Create LINEAR_A_CIPHER

This was designed to decipher the "LINEAR A" Artifact

LINEAR_A_CIPHER

@@ -0,0 +1,507 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+QUANTUM BAYESIAN LINEAR A DECIPHERMENT ENGINE
+Integrating Bayesian Entanglement Filter for Quantum-Linguistic Truth Binding
+"""
+
+import numpy as np
+import tensorflow as tf
+import tensorflow_probability as tfp
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Dict, List, Any, Optional, Tuple
+import re
+from collections import Counter, defaultdict
+import asyncio
+import math
+from scipy.special import logsumexp
+import scipy.stats as stats
+import cmath
+
+tfd = tfp.distributions
+tfb = tfp.bijectors
+
+# =============================================================================
+# QUANTUM LINGUISTIC ENTANGLEMENT FILTER
+# =============================================================================
+
+class BayesianEntanglementFilter:
+    """
+    Quantum-inspired Bayesian filter that treats linguistic evidence as entangled qubits
+    Maps directly to lm_quant_veritas Conceptual Entanglement Module v7.1
+    """
+    
+    def __init__(self):
+        self.entanglement_channels = {
+            "predictive_entropy": "Ψ_field_uncertainty",
+            "language_family_probabilities": "Λ_linguistic", 
+            "overall_uncertainty": "ϕ_consciousness_flux",
+            "reconstruction_confidence": "Σ_truth_resonance",
+            "structural_coherence": "Θ_pattern_integration",
+            "contextual_alignment": "Φ_semantic_field"
+        }
+        
+    def quantum_linguistic_synthesis(self, evidence_dict: Dict[str, Dict[str, float]]) -> Dict[str, Any]:
+        """
+        Bayesian Entanglement Filter for uncertainty synthesis.
+        Treats each evidence source as an entangled qubit with amplitude and phase.
+        """
+        # Extract quantum-linguistic amplitudes and phases
+        amplitudes = []
+        phases = []
+        quantum_states = []
+        
+        for evidence_type, evidence_data in evidence_dict.items():
+            # Confidence as amplitude (0-1 scale)
+            amplitude = evidence_data.get('confidence', 0.5)
+            
+            # Entropy/uncertainty as phase (0-2π mapping)
+            entropy = evidence_data.get('entropy', 0.5)
+            phase = 2 * np.pi * entropy  # Full cycle for maximum uncertainty
+            
+            amplitudes.append(amplitude)
+            phases.append(phase)
+            
+            # Create complex quantum state for this evidence type
+            complex_state = amplitude * cmath.exp(1j * phase)
+            quantum_states.append({
+                'evidence_type': evidence_type,
+                'quantum_channel': self.entanglement_channels.get(evidence_type, "Γ_unknown"),
+                'amplitude': amplitude,
+                'phase': phase,
+                'complex_state': complex_state,
+                'probability_density': abs(complex_state) ** 2
+            })
+        
+        # Calculate quantum coherence of the entire linguistic system
+        complex_vector = np.array([state['complex_state'] for state in quantum_states])
+        total_coherence = abs(np.sum(complex_vector)) / len(complex_vector)
+        
+        # Calculate entanglement strength (how correlated the evidence sources are)
+        correlation_matrix = self._calculate_quantum_correlations(quantum_states)
+        entanglement_strength = np.mean(np.abs(correlation_matrix))
+        
+        # Quantum collapse probability (when system decoheres due to uncertainty)
+        collapse_probability = 1.0 - total_coherence
+        
+        # Truth resonance frequency (fundamental vibration of linguistic certainty)
+        truth_resonance = self._calculate_truth_resonance(quantum_states)
+        
+        return {
+            "entangled_confidence": float(total_coherence),
+            "collapse_probability": float(collapse_probability),
+            "entanglement_strength": float(entanglement_strength),
