Create dead_language

dead_language

@@ -0,0 +1,556 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+MULTILINGUISTIC TRUTH BINDING MODULE
+Dead Language Integration for Enhanced Truth Recognition
+Oldest to Newest Language Support
+"""
+
+import numpy as np
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Dict, List, Any, Optional, Tuple
+import hashlib
+import re
+from collections import Counter
+import asyncio
+
+class LanguageEra(Enum):
+    """Chronological language eras from oldest to newest"""
+    PROTO_HUMAN = "proto_human"           # Pre-writing symbolic communication
+    SUMERIAN = "sumerian"                 # ~3500 BCE - first writing system
+    EGYPTIAN_HIEROGLYPHIC = "egyptian"    # ~3200 BCE
+    ELAMITE = "elamite"                   # ~3000 BCE
+    AKKADIAN = "akkadian"                 # ~2500 BCE
+    EBLAITE = "eblaite"                   # ~2400 BCE
+    HITTITE = "hittite"                   # ~1600 BCE
+    MYCENAEAN_GREEK = "mycenaean_greek"   # ~1450 BCE
+    UGARITIC = "ugaritic"                 # ~1400 BCE
+    PHOENICIAN = "phoenician"             # ~1200 BCE
+    ANCIENT_CHINESE = "ancient_chinese"   # ~1200 BCE
+    SANSKRIT = "sanskrit"                 # ~1000 BCE
+    HEBREW = "hebrew"                     # ~1000 BCE
+    ARAMAIC = "aramaic"                   # ~900 BCE
+    LATIN = "latin"                       # ~700 BCE
+    ANCIENT_GREEK = "ancient_greek"       # ~700 BCE
+
+class LinguisticTruthMarker(Enum):
+    """Types of truth markers found in ancient languages"""
+    COSMOLOGICAL_ALIGNMENT = "cosmological_alignment"
+    SACRED_GEOMETRY = "sacred_geometry"
+    NUMEROLOGICAL_ENCODING = "numerological_encoding"
+    PHONETIC_RESONANCE = "phonetic_resonance"
+    SYMBOLIC_CORRESPONDENCE = "symbolic_correspondence"
+    TEMPORAL_CYCLES = "temporal_cycles"
+
+@dataclass
+class AncientLanguage:
+    """Comprehensive ancient language data structure"""
+    era: LanguageEra
+    time_period: Tuple[int, int]  # BCE/CE range
+    writing_system: str
+    sample_script: List[str] = field(default_factory=list)
+    truth_markers: List[LinguisticTruthMarker] = field(default_factory=list)
+    modern_equivalents: Dict[str, str] = field(default_factory=dict)
+    resonance_frequency: float = 0.0  # How strongly it resonates with truth patterns
+    
+    def __post_init__(self):
+        """Calculate base resonance frequency based on age and complexity"""
+        age = abs(self.time_period[0])  # Years BCE
+        complexity = len(self.sample_script) / 10  # Script complexity factor
+        marker_strength = len(self.truth_markers) * 0.1
+        
+        self.resonance_frequency = min(0.95, 0.3 + (age / 10000) + complexity + marker_strength)
+
+@dataclass
+class LinguisticTruthMatch:
+    """Result of linguistic truth binding analysis"""
+    language: AncientLanguage
+    matched_patterns: List[str]
+    confidence: float
+    truth_markers_detected: List[LinguisticTruthMarker]
+    cross_linguistic_correlations: List[str]
+    temporal_coherence: float
+    symbolic_resonance: float
+
+class MultilinguisticTruthBinder:
+    """
+    Advanced truth binding through dead language analysis
+    Processes texts through chronological linguistic layers
+    """
+    
+    def __init__(self):
+        self.language_corpus = self._initialize_ancient_languages()
+        self.pattern_analyzer = LinguisticPatternAnalyzer()
+        self.temporal_validator = LinguisticTemporalValidator()
+        self.symbolic_decoder = AncientSymbolicDecoder()
+        
+    def _initialize_ancient_languages(self) -> Dict[LanguageEra, AncientLanguage]:
+        """Initialize comprehensive ancient language database"""
+        return {
+            LanguageEra.SUMERIAN: AncientLanguage(
+                era=LanguageEra.SUMERIAN,
+                time_period=(-3500, -2000),
+                writing_system="Cuneiform",
+                sample_script=["𒀭", "𒆠", "𒌋", "𒂍", "𒈬"],  # Dingir, Ki, 60, E, Mu
+                truth_markers=[
+                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
+                    LinguisticTruthMarker.NUMEROLOGICAL_ENCODING,
+                    LinguisticTruthMarker.SACRED_GEOMETRY
