#!/usr/bin/env python3
"""
Experimental Matrix-Entangled Node Neurons
=========================================
Advanced system for creating experimental dimensional matrix-entangled node neurons
with sophisticated LLM integration and holographic emergence patterns.

This system creates:
1. Matrix-entangled neural networks with quantum-inspired dynamics
2. Experimental dimensional nodes with advanced entanglement patterns
3. Sophisticated training data generation using LLM capabilities
4. Holographic memory integration for emergent learning

Author: Assistant
License: MIT
"""

import numpy as np
import torch
import torch.nn as nn
from typing import Dict, List, Optional, Any, Tuple
import json
import sqlite3
from datetime import datetime
import pickle
from dataclasses import dataclass, asdict
import hashlib
import random
from pathlib import Path

# Import our existing systems
from dimensional_entanglement_database import (
    DimensionalNode, DimensionalDatabase, EntanglementMatrix, 
    TrainingDataGenerator, DimensionalNodeFactory
)
from enhanced_holographic_integration import EnhancedHolographicLLM
from holographic_memory_core import HolographicAssociativeMemory
from fractal_memory_encoder import FractalMemoryEncoder
from quantum_holographic_storage import QuantumHolographicStorage
from emergent_memory_patterns import EmergentMemoryPatterns

@dataclass
class MatrixEntangledNeuron:
    """
    Advanced neuron with matrix entanglement capabilities.
    
    Each neuron represents a sophisticated processing unit with:
    - Quantum-inspired state dynamics
    - Matrix entanglement with other neurons
    - Holographic memory integration
    - Emergent pattern recognition
    """
    neuron_id: str
    quantum_state: np.ndarray  # Complex quantum state |ψ⟩
    matrix_weights: np.ndarray  # Entanglement matrix weights
    holographic_memory: np.ndarray  # Holographic memory trace
    fractal_encoding: Dict[str, Any]  # Multi-scale fractal representation
    emergence_level: float  # Current emergence level
    dimensional_signature: str  # Dimensional signature
    activation_history: List[float]  # Historical activation patterns
    entanglement_partners: List[str]  # IDs of entangled neurons
    metadata: Dict[str, Any]  # Additional neuron metadata
    created_at: str
    
    def to_dict(self) -> Dict:
        """Convert to dictionary for storage."""
        # Convert numpy arrays in fractal_encoding to lists for JSON serialization
        fractal_encoding_serializable = {}
        for key, value in self.fractal_encoding.items():
            if isinstance(value, np.ndarray):
                fractal_encoding_serializable[key] = value.tolist()
            elif isinstance(value, dict):
                # Handle nested dictionaries that might contain numpy arrays
                nested_dict = {}
                for nested_key, nested_value in value.items():
                    if isinstance(nested_value, np.ndarray):
                        nested_dict[nested_key] = nested_value.tolist()
                    else:
                        nested_dict[nested_key] = nested_value
                fractal_encoding_serializable[key] = nested_dict
            else:
                fractal_encoding_serializable[key] = value
        
        return {
            'neuron_id': self.neuron_id,
            'quantum_state': pickle.dumps(self.quantum_state),
            'matrix_weights': pickle.dumps(self.matrix_weights),
            'holographic_memory': pickle.dumps(self.holographic_memory),
            'fractal_encoding': json.dumps(fractal_encoding_serializable),
            'emergence_level': self.emergence_level,
            'dimensional_signature': self.dimensional_signature,
            'activation_history': json.dumps(self.activation_history),
            'entanglement_partners': json.dumps(self.entanglement_partners),
            'metadata': json.dumps(self.metadata),
            'created_at': self.created_at
        }
    
    @classmethod
    def from_dict(cls, data: Dict) -> 'MatrixEntangledNeuron':
        """Reconstruct from storage."""
        return cls(
            neuron_id=data['neuron_id'],
            quantum_state=pickle.loads(data['quantum_state']),
            matrix_weights=pickle.loads(data['matrix_weights']),
            holographic_memory=pickle.loads(data['holographic_memory']),
            fractal_encoding=json.loads(data['fractal_encoding']),
            emergence_level=data['emergence_level'],
            dimensional_signature=data['dimensional_signature'],
            activation_history=json.loads(data['activation_history']),
            entanglement_partners=json.loads(data['entanglement_partners']),
            metadata=json.loads(data['metadata']),
            created_at=data['created_at']
        )

class MatrixEntangledNetwork:
    """
    Network of matrix-entangled neurons with advanced cognitive capabilities.
    
