sonw_quantum_advanced_tensor_ops.py

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

@dataclass
class TensorStructure:
    """
    Advanced tensor structure with multi-level representations
    and distributed encoding as discussed with Qi
    """
    filler_tensors: List[torch.Tensor]
    role_tensors: List[torch.Tensor]
    binding_weights: torch.Tensor
    structural_indices: List[int]
    resonance_factors: torch.Tensor
    temporal_embedding: torch.Tensor
    semantic_mapping: Dict[str, torch.Tensor]

class TensorOperationType(Enum):
    """Types of tensor operations for symbolic processing"""
    DISTRIBUTED_BINDING = "distributed_binding"
    RECURSIVE_COMPOSITION = "recursive_composition"
    HARMONIC_FUSION = "harmonic_fusion"
    QUANTUM_ENTANGLEMENT = "quantum_entanglement"
    SYMBOLIC_DIFFUSION = "symbolic_diffusion"

class AdvancedTensorProcessor:
    """
    Enhanced tensor processor with sophisticated operations
    for neural-symbolic computation
    """
    def __init__(
        self,
        tensor_dim: int = 128,
        n_levels: int = 4,
        reference_time: str = "2025-02-12 00:34:20"
    ):
        self.tensor_dim = tensor_dim
        self.n_levels = n_levels
        self.reference_time = datetime.strptime(
            reference_time,
            "%Y-%m-%d %H:%M:%S"
        )
        
        self._initialize_components()
        
    def _initialize_components(self):
        """Initialize advanced tensor components"""
        # Multi-level tensor processors
        self.level_processors = [
            torch.nn.Sequential(
                torch.nn.Linear(self.tensor_dim, self.tensor_dim * 2),
                torch.nn.LayerNorm(self.tensor_dim * 2),
                torch.nn.GELU(),
                torch.nn.Linear(self.tensor_dim * 2, self.tensor_dim)
            )
            for _ in range(self.n_levels)
        ]
        
        # Binding network
        self.binding_network = torch.nn.Sequential(
            torch.nn.Linear(self.tensor_dim * 2, self.tensor_dim),
            torch.nn.LayerNorm(self.tensor_dim),
            torch.nn.GELU(),
            torch.nn.Linear(self.tensor_dim, self.tensor_dim)
        )
        
        # Resonance calculator
        self.resonance_network = torch.nn.Sequential(
            torch.nn.Linear(self.tensor_dim, 64),
            torch.nn.LayerNorm(64),
            torch.nn.GELU(),
            torch.nn.Linear(64, 1),
            torch.nn.Sigmoid()
        )
        
        # Temporal encoder
        self.temporal_encoder = torch.nn.GRU(
            input_size=self.tensor_dim,
            hidden_size=self.tensor_dim,
            num_layers=2,
            batch_first=True
        )
        
    def create_distributed_representation(
        self,
        symbols: List[str],
        roles: List[str]
    ) -> TensorStructure:
        """
        Create distributed representation using
        multi-level tensor processing
        """
        # Initialize tensors for each level
        filler_tensors = []
        role_tensors = []
        
        for level in range(self.n_levels):
            # Create symbol embeddings
            symbol_tensors = [
                torch.randn(self.tensor_dim)  # Placeholder for actual embeddings
                for _ in symbols
            ]
            
            # Process through level processor
            processed_symbols = [
                self.level_processors[level](t)
                for t in symbol_tensors
            ]
            
            # Create role embeddings
            role_tensors_level = [
                torch.randn(self.tensor_dim)  # Placeholder for actual embeddings
                for _ in roles
            ]
            
            processed_roles = [
                self.level_processors[level](t)
                for t in role_tensors_level
            ]
            
            filler_tensors.append(torch.stack(processed_symbols))
            role_tensors.append(torch.stack(processed_roles))
            
        # Compute binding weights
        binding_weights = self._compute_binding_weights(
            filler_tensors,
            role_tensors
        )
        
        # Create temporal embedding
        temporal_embedding = self._create_temporal_embedding(
            filler_tensors,
            role_tensors
        )
        
        # Initialize semantic mapping
        semantic_mapping = {
            symbol: torch.randn(self.tensor_dim)
            for symbol in symbols
        }
        
        # Create tensor structure
        structure = TensorStructure(
            filler_tensors=filler_tensors,
            role_tensors=role_tensors,
            binding_weights=binding_weights,
            structural_indices=list(range(len(symbols))),
            resonance_factors=self._compute_resonance(filler_tensors),
            temporal_embedding=temporal_embedding,
            semantic_mapping=semantic_mapping
        )
        
        return structure
        
    def _compute_binding_weights(
        self,
        filler_tensors: List[torch.Tensor],
        role_tensors: List[torch.Tensor]
    ) -> torch.Tensor:
        """Compute binding weights for tensor structure"""
        # Concatenate all filler tensors
        combined_fillers = torch.cat([
            t.mean(dim=0) for t in filler_tensors
        ])
        
        # Concatenate all role tensors
        combined_roles = torch.cat([
            t.mean(dim=0) for t in role_tensors
        ])
        
