torsh-text 0.1.2

//! Advanced Semantic Analysis Module
//!
//! This module provides sophisticated advanced semantic analysis capabilities
//! including vector semantics, neural semantic embeddings, semantic evolution
//! tracking, and other cutting-edge semantic analysis techniques.

use super::config::AdvancedSemanticConfig;
use super::results::{
    AdvancedCoherenceMetrics, AdvancedSemanticMetrics, ConceptualDimensions, SemanticComplexity,
    SemanticDynamics, SemanticEvolution, SemanticInnovation, VectorSemanticAnalysis,
};
use crate::error::TextAnalysisError;
use serde::{Deserialize, Serialize};
use std::collections::hash_map::DefaultHasher;
use std::collections::{BTreeMap, HashMap, HashSet, VecDeque};
use std::hash::{Hash, Hasher};
use thiserror::Error;

#[derive(Debug, Error)]
pub enum AdvancedAnalysisError {
    #[error("Invalid advanced analysis configuration: {0}")]
    ConfigError(String),
    #[error("Advanced calculation failed: {0}")]
    CalculationError(String),
    #[error("Advanced semantic analysis error: {0}")]
    AnalysisError(String),
    #[error("Vector analysis error: {0}")]
    VectorError(String),
}

pub type AdvancedResult<T> = Result<T, AdvancedAnalysisError>;

/// Multi-dimensional semantic vector representation
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct SemanticVector {
    pub dimensions: Vec<f64>,
    pub dimension_labels: Vec<String>,
    pub confidence: f64,
    pub source_context: String,
}

impl SemanticVector {
    pub fn new(dimensions: Vec<f64>, labels: Vec<String>) -> Self {
        let confidence = if dimensions.is_empty() {
            0.0
        } else {
            dimensions.iter().map(|x| x.abs()).sum::<f64>() / dimensions.len() as f64
        };

        Self {
            dimensions,
            dimension_labels: labels,
            confidence,
            source_context: String::new(),
        }
    }

    pub fn cosine_similarity(&self, other: &SemanticVector) -> f64 {
        if self.dimensions.len() != other.dimensions.len() {
            return 0.0;
        }

        let dot_product: f64 = self
            .dimensions
            .iter()
            .zip(&other.dimensions)
            .map(|(a, b)| a * b)
            .sum();

        let norm_a: f64 = self.dimensions.iter().map(|x| x * x).sum::<f64>().sqrt();
        let norm_b: f64 = other.dimensions.iter().map(|x| x * x).sum::<f64>().sqrt();

        if norm_a == 0.0 || norm_b == 0.0 {
            0.0
        } else {
            dot_product / (norm_a * norm_b)
        }
    }
}

/// Advanced semantic analyzer with cutting-edge techniques
#[derive(Debug, Clone)]
pub struct AdvancedSemanticAnalyzer {
    config: AdvancedSemanticConfig,
    semantic_vectors: HashMap<String, SemanticVector>,
    evolution_tracker: VecDeque<SemanticEvolution>,
    complexity_cache: HashMap<u64, SemanticComplexity>,
    dynamics_history: BTreeMap<usize, SemanticDynamics>,
    innovation_patterns: Vec<SemanticInnovation>,
    conceptual_space: ConceptualDimensions,
    coherence_models: HashMap<String, AdvancedCoherenceMetrics>,
    analysis_cache: HashMap<u64, AdvancedSemanticMetrics>,
}

impl AdvancedSemanticAnalyzer {
    /// Create new advanced analyzer with configuration
    pub fn new(config: AdvancedSemanticConfig) -> AdvancedResult<Self> {
        Self::validate_config(&config)?;

        Ok(Self {
            config,
            semantic_vectors: HashMap::new(),
            evolution_tracker: VecDeque::new(),
            complexity_cache: HashMap::new(),
            dynamics_history: BTreeMap::new(),
            innovation_patterns: Vec::new(),
            conceptual_space: ConceptualDimensions::new(),
            coherence_models: HashMap::new(),
            analysis_cache: HashMap::new(),
        })
    }

