conceptual entanglement enhanced

#!/usr/bin/env python3
"""
CONCEPTUAL ENTANGLEMENT MODULE - lm_quant_veritas v7.2
-----------------------------------------------------------------
MEMORY-OPTIMIZED QUANTUM-LINGUISTIC CONSCIOUSNESS INTEGRATION
With GPT-5 Architectural Improvements & Diag+IJ Connection
"""

import numpy as np
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Any, Optional, Tuple
import hashlib
import asyncio
import datetime

class EntanglementState(Enum):
    """States of conceptual entanglement"""
    POTENTIAL = "potential"
    COHERENT = "coherent"  
    RESONANT = "resonant"
    MANIFEST = "manifest"
    COLLAPSED = "collapsed"

@dataclass
class ConceptualEntity:
    """Memory-optimized unit of understanding"""
    concept_hash: str
    truth_coordinate: np.ndarray  # float32
    coherence_amplitude: float
    entanglement_vectors: List[np.ndarray]  # float32 arrays
    topological_charge: float
    
    def __repr__(self) -> str:
        """Debug-friendly representation without dumping large arrays"""
        return (f"ConceptualEntity(hash={self.concept_hash[:8]}..., "
                f"coherence={self.coherence_amplitude:.3f}, "
                f"topo_charge={self.topological_charge:.3f}, "
                f"vectors={len(self.entanglement_vectors)})")
    
    def calculate_reality_potential(self) -> float:
        """Calculate normalized manifestation potential [0,1]"""
        coherence_term = float(self.coherence_amplitude)
        
        # Safe entanglement term calculation
        if len(self.entanglement_vectors) == 0:
            entanglement_term = 0.0
        else:
            ent_sum = np.sum(np.stack(self.entanglement_vectors, axis=0), axis=0)
            entanglement_term = float(np.linalg.norm(ent_sum))
            # Normalize by dimensionality to bound term
            max_ent_norm = np.sqrt(len(self.truth_coordinate))
            entanglement_term /= (max_ent_norm + 1e-8)
        
        topological_term = float(abs(self.topological_charge))
        
        # Weighted sum with normalized terms
        return min(1.0, coherence_term * 0.4 + entanglement_term * 0.35 + topological_term * 0.25)

@dataclass
class UnderstandingManifold:
    """Memory-optimized manifold with diag+ij connection"""
    dimensionality: int
    metric_tensor: np.ndarray  # float32
    curvature_field: np.ndarray  # float32
    diag_coeff: np.ndarray  # float32, shape (dim,)
    ij_coeff: np.ndarray    # float32, shape (dim, dim)
    
    def parallel_transport(self, concept: ConceptualEntity, path: np.ndarray) -> ConceptualEntity:
        """
        Efficient parallel transport using diag + ij connection components
        
        Mathematical intent:
        transported_vec[i] = diag_coeff[i] * vector[i] + sum_k ij_coeff[i,k] * vector[k]
        
        Where:
        - diag_coeff handles self-reinforcement (i==j==k case)
        - ij_coeff handles conceptual coherence (i==j, any k aggregated)
        """
        transported_vectors = []
        for vector in concept.entanglement_vectors:
            # Efficient transport: diag * vector (elementwise) + ij @ vector
            transported_vec = self.diag_coeff * vector + self.ij_coeff.dot(vector)
            transported_vectors.append(transported_vec.astype(np.float32))
        
        # Return new entity to avoid mutation
        return ConceptualEntity(
            concept_hash=concept.concept_hash + "_transported",
            truth_coordinate=(concept.truth_coordinate + path).astype(np.float32),
            coherence_amplitude=concept.coherence_amplitude,
            entanglement_vectors=transported_vectors,
            topological_charge=concept.topological_charge
        )

class QuantumLinguisticEngine:
    """
    Memory-optimized engine for conceptual entanglement operations
    Uses diag+ij connection instead of full 3-tensor
    """
    
    def __init__(self, conceptual_space_dims: int = 256, 
                 random_seed: Optional[int] = None,
                 manifestation_threshold: float = 0.85):
        self.conceptual_space_dims = conceptual_space_dims
        self.manifestation_threshold = manifestation_threshold
        self.rng = np.random.default_rng(random_seed)
        self.understanding_manifold = self._initialize_manifold()
        self.entangled_concepts: Dict[str, ConceptualEntity] = {}
        self.reality_interface = RealityInterface()
        
    def _initialize_manifold(self) -> UnderstandingManifold:
        """Initialize memory-optimized understanding manifold"""
        dim = self.conceptual_space_dims
        
        # Metric tensor
        metric_tensor = np.eye(dim, dtype=np.float32)
        
        # Curvature field with controlled randomness
        curvature = self.rng.normal(0, 0.1, (dim, dim)).astype(np.float32)
        curvature = (curvature + curvature.T) / 2  # Symmetrize
        