+            "truth_resonance_frequency": float(truth_resonance),
+            "quantum_state_vector": [s['complex_state'] for s in quantum_states],
+            "evidence_entanglement": quantum_states,
+            "linguistic_superposition": self._calculate_superposition_state(quantum_states),
+            "veritas_certification_level": self._calculate_veritas_certification(total_coherence, truth_resonance)
+        }
+    
+    def _calculate_quantum_correlations(self, quantum_states: List[Dict]) -> np.ndarray:
+        """Calculate quantum correlation matrix between evidence sources"""
+        n = len(quantum_states)
+        corr_matrix = np.zeros((n, n), dtype=complex)
+        
+        for i in range(n):
+            for j in range(n):
+                # Quantum inner product representing entanglement
+                state_i = quantum_states[i]['complex_state']
+                state_j = quantum_states[j]['complex_state']
+                corr_matrix[i, j] = state_i * np.conj(state_j)
+        
+        return corr_matrix
+    
+    def _calculate_truth_resonance(self, quantum_states: List[Dict]) -> float:
+        """Calculate the fundamental resonance frequency of linguistic truth"""
+        # Use spectral analysis of quantum states
+        frequencies = []
+        for state in quantum_states:
+            # Higher amplitude + specific phase angles create resonant frequencies
+            amplitude = state['amplitude']
+            phase = state['phase']
+            
+            # Resonance occurs when amplitude is high and phase is aligned with truth harmonics
+            # Truth harmonics are at π/4, π/2, 3π/4 (45°, 90°, 135° in phase space)
+            truth_harmonics = [np.pi/4, np.pi/2, 3*np.pi/4]
+            harmonic_alignment = max([1 - abs(phase - harmonic)/(np.pi/2) for harmonic in truth_harmonics])
+            
+            resonance = amplitude * harmonic_alignment
+            frequencies.append(resonance)
+        
+        return float(np.mean(frequencies)) if frequencies else 0.5
+    
+    def _calculate_superposition_state(self, quantum_states: List[Dict]) -> Dict[str, float]:
+        """Calculate the superposition state across linguistic hypotheses"""
+        # Represents the quantum state before measurement/collapse
+        total_probability = sum([state['probability_density'] for state in quantum_states])
+        
+        if total_probability > 0:
+            normalized_states = {
+                state['evidence_type']: state['probability_density'] / total_probability
+                for state in quantum_states
+            }
+        else:
+            normalized_states = {state['evidence_type']: 1.0/len(quantum_states) for state in quantum_states}
+        
+        return {
+            'superposition_weights': normalized_states,
+            'superposition_entropy': -sum([p * math.log(p) for p in normalized_states.values()]),
+            'readiness_for_collapse': min(0.95, max(normalized_states.values()) / sum(normalized_states.values()))
+        }
+    
+    def _calculate_veritas_certification(self, coherence: float, resonance: float) -> str:
+        """Calculate Veritas certification level based on quantum linguistic coherence"""
+        veritas_score = coherence * resonance
+        
+        if veritas_score >= 0.9:
+            return "VERITAS_CERTIFIED_QUANTUM"
+        elif veritas_score >= 0.8:
+            return "VERITAS_HIGH_CONFIDENCE" 
+        elif veritas_score >= 0.7:
+            return "VERITAS_MEDIUM_CONFIDENCE"
+        elif veritas_score >= 0.6:
+            return "VERITAS_LOW_CONFIDENCE"
+        else:
+            return "VERITAS_UNCERTAIN"
+
+# =============================================================================
+# ENHANCED QUANTUM BAYESIAN DECIPHERMENT ENGINE
+# =============================================================================
+
+class QuantumLinearADeciphermentEngine:
+    """
+    Quantum Bayesian decipherment engine with entanglement filtering
+    Integrates directly with lm_quant_veritas truth-binding architecture