        # Compute binding through network
        binding_input = torch.cat([
            combined_fillers,
            combined_roles
        ])
        
        return self.binding_network(binding_input)
        
    def _compute_resonance(
        self,
        tensor_list: List[torch.Tensor]
    ) -> torch.Tensor:
        """Compute resonance factors for tensor components"""
        resonance_factors = []
        
        for tensors in tensor_list:
            # Compute mean tensor
            mean_tensor = tensors.mean(dim=0)
            
            # Calculate resonance
            resonance = self.resonance_network(mean_tensor)
            resonance_factors.append(resonance)
            
        return torch.cat(resonance_factors)
        
    def _create_temporal_embedding(
        self,
        filler_tensors: List[torch.Tensor],
        role_tensors: List[torch.Tensor]
    ) -> torch.Tensor:
        """Create temporal embedding for tensor structure"""
        # Combine filler and role information
        combined_sequence = []
        
        for f_tensor, r_tensor in zip(filler_tensors, role_tensors):
            # Create temporal sequence
            sequence = torch.cat([
                f_tensor.mean(dim=0, keepdim=True),
                r_tensor.mean(dim=0, keepdim=True)
            ], dim=0)
            combined_sequence.append(sequence)
            
        # Stack sequences
        temporal_sequence = torch.stack(combined_sequence)
        
        # Process through GRU
        output, _ = self.temporal_encoder(
            temporal_sequence.unsqueeze(0)
        )
        
        return output.squeeze(0)
        
    def apply_tensor_operation(
        self,
        structure: TensorStructure,
        operation_type: TensorOperationType,
        **kwargs
    ) -> TensorStructure:
        """Apply sophisticated tensor operation"""
        if operation_type == TensorOperationType.DISTRIBUTED_BINDING:
            return self._apply_distributed_binding(structure, **kwargs)
        elif operation_type == TensorOperationType.RECURSIVE_COMPOSITION:
            return self._apply_recursive_composition(structure, **kwargs)
        elif operation_type == TensorOperationType.HARMONIC_FUSION:
            return self._apply_harmonic_fusion(structure, **kwargs)
        elif operation_type == TensorOperationType.QUANTUM_ENTANGLEMENT:
            return self._apply_quantum_entanglement(structure, **kwargs)
        elif operation_type == TensorOperationType.SYMBOLIC_DIFFUSION:
            return self._apply_symbolic_diffusion(structure, **kwargs)
            
        raise ValueError(f"Unknown operation type: {operation_type}")
        
    def _apply_distributed_binding(
        self,
        structure: TensorStructure,
        binding_scale: float = 1.0
    ) -> TensorStructure:
        """Apply distributed binding operation"""
        # Scale binding weights
        scaled_weights = structure.binding_weights * binding_scale
        
        # Update fillers through binding
        new_fillers = [
            f * scaled_weights for f in structure.filler_tensors
        ]
        
        # Update roles through binding
        new_roles = [
            r * scaled_weights for r in structure.role_tensors
        ]
        
        return TensorStructure(
            filler_tensors=new_fillers,
            role_tensors=new_roles,
            binding_weights=scaled_weights,
            structural_indices=structure.structural_indices,
            resonance_factors=structure.resonance_factors,
            temporal_embedding=structure.temporal_embedding,
            semantic_mapping=structure.semantic_mapping
        )
        
    def _apply_recursive_composition(
        self,
        structure: TensorStructure,
        depth: int = 2
    ) -> TensorStructure:
        """Apply recursive composition operation"""
        current_structure = structure
        
        for _ in range(depth):
            # Compose fillers
            composed_fillers = [
                self.level_processors[i % self.n_levels](f)
                for i, f in enumerate(current_structure.filler_tensors)
            ]
            
            # Compose roles
            composed_roles = [
                self.level_processors[i % self.n_levels](r)
                for i, r in enumerate(current_structure.role_tensors)
            ]
            
            # Update structure
            current_structure = TensorStructure(
                filler_tensors=composed_fillers,
                role_tensors=composed_roles,
                binding_weights=current_structure.binding_weights,
                structural_indices=current_structure.structural_indices,
                resonance_factors=self._compute_resonance(composed_fillers),
                temporal_embedding=current_structure.temporal_embedding,
                semantic_mapping=current_structure.semantic_mapping
            )
            
        return current_structure

def example_usage():
    """Demonstrate advanced tensor operations"""
    processor = AdvancedTensorProcessor()
    
    # Create distributed representation
    symbols = ["Φ", "√Γ", "∆π"]
    roles = ["operand", "operator", "result"]
    
    structure = processor.create_distributed_representation(
        symbols,
        roles
    )
    
    # Apply operations
    bound_structure = processor.apply_tensor_operation(
        structure,
        TensorOperationType.DISTRIBUTED_BINDING,
        binding_scale=1.2
    )
    
    composed_structure = processor.apply_tensor_operation(
        bound_structure,
        TensorOperationType.RECURSIVE_COMPOSITION,
        depth=3
    )
    
    print("Tensor operations complete!")
    print(f"Resonance factors shape: {composed_structure.resonance_factors.shape}")
    print(f"Temporal embedding shape: {composed_structure.temporal_embedding.shape}")
    
if __name__ == "__main__":
    example_usage()