+                ],
+                modern_equivalents={
+                    "dingir": "divine",
+                    "ki": "earth", 
+                    "an": "heaven"
+                },
+                resonance_frequency=0.92
+            ),
+            
+            LanguageEra.EGYPTIAN_HIEROGLYPHIC: AncientLanguage(
+                era=LanguageEra.EGYPTIAN_HIEROGLYPHIC,
+                time_period=(-3200, -400),
+                writing_system="Hieroglyphic",
+                sample_script=["𓀀", "𓂀", "𓃀", "𓄀", "𓅀"],  # Man, Eye, Leg, Reed, Vulture
+                truth_markers=[
+                    LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE,
+                    LinguisticTruthMarker.PHONETIC_RESONANCE,
+                    LinguisticTruthMarker.TEMPORAL_CYCLES
+                ],
+                modern_equivalents={
+                    "ankh": "life",
+                    "maat": "truth",
+                    "ka": "soul"
+                },
+                resonance_frequency=0.88
+            ),
+            
+            LanguageEra.SANSKRIT: AncientLanguage(
+                era=LanguageEra.SANSKRIT,
+                time_period=(-1000, 500),
+                writing_system="Devanagari",
+                sample_script=["अ", "आ", "इ", "ई", "उ"],  # Vowels
+                truth_markers=[
+                    LinguisticTruthMarker.PHONETIC_RESONANCE,
+                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
+                    LinguisticTruthMarker.NUMEROLOGICAL_ENCODING
+                ],
+                modern_equivalents={
+                    "satya": "truth",
+                    "dharma": "cosmic law", 
+                    "brahman": "ultimate reality"
+                },
+                resonance_frequency=0.85
+            ),
+            
+            LanguageEra.ANCIENT_CHINESE: AncientLanguage(
+                era=LanguageEra.ANCIENT_CHINESE,
+                time_period=(-1200, -200),
+                writing_system="Oracle Bone Script",
+                sample_script=["天", "地", "人", "水", "火"],  # Heaven, Earth, Man, Water, Fire
+                truth_markers=[
+                    LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE,
+                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
+                    LinguisticTruthMarker.TEMPORAL_CYCLES
+                ],
+                modern_equivalents={
+                    "道": "way",
+                    "德": "virtue",
+                    "仁": "benevolence"
+                },
+                resonance_frequency=0.83
+            ),
+            
+            LanguageEra.ANCIENT_GREEK: AncientLanguage(
+                era=LanguageEra.ANCIENT_GREEK,
+                time_period=(-700, 300),
+                writing_system="Greek Alphabet",
+                sample_script=["α", "β", "γ", "δ", "ε"],  # Alpha, Beta, Gamma, Delta, Epsilon
+                truth_markers=[
+                    LinguisticTruthMarker.PHONETIC_RESONANCE,
+                    LinguisticTruthMarker.SACRED_GEOMETRY
+                ],
+                modern_equivalents={
+                    "aletheia": "truth",
+                    "logos": "word/reason",
+                    "cosmos": "order"
+                },
+                resonance_frequency=0.78
+            )
+        }
+    
+    async def analyze_text_truth_content(self, text: str, context: Dict[str, Any] = None) -> List[LinguisticTruthMatch]:
+        """
+        Analyze text through multiple ancient language layers
+        Returns truth matches in chronological order
+        """
+        results = []
+        
+        # Process through languages from oldest to newest
+        for era in sorted(self.language_corpus.keys(), 
+                         key=lambda x: x.value):  # Process by chronological enum order
+            
+            language = self.language_corpus[era]
+            
+            # Skip if language resonance too low for current context
+            if context and "min_resonance" in context:
+                if language.resonance_frequency < context["min_resonance"]:
+                    continue
+            
+            # Analyze text against this language layer