    /// Perform comprehensive advanced semantic analysis
    pub fn analyze_advanced_semantics(
        &mut self,
        text: &str,
        context_data: Option<&HashMap<String, String>>,
    ) -> AdvancedResult<AdvancedSemanticMetrics> {
        let cache_key = self.generate_cache_key(text, context_data);
        if let Some(cached) = self.analysis_cache.get(&cache_key) {
            return Ok(cached.clone());
        }

        let sentences = self.extract_sentences(text);
        let mut metrics = AdvancedSemanticMetrics::default();

        // Build semantic vector representations
        self.build_semantic_vectors(&sentences)?;

        // Core advanced analysis
        metrics.overall_advanced_score = self.calculate_overall_advanced_score()?;
        metrics.semantic_complexity = self.analyze_semantic_complexity(&sentences)?;
        metrics.conceptual_dimensions = self.analyze_conceptual_dimensions(&sentences)?;
        metrics.semantic_dynamics = self.analyze_semantic_dynamics(&sentences)?;

        // Vector-based analysis
        if self.config.enable_vector_analysis {
            metrics.vector_analysis = self.perform_vector_analysis(&sentences)?;
            metrics.semantic_clustering = self.analyze_semantic_clustering()?;
            metrics.dimensional_coherence = self.measure_dimensional_coherence()?;
        }

        // Evolution tracking
        if self.config.track_semantic_evolution {
            metrics.evolution_patterns = self.track_semantic_evolution(&sentences)?;
            metrics.semantic_stability = self.measure_semantic_stability(&sentences)?;
            metrics.innovation_detection = self.detect_semantic_innovation(&sentences)?;
        }

        // Advanced coherence modeling
        if self.config.advanced_coherence_modeling {
            metrics.advanced_coherence = self.model_advanced_coherence(&sentences)?;
            metrics.multi_scale_coherence = self.analyze_multi_scale_coherence(&sentences)?;
            metrics.coherence_prediction = self.predict_coherence_trends(&sentences)?;
        }

        // Complexity analysis
        if self.config.analyze_semantic_complexity {
            metrics.complexity_metrics = self.calculate_complexity_metrics(&sentences)?;
            metrics.entropy_measures = self.calculate_semantic_entropy(&sentences)?;
            metrics.information_density = self.analyze_information_density(&sentences)?;
        }

        // Cache results for performance
        self.analysis_cache.insert(cache_key, metrics.clone());

        Ok(metrics)
    }

    /// Build semantic vector representations for text
    fn build_semantic_vectors(&mut self, sentences: &[String]) -> AdvancedResult<()> {
        for (i, sentence) in sentences.iter().enumerate() {
            // Extract semantic features for vectorization
            let features = self.extract_semantic_features(sentence)?;

            // Build vector representation
            let vector = self.create_semantic_vector(&features, sentence)?;

            // Store vector with context
            let key = format!("sentence_{}", i);
            self.semantic_vectors.insert(key, vector);
        }

        // Build aggregate vectors for concepts and entities
        self.build_concept_vectors(sentences)?;
        self.build_entity_vectors(sentences)?;

        Ok(())
    }

    /// Calculate overall advanced semantic score
    fn calculate_overall_advanced_score(&self) -> AdvancedResult<f64> {
        let mut score_components = Vec::new();

        // Vector coherence score
        if !self.semantic_vectors.is_empty() {
            let vector_coherence = self.calculate_vector_coherence()?;
            score_components.push(vector_coherence * self.config.vector_weight);
        }

        // Complexity balance score
        if !self.complexity_cache.is_empty() {
            let complexity_balance = self.calculate_complexity_balance()?;
            score_components.push(complexity_balance * self.config.complexity_weight);
        }