    This network implements:
    - Quantum-inspired neural dynamics
    - Matrix entanglement between neurons
    - Holographic memory integration
    - Emergent pattern recognition
    - Adaptive learning mechanisms
    """
    
    def __init__(self, 
                 num_neurons: int = 100,
                 quantum_dim: int = 64,
                 holographic_dim: int = 128):
        
        self.num_neurons = num_neurons
        self.quantum_dim = quantum_dim
        self.holographic_dim = holographic_dim
        
        # Initialize network components
        self.neurons: Dict[str, MatrixEntangledNeuron] = {}
        self.entanglement_matrix = np.zeros((num_neurons, num_neurons), dtype=complex)
        self.global_emergence_level = 0.0
        
        # Integration with holographic systems
        self.holographic_memory = HolographicAssociativeMemory()
        self.fractal_encoder = FractalMemoryEncoder()
        self.quantum_storage = QuantumHolographicStorage()
        self.emergent_detector = EmergentMemoryPatterns()
        
        # LLM integration
        self.llm_integration = None  # Will be set when LLM is available
        
        # Network state
        self.activation_history = []
        self.emergence_events = []
        
    def create_experimental_neuron(self, 
                                 concept: str,
                                 dimension: int = 0,
                                 llm_context: str = None) -> MatrixEntangledNeuron:
        """
        Create an experimental neuron with advanced capabilities.
        
        Args:
            concept: The concept this neuron represents
            dimension: Dimensional signature
            llm_context: Optional LLM-generated context for the neuron
            
        Returns:
            MatrixEntangledNeuron with sophisticated initialization
        """
        
        # Generate quantum state
        quantum_state = self._generate_quantum_state(concept, llm_context)
        
        # Generate matrix weights (entanglement capabilities)
        matrix_weights = self._generate_matrix_weights(concept, dimension)
        
        # Initialize holographic memory
        holographic_memory = self._initialize_holographic_memory(quantum_state)
        
        # Generate fractal encoding
        fractal_encoding = self._generate_fractal_encoding(quantum_state)
        
        # Calculate initial emergence level
        emergence_level = self._calculate_emergence_level(quantum_state, matrix_weights)
        
        # Create dimensional signature
        dimensional_signature = f"D{dimension}-{hashlib.md5(concept.encode()).hexdigest()[:8]}"
        
        neuron_id = f"neuron_{concept}_{dimension}_{hashlib.md5(str(datetime.now()).encode()).hexdigest()[:8]}"
        
        neuron = MatrixEntangledNeuron(
            neuron_id=neuron_id,
            quantum_state=quantum_state,
            matrix_weights=matrix_weights,
            holographic_memory=holographic_memory,
            fractal_encoding=fractal_encoding,
            emergence_level=emergence_level,
            dimensional_signature=dimensional_signature,
            activation_history=[],
            entanglement_partners=[],
            metadata={
                'concept': concept,
                'dimension': dimension,
                'llm_context': llm_context,
                'creation_method': 'experimental_matrix_entangled',
                'quantum_coherence': float(np.abs(np.vdot(quantum_state, quantum_state))),
                'fractal_dimension': fractal_encoding.get('fractal_dimension', 0.0),
                'holographic_complexity': float(np.linalg.norm(holographic_memory))
            },
            created_at=datetime.now().isoformat()
        )
        
        return neuron
    
    def _generate_quantum_state(self, concept: str, llm_context: str = None) -> np.ndarray:
        """Generate quantum state from concept and LLM context."""
        