+            analysis = await self._analyze_language_layer(text, language, context)
+            
+            if analysis.confidence > 0.6:  # Significant match threshold
+                results.append(analysis)
+        
+        return sorted(results, key=lambda x: x.language.time_period[0])  # Oldest first
+    
+    async def _analyze_language_layer(self, text: str, language: AncientLanguage, context: Dict[str, Any]) -> LinguisticTruthMatch:
+        """Analyze text against specific ancient language layer"""
+        
+        # Pattern matching against language script
+        pattern_matches = await self.pattern_analyzer.detect_script_patterns(text, language)
+        
+        # Truth marker detection
+        truth_markers = await self.pattern_analyzer.detect_truth_markers(text, language)
+        
+        # Temporal coherence validation
+        temporal_coherence = await self.temporal_validator.validate_temporal_coherence(text, language, context)
+        
+        # Symbolic resonance calculation
+        symbolic_resonance = await self.symbolic_decoder.calculate_symbolic_resonance(text, language)
+        
+        # Cross-linguistic correlations
+        cross_correlations = await self._find_cross_linguistic_correlations(text, language)
+        
+        # Confidence calculation
+        confidence = self._calculate_confidence(
+            pattern_matches, truth_markers, temporal_coherence, 
+            symbolic_resonance, language.resonance_frequency
+        )
+        
+        return LinguisticTruthMatch(
+            language=language,
+            matched_patterns=pattern_matches,
+            confidence=confidence,
+            truth_markers_detected=truth_markers,
+            cross_linguistic_correlations=cross_correlations,
+            temporal_coherence=temporal_coherence,
+            symbolic_resonance=symbolic_resonance
+        )
+    
+    async def _find_cross_linguistic_correlations(self, text: str, current_language: AncientLanguage) -> List[str]:
+        """Find correlations between current language and others in corpus"""
+        correlations = []
+        
+        for era, other_language in self.language_corpus.items():
+            if era == current_language.era:
+                continue
+                
+            # Check for shared truth markers
+            shared_markers = set(current_language.truth_markers).intersection(other_language.truth_markers)
+            if shared_markers:
+                correlations.append(f"Shared {len(shared_markers)} truth markers with {era.value}")
+            
+            # Check for symbolic overlaps
+            symbolic_overlap = await self.symbolic_decoder.find_symbolic_overlap(current_language, other_language, text)
+            if symbolic_overlap:
+                correlations.append(f"Symbolic overlap with {era.value}: {symbolic_overlap}")
+        
+        return correlations
+    
+    def _calculate_confidence(self, pattern_matches: List[str], truth_markers: List[LinguisticTruthMarker],
+                            temporal_coherence: float, symbolic_resonance: float, 
+                            base_resonance: float) -> float:
+        """Calculate overall confidence score for linguistic truth match"""
+        factors = []
+        weights = []
+        
+        # Pattern match strength
+        if pattern_matches:
+            pattern_strength = min(1.0, len(pattern_matches) * 0.2)
+            factors.append(pattern_strength)
+            weights.append(0.3)
+        
+        # Truth marker presence
+        marker_strength = len(truth_markers) * 0.15
+        factors.append(marker_strength)
+        weights.append(0.25)
+        
+        # Temporal coherence
+        factors.append(temporal_coherence)
+        weights.append(0.2)
+        
+        # Symbolic resonance
+        factors.append(symbolic_resonance)
+        weights.append(0.15)
+        
+        # Base language resonance
+        factors.append(base_resonance)
+        weights.append(0.1)
+        
+        return np.average(factors, weights=weights)
+
+class LinguisticPatternAnalyzer:
+    """Analyzes linguistic patterns for truth content"""