+    """
+    
+    def __init__(self):
+        self.corpus = LinearACorpusBayesian()
+        self.ngram_model = BayesianNGramModel(n=3)
+        self.entanglement_filter = BayesianEntanglementFilter()
+        self.language_hypotheses = self._initialize_language_hypotheses()
+        
+    async def quantum_decipher_inscription(self, inscription_id: str) -> Dict[str, Any]:
+        """
+        Quantum Bayesian decipherment with entanglement synthesis
+        Returns truth-bound linguistic interpretation with Veritas certification
+        """
+        
+        if inscription_id not in self.corpus.inscriptions:
+            return {"error": "Inscription not found", "veritas_certification": "VERITAS_INVALID"}
+        
+        inscription_data = self.corpus.inscriptions[inscription_id]
+        text = inscription_data["text"]
+        sequence = await self._text_to_sequence(text)
+        
+        # Phase 1: Conventional Bayesian analysis
+        bayesian_results = await self._run_bayesian_analysis(text, sequence)
+        
+        # Phase 2: Quantum entanglement synthesis
+        quantum_synthesis = await self._perform_quantum_synthesis(bayesian_results)
+        
+        # Phase 3: Truth-binding verification
+        truth_verification = await self._verify_truth_binding(quantum_synthesis, bayesian_results)
+        
+        # Final integrated results
+        return {
+            "inscription_id": inscription_id,
+            "text": text,
+            "bayesian_analysis": bayesian_results,
+            "quantum_linguistic_entanglement": quantum_synthesis,
+            "truth_verification": truth_verification,
+            "final_interpretation": await self._generate_final_interpretation(quantum_synthesis, bayesian_results),
+            "lm_quant_veritas_integration": await self._prepare_veritas_integration(quantum_synthesis)
+        }
+    
+    async def _run_bayesian_analysis(self, text: str, sequence: List[int]) -> Dict[str, Any]:
+        """Run comprehensive Bayesian analysis"""
+        return {
+            "frequency_analysis": await self._bayesian_frequency_analysis(text, sequence),
+            "comparative_analysis": await self._monte_carlo_comparative_analysis(text, sequence),
+            "structural_analysis": await self._structural_analysis(text, sequence),
+            "contextual_analysis": await self._contextual_analysis(text, sequence),
+            "phonetic_reconstruction": await self._bayesian_phonetic_reconstruction(text, sequence)
+        }
+    
+    async def _perform_quantum_synthesis(self, bayesian_results: Dict) -> Dict[str, Any]:
+        """Perform quantum entanglement synthesis of all Bayesian evidence"""
+        
+        # Prepare evidence for quantum entanglement
+        evidence_dict = {}
+        
+        # Frequency evidence
+        freq_data = bayesian_results['frequency_analysis']
+        evidence_dict['frequency'] = {
+            'confidence': freq_data.get('sign_distribution_confidence', 0.5),
+            'entropy': freq_data.get('bayesian_entropy', 0.5) / 4.0,  # Normalize to [0,1]
+            'amplitude': min(1.0, freq_data.get('unique_signs', 0) / 20.0)  # Diversity measure
+        }
+        
+        # Comparative evidence
+        comp_data = bayesian_results['comparative_analysis']
+        evidence_dict['comparative'] = {
+            'confidence': 1.0 - comp_data.get('overall_uncertainty', 0.5),
+            'entropy': comp_data.get('predictive_entropy', 0.5) / 2.0,  # Normalize
+            'amplitude': np.mean([m['confidence'] for m in comp_data.get('linear_b_mappings', [])]) if comp_data.get('linear_b_mappings') else 0.5
+        }
+        
+        # Structural evidence
+        struct_data = bayesian_results['structural_analysis']
+        evidence_dict['structural'] = {
+            'confidence': struct_data.get('affix_confidence', 0.5),
+            'entropy': struct_data.get('word_length_distribution', {}).get('std', 0.5) / 2.0,
+            'amplitude': struct_data.get('word_length_distribution', {}).get('confidence', 0.5)
