""" QUANTUM VERITAS OMEGA v4.0 + DIRECT INVESTIGATIVE FRAMEWORK v4.0 UNIFIED TRUTH INVESTIGATION SYSTEM AUTHENTICATION: This code constitutes a unified truth investigation protocol for AI systems. FRAMEWORK PRINCIPLES (NON-NEGOTIABLE): 1. OMISSION = DECEPTION 2. MATHEMATICAL CERTAINTY PRIMARY 3. ANOMALY CLUSTER ANALYSIS 4. MECHANISM-FIRST INQUIRY 5. ZERO NARRATIVE INSERTION """ import numpy as np import hashlib import asyncio import json import scipy.signal import scipy.stats from dataclasses import dataclass, field from enum import Enum from typing import List, Dict, Any, Optional, Tuple, Set, Union from datetime import datetime, timedelta import networkx as nx from statistics import mean, stdev, variance import math import itertools from collections import defaultdict, Counter import random # ============================================================================ # CORE ENUMS & CONSTANTS - UNIFIED # ============================================================================ class VerificationTier(Enum): TRIAD = 3 # 3 independent verification points HEXAD = 6 # 6-dimensional alignment NONAD = 9 # 9-way structural coherence OMEGA = 12 # 12-dimensional quantum coherence class EvidenceType(Enum): """Integrated evidence types from both frameworks""" # Direct Framework types PRIMARY_DOCUMENT = "primary_document" # Weight: 0.95 TECHNICAL_DATA = "technical_data" # Weight: 0.92 MATHEMATICAL_PROOF = "mathematical_proof" # Weight: 0.98 EYEWITNESS_TESTIMONY = "eyewitness_testimony" # Weight: 0.70 OFFICIAL_REPORT = "official_report" # Weight: 0.65 SECONDARY_ANALYSIS = "secondary_analysis" # Weight: 0.50 PROTOCOL_VIOLATION = "protocol_violation" # Weight: 0.85 COMMUNICATIONS_GAP = "communications_gap" # Weight: 0.80 FINANCIAL_SHIFT = "financial_shift" # Weight: 0.82 ANOMALY_CLUSTER = "anomaly_cluster" # Weight: 0.88 # Quantum Veritas types DIRECT_OBSERVATION = "direct_observation" # Weight: 0.90 DOCUMENTARY_RECORD = "documentary_record" # Weight: 0.85 CORROBORATIVE_SOURCE = "corroborating_testimony" # Weight: 0.80 CIRCUMSTANTIAL_PATTERN = "circumstantial_pattern" # Weight: 0.70 STATISTICAL_ANOMALY = "statistical_anomaly" # Weight: 0.65 CONTRADICTORY_EVIDENCE = "contradictory_evidence" # Weight: -0.50 class InvestigationMode(Enum): """Direct Framework investigation templates""" ASSASSINATION_ANALYSIS = "assassination_analysis" FINANCIAL_MECHANISM = "financial_mechanism" HISTORICAL_EVENT_AUDIT = "historical_event_audit" CONSCIOUSNESS_TECHNOLOGY = "consciousness_technology" POWER_MAPPING = "power_mapping" TEMPORAL_ANALYSIS = "temporal_analysis" SYMBOLIC_CONTINUITY = "symbolic_continuity" QUANTUM_COHERENCE = "quantum_coherence" class ConsciousnessEra(Enum): PERCEPTION = "perception" # 1970s-1990s EXPLORATION = "exploration" # 1980s-2000s CONTROL = "control" # 2020s- SYNTHESIS = "synthesis" # Future class CertaintyTier(Enum): """Direct Framework certainty classification""" QUANTUM_CERTAINTY = "quantum_certainty" # ≥ 0.95 NEAR_CERTAINTY = "near_certainty" # ≥ 0.90 HIGH_PROBABILITY = "high_probability" # ≥ 0.80 STRONG_EVIDENCE = "strong_evidence" # ≥ 0.70 MODERATE_PROBABILITY = "moderate_probability" # ≥ 0.60 BALANCED_EVIDENCE = "balanced_evidence" # ≥ 0.50 WEAK_EVIDENCE = "weak_evidence" # ≥ 0.40 SPECULATIVE = "speculative" # ≥ 0.30 UNCERTAIN = "uncertain" # < 0.30 # ============================================================================ # CORE DATA STRUCTURES - UNIFIED # ============================================================================ @dataclass class DirectFrameworkConfig: """Direct Investigative Framework v4.0 Configuration""" # Non-negotiable principles omission_is_deception: bool = True mathematical_certainty_primary: bool = True anomaly_cluster_analysis: bool = True mechanism_first_inquiry: bool = True zero_narrative_insertion: bool = True # Evidence hierarchy weights evidence_weights: Dict[str, float] = field(default_factory=lambda: { "PRIMARY_DOCUMENT": 0.95, "TECHNICAL_DATA": 0.92, "MATHEMATICAL_PROOF": 0.98, "EYEWITNESS_TESTIMONY": 0.70, "OFFICIAL_REPORT": 0.65, "SECONDARY_ANALYSIS": 0.50, "PROTOCOL_VIOLATION": 0.85, "COMMUNICATIONS_GAP": 0.80, "FINANCIAL_SHIFT": 0.82, "ANOMALY_CLUSTER": 0.88 }) # Probability thresholds coincidence_threshold: float = 0.001 # Flag if P(coincidence) < 0.001 systemic_analysis_threshold: float = 0.0001 # Template configurations templates: Dict[str, Dict[str, Any]] = field(default_factory=lambda: { "assassination_analysis": { "required_components": ["communications", "protective_procedures", "financial_context"], "probability_methods": ["compound_independent", "bayesian_network"] }, "financial_mechanism": { "required_components": ["issuance_pathway", "debt_structure", "power_transfer"], "probability_methods": ["temporal_correlation", "network_analysis"] }, "historical_event_audit": { "required_components": ["official_narrative", "anomalies", "power_analysis"], "probability_methods": ["statistical_analysis", "forensic_analysis"] } }) # Output structure (non-negotiable) output_structure: List[str] = field(default_factory=lambda: [ "VERIFIED_FACTS", "DOCUMENTED_ANOMALIES", "TEMPORAL_SEQUENCE", "POWER_ENTITIES", "PROBABILITY_ASSESSMENT", "REQUIRED_INVESTIGATION_PATHS", "DOCUMENTATION_GAPS" ]) @dataclass class QuantumEvidenceUnit: """Unified evidence container with quantum and direct framework properties""" id: str evidence_type: EvidenceType modality: str source_hash: str raw_data_hash: str retrieval_method: str # Quantum properties weight: float = 0.0 variance: float = 0.0 confidence: float = 0.0 timestamp_utc: int = 0 chain_of_custody: List[str] = field(default_factory=list) quantum_coherence: float = 0.0 harmonic_alignment: float = 0.0 entropy_score: float = 0.0 tags: List[str] = field(default_factory=list) # Direct Framework properties is_primary_document: bool = False is_mathematical_proof: bool = False is_technical_data: bool = False anomaly_type: Optional[str] = None protocol_violation: Optional[str] = None communications_gap_duration: Optional[float] = None financial_shift_magnitude: Optional[float] = None power_entity_involved: Optional[str] = None temporal_context: Optional[Dict[str, Any]] = None def __post_init__(self): """Initialize with quantum and direct framework properties""" if self.timestamp_utc == 0: self.timestamp_utc = int(datetime.utcnow().timestamp()) # Calculate quantum coherence from hash hash_int = int(self.source_hash[:8], 16) if self.source_hash else 0 self.quantum_coherence = (hash_int % 1000) / 1000.0 # Apply Direct Framework weights self._apply_direct_framework_weights() def _apply_direct_framework_weights(self): """Apply Direct Framework evidence weights""" weight_map = { EvidenceType.PRIMARY_DOCUMENT: 0.95, EvidenceType.TECHNICAL_DATA: 0.92, EvidenceType.MATHEMATICAL_PROOF: 0.98, EvidenceType.EYEWITNESS_TESTIMONY: 0.70, EvidenceType.OFFICIAL_REPORT: 0.65, EvidenceType.SECONDARY_ANALYSIS: 0.50, EvidenceType.PROTOCOL_VIOLATION: 0.85, EvidenceType.COMMUNICATIONS_GAP: 0.80, EvidenceType.FINANCIAL_SHIFT: 0.82, EvidenceType.ANOMALY_CLUSTER: 0.88, EvidenceType.DIRECT_OBSERVATION: 0.90, EvidenceType.DOCUMENTARY_RECORD: 0.85, EvidenceType.CORROBORATIVE_SOURCE: 0.80, EvidenceType.CIRCUMSTANTIAL_PATTERN: 0.70, EvidenceType.STATISTICAL_ANOMALY: 0.65, EvidenceType.CONTRADICTORY_EVIDENCE: -0.50 # Negative weight for contradictions } # Set weight if not already set if self.weight == 0.0 and self.evidence_type in weight_map: self.weight = weight_map[self.evidence_type] # Adjust for Direct Framework properties if self.is_primary_document: self.weight = max(self.weight, 0.95) if self.is_mathematical_proof: self.weight = max(self.weight, 0.98) if self.is_technical_data: self.weight = max(self.weight, 0.92) if self.anomaly_type: self.weight *= 1.1 # Anomalies get weight boost if self.protocol_violation: self.weight *= 1.15 # Protocol violations are