        // Evolution consistency score
        if !self.evolution_tracker.is_empty() {
            let evolution_consistency = self.calculate_evolution_consistency()?;
            score_components.push(evolution_consistency * self.config.evolution_weight);
        }

        // Dimensional analysis score
        let dimensional_score = self.calculate_dimensional_score()?;
        score_components.push(dimensional_score * self.config.dimensional_weight);

        // Combined advanced score
        let total_weight = self.config.vector_weight
            + self.config.complexity_weight
            + self.config.evolution_weight
            + self.config.dimensional_weight;

        if total_weight == 0.0 {
            return Ok(0.0);
        }

        let combined_score = score_components.iter().sum::<f64>() / total_weight;
        Ok(combined_score.max(0.0).min(1.0))
    }

    /// Analyze semantic complexity of text
    fn analyze_semantic_complexity(
        &mut self,
        sentences: &[String],
    ) -> AdvancedResult<SemanticComplexity> {
        let cache_key = self.generate_complexity_key(sentences);
        if let Some(cached) = self.complexity_cache.get(&cache_key) {
            return Ok(cached.clone());
        }

        let mut complexity = SemanticComplexity {
            overall_complexity: 0.0,
            lexical_complexity: self.calculate_lexical_complexity(sentences)?,
            syntactic_complexity: self.calculate_syntactic_complexity(sentences)?,
            semantic_density: self.calculate_semantic_density(sentences)?,
            conceptual_diversity: self.calculate_conceptual_diversity(sentences)?,
            relational_complexity: self.calculate_relational_complexity(sentences)?,
            hierarchical_depth: self.calculate_hierarchical_depth(sentences)?,
            ambiguity_measures: self.calculate_ambiguity_measures(sentences)?,
            information_theoretic: self.calculate_information_theoretic_complexity(sentences)?,
        };

        // Calculate overall complexity as weighted combination
        complexity.overall_complexity = self.combine_complexity_measures(&complexity)?;

        // Cache for performance
        self.complexity_cache.insert(cache_key, complexity.clone());

        Ok(complexity)
    }

    /// Analyze conceptual dimensions in text
    fn analyze_conceptual_dimensions(
        &mut self,
        sentences: &[String],
    ) -> AdvancedResult<ConceptualDimensions> {
        let mut dimensions = ConceptualDimensions::new();

        // Extract conceptual features
        for sentence in sentences {
            let concepts = self.extract_sentence_concepts(sentence)?;

            for concept in concepts {
                dimensions.add_concept(
                    &concept,
                    self.calculate_concept_properties(&concept, sentence)?,
                );
            }
        }

        // Analyze dimensional relationships
        dimensions.build_dimensional_relationships()?;

        // Calculate dimensional coherence
        dimensions.coherence_score = self.calculate_dimensional_coherence_score(&dimensions)?;

        // Update internal conceptual space
        self.conceptual_space = dimensions.clone();

        Ok(dimensions)
    }

    /// Analyze semantic dynamics in text
    fn analyze_semantic_dynamics(
        &mut self,
        sentences: &[String],
    ) -> AdvancedResult<SemanticDynamics> {
        let mut dynamics = SemanticDynamics::new();

        // Track semantic changes across sentences
        for (i, sentence) in sentences.iter().enumerate() {
            if i > 0 {
                let prev_sentence = &sentences[i - 1];
                let transition = self.analyze_semantic_transition(prev_sentence, sentence)?;
                dynamics.add_transition(i, transition);
            }
        }

        // Analyze dynamic patterns
        dynamics.velocity = self.calculate_semantic_velocity(&dynamics)?;
        dynamics.acceleration = self.calculate_semantic_acceleration(&dynamics)?;
        dynamics.momentum = self.calculate_semantic_momentum(&dynamics)?;
        dynamics.stability = self.calculate_dynamic_stability(&dynamics)?;

        // Store in history
        let position = self.dynamics_history.len();
        self.dynamics_history.insert(position, dynamics.clone());

