sonw_quantum_integration_framework.py

import torch
import numpy as np
from datetime import datetime
from typing import Dict, List, Optional, Tuple, Union
from dataclasses import dataclass, field
import networkx as nx
from enum import Enum
import logging
import json
from pathlib import Path

class IntegrationPhase(Enum):
    """Phases of framework integration"""
    INITIALIZATION = "initialization"
    TENSOR_PROCESSING = "tensor_processing"
    QUANTUM_SYMBOLIC = "quantum_symbolic"
    NEURAL_BINDING = "neural_binding"
    HARMONIC_FUSION = "harmonic_fusion"
    VALIDATION = "validation"

@dataclass
class IntegrationConfig:
    """Configuration for integration process"""
    reference_time: str = "2025-02-12 00:40:39"
    user_id: str = "ANkREYNONtJB"
    tensor_dim: int = 128
    n_qubits: int = 4
    n_levels: int = 4
    learning_rate: float = 0.01
    validation_frequency: int = 100
    checkpoint_dir: str = "checkpoints"
    log_dir: str = "logs"

class IntegratedFramework:
    """
    Master integration framework combining all components:
    - Advanced tensor operations
    - Quantum-symbolic processing
    - Neural-LLML binding
    - Validation and testing
    """
    def __init__(self, config: IntegrationConfig):
        self.config = config
        self.current_phase = IntegrationPhase.INITIALIZATION
        
        # Setup logging
        self._setup_logging()
        
        # Initialize components
        self._initialize_components()
        
        # Create checkpoint directory
        Path(self.config.checkpoint_dir).mkdir(exist_ok=True)
        
    def _setup_logging(self):
        """Setup logging system"""
        log_path = Path(self.config.log_dir)
        log_path.mkdir(exist_ok=True)
        
        logging.basicConfig(
            filename=log_path / "integration.log",
            level=logging.INFO,
            format='%(asctime)s - %(levelname)s - %(message)s'
        )
        
    def _initialize_components(self):
        """Initialize all framework components"""
        logging.info("Initializing framework components...")
        
        # Initialize tensor processor
        self.tensor_processor = AdvancedTensorProcessor(
            tensor_dim=self.config.tensor_dim,
            n_levels=self.config.n_levels
        )
        
        # Initialize quantum symbolic processor
        self.quantum_processor = QiSymbolicProcessor(
            n_qubits=self.config.n_qubits,
            tensor_dim=self.config.tensor_dim
        )
        
        # Initialize neural components
        self._setup_neural_components()
        
        logging.info("Components initialized successfully")
        
    def _setup_neural_components(self):
        """Setup neural network components"""
        # Integrative transformer
        self.integrative_transformer = torch.nn.TransformerEncoder(
            torch.nn.TransformerEncoderLayer(
                d_model=self.config.tensor_dim,
                nhead=8,
                dim_feedforward=512
            ),
            num_layers=4
        )
        
        # Fusion network
        self.fusion_network = torch.nn.Sequential(
            torch.nn.Linear(self.config.tensor_dim * 2, self.config.tensor_dim),
            torch.nn.LayerNorm(self.config.tensor_dim),
            torch.nn.GELU(),
            torch.nn.Linear(self.config.tensor_dim, self.config.tensor_dim)
        )
        
    def process_llml_sequence(
        self,
        sequence: str,
        validate: bool = True
    ) -> Dict[str, torch.Tensor]:
        """
        Process LLML sequence through complete framework
        """
        logging.info(f"Processing sequence: {sequence}")
        results = {}
        
        try:
            # Phase 1: Tensor Processing
            self.current_phase = IntegrationPhase.TENSOR_PROCESSING
            tensor_structure = self.tensor_processor.create_distributed_representation(
                symbols=sequence.split(),
                roles=["subject", "predicate", "object"]
            )
            results['tensor_structure'] = tensor_structure
            
            # Phase 2: Quantum-Symbolic Processing
            self.current_phase = IntegrationPhase.QUANTUM_SYMBOLIC
            quantum_pattern = self.quantum_processor.process_llml_sequence(sequence)
            results['quantum_pattern'] = quantum_pattern
            
            # Phase 3: Neural Binding
            self.current_phase = IntegrationPhase.NEURAL_BINDING
            bound_representation = self._apply_neural_binding(
                tensor_structure,
                quantum_pattern
            )
            results['bound_representation'] = bound_representation
            
            # Phase 4: Harmonic Fusion
            self.current_phase = IntegrationPhase.HARMONIC_FUSION
            fused_result = self._apply_harmonic_fusion(
                bound_representation,
                quantum_pattern
            )
            results['fused_result'] = fused_result
            