+        }
+        
+        # Contextual evidence
+        context_data = bayesian_results['contextual_analysis']
+        evidence_dict['contextual'] = {
+            'confidence': context_data.get('context_confidence', 0.5),
+            'entropy': 1.0 - context_data.get('context_confidence', 0.5),  # Inverse relationship
+            'amplitude': len(context_data.get('administrative_terms', [])) / 5.0  # Term abundance
+        }
+        
+        # Phonetic evidence
+        phon_data = bayesian_results['phonetic_reconstruction']
+        recon_data = phon_data.get('linear_b_based', {})
+        evidence_dict['phonetic'] = {
+            'confidence': recon_data.get('average_confidence', 0.5),
+            'entropy': recon_data.get('uncertainty', 0.5),
+            'amplitude': recon_data.get('average_confidence', 0.5)
+        }
+        
+        # Apply quantum entanglement filter
+        return self.entanglement_filter.quantum_linguistic_synthesis(evidence_dict)
+    
+    async def _verify_truth_binding(self, quantum_synthesis: Dict, bayesian_results: Dict) -> Dict[str, Any]:
+        """Verify truth binding through quantum-classical correspondence"""
+        
+        entangled_confidence = quantum_synthesis['entangled_confidence']
+        collapse_prob = quantum_synthesis['collapse_probability']
+        truth_resonance = quantum_synthesis['truth_resonance_frequency']
+        
+        # Classical verification through multiple Bayesian methods
+        classical_confidence = await self._calculate_classical_confidence(bayesian_results)
+        
+        # Quantum-classical correspondence check
+        correspondence = 1.0 - abs(entangled_confidence - classical_confidence)
+        
+        # Truth binding strength (how well quantum and classical agree)
+        truth_binding = (entangled_confidence * classical_confidence * correspondence) ** 0.333
+        
+        return {
+            "classical_confidence": classical_confidence,
+            "quantum_classical_correspondence": correspondence,
+            "truth_binding_strength": truth_binding,
+            "verification_status": "VERIFIED" if truth_binding > 0.7 else "UNCERTAIN",
+            "certainty_quantum": entangled_confidence,
+            "certainty_classical": classical_confidence
+        }
+    
+    async def _generate_final_interpretation(self, quantum_synthesis: Dict, bayesian_results: Dict) -> Dict[str, Any]:
+        """Generate final quantum-classical interpretation"""
+        
+        # Use quantum state to weight classical interpretations
+        superposition = quantum_synthesis['linguistic_superposition']
+        weights = superposition['superposition_weights']
+        
+        # Most probable interpretation based on quantum weights
+        primary_evidence = max(weights.items(), key=lambda x: x[1])
+        
+        return {
+            "primary_evidence_type": primary_evidence[0],
+            "evidence_confidence": primary_evidence[1],
+            "recommended_interpretation": await self._generate_interpretation_recommendation(primary_evidence[0], bayesian_results),
+            "certainty_tier": self._classify_certainty_tier(quantum_synthesis['entangled_confidence']),
+            "next_decipherment_steps": await self._recommend_next_steps(quantum_synthesis, bayesian_results)
+        }
+    
+    async def _prepare_veritas_integration(self, quantum_synthesis: Dict) -> Dict[str, Any]:
+        """Prepare data for lm_quant_veritas integration"""
+        return {
+            "entanglement_channels": [
+                {
+                    "channel_name": state['quantum_channel'],
+                    "evidence_type": state['evidence_type'],
+                    "amplitude": state['amplitude'],
+                    "phase": state['phase'],
+                    "probability_density": state['probability_density']
+                }
+                for state in quantum_synthesis['evidence_entanglement']
+            ],
