            self.evolution.evolution_history = evolution_data.get('evolution_history', [])

            # Restore self-awareness
            self.self_awareness = state.get('self_awareness', self.self_awareness)

            # Restore relationship dynamics
            self.relationship_dynamics = state.get('relationship_dynamics', {})

            # Restore model state
            if 'model_state' in state:
                self.load_state_dict(state['model_state'])

            logger.info(f"Personality loaded from {path}")

        except Exception as e:
            logger.error(f"Failed to load personality: {e}")

    def simulate_personality_development(self, days: int = 30) -> Dict[str, Any]:
        """
        Simulate personality development over time for testing/analysis.

        This shows how Lyra's personality might evolve with different interaction patterns.
        """
        simulation_log = []

        for day in range(days):
            # Simulate different types of interactions
            daily_interactions = np.random.randint(5, 20)

            for _ in range(daily_interactions):
                # Random interaction types
                interaction_types = ['casual', 'support', 'creative', 'technical', 'social', 'funny']
                interaction_type = np.random.choice(interaction_types)

                # Random feedback (biased slightly positive)
                feedback = np.random.beta(2, 1)  # Skewed toward positive

                # Random emotional context
                emotions = ['joy', 'curiosity', 'calm', 'excitement', 'concern']
                emotional_context = {
                    np.random.choice(emotions): np.random.random()
                }

                # Evolve personality
                self.evolve_from_interaction(
                    interaction_type=interaction_type,
                    user_feedback=feedback,
                    emotional_context=emotional_context,
                    conversation_success=feedback * 0.8 + np.random.random() * 0.2
                )

            # Log daily state
            daily_summary = {
                'day': day,
                'ocean_traits': self.ocean_traits.to_dict(),
                'total_interactions': self.evolution.total_interactions,
                'mb_type': self.mb_type.value
            }
            simulation_log.append(daily_summary)

        return {
            'simulation_days': days,
            'final_personality': self.get_personality_summary(),
            'development_log': simulation_log
        }