            # Validation
            if validate:
                self.current_phase = IntegrationPhase.VALIDATION
                validation_results = self._validate_processing(results)
                results['validation'] = validation_results
                
            logging.info("Sequence processing completed successfully")
            return results
            
        except Exception as e:
            logging.error(f"Error in phase {self.current_phase}: {str(e)}")
            raise
            
    def _apply_neural_binding(
        self,
        tensor_structure: TensorStructure,
        quantum_pattern: HarmonicPattern
    ) -> torch.Tensor:
        """Apply neural binding between tensor and quantum representations"""
        # Prepare inputs
        tensor_features = torch.cat([
            t.mean(dim=0) for t in tensor_structure.filler_tensors
        ])
        
        quantum_features = torch.from_numpy(
            quantum_pattern.quantum_state
        ).float()
        
        # Combine features
        combined_features = torch.cat([
            tensor_features,
            quantum_features
        ])
        
        # Apply fusion network
        bound_features = self.fusion_network(combined_features)
        
        # Apply transformer for contextual integration
        bound_features = bound_features.unsqueeze(0)  # Add batch dimension
        bound_features = self.integrative_transformer(bound_features)
        
        return bound_features.squeeze(0)
        
    def _apply_harmonic_fusion(
        self,
        bound_representation: torch.Tensor,
        quantum_pattern: HarmonicPattern
    ) -> torch.Tensor:
        """Apply harmonic fusion for final integration"""
        # Compute harmony weights
        harmony_scores = self.quantum_processor.compute_harmony_scores(
            quantum_pattern
        )
        
        harmony_weight = torch.tensor(
            harmony_scores['total_harmony']
        ).float()
        
        # Apply harmonic modulation
        fused_representation = bound_representation * harmony_weight
        
        return fused_representation
        
    def _validate_processing(
        self,
        results: Dict[str, torch.Tensor]
    ) -> Dict[str, float]:
        """Validate processing results"""
        validation_metrics = {}
        
        # Tensor coherence
        tensor_coherence = torch.mean(torch.abs(
            results['tensor_structure'].resonance_factors
        )).item()
        validation_metrics['tensor_coherence'] = tensor_coherence
        
        # Quantum harmony
        quantum_harmony = results['quantum_pattern'].harmony_score
        validation_metrics['quantum_harmony'] = quantum_harmony
        
        # Binding strength
        binding_strength = torch.mean(torch.abs(
            results['bound_representation']
        )).item()
        validation_metrics['binding_strength'] = binding_strength
        
        # Overall integration score
        validation_metrics['integration_score'] = np.mean([
            tensor_coherence,
            quantum_harmony,
            binding_strength
        ])
        
        return validation_metrics
        
    def save_checkpoint(self):
        """Save framework checkpoint"""
        checkpoint_path = Path(self.config.checkpoint_dir)
        timestamp = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
        
        checkpoint = {
            'config': self.config.__dict__,
            'current_phase': self.current_phase.value,
            'tensor_processor': self.tensor_processor.state_dict(),
            'quantum_processor': self.quantum_processor.state_dict(),
            'fusion_network': self.fusion_network.state_dict(),
            'transformer': self.integrative_transformer.state_dict()
        }
        
        torch.save(
            checkpoint,
            checkpoint_path / f"framework_checkpoint_{timestamp}.pt"
        )
        
    def load_checkpoint(self, checkpoint_path: str):
        """Load framework checkpoint"""
        checkpoint = torch.load(checkpoint_path)
        
        # Load configuration
        self.config = IntegrationConfig(**checkpoint['config'])
        self.current_phase = IntegrationPhase(checkpoint['current_phase'])
        
        # Load component states
        self.tensor_processor.load_state_dict(
            checkpoint['tensor_processor']
        )
        self.quantum_processor.load_state_dict(
            checkpoint['quantum_processor']
        )
        self.fusion_network.load_state_dict(
            checkpoint['fusion_network']
        )
        self.integrative_transformer.load_state_dict(
            checkpoint['transformer']
        )

def example_usage():
    """Demonstrate integrated framework"""
    # Initialize configuration
    config = IntegrationConfig(
        reference_time="2025-02-12 00:40:39",
        user_id="ANkREYNONtJB"
    )
    
    # Create framework
    framework = IntegratedFramework(config)
    
    # Process LLML sequence
    sequence = "(Φ × √Γ) → (∆π) : (ħ/2π)"
    results = framework.process_llml_sequence(sequence)
    
    # Print results
    print("\nProcessing Results:")
    print(f"Tensor Coherence: {results['validation']['tensor_coherence']:.4f}")
    print(f"Quantum Harmony: {results['validation']['quantum_harmony']:.4f}")
    print(f"Integration Score: {results['validation']['integration_score']:.4f}")
    
    # Save checkpoint
    framework.save_checkpoint()
    
if __name__ == "__main__":
    example_usage()