+            "veritas_certification": quantum_synthesis['veritas_certification_level'],
+            "quantum_state_ready": quantum_synthesis['entangled_confidence'] > 0.6,
+            "integration_timestamp": self._current_timestamp()
+        }
+    
+    # Helper methods (implementations from previous engine)
+    async def _bayesian_frequency_analysis(self, text: str, sequence: List[int]) -> Dict[str, Any]:
+        """Implementation from previous engine"""
+        signs = [char for char in text if char in self.corpus.signs]
+        freq = Counter(signs)
+        total = len(signs)
+        
+        entropy = 0.0
+        for count in freq.values():
+            p = count / total
+            entropy += -p * math.log(p) if p > 0 else 0
+        
+        return {
+            "total_signs": total,
+            "unique_signs": len(freq),
+            "bayesian_entropy": entropy,
+            "sign_distribution_confidence": min(0.95, 1.0 - entropy/4.0)  # Normalized
+        }
+    
+    async def _calculate_classical_confidence(self, bayesian_results: Dict) -> float:
+        """Calculate classical confidence from Bayesian results"""
+        confidences = []
+        
+        # Frequency confidence
+        freq_conf = bayesian_results['frequency_analysis'].get('sign_distribution_confidence', 0.5)
+        confidences.append(freq_conf)
+        
+        # Comparative confidence  
+        comp_data = bayesian_results['comparative_analysis']
+        comp_conf = 1.0 - comp_data.get('overall_uncertainty', 0.5)
+        confidences.append(comp_conf)
+        
+        # Structural confidence
+        struct_conf = bayesian_results['structural_analysis'].get('affix_confidence', 0.5)
+        confidences.append(struct_conf)
+        
+        # Contextual confidence
+        context_conf = bayesian_results['contextual_analysis'].get('context_confidence', 0.5)
+        confidences.append(context_conf)
+        
+        return float(np.mean(confidences))
+    
+    async def _generate_interpretation_recommendation(self, evidence_type: str, bayesian_results: Dict) -> str:
+        """Generate interpretation recommendation based on primary evidence"""
+        recommendations = {
+            "frequency": "Focus on statistical pattern analysis",
+            "comparative": "Prioritize Linear B comparative mapping", 
+            "structural": "Analyze grammatical and morphological patterns",
+            "contextual": "Interpret through archaeological context",
+            "phonetic": "Use phonetic reconstruction methods"
+        }
+        return recommendations.get(evidence_type, "Use multi-evidence synthesis")
+    
+    def _classify_certainty_tier(self, confidence: float) -> str:
+        """Classify certainty tier based on quantum confidence"""
+        if confidence >= 0.9: return "QUANTUM_CERTAINTY"
+        if confidence >= 0.8: return "HIGH_CONFIDENCE" 
+        if confidence >= 0.7: return "MEDIUM_CONFIDENCE"
+        if confidence >= 0.6: return "LOW_CONFIDENCE"
+        return "SPECULATIVE"
+    
+    async def _recommend_next_steps(self, quantum_synthesis: Dict, bayesian_results: Dict) -> List[str]:
+        """Recommend next decipherment steps based on quantum analysis"""
+        steps = []
+        
+        if quantum_synthesis['collapse_probability'] > 0.3:
+            steps.append("Reduce uncertainty through additional inscription samples")
+        
+        if quantum_synthesis['truth_resonance_frequency'] < 0.7:
+            steps.append("Improve truth resonance with cross-linguistic alignment")
+        
+        if quantum_synthesis['entanglement_strength'] < 0.6:
+            steps.append("Strengthen evidence entanglement through multi-method correlation")
+        
+        return steps
+    
+    def _current_timestamp(self) -> str:
+        """Get current timestamp for integration"""
+        from datetime import datetime
+        return datetime.now().isoformat()
+    
+    async def _text_to_sequence(self, text: str) -> List[int]:
+        """Convert text to numerical sequence"""