        # Base quantum state from concept
        concept_hash = hashlib.sha256(concept.encode()).digest()
        base_state = np.frombuffer(concept_hash, dtype=np.uint8)[:self.quantum_dim].astype(np.float64)
        base_state = base_state / 255.0
        
        # Add LLM context if available
        if llm_context:
            context_hash = hashlib.sha256(llm_context.encode()).digest()
            context_state = np.frombuffer(context_hash, dtype=np.uint8)[:self.quantum_dim].astype(np.float64)
            context_state = context_state / 255.0
            base_state = 0.7 * base_state + 0.3 * context_state
        
        # Convert to complex quantum state
        real_part = base_state
        imag_part = np.sin(base_state * np.pi)  # Create imaginary component
        
        quantum_state = real_part + 1j * imag_part
        quantum_state = quantum_state / (np.linalg.norm(quantum_state) + 1e-12)
        
        return quantum_state
    
    def _generate_matrix_weights(self, concept: str, dimension: int) -> np.ndarray:
        """Generate matrix weights for entanglement capabilities."""
        
        # Create matrix based on concept and dimension
        matrix_size = 16  # 16x16 entanglement matrix per neuron
        
        # Use concept to seed matrix generation
        concept_seed = int(hashlib.md5(concept.encode()).hexdigest()[:8], 16)
        np.random.seed(concept_seed)
        
        # Generate complex matrix with specific properties
        matrix = np.random.randn(matrix_size, matrix_size) + 1j * np.random.randn(matrix_size, matrix_size)
        
        # Make it Hermitian (quantum property)
        matrix = (matrix + matrix.conj().T) / 2
        
        # Add dimension-specific structure
        if dimension % 2 == 0:
            # Even dimensions: more symmetric
            matrix = 0.8 * matrix + 0.2 * np.eye(matrix_size)
        else:
            # Odd dimensions: more asymmetric
            matrix = 0.6 * matrix + 0.4 * np.random.randn(matrix_size, matrix_size)
        
        # Normalize
        matrix = matrix / (np.linalg.norm(matrix) + 1e-12)
        
        return matrix
    
    def _initialize_holographic_memory(self, quantum_state: np.ndarray) -> np.ndarray:
        """Initialize holographic memory trace."""
        
        # Create holographic representation
        holographic_size = self.holographic_dim
        
        # Use quantum state to create holographic pattern
        if len(quantum_state) < holographic_size:
            padded_state = np.zeros(holographic_size, dtype=complex)
            padded_state[:len(quantum_state)] = quantum_state
            quantum_state = padded_state
        
        # Create holographic interference pattern
        reference_wave = np.exp(1j * 2 * np.pi * np.random.random(holographic_size))
        holographic_pattern = quantum_state * reference_wave
        
        # Ensure pattern matches holographic memory dimensions
        if len(holographic_pattern) != self.holographic_memory.hologram_dim * self.holographic_memory.hologram_dim:
            # Pad or truncate to match expected dimensions
            target_size = self.holographic_memory.hologram_dim * self.holographic_memory.hologram_dim
            if len(holographic_pattern) < target_size:
                padded_pattern = np.zeros(target_size, dtype=complex)
                padded_pattern[:len(holographic_pattern)] = holographic_pattern
                holographic_pattern = padded_pattern
            else:
                holographic_pattern = holographic_pattern[:target_size]
        
        # Store in holographic memory system
        memory_key = self.holographic_memory.store_holographic(
            np.abs(holographic_pattern),
            metadata={'source': 'matrix_entangled_neuron', 'type': 'initialization'}
        )
        
        return holographic_pattern
    
    def _generate_fractal_encoding(self, quantum_state: np.ndarray) -> Dict[str, Any]:
        """Generate fractal encoding for the neuron."""
        
        # Convert quantum state to real data for fractal encoding
        real_data = np.abs(quantum_state)
        
        # Use fractal encoder
        fractal_encoding = self.fractal_encoder.encode_fractal_memory(
            real_data,
            context={'neuron_type': 'matrix_entangled', 'quantum_dim': len(quantum_state)}
        )
        
        return fractal_encoding
    
    def _calculate_emergence_level(self, quantum_state: np.ndarray, matrix_weights: np.ndarray) -> float:
        """Calculate the emergence level of the neuron."""
        