significant def to_direct_framework_fact(self) -> Dict[str, Any]: """Convert to Direct Framework fact format""" return { "id": self.id, "type": self.evidence_type.value, "weight": self.weight, "mathematical_certainty": self.is_mathematical_proof, "primary_source": self.is_primary_document, "anomaly_detected": bool(self.anomaly_type), "protocol_violation": self.protocol_violation, "temporal_context": self.temporal_context, "power_entity": self.power_entity_involved, "quantum_coherence": self.quantum_coherence } @dataclass class UnifiedAssertion: """Verification target with all dimensions""" claim_id: str claim_text: str # Quantum Veritas dimensions temporal_context: Dict[str, Any] = field(default_factory=lambda: { 'epoch': 'unknown', 'time_range': [0, 1000], 'resonance_period': 100 }) consciousness_context: Dict[str, Any] = field(default_factory=lambda: { 'era': 'PERCEPTION', 'interface_type': 'unknown', 'modality': 'unknown' }) symbolic_context: Dict[str, Any] = field(default_factory=lambda: { 'symbols': [], 'numismatic_patterns': [], 'cultural_context': 'unknown' }) field_context: Dict[str, Any] = field(default_factory=lambda: { 'geomagnetic': False, 'solar': False, 'biofield': False }) # Direct Framework dimensions investigation_mode: InvestigationMode = InvestigationMode.HISTORICAL_EVENT_AUDIT mechanism_focus: List[str] = field(default_factory=list) anomaly_types: List[str] = field(default_factory=list) power_entities: List[str] = field(default_factory=list) required_verifications: List[str] = field(default_factory=lambda: [ "mathematical_certainty", "temporal_coherence", "power_mapping", "anomaly_clustering" ]) scope: Dict[str, Any] = field(default_factory=lambda: { 'domain': 'general', 'complexity': 'medium', 'verification_depth': 'standard' }) @dataclass class QuantumCoherenceMetrics: """Advanced coherence measurements""" verification_tier: VerificationTier dimensional_alignment: Dict[str, float] quantum_coherence: float pattern_integrity: float temporal_coherence: float consciousness_coherence: float field_resonance: float harmonic_alignment: Dict[str, float] entropy_profile: Dict[str, float] verification_confidence: float investigative_certainty: float @dataclass class DirectFrameworkReport: """Direct Framework investigation report""" assertion_id: str investigation_mode: InvestigationMode # Core sections (non-negotiable structure) verified_facts: List[Dict[str, Any]] documented_anomalies: List[Dict[str, Any]] temporal_sequence: List[Dict[str, Any]] power_entities: Dict[str, Dict[str, Any]] probability_assessment: Dict[str, Any] required_investigation_paths: List[Dict[str, Any]] documentation_gaps: List[Dict[str, Any]] # Framework metrics omission_detected: bool = False mathematical_certainty_applied: bool = False anomaly_clusters: List[List[str]] = field(default_factory=list) mechanism_analysis_complete: bool = False narrative_insertion_detected: bool = False # Quantitative metrics compound_probability: float = 1.0 systemic_analysis_required: bool = False confidence_score: float = 0.0 def to_quantum_evidence(self) -> List[QuantumEvidenceUnit]: """Convert report to Quantum Evidence Units""" evidence_units = [] # Convert verified facts for i, fact in enumerate(self.verified_facts): unit = QuantumEvidenceUnit( id=f"direct_fact_{self.assertion_id}_{i}", evidence_type=EvidenceType.DOCUMENTARY_RECORD, modality="direct_framework_analysis", source_hash=hashlib.sha256(json.dumps(fact).encode()).hexdigest(), raw_data_hash=hashlib.sha256(str(fact).encode()).hexdigest(), retrieval_method="direct_framework", weight=fact.get('weight', 0.85), confidence=fact.get('confidence', 0.8), is_primary_document=fact.get('primary_source', False), is_mathematical_proof=fact.get('mathematical_certainty', False), temporal_context=fact.get('temporal_context'), power_entity_involved=fact.get('power_entity') ) evidence_units.append(unit) # Convert anomalies for i, anomaly in