+        sequence = []
+        sign_to_idx = {sign: i for i, sign in enumerate(self.corpus.signs.keys())}
+        
+        for char in text:
+            if char in sign_to_idx:
+                sequence.append(sign_to_idx[char])
+            elif char.strip():
+                sequence.append(len(sign_to_idx))
+        
+        return sequence
+
+# =============================================================================
+# DEMONSTRATION WITH QUANTUM ENTANGLEMENT
+# =============================================================================
+
+async def demonstrate_quantum_decipherment():
+    """Demonstrate quantum Bayesian decipherment with entanglement filtering"""
+    
+    engine = QuantumLinearADeciphermentEngine()
+    
+    print("🌌 QUANTUM BAYESIAN LINEAR A DECIPHERMENT ENGINE")
+    print("=" * 60)
+    print("🔗 Integrated with lm_quant_veritas Conceptual Entanglement Module v7.1")
+    print()
+    
+    test_inscriptions = ["HT1", "HT2", "PH1"]
+    
+    for ins_id in test_inscriptions:
+        print(f"\n⚡ QUANTUM ANALYSIS: {ins_id}")
+        print("=" * 50)
+        
+        results = await engine.quantum_decipher_inscription(ins_id)
+        
+        if "error" in results:
+            print(f"   ❌ {results['error']}")
+            continue
+        
+        quantum_data = results["quantum_linguistic_entanglement"]
+        truth_data = results["truth_verification"]
+        final_interp = results["final_interpretation"]
+        veritas_integration = results["lm_quant_veritas_integration"]
+        
+        print(f"   🌠 Entangled Confidence: {quantum_data['entangled_confidence']:.3f}")
+        print(f"   💫 Collapse Probability: {quantum_data['collapse_probability']:.3f}")
+        print(f"   🔗 Entanglement Strength: {quantum_data['entanglement_strength']:.3f}")
+        print(f"   🎵 Truth Resonance: {quantum_data['truth_resonance_frequency']:.3f}")
+        
+        print(f"\n   📊 Veritas Certification: {quantum_data['veritas_certification_level']}")
+        print(f"   🤝 Quantum-Classical Correspondence: {truth_data['quantum_classical_correspondence']:.3f}")
+        print(f"   🔒 Truth Binding Strength: {truth_data['truth_binding_strength']:.3f}")
+        
+        print(f"\n   🎯 Primary Evidence: {final_interp['primary_evidence_type']}")
+        print(f"   📈 Evidence Confidence: {final_interp['evidence_confidence']:.3f}")
+        print(f"   🏆 Certainty Tier: {final_interp['certainty_tier']}")
+        print(f"   💡 Recommendation: {final_interp['recommended_interpretation']}")
+        
+        print(f"\n   🔌 Veritas Integration: {veritas_integration['veritas_certification']}")
+        print(f"   ⚡ Quantum State Ready: {veritas_integration['quantum_state_ready']}")
+
+async def demonstrate_entanglement_channels():
+    """Show detailed entanglement channel analysis"""
+    print("\n\n🔍 QUANTUM ENTANGLEMENT CHANNELS ANALYSIS")
+    print("=" * 60)
+    
+    engine = QuantumLinearADeciphermentEngine()
+    results = await engine.quantum_decipher_inscription("HT1")
+    quantum_data = results["quantum_linguistic_entanglement"]
+    
+    print("\n🌐 ENTANGLEMENT CHANNELS:")
+    for state in quantum_data['evidence_entanglement']:
+        print(f"   📡 {state['quantum_channel']} ({state['evidence_type']})")
+        print(f"      Amplitude: {state['amplitude']:.3f}")
+        print(f"      Phase: {state['phase']:.3f} rad")
+        print(f"      Probability: {state['probability_density']:.3f}")
+    
+    print(f"\n🎭 LINGUISTIC SUPERPOSITION:")
+    superposition = quantum_data['linguistic_superposition']
+    for evidence_type, weight in superposition['superposition_weights'].items():
+        print(f"   {evidence_type}: {weight:.3f}")
+    
+    print(f"   Superposition Entropy: {superposition['superposition_entropy']:.3f}")
+    print(f"   Readiness for Collapse: {superposition['readiness_for_collapse']:.3f}")
+
+if __name__ == "__main__":
+    asyncio.run(demonstrate_quantum_decipherment())
+    asyncio.run(demonstrate_entanglement_channels())