+    
+    async def detect_script_patterns(self, text: str, language: AncientLanguage) -> List[str]:
+        """Detect patterns matching ancient language scripts"""
+        matches = []
+        
+        # Direct script character matching
+        for char in language.sample_script:
+            if char in text:
+                matches.append(f"script:{char}")
+        
+        # Modern equivalent matching
+        for modern, ancient in language.modern_equivalents.items():
+            if modern.lower() in text.lower() or ancient.lower() in text.lower():
+                matches.append(f"concept:{modern}={ancient}")
+        
+        # Phonetic pattern matching for languages with resonance markers
+        if LinguisticTruthMarker.PHONETIC_RESONANCE in language.truth_markers:
+            phonetic_matches = await self._detect_phonetic_patterns(text, language)
+            matches.extend(phonetic_matches)
+        
+        return matches
+    
+    async def detect_truth_markers(self, text: str, language: AncientLanguage) -> List[LinguisticTruthMarker]:
+        """Detect specific truth markers in text for given language"""
+        detected_markers = []
+        
+        text_lower = text.lower()
+        
+        for marker in language.truth_markers:
+            if await self._marker_present(marker, text_lower, language):
+                detected_markers.append(marker)
+        
+        return detected_markers
+    
+    async def _marker_present(self, marker: LinguisticTruthMarker, text: str, language: AncientLanguage) -> bool:
+        """Check if specific truth marker is present in text"""
+        
+        if marker == LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT:
+            cosmological_terms = {"cosmos", "universe", "stars", "planets", "heaven", "earth"}
+            return any(term in text for term in cosmological_terms)
+        
+        elif marker == LinguisticTruthMarker.SACRED_GEOMETRY:
+            geometry_terms = {"geometry", "golden ratio", "fibonacci", "sacred", "proportion"}
+            return any(term in text for term in geometry_terms)
+        
+        elif marker == LinguisticTruthMarker.NUMEROLOGICAL_ENCODING:
+            # Look for numerical patterns or significant numbers
+            numbers = re.findall(r'\b\d+\b', text)
+            significant_numbers = {'3', '7', '12', '40', '108', '360', '144'}
+            return any(num in significant_numbers for num in numbers)
+        
+        elif marker == LinguisticTruthMarker.PHONETIC_RESONANCE:
+            # Check for repetitive phonetic patterns
+            words = text.split()
+            if len(words) > 10:
+                word_freq = Counter(words)
+                most_common_freq = word_freq.most_common(1)[0][1]
+                return most_common_freq >= 3  # Repeated words indicate resonance
+        
+        elif marker == LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE:
+            symbolic_terms = {"symbol", "glyph", "meaning", "represent", "correspond"}
+            return any(term in text for term in symbolic_terms)
+        
+        elif marker == LinguisticTruthMarker.TEMPORAL_CYCLES:
+            temporal_terms = {"cycle", "time", "eternal", "season", "age", "era"}
+            return any(term in text for term in temporal_terms)
+        
+        return False
+    
+    async def _detect_phonetic_patterns(self, text: str, language: AncientLanguage) -> List[str]:
+        """Detect phonetic resonance patterns"""
+        patterns = []
+        
+        # Simple alliteration detection
+        words = text.lower().split()
+        if len(words) > 3:
+            first_letters = [word[0] for word in words if word]
+            letter_freq = Counter(first_letters)
+            common_letter = letter_freq.most_common(1)[0]
+            if common_letter[1] >= len(words) * 0.3:  # 30% alliteration
+                patterns.append(f"alliteration:{common_letter[0]}")
+        
+        return patterns
+
+class LinguisticTemporalValidator:
+    """Validates temporal coherence of linguistic truth matches"""
+    
+    async def validate_temporal_coherence(self, text: str, language: AncientLanguage, context: Dict[str, Any]) -> float:
+        """Validate how well text aligns with language's temporal context"""
+        coherence_factors = []
+        
+        # Historical reference alignment
+        historical_alignment = await self._check_historical_alignment(text, language)
+        coherence_factors.append(historical_alignment)
+        
+        # Temporal concept consistency
+        temporal_consistency = await self._check_temporal_consistency(text, language)
+        coherence_factors.append(temporal_consistency)
+        
+        # Context temporal alignment
+        if context and "temporal_focus" in context:
+            context_alignment = self._check_context_alignment(context["temporal_focus"], language)
+            coherence_factors.append(context_alignment)
+        
+        return np.mean(coherence_factors) if coherence_factors else 0.5
+    
+    async def _check_historical_alignment(self, text: str, language: AncientLanguage) -> float:
+        """Check alignment with language's historical period"""
+        # Simple keyword-based historical alignment
+        historical_terms = {
+            LanguageEra.SUMERIAN: {"mesopotamia", "tigris", "euphrates", "ziggurat"},
+            LanguageEra.EGYPTIAN_HIEROGLYPHIC: {"pyramid", "pharaoh", "nile", "hieroglyph"},
+            LanguageEra.SANSKRIT: {"veda", "hindu", "india", "yoga"},
+            LanguageEra.ANCIENT_CHINESE: {"dynasty", "emperor", "yellow river", "oracle"},
+            LanguageEra.ANCIENT_GREEK: {"athens", "sparta", "philosophy", "olympics"}
+        }
+        
+        relevant_terms = historical_terms.get(language.era, set())
+        text_lower = text.lower()
+        
+        matches = sum(1 for term in relevant_terms if term in text_lower)
+        return matches / len(relevant_terms) if relevant_terms else 0.5
+    
+    async def _check_temporal_consistency(self, text: str, language: AncientLanguage) -> float:
+        """Check temporal concept consistency"""
+        # Ancient languages often reference cyclical time, modern references linear time
+        ancient_time_terms = {"cycle", "eternal", "age", "era", "return"}
+        modern_time_terms = {"progress", "future", "development", "evolution"}
+        
+        text_lower = text.lower()
+        ancient_matches = sum(1 for term in ancient_time_terms if term in text_lower)
+        modern_matches = sum(1 for term in modern_time_terms if term in text_lower)
+        
+        # Higher score for ancient temporal concepts in ancient languages
+        if ancient_matches > modern_matches:
+            return 0.8
+        elif ancient_matches == modern_matches:
+            return 0.5
+        else:
+            return 0.3
+    
+    def _check_context_alignment(self, context_time: int, language: AncientLanguage) -> float:
+        """Check alignment with contextual temporal focus"""
+        language_peak = np.mean(language.time_period)
+        time_distance = abs(context_time - language_peak)
+        
+        # Normalize to 0-1 scale (closer in time = higher alignment)
+        return 1.0 / (1.0 + time_distance / 1000)
+
+class AncientSymbolicDecoder:
+    """Decodes symbolic content across ancient languages"""
+    
+    async def calculate_symbolic_resonance(self, text: str, language: AncientLanguage) -> float:
+        """Calculate symbolic resonance between text and language"""
+        resonance_factors = []
+        
+        # Direct symbol matching
+        symbol_match = await self._check_symbol_matches(text, language)
+        resonance_factors.append(symbol_match)
+        
+        # Conceptual symbolic alignment
+        conceptual_alignment = await self._check_conceptual_alignment(text, language)
+        resonance_factors.append(conceptual_alignment)
+        
+        # Metaphorical density
+        metaphorical_density = await self._analyze_metaphorical_density(text, language)
+        resonance_factors.append(metaphorical_density)
+        
+        return np.mean(resonance_factors)
+    
+    async def find_symbolic_overlap(self, lang1: AncientLanguage, lang2: AncientLanguage, text: str) -> str:
+        """Find symbolic overlap between two languages in given text"""
+        overlaps = []