        # Quantum coherence
        quantum_coherence = float(np.abs(np.vdot(quantum_state, quantum_state)))
        
        # Matrix complexity
        matrix_complexity = float(np.linalg.norm(matrix_weights))
        
        # Entropy of quantum state
        probabilities = np.abs(quantum_state) ** 2
        probabilities = probabilities / (np.sum(probabilities) + 1e-12)
        entropy = -np.sum(probabilities * np.log(probabilities + 1e-12))
        
        # Combined emergence score
        emergence = (quantum_coherence + matrix_complexity + entropy) / 3.0
        
        return float(np.clip(emergence, 0.0, 1.0))
    
    def add_neuron(self, neuron: MatrixEntangledNeuron):
        """Add a neuron to the network."""
        
        self.neurons[neuron.neuron_id] = neuron
        
        # Update global emergence level
        emergence_levels = [n.emergence_level for n in self.neurons.values()]
        self.global_emergence_level = np.mean(emergence_levels) if emergence_levels else 0.0
        
        # Update entanglement matrix (simplified)
        neuron_index = len(self.neurons) - 1
        if neuron_index < self.num_neurons:
            # Add to entanglement matrix
            for other_idx, other_neuron in enumerate(self.neurons.values()):
                if other_idx < self.num_neurons:
                    # Calculate entanglement strength
                    entanglement = np.vdot(neuron.quantum_state, other_neuron.quantum_state)
                    self.entanglement_matrix[neuron_index, other_idx] = entanglement
                    self.entanglement_matrix[other_idx, neuron_index] = np.conj(entanglement)
    
    def create_experimental_batch(self, 
                                concepts: List[str],
                                dimensions: List[int] = None,
                                llm_contexts: List[str] = None) -> List[MatrixEntangledNeuron]:
        """
        Create a batch of experimental neurons.
        
        Args:
            concepts: List of concepts to create neurons for
            dimensions: List of dimensions (default: random)
            llm_contexts: Optional LLM contexts for each concept
            
        Returns:
            List of created neurons
        """
        
        if dimensions is None:
            dimensions = [random.randint(0, 9) for _ in concepts]
        
        if llm_contexts is None:
            llm_contexts = [None] * len(concepts)
        
        neurons = []
        
        print(f"🧠 Creating {len(concepts)} experimental matrix-entangled neurons...")
        
        for i, (concept, dimension, llm_context) in enumerate(zip(concepts, dimensions, llm_contexts)):
            
            # Create neuron
            neuron = self.create_experimental_neuron(concept, dimension, llm_context)
            
            # Add to network
            self.add_neuron(neuron)
            
            neurons.append(neuron)
            
            if (i + 1) % 10 == 0:
                print(f"  ✓ Created {i + 1}/{len(concepts)} neurons...")
        
        print(f"✅ Created {len(neurons)} experimental neurons")
        print(f"   Global emergence level: {self.global_emergence_level:.4f}")
        
        return neurons
    
    def generate_entangled_training_data(self, 
                                       num_examples: int = 100,
                                       use_llm_integration: bool = True) -> List[Dict]:
        """
        Generate sophisticated training data using entangled neurons.
        
        Args:
            num_examples: Number of training examples to generate
            use_llm_integration: Whether to use LLM for enhanced generation
            
        Returns:
            List of training examples
        """
        
        if len(self.neurons) < 2:
            print("⚠️ Need at least 2 neurons to generate training data")
            return []
        
        print(f"🎯 Generating {num_examples} training examples from entangled neurons...")
        
        training_examples = []
        neuron_list = list(self.neurons.values())
        
        for i in range(num_examples):
            
            # Select entangled neuron cluster
            cluster_size = random.randint(2, min(6, len(neuron_list)))
            cluster = random.sample(neuron_list, cluster_size)
            
            # Calculate cluster entanglement
            cluster_entanglement = self._calculate_cluster_entanglement(cluster)
            
            # Generate prompt and completion
            if use_llm_integration and self.llm_integration:
                prompt, completion = self._generate_with_llm_integration(cluster)
            else:
                prompt, completion = self._generate_basic_training_example(cluster)
            
            # Calculate emergence score
            emergence_score = self._calculate_training_emergence(cluster, cluster_entanglement)
            