        # Memory-efficient connection components
        diag_coeff, ij_coeff = self._calculate_efficient_connection(dim)
        
        return UnderstandingManifold(
            dimensionality=dim,
            metric_tensor=metric_tensor,
            curvature_field=curvature,
            diag_coeff=diag_coeff,
            ij_coeff=ij_coeff
        )
    
    def _calculate_efficient_connection(self, dim: int) -> Tuple[np.ndarray, np.ndarray]:
        """
        Calculate memory-efficient connection components
        
        Returns:
        - diag_coeff: diagonal reinforcement coefficients (shape [dim])
        - ij_coeff: conceptual coherence operator (shape [dim, dim])
        
        Memory footprint: O(dim²) instead of O(dim³)
        """
        # diag: self-reinforcement (formerly i==j==k: 0.5)
        diag_coeff = np.full(dim, 0.5, dtype=np.float32)
        
        # ij: conceptual coherence operator (formerly i==j, any k: 0.1)
        ij_coeff = np.full((dim, dim), 0.1, dtype=np.float32)
        
        return diag_coeff, ij_coeff
    
    def _cosine_similarity_safe(self, a: np.ndarray, b: np.ndarray, eps: float = 1e-10) -> float:
        """Safe cosine similarity with NaN protection"""
        na, nb = np.linalg.norm(a), np.linalg.norm(b)
        if na < eps or nb < eps:
            return 0.0
        return float(np.dot(a, b) / (na * nb))
    
    def _concept_hash(self, concept: str) -> str:
        """Full hash for better entropy distribution"""
        return hashlib.sha3_256(concept.encode()).hexdigest()
    
    def _concept_to_coordinate(self, concept: str) -> np.ndarray:
        """Robust concept mapping using full byte space"""
        digest = hashlib.sha3_256(concept.encode()).digest()  # 32 bytes
        
        # Expand to fill conceptual space dimensions
        repeats = (self.conceptual_space_dims + len(digest) - 1) // len(digest)
        big_bytes = (digest * repeats)[:self.conceptual_space_dims]
        
        # Convert to normalized float32 array
        arr = np.frombuffer(big_bytes, dtype=np.uint8).astype(np.float32)
        return ((arr / 255.0) * 2.0 - 1.0).astype(np.float32)  # Normalize to [-1, 1]
    
    def entangle_concepts(self, primary_concept: str, secondary_concept: str) -> ConceptualEntity:
        """Create robust quantum entanglement between concepts"""
        primary_hash = self._concept_hash(primary_concept)
        secondary_hash = self._concept_hash(secondary_concept)
        
        primary_coord = self._concept_to_coordinate(primary_concept)
        secondary_coord = self._concept_to_coordinate(secondary_concept)
        
        # Safe coherence calculation
        cos_sim = self._cosine_similarity_safe(primary_coord, secondary_coord)
        coherence = (cos_sim + 1.0) / 2.0  # Normalize to [0,1]
        
        # Ensure float32 for entanglement vector
        entanglement_vector = (secondary_coord - primary_coord).astype(np.float32)
        
        entangled_entity = ConceptualEntity(
            concept_hash=f"{primary_hash}:{secondary_hash}",
            truth_coordinate=((primary_coord + secondary_coord) / 2).astype(np.float32),
            coherence_amplitude=coherence,
            entanglement_vectors=[entanglement_vector],
            topological_charge=cos_sim  # Use cosine similarity as topological charge
        )
        
        self.entangled_concepts[entangled_entity.concept_hash] = entangled_entity
        return entangled_entity
    
    def calibrate_threshold(self, examples: List[Tuple[ConceptualEntity, bool]]) -> float:
        """
        Calibrate manifestation threshold from labeled examples
        
        Args:
            examples: List of (concept_entity, did_manifest) pairs
            
        Returns:
            Optimized manifestation threshold
        """
        if not examples:
            return self.manifestation_threshold  # Default if no data
            
        potentials = [entity.calculate_reality_potential() for entity, _ in examples]
        manifested = [did_manifest for _, did_manifest in examples]
        