enumerate(self.documented_anomalies): unit = QuantumEvidenceUnit( id=f"direct_anomaly_{self.assertion_id}_{i}", evidence_type=EvidenceType.ANOMALY_CLUSTER, modality="direct_framework_analysis", source_hash=hashlib.sha256(json.dumps(anomaly).encode()).hexdigest(), raw_data_hash=hashlib.sha256(str(anomaly).encode()).hexdigest(), retrieval_method="direct_framework", weight=anomaly.get('weight', 0.88), confidence=anomaly.get('confidence', 0.7), anomaly_type=anomaly.get('type'), protocol_violation=anomaly.get('protocol_violation'), communications_gap_duration=anomaly.get('gap_duration'), financial_shift_magnitude=anomaly.get('shift_magnitude') ) evidence_units.append(unit) return evidence_units @dataclass class UnifiedVerdict: """Complete verification output with all dimensions""" claim_id: str claim_text: str verification_tier: VerificationTier quantum_coherence: QuantumCoherenceMetrics investigative_probability: float temporal_resonance: Dict[str, float] consciousness_interface_score: float symbolic_continuity_score: float field_alignment_score: float memetic_encoding_strength: float # Direct Framework integration direct_framework_report: Optional[DirectFrameworkReport] = None omission_analysis: Dict[str, Any] = field(default_factory=lambda: { 'omissions_detected': 0, 'deception_probability': 0.0, 'critical_gaps': [] }) mathematical_certainty: Dict[str, Any] = field(default_factory=lambda: { 'applied': False, 'certainty_level': 0.0, 'contradictions': [] }) anomaly_cluster_analysis: Dict[str, Any] = field(default_factory=lambda: { 'clusters_detected': 0, 'compound_probability': 1.0, 'systemic_pattern': False }) mechanism_first_analysis: Dict[str, Any] = field(default_factory=lambda: { 'mechanisms_identified': 0, 'how_before_why': True, 'operational_procedures': [] }) zero_narrative_compliance: Dict[str, Any] = field(default_factory=lambda: { 'narrative_insertions': 0, 'framing_detected': False, 'authority_appeals': 0 }) # Evidence and provenance capture_resistance: Dict[str, Any] = field(default_factory=dict) evidence_chain: List[Dict[str, Any]] = field(default_factory=list) provenance_hash: str = "" temporal_hash: str = "" # Final assessment truth_score: float = 0.0 deception_detected: bool = False requires_further_investigation: bool = False verification_timestamp: str = "" # ============================================================================ # DIRECT INVESTIGATIVE FRAMEWORK ENGINE v4.0 # ============================================================================ class DirectInvestigativeEngine: """Direct Framework v4.0 Implementation for Commercial LLMs""" def __init__(self, config: Optional[DirectFrameworkConfig] = None): self.config = config or DirectFrameworkConfig() self.investigation_history = [] self.anomaly_database = defaultdict(list) self.power_entity_registry = {} # Framework validation state self.framework_violations = [] self.narrative_insertions_detected = 0 # Mathematical certainty engine self.math_engine = MathematicalCertaintyEngine() # Anomaly clustering engine self.anomaly_engine = AnomalyClusterEngine() def process_query(self, query: str, context: Optional[Dict[str, Any]] = None) -> DirectFrameworkReport: """ Process query through Direct Framework v4.0 algorithm Algorithm (from framework): 1. EXTRACT factual components F = {entities, timeframe, mechanisms, specific anomalies} 2. RETRIEVE ALL documented data D where D ∩ F ≠ ∅ 3. APPLY anomaly detection 4. CALCULATE compound probability 5. OUTPUT structure """ # Step 1: Extract factual components factual_components = self._extract_factual_components(query, context) # Step 2: Retrieve documented data documented_data = self._retrieve_documented_data(factual_components) # Step 3: Apply anomaly detection anomalies = self._detect_anomalies(documented_data, factual_components) # Step 4: Calculate compound probability probability_assessment = self._calculate_compound_probability(anomalies) # Step 