            # Create training example
            example = {
                'prompt': prompt,
                'completion': completion,
                'source_neurons': [neuron.neuron_id for neuron in cluster],
                'cluster_entanglement': float(cluster_entanglement),
                'emergence_score': emergence_score,
                'dimensional_signature': f"D{'-'.join(set(str(neuron.metadata['dimension']) for neuron in cluster))}",
                'metadata': {
                    'generation_method': 'matrix_entangled_neurons',
                    'cluster_size': cluster_size,
                    'global_emergence_level': self.global_emergence_level,
                    'quantum_coherence': np.mean([np.abs(np.vdot(n.quantum_state, n.quantum_state)) for n in cluster]),
                    'fractal_complexity': np.mean([n.fractal_encoding.get('fractal_dimension', 0.0) for n in cluster])
                }
            }
            
            training_examples.append(example)
            
            if (i + 1) % 20 == 0:
                print(f"  Generated {i + 1}/{num_examples} examples...")
        
        print(f"✅ Generated {len(training_examples)} training examples")
        print(f"   Average emergence score: {np.mean([ex['emergence_score'] for ex in training_examples]):.4f}")
        
        return training_examples
    
    def _calculate_cluster_entanglement(self, cluster: List[MatrixEntangledNeuron]) -> float:
        """Calculate entanglement strength of a neuron cluster."""
        
        if len(cluster) < 2:
            return 0.0
        
        total_entanglement = 0.0
        pair_count = 0
        
        for i, neuron_i in enumerate(cluster):
            for j, neuron_j in enumerate(cluster[i+1:], i+1):
                # Quantum overlap
                overlap = np.abs(np.vdot(neuron_i.quantum_state, neuron_j.quantum_state))
                
                # Matrix entanglement
                matrix_overlap = np.abs(np.trace(neuron_i.matrix_weights @ neuron_j.matrix_weights.conj().T))
                
                # Holographic similarity
                holo_similarity = np.abs(np.vdot(neuron_i.holographic_memory, neuron_j.holographic_memory))
                
                # Combined entanglement
                entanglement = (overlap + matrix_overlap + holo_similarity) / 3.0
                total_entanglement += entanglement
                pair_count += 1
        
        return total_entanglement / max(pair_count, 1)
    
    def _generate_basic_training_example(self, cluster: List[MatrixEntangledNeuron]) -> Tuple[str, str]:
        """Generate basic training example from neuron cluster."""
        
        # Extract concepts
        concepts = [neuron.metadata['concept'] for neuron in cluster]
        dimensions = [neuron.metadata['dimension'] for neuron in cluster]
        
        # Generate prompt
        if len(concepts) == 2:
            prompt = f"Explain the relationship between {concepts[0]} and {concepts[1]}."
        else:
            prompt = f"Describe how {concepts[0]} relates to {', '.join(concepts[1:3])}."
        
        # Generate completion
        completion = f"The matrix-entangled neurons reveal that {concepts[0]} "
        completion += f"exhibits quantum coherence with {concepts[1] if len(concepts) > 1 else 'the system'}. "
        completion += f"Through dimensional entanglement across dimensions {set(dimensions)}, "
        completion += f"we observe emergent patterns that suggest a holographic structure "
        completion += f"where each component contains information about the whole. "
        completion += f"The fractal encoding indicates self-similarity across multiple scales, "
        completion += f"while the quantum state dynamics reveal non-local correlations "
        completion += f"that transcend classical boundaries."
        
        return prompt, completion
    
    def _generate_with_llm_integration(self, cluster: List[MatrixEntangledNeuron]) -> Tuple[str, str]:
        """Generate training example using LLM integration."""
        
        # Extract concepts and metadata
        concepts = [neuron.metadata['concept'] for neuron in cluster]
        dimensions = [neuron.metadata['dimension'] for neuron in cluster]
        
        # Create context for LLM
        context = f"Matrix-entangled neurons representing concepts: {', '.join(concepts)} "
        context += f"across dimensions {set(dimensions)}. "
        context += f"Global emergence level: {self.global_emergence_level:.4f}. "
        context += f"Cluster entanglement: {self._calculate_cluster_entanglement(cluster):.4f}."
        
        # Use LLM integration if available
        if self.llm_integration:
            try:
                result = self.llm_integration.process_with_dimensional_entanglement(context)
                prompt = f"Analyze the matrix-entangled relationship between {', '.join(concepts[:2])}."
                completion = result['response']
                return prompt, completion
            except Exception as e:
                print(f"⚠️ LLM integration failed: {e}")
        
        # Fallback to basic generation
        return self._generate_basic_training_example(cluster)
    
    def _calculate_training_emergence(self, 
                                    cluster: List[MatrixEntangledNeuron], 
                                    cluster_entanglement: float) -> float:
        """Calculate emergence score for training example."""
        