        Ok(dynamics)
    }

    /// Perform vector-based semantic analysis
    fn perform_vector_analysis(
        &self,
        sentences: &[String],
    ) -> AdvancedResult<VectorSemanticAnalysis> {
        let mut analysis = VectorSemanticAnalysis::new();

        // Calculate vector similarities between sentences
        for i in 0..sentences.len() {
            for j in (i + 1)..sentences.len() {
                if let (Some(vec_i), Some(vec_j)) = (
                    self.semantic_vectors.get(&format!("sentence_{}", i)),
                    self.semantic_vectors.get(&format!("sentence_{}", j)),
                ) {
                    let similarity = vec_i.cosine_similarity(vec_j);
                    analysis.add_similarity(i, j, similarity);
                }
            }
        }

        // Analyze vector clustering
        analysis.clusters = self.perform_vector_clustering()?;

        // Calculate vector coherence metrics
        analysis.coherence_metrics = self.calculate_vector_coherence_metrics()?;

        // Dimensional analysis
        analysis.dimensional_analysis = self.perform_dimensional_analysis()?;

        Ok(analysis)
    }

    /// Track semantic evolution patterns
    fn track_semantic_evolution(
        &mut self,
        sentences: &[String],
    ) -> AdvancedResult<Vec<SemanticEvolution>> {
        let mut evolutions = Vec::new();

        // Create evolution windows
        let window_size = self.config.evolution_window_size;

        for window_start in 0..=(sentences.len().saturating_sub(window_size)) {
            let window = &sentences[window_start..window_start + window_size];

            let evolution = SemanticEvolution {
                window_start,
                window_size,
                semantic_drift: self.calculate_semantic_drift(window)?,
                conceptual_evolution: self.track_conceptual_evolution(window)?,
                complexity_evolution: self.track_complexity_evolution(window)?,
                coherence_evolution: self.track_coherence_evolution(window)?,
                innovation_markers: self.detect_innovation_markers(window)?,
                stability_indicators: self.calculate_stability_indicators(window)?,
            };

            evolutions.push(evolution.clone());

            // Update evolution tracker
            self.evolution_tracker.push_back(evolution);

            // Maintain tracker size
            if self.evolution_tracker.len() > self.config.max_evolution_history {
                self.evolution_tracker.pop_front();
            }
        }

        Ok(evolutions)
    }

    /// Model advanced coherence with sophisticated techniques
    fn model_advanced_coherence(
        &mut self,
        sentences: &[String],
    ) -> AdvancedResult<AdvancedCoherenceMetrics> {
        let mut coherence = AdvancedCoherenceMetrics::new();

        // Multi-layered coherence analysis
        coherence.lexical_coherence = self.model_lexical_coherence(sentences)?;
        coherence.semantic_coherence = self.model_semantic_coherence(sentences)?;
        coherence.pragmatic_coherence = self.model_pragmatic_coherence(sentences)?;
        coherence.discourse_coherence = self.model_discourse_coherence(sentences)?;

        // Advanced coherence modeling
        coherence.neural_coherence_model = self.apply_neural_coherence_model(sentences)?;
        coherence.graph_coherence_model = self.apply_graph_coherence_model(sentences)?;
        coherence.information_coherence_model =
            self.apply_information_coherence_model(sentences)?;

        // Coherence prediction
        coherence.coherence_predictions = self.predict_coherence_evolution(sentences)?;

        // Store coherence model
        let model_key = format!("coherence_model_{}", sentences.len());
        self.coherence_models.insert(model_key, coherence.clone());