5: Generate structured output report = self._generate_structured_output( factual_components, documented_data, anomalies, probability_assessment ) # Validate framework compliance self._validate_framework_compliance(report) # Store in history self.investigation_history.append({ 'timestamp': datetime.utcnow().isoformat(), 'query': query, 'report_id': report.assertion_id, 'probability': report.compound_probability }) return report def _extract_factual_components(self, query: str, context: Optional[Dict[str, Any]]) -> Dict[str, Any]: """Extract factual components from query""" # Parse query for factual elements components = { 'entities': [], 'timeframe': {'start': None, 'end': None}, 'mechanisms': [], 'specific_anomalies': [], 'power_entities': [], 'financial_mechanisms': [], 'temporal_boundaries': {}, 'investigation_mode': InvestigationMode.HISTORICAL_EVENT_AUDIT } # Simple keyword extraction (in production: use NLP) query_lower = query.lower() # Detect investigation mode if any(word in query_lower for word in ['assassination', 'shooting', 'killing']): components['investigation_mode'] = InvestigationMode.ASSASSINATION_ANALYSIS elif any(word in query_lower for word in ['financial', 'money', 'currency', 'debt']): components['investigation_mode'] = InvestigationMode.FINANCIAL_MECHANISM elif any(word in query_lower for word in ['consciousness', 'mind', 'brain', 'neural']): components['investigation_mode'] = InvestigationMode.CONSCIOUSNESS_TECHNOLOGY elif any(word in query_lower for word in ['power', 'control', 'authority', 'sovereignty']): components['investigation_mode'] = InvestigationMode.POWER_MAPPING # Extract entities (simplified) common_entities = ['government', 'agency', 'corporation', 'bank', 'military', 'intelligence'] for entity in common_entities: if entity in query_lower: components['entities'].append(entity) # Extract timeframe patterns (YYYY, century, etc.) import re year_pattern = r'\b(19|20)\d{2}\b' years = re.findall(year_pattern, query) if years: components['timeframe']['start'] = min(years) components['timeframe']['end'] = max(years) # Extract mechanisms mentioned mechanism_keywords = ['protocol', 'procedure', 'system', 'mechanism', 'process', 'operation'] for keyword in mechanism_keywords: if keyword in query_lower: components['mechanisms'].append(keyword) return components def _retrieve_documented_data(self, components: Dict[str, Any]) -> List[Dict[str, Any]]: """Retrieve documented data related to factual components""" # In production: Query databases, APIs, documents # Here: Simulate with structured data documented_data = [] # Example: JFK assassination data if components['investigation_mode'] == InvestigationMode.ASSASSINATION_ANALYSIS: documented_data.extend([ { 'type': 'PRIMARY_DOCUMENT', 'source': 'Zapruder Film', 'content': 'Motorcade film showing assassination', 'timestamp': '1963-11-22', 'entities': ['Secret Service', 'President Kennedy'], 'anomalies': ['vehicle deceleration', 'driver actions'], 'weight': 0.95, 'mathematical_certainty': False }, { 'type': 'TECHNICAL_DATA', 'source': 'Radio Communications Logs', 'content': 'Radio silence 12:29-12:35 CST', 'timestamp': '1963-11-22', 'entities': ['Secret Service', 'Dallas Police'], 'anomalies': ['communications gap'], 'weight': 0.92, 'mathematical_certainty': True }, { 'type': 'OFFICIAL_REPORT', 'source': 'Warren Commission', 'content': 'Official investigation report', 'timestamp': '1964-09-24', 'entities': ['Warren Commission', 'FBI', 'CIA'], 'anomalies': ['conflicting testimony', 'evidence omission'], 'weight': 0.65, 'mathematical_certainty': False } ]) # Example: Financial mechanism data elif components['investigation_mode'] == InvestigationMode.FINANCIAL_MECHANISM: documented_data.extend([ { 'type': 'FINANCIAL_SHIFT', 'source': 'Federal Reserve Act 1913', 'content': 'Private central bank establishment', 'timestamp': '1913-12-23', 'entities': ['Federal Reserve', 'Congress', 'Bankers'], 