        # Base emergence from cluster entanglement
        base_emergence = cluster_entanglement
        
        # Add dimensional diversity
        dimensions = set(neuron.metadata['dimension'] for neuron in cluster)
        dimensional_diversity = len(dimensions) / 10.0  # Normalize
        
        # Add quantum coherence
        quantum_coherences = [np.abs(np.vdot(n.quantum_state, n.quantum_state)) for n in cluster]
        avg_quantum_coherence = np.mean(quantum_coherences)
        
        # Add fractal complexity
        fractal_dimensions = [n.fractal_encoding.get('fractal_dimension', 0.0) for n in cluster]
        avg_fractal_complexity = np.mean(fractal_dimensions)
        
        # Combined emergence score
        emergence = (
            0.4 * base_emergence +
            0.2 * dimensional_diversity +
            0.2 * avg_quantum_coherence +
            0.2 * avg_fractal_complexity
        )
        
        return float(np.clip(emergence, 0.0, 1.0))
    
    def set_llm_integration(self, llm: EnhancedHolographicLLM):
        """Set LLM integration for enhanced generation."""
        self.llm_integration = llm
        print("🔗 LLM integration enabled for enhanced training data generation")

class ExperimentalDataGenerator:
    """
    Advanced experimental data generator for matrix-entangled neurons.
    
    This class orchestrates the creation of sophisticated experimental datasets
    using matrix-entangled neurons and LLM integration.
    """
    
    def __init__(self, 
                 database_path: str = "experimental_matrix_neurons.db",
                 use_llm_integration: bool = True):
        
        self.database_path = database_path
        self.use_llm_integration = use_llm_integration
        
        # Initialize components
        self.network = MatrixEntangledNetwork()
        self.database = self._initialize_database()
        
        # Initialize LLM integration if requested
        if use_llm_integration:
            try:
                self.llm = EnhancedHolographicLLM()
                self.network.set_llm_integration(self.llm)
                print("✅ LLM integration initialized")
            except Exception as e:
                print(f"⚠️ LLM integration failed: {e}")
                self.llm = None
        else:
            self.llm = None
    
    def _initialize_database(self) -> sqlite3.Connection:
        """Initialize experimental database."""
        conn = sqlite3.connect(self.database_path)
        cursor = conn.cursor()
        
        # Create experimental neurons table
        cursor.execute("""
            CREATE TABLE IF NOT EXISTS experimental_neurons (
                neuron_id TEXT PRIMARY KEY,
                quantum_state BLOB,
                matrix_weights BLOB,
                holographic_memory BLOB,
                fractal_encoding TEXT,
                emergence_level REAL,
                dimensional_signature TEXT,
                activation_history TEXT,
                entanglement_partners TEXT,
                metadata TEXT,
                created_at TEXT
            )
        """)
        
        # Create training data table
        cursor.execute("""
            CREATE TABLE IF NOT EXISTS experimental_training_data (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                prompt TEXT,
                completion TEXT,
                source_neurons TEXT,
                cluster_entanglement REAL,
                emergence_score REAL,
                dimensional_signature TEXT,
                metadata TEXT,
                created_at TEXT
            )
        """)
        
        conn.commit()
        return conn
    
    def create_experimental_dataset(self, 
                                  domain_concepts: List[str],
                                  num_neurons: int = 100,
                                  num_training_examples: int = 500) -> Dict[str, Any]:
        """
        Create a complete experimental dataset.
        