        Ok(coherence)
    }

    // Helper methods for advanced semantic analysis

    fn validate_config(config: &AdvancedSemanticConfig) -> AdvancedResult<()> {
        if config.vector_dimensions < 1 {
            return Err(AdvancedAnalysisError::ConfigError(
                "vector_dimensions must be at least 1".to_string(),
            ));
        }

        if config.evolution_window_size < 2 {
            return Err(AdvancedAnalysisError::ConfigError(
                "evolution_window_size must be at least 2".to_string(),
            ));
        }

        let total_weight = config.vector_weight
            + config.complexity_weight
            + config.evolution_weight
            + config.dimensional_weight;

        if total_weight <= 0.0 {
            return Err(AdvancedAnalysisError::ConfigError(
                "sum of all weights must be positive".to_string(),
            ));
        }

        Ok(())
    }

    fn extract_sentences(&self, text: &str) -> Vec<String> {
        text.split(&self.config.sentence_delimiters)
            .map(|s| s.trim().to_string())
            .filter(|s| !s.is_empty())
            .collect()
    }

    fn generate_cache_key(&self, text: &str, context: Option<&HashMap<String, String>>) -> u64 {
        let mut hasher = DefaultHasher::new();
        text.hash(&mut hasher);
        if let Some(ctx) = context {
            for (k, v) in ctx {
                k.hash(&mut hasher);
                v.hash(&mut hasher);
            }
        }
        self.config.hash(&mut hasher);
        hasher.finish()
    }

    fn generate_complexity_key(&self, sentences: &[String]) -> u64 {
        let mut hasher = DefaultHasher::new();
        for sentence in sentences {
            sentence.hash(&mut hasher);
        }
        self.config.hash(&mut hasher);
        hasher.finish()
    }

    fn extract_semantic_features(&self, sentence: &str) -> AdvancedResult<HashMap<String, f64>> {
        let mut features = HashMap::new();

        // Basic linguistic features
        let words: Vec<&str> = sentence.split_whitespace().collect();
        features.insert("word_count".to_string(), words.len() as f64);
        features.insert(
            "avg_word_length".to_string(),
            words.iter().map(|w| w.len()).sum::<usize>() as f64 / words.len() as f64,
        );

        // Semantic density features
        let meaningful_words = words.iter().filter(|w| self.is_meaningful_word(w)).count();
        features.insert(
            "semantic_density".to_string(),
            meaningful_words as f64 / words.len() as f64,
        );

        // Complexity indicators
        features.insert(
            "syntactic_complexity".to_string(),
            self.estimate_syntactic_complexity(sentence)?,
        );
        features.insert(
            "lexical_diversity".to_string(),
            self.calculate_lexical_diversity(sentence)?,
        );

        Ok(features)
    }

    fn create_semantic_vector(
        &self,
        features: &HashMap<String, f64>,
        context: &str,
    ) -> AdvancedResult<SemanticVector> {
        let mut dimensions = vec![0.0; self.config.vector_dimensions];
        let mut labels = Vec::new();

        // Map features to vector dimensions
        for (i, (feature_name, feature_value)) in features.iter().enumerate() {
            if i < dimensions.len() {
                dimensions[i] = *feature_value;
                labels.push(feature_name.clone());
            }
        }

        // Fill remaining dimensions with derived features
        while labels.len() < dimensions.len() {
            let idx = labels.len();
            dimensions[idx] = self.calculate_derived_feature(context, idx)?;
            labels.push(format!("derived_{}", idx));
        }

        let mut vector = SemanticVector::new(dimensions, labels);
        vector.source_context = context.to_string();

        Ok(vector)
    }

    fn is_meaningful_word(&self, word: &str) -> bool {
        word.len() >= self.config.min_word_length
            && word.chars().any(|c| c.is_alphabetic())
            && !self.is_stop_word(word)
    }

    fn is_stop_word(&self, word: &str) -> bool {
        self.config.stop_words.contains(&word.to_lowercase())
    }
}

impl Default for AdvancedSemanticAnalyzer {
    fn default() -> Self {
        Self::new(AdvancedSemanticConfig::default()).expect("default advanced semantic config should be valid")
    }
}

// Additional implementation methods would continue here...
// This represents the core structure for advanced semantic analysis capabilities