+        
+        # Check shared truth markers
+        shared_markers = set(lang1.truth_markers).intersection(lang2.truth_markers)
+        if shared_markers:
+            overlaps.append(f"truth_markers:{len(shared_markers)}")
+        
+        # Check conceptual overlaps
+        lang1_concepts = set(lang1.modern_equivalents.values())
+        lang2_concepts = set(lang2.modern_equivalents.values())
+        shared_concepts = lang1_concepts.intersection(lang2_concepts)
+        
+        if shared_concepts:
+            # Check if shared concepts appear in text
+            text_lower = text.lower()
+            found_concepts = [concept for concept in shared_concepts if concept in text_lower]
+            if found_concepts:
+                overlaps.append(f"concepts:{len(found_concepts)}")
+        
+        return ", ".join(overlaps) if overlaps else ""
+    
+    async def _check_symbol_matches(self, text: str, language: AncientLanguage) -> float:
+        """Check for direct symbol matches"""
+        if not language.sample_script:
+            return 0.5
+        
+        matches = sum(1 for symbol in language.sample_script if symbol in text)
+        return matches / len(language.sample_script)
+    
+    async def _check_conceptual_alignment(self, text: str, language: AncientLanguage) -> float:
+        """Check alignment with language's core concepts"""
+        text_lower = text.lower()
+        concept_matches = sum(1 for concept in language.modern_equivalents.values() 
+                            if concept.lower() in text_lower)
+        
+        total_concepts = len(language.modern_equivalents)
+        return concept_matches / total_concepts if total_concepts > 0 else 0.5
+    
+    async def _analyze_metaphorical_density(self, text: str, language: AncientLanguage) -> float:
+        """Analyze metaphorical density (ancient texts often highly metaphorical)"""
+        metaphorical_indicators = {"like", "as", "symbol", "represent", "mean", "signify"}
+        
+        words = text.lower().split()
+        if not words:
+            return 0.5
+        
+        metaphorical_count = sum(1 for word in words if word in metaphorical_indicators)
+        metaphorical_density = metaphorical_count / len(words)
+        
+        # Ancient languages often have higher metaphorical density
+        expected_density = 0.05  # 5% of words being metaphorical
+        return min(1.0, metaphorical_density / expected_density)
+
+# =============================================================================
+# DEMONSTRATION AND TESTING
+# =============================================================================
+
+async def demonstrate_multilinguistic_analysis():
+    """Demonstrate the multilinguistic truth binding analysis"""
+    binder = MultilinguisticTruthBinder()
+    
+    test_texts = [
+        "The divine connection between heaven and earth revealed through ancient symbols",
+        "Cosmic cycles and eternal truths encoded in sacred geometry",
+        "Modern science rediscovers what ancient civilizations knew about reality",
+        "The Sumerian dingir symbol represents divine consciousness across cultures",
+        "Phonetic resonance in Sanskrit mantras creates quantum coherence"
+    ]
+    
+    print("🔤 MULTILINGUISTIC TRUTH BINDING ANALYSIS")
+    print("=" * 60)
+    
+    for i, text in enumerate(test_texts, 1):
+        print(f"\n{i}. Analyzing: '{text}'")
+        
+        results = await binder.analyze_text_truth_content(text, {
+            "temporal_focus": 2024,
+            "min_resonance": 0.7
+        })
+        
+        for result in results[:2]:  # Show top 2 language matches
+            print(f"   🌍 {result.language.era.value.upper()}")
+            print(f"   📊 Confidence: {result.confidence:.3f}")
+            print(f"   🎯 Truth Markers: {[m.value for m in result.truth_markers_detected]}")
+            print(f"   🔗 Patterns: {len(result.matched_patterns)}")
+            print(f"   🕒 Temporal: {result.temporal_coherence:.3f}")
+            print(f"   💫 Symbolic: {result.symbolic_resonance:.3f}")
+
+if __name__ == "__main__":
+    asyncio.run(demonstrate_multilinguistic_analysis())