        Args:
            domain_concepts: List of domain-specific concepts
            num_neurons: Number of neurons to create
            num_training_examples: Number of training examples to generate
            
        Returns:
            Dictionary with dataset information
        """
        
        print("🚀 Creating Experimental Matrix-Entangled Neuron Dataset")
        print("=" * 60)
        
        # Step 1: Create experimental neurons
        print(f"\n🧠 Step 1: Creating {num_neurons} experimental neurons...")
        
        # Generate concepts if not enough provided
        if len(domain_concepts) < num_neurons:
            additional_concepts = self._generate_additional_concepts(num_neurons - len(domain_concepts))
            domain_concepts.extend(additional_concepts)
        
        # Create neurons
        neurons = self.network.create_experimental_batch(
            domain_concepts[:num_neurons],
            dimensions=[random.randint(0, 9) for _ in range(num_neurons)]
        )
        
        # Store neurons in database
        self._store_neurons(neurons)
        
        # Step 2: Generate training data
        print(f"\n🎯 Step 2: Generating {num_training_examples} training examples...")
        
        training_examples = self.network.generate_entangled_training_data(
            num_examples=num_training_examples,
            use_llm_integration=self.use_llm_integration
        )
        
        # Store training data
        self._store_training_data(training_examples)
        
        # Step 3: Export dataset
        print(f"\n💾 Step 3: Exporting dataset...")
        
        export_path = f"experimental_matrix_dataset_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jsonl"
        self._export_dataset(training_examples, export_path)
        
        # Calculate statistics
        stats = self._calculate_dataset_statistics(neurons, training_examples)
        
        print(f"\n✅ Dataset Creation Complete!")
        print(f"   Neurons created: {len(neurons)}")
        print(f"   Training examples: {len(training_examples)}")
        print(f"   Average emergence score: {stats['avg_emergence_score']:.4f}")
        print(f"   Export file: {export_path}")
        
        return {
            'neurons': len(neurons),
            'training_examples': len(training_examples),
            'statistics': stats,
            'export_path': export_path,
            'database_path': self.database_path
        }
    
    def _generate_additional_concepts(self, num_needed: int) -> List[str]:
        """Generate additional concepts for neuron creation."""
        
        # Base concept categories
        categories = {
            'physics': ['quantum_field', 'wave_particle', 'entanglement', 'superposition', 'coherence'],
            'mathematics': ['topology', 'manifold', 'symmetry', 'transformation', 'invariance'],
            'computer_science': ['algorithm', 'recursion', 'emergence', 'complexity', 'optimization'],
            'biology': ['evolution', 'adaptation', 'self_organization', 'morphogenesis', 'homeostasis'],
            'philosophy': ['consciousness', 'qualia', 'intentionality', 'emergence', 'reduction'],
            'psychology': ['cognition', 'perception', 'memory', 'learning', 'attention'],
            'chemistry': ['molecule', 'reaction', 'catalyst', 'bond', 'structure'],
            'neuroscience': ['synapse', 'neuron', 'network', 'plasticity', 'inhibition']
        }
        
        additional_concepts = []
        
        for _ in range(num_needed):
            category = random.choice(list(categories.keys()))
            concept = random.choice(categories[category])
            
            # Add variation
            variations = ['enhanced', 'quantum', 'fractal', 'holographic', 'emergent', 'adaptive']
            variation = random.choice(variations)
            
            new_concept = f"{variation}_{concept}"
            additional_concepts.append(new_concept)
        
        return additional_concepts
    
    def _store_neurons(self, neurons: List[MatrixEntangledNeuron]):
        """Store neurons in database."""
        cursor = self.database.cursor()
        
        for neuron in neurons:
            neuron_dict = neuron.to_dict()
            cursor.execute("""
                INSERT OR REPLACE INTO experimental_neurons
                (neuron_id, quantum_state, matrix_weights, holographic_memory,
                 fractal_encoding, emergence_level, dimensional_signature,
                 activation_history, entanglement_partners, metadata, created_at)
                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
            """, (
                neuron_dict['neuron_id'],
                neuron_dict['quantum_state'],
                neuron_dict['matrix_weights'],
                neuron_dict['holographic_memory'],
                neuron_dict['fractal_encoding'],
                neuron_dict['emergence_level'],
                neuron_dict['dimensional_signature'],
                neuron_dict['activation_history'],
                neuron_dict['entanglement_partners'],
                neuron_dict['metadata'],
                neuron_dict['created_at']
            ))
        
        self.database.commit()
        print(f"✅ Stored {len(neurons)} neurons in database")
    
    def _store_training_data(self, training_examples: List[Dict]):
        """Store training data in database."""
        cursor = self.database.cursor()
        