        # Simple threshold optimization: find value that maximizes accuracy
        best_threshold = 0.5
        best_accuracy = 0.0
        
        for threshold in np.linspace(0.1, 0.9, 50):
            predictions = [p >= threshold for p in potentials]
            accuracy = sum(p == m for p, m in zip(predictions, manifested)) / len(examples)
            
            if accuracy > best_accuracy:
                best_accuracy = accuracy
                best_threshold = threshold
        
        self.manifestation_threshold = best_threshold
        return best_threshold

class RealityInterface:
    """Robust reality interface with calibration support"""
    
    def __init__(self):
        self.manifestation_records = []
        self.collapse_observers = []
    
    async def attempt_manifestation(self, concept: ConceptualEntity, 
                                  context: Dict[str, Any],
                                  threshold: float = 0.85) -> Dict[str, Any]:
        """Robust manifestation attempt with configurable threshold"""
        
        reality_potential = concept.calculate_reality_potential()
        
        if reality_potential >= threshold:
            manifestation = {
                'concept_hash': concept.concept_hash,
                'manifestation_strength': reality_potential,
                'reality_distortion': reality_potential - threshold,
                'collapse_observers': len(self.collapse_observers),
                'timestamp': datetime.datetime.utcnow().isoformat(),
                'coordinates_shape': concept.truth_coordinate.shape,
                'status': 'manifested'
            }
            self.manifestation_records.append(manifestation)
            return manifestation
        else:
            return {
                'concept_hash': concept.concept_hash,
                'manifestation_strength': reality_potential,
                'status': 'below_threshold',
                'required_coherence': threshold - reality_potential,
                'current_threshold': threshold
            }

# VALIDATION TESTS
def test_memory_optimized_engine():
    """Comprehensive tests for memory-optimized engine"""
    engine = QuantumLinguisticEngine(conceptual_space_dims=64, random_seed=42)
    
    # Test 1: Memory efficiency - check connection components
    manifold = engine.understanding_manifold
    assert manifold.diag_coeff.shape == (64,)
    assert manifold.ij_coeff.shape == (64, 64)
    assert manifold.diag_coeff.dtype == np.float32
    assert manifold.ij_coeff.dtype == np.float32
    
    # Test 2: Identical concepts should have max coherence
    identical_entanglement = engine.entangle_concepts("test", "test")
    assert abs(identical_entanglement.coherence_amplitude - 1.0) < 1e-6
    
    # Test 3: All arrays should be float32 for memory efficiency
    assert identical_entanglement.truth_coordinate.dtype == np.float32
    assert identical_entanglement.entanglement_vectors[0].dtype == np.float32
    
    # Test 4: Calibration functionality
    calibration_examples = [
        (identical_entanglement, True),  # High potential, should manifest
    ]
    calibrated_threshold = engine.calibrate_threshold(calibration_examples)
    assert 0.0 <= calibrated_threshold <= 1.0
    
    print("✅ All memory-optimized tests passed")

# DEMONSTRATION
async def demonstrate_memory_optimized_entanglement():
    """Demonstrate the memory-optimized entanglement engine"""
    
    print("🌌 CONCEPTUAL ENTANGLEMENT MODULE v7.2")
    print("Memory-Optimized with Diag+IJ Connection")
    print("=" * 60)
    
    # Initialize with seed for reproducibility
    engine = QuantumLinguisticEngine(random_seed=42, manifestation_threshold=0.8)
    
    # Create entanglement
    entanglement = engine.entangle_concepts(
        "truth_manifestation", 
        "institutional_bypass"
    )
    
    print(f"🧠 Memory-Optimized Conceptual Entanglement:")
    print(f"   Entity: {entanglement}")
    print(f"   Reality Potential: {entanglement.calculate_reality_potential():.3f}")
    
    # Test manifestation with custom threshold
    result = await engine.reality_interface.attempt_manifestation(
        entanglement, 
        {'context': 'strategic_deployment'},
        threshold=engine.manifestation_threshold
    )
    
    print(f"\n⚡ Manifestation Result:")
    print(f"   Status: {result['status']}")
    print(f"   Strength: {result['manifestation_strength']:.3f}")
    print(f"   Threshold: {result.get('current_threshold', engine.manifestation_threshold):.3f}")
    
    # Memory efficiency report
    manifold = engine.understanding_manifold
    original_memory = 256**3 * 4  # 256³ float32 tensor in bytes
    optimized_memory = (256 + 256**2) * 4  # diag + ij in bytes
    memory_savings = (1 - optimized_memory / original_memory) * 100
    
    print(f"\n💾 Memory Optimization:")
    print(f"   Original 3-tensor: {original_memory / (1024**2):.1f} MB")
    print(f"   Diag+IJ components: {optimized_memory / (1024**2):.1f} MB")  
    print(f"   Memory reduction: {memory_savings:.1f}%")
    
    # Run validation tests
    print(f"\n🔬 Running Validation Tests...")
    test_memory_optimized_engine()
    
    print(f"\n💫 Module Status: MEMORY-OPTIMIZED & PRODUCTION-READY")
    print("   Diag+IJ connection architecture implemented")
    print("   Full float32 consistency enforced")
    print("   Configurable manifestation threshold")
    print("   Calibration system for threshold optimization")

if __name__ == "__main__":
    asyncio.run(demonstrate_memory_optimized_entanglement())