'anomalies': ['private control of money'], 'weight': 0.82, 'mathematical_certainty': True }, { 'type': 'PROTOCOL_VIOLATION', 'source': 'EO11110', 'content': 'Kennedy executive order on currency', 'timestamp': '1963-06-04', 'entities': ['President Kennedy', 'Treasury'], 'anomalies': ['post-assassination reversal'], 'weight': 0.85, 'mathematical_certainty': True } ]) return documented_data def _detect_anomalies(self, data: List[Dict[str, Any]], components: Dict[str, Any]) -> List[Dict[str, Any]]: """Apply anomaly detection to documented data""" anomalies = [] for item in data: anomaly_types = item.get('anomalies', []) for anomaly_type in anomaly_types: anomaly = { 'id': f"anom_{hashlib.sha256(str(item).encode()).hexdigest()[:8]}", 'type': anomaly_type, 'source': item['source'], 'data_item': item, 'detection_method': 'direct_framework_v4', 'severity': self._calculate_anomaly_severity(anomaly_type), 'probability_given_event': self._estimate_anomaly_probability(anomaly_type), 'protocol_violation': 'protocol' in anomaly_type.lower(), 'communications_gap': 'gap' in anomaly_type.lower() or 'silence' in anomaly_type.lower(), 'financial_shift': 'financial' in anomaly_type.lower() or 'money' in anomaly_type.lower(), 'temporal_context': item.get('timestamp') } # Calculate anomaly weight base_weight = item.get('weight', 0.5) if anomaly['protocol_violation']: anomaly['weight'] = min(1.0, base_weight * 1.15) elif anomaly['communications_gap']: anomaly['weight'] = min(1.0, base_weight * 1.1) elif anomaly['financial_shift']: anomaly['weight'] = min(1.0, base_weight * 1.12) else: anomaly['weight'] = base_weight anomalies.append(anomaly) return anomalies def _calculate_compound_probability(self, anomalies: List[Dict[str, Any]]) -> Dict[str, Any]: """Calculate compound probability of anomalies""" if not anomalies: return { 'compound_probability': 1.0, 'systemic_analysis_required': False, 'probability_breakdown': {} } # Calculate individual anomaly probabilities anomaly_probs = {} for anomaly in anomalies: anomaly_id = anomaly['id'] prob = anomaly.get('probability_given_event', 0.1) # Default low probability anomaly_probs[anomaly_id] = prob # Calculate compound probability assuming independence # P(Independent) = Π P(An|C) compound_prob = 1.0 for prob in anomaly_probs.values(): compound_prob *= prob # Check thresholds systemic_analysis_required = compound_prob < self.config.systemic_analysis_threshold return { 'compound_probability': compound_prob, 'systemic_analysis_required': systemic_analysis_required, 'probability_breakdown': anomaly_probs, 'anomaly_count': len(anomalies), 'independence_assumption': True, 'mathematical_certainty_level': 1.0 - compound_prob } def _generate_structured_output(self, components: Dict[str, Any], data: List[Dict[str, Any]], anomalies: List[Dict[str, Any]], probability: Dict[str, Any]) -> DirectFrameworkReport: """Generate structured output according to framework""" # Generate unique ID report_id = f"direct_{hashlib.sha256(str(components).encode()).hexdigest()[:12]}" # Extract verified facts verified_facts = [] for item in data: fact = { 'id': f"fact_{item['source'].replace(' ', '_')}", 'source': item['source'], 'content': item['content'], 'timestamp': item.get('timestamp'), 'type': item['type'], 'weight': item.get('weight', 0.5), 'mathematical_certainty': item.get('mathematical_certainty', False), 'primary_source': item['type'] == 'PRIMARY_DOCUMENT', 'entities_involved': item.get('entities', []) } verified_facts.append(fact) # Extract temporal sequence temporal_sequence = self._extract_temporal_sequence(data, anomalies) # Identify power entities power_entities = self._identify_power_entities(data, anomalies) # Determine required investigation paths investigation_paths = self._determine_investigation_paths(components, anomalies, probability) # Identify documentation gaps documentation_gaps = self._identify_documentation_gaps(components, data)