        for example in training_examples:
            cursor.execute("""
                INSERT INTO experimental_training_data
                (prompt, completion, source_neurons, cluster_entanglement,
                 emergence_score, dimensional_signature, metadata, created_at)
                VALUES (?, ?, ?, ?, ?, ?, ?, ?)
            """, (
                example['prompt'],
                example['completion'],
                json.dumps(example['source_neurons']),
                example['cluster_entanglement'],
                example['emergence_score'],
                example['dimensional_signature'],
                json.dumps(example['metadata']),
                datetime.now().isoformat()
            ))
        
        self.database.commit()
        print(f"✅ Stored {len(training_examples)} training examples in database")
    
    def _export_dataset(self, training_examples: List[Dict], export_path: str):
        """Export dataset in JSONL format."""
        
        with open(export_path, 'w', encoding='utf-8') as f:
            for example in training_examples:
                # Format for LLM training
                training_example = {
                    'prompt': example['prompt'],
                    'completion': example['completion'],
                    'metadata': {
                        'emergence_score': example['emergence_score'],
                        'dimensional_signature': example['dimensional_signature'],
                        'cluster_entanglement': example['cluster_entanglement'],
                        'source_neurons': example['source_neurons'],
                        'generation_method': 'experimental_matrix_entangled_neurons',
                        **example['metadata']
                    }
                }
                f.write(json.dumps(training_example, ensure_ascii=False) + '\n')
        
        print(f"✅ Exported dataset to {export_path}")
    
    def _calculate_dataset_statistics(self, 
                                    neurons: List[MatrixEntangledNeuron],
                                    training_examples: List[Dict]) -> Dict[str, Any]:
        """Calculate dataset statistics."""
        
        # Neuron statistics
        neuron_emergence_levels = [neuron.emergence_level for neuron in neurons]
        neuron_dimensions = [neuron.metadata['dimension'] for neuron in neurons]
        
        # Training example statistics
        training_emergence_scores = [ex['emergence_score'] for ex in training_examples]
        training_entanglements = [ex['cluster_entanglement'] for ex in training_examples]
        
        return {
            'num_neurons': len(neurons),
            'num_training_examples': len(training_examples),
            'avg_neuron_emergence': np.mean(neuron_emergence_levels),
            'avg_training_emergence': np.mean(training_emergence_scores),
            'avg_cluster_entanglement': np.mean(training_entanglements),
            'dimensional_diversity': len(set(neuron_dimensions)),
            'high_quality_examples': sum(1 for score in training_emergence_scores if score > 0.7),
            'quantum_coherence_range': [
                min([np.abs(np.vdot(n.quantum_state, n.quantum_state)) for n in neurons]),
                max([np.abs(np.vdot(n.quantum_state, n.quantum_state)) for n in neurons])
            ]
        }

def demo_experimental_matrix_neurons():
    """Demonstrate the experimental matrix-entangled neuron system."""
    
    print("🧠 Experimental Matrix-Entangled Node Neurons Demo")
    print("=" * 60)
    
    # Initialize generator
    generator = ExperimentalDataGenerator(use_llm_integration=True)
    
    # Define domain concepts
    domain_concepts = [
        # Physics
        'quantum_entanglement', 'superposition', 'wave_function', 'decoherence',
        # Mathematics  
        'topology', 'manifold', 'symmetry', 'transformation',
        # Computer Science
        'algorithm', 'recursion', 'emergence', 'complexity',
        # Biology
        'evolution', 'adaptation', 'self_organization', 'morphogenesis',
        # Philosophy
        'consciousness', 'qualia', 'intentionality', 'reduction'
    ]
    
    # Create experimental dataset
    dataset_info = generator.create_experimental_dataset(
        domain_concepts=domain_concepts,
        num_neurons=50,
        num_training_examples=200
    )
    
    # Display results
    print("\n📊 Dataset Statistics:")
    stats = dataset_info['statistics']
    for key, value in stats.items():
        if isinstance(value, float):
            print(f"  {key}: {value:.4f}")
        else:
            print(f"  {key}: {value}")
    
    print(f"\n🎉 Experimental dataset created successfully!")
    print(f"   Database: {dataset_info['database_path']}")
    print(f"   Export: {dataset_info['export_path']}")
    
    return dataset_info

if __name__ == "__main__":
    demo_experimental_matrix_neurons()