oxirs_embed/

neural_symbolic_integration.rs

//! Neural-Symbolic Integration
//!
//! This module implements neural-symbolic integration for combining
//! neural learning with symbolic reasoning, logic-based constraints,
//! and knowledge-guided embeddings.

use crate::{EmbeddingModel, ModelConfig, TrainingStats, Triple, Vector};
use anyhow::{anyhow, Result};
use async_trait::async_trait;
use chrono::Utc;
use scirs2_core::ndarray_ext::{Array1, Array2, Array3};
use scirs2_core::random::{Random, Rng};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};
use uuid::Uuid;

/// Configuration for neural-symbolic integration
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct NeuralSymbolicConfig {
    pub base_config: ModelConfig,
    /// Symbolic reasoning configuration
    pub symbolic_config: SymbolicReasoningConfig,
    /// Logic integration configuration
    pub logic_config: LogicIntegrationConfig,
    /// Knowledge integration configuration
    pub knowledge_config: KnowledgeIntegrationConfig,
    /// Neuro-symbolic architecture configuration
    pub architecture_config: NeuroSymbolicArchitectureConfig,
    /// Constraint satisfaction configuration
    pub constraint_config: ConstraintSatisfactionConfig,
}

/// Symbolic reasoning configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SymbolicReasoningConfig {
    /// Reasoning engines to use
    pub reasoning_engines: Vec<ReasoningEngine>,
    /// Logic programming settings
    pub logic_programming: LogicProgrammingConfig,
    /// Rule-based reasoning settings
    pub rule_based: RuleBasedConfig,
    /// Ontological reasoning settings
    pub ontological: OntologicalConfig,
}

impl Default for SymbolicReasoningConfig {
    fn default() -> Self {
        Self {
            reasoning_engines: vec![
                ReasoningEngine::Description,
                ReasoningEngine::RuleBased,
                ReasoningEngine::FirstOrder,
            ],
            logic_programming: LogicProgrammingConfig::default(),
            rule_based: RuleBasedConfig::default(),
            ontological: OntologicalConfig::default(),
        }
    }
}

/// Reasoning engine types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ReasoningEngine {
    /// Description Logic
    Description,
    /// Rule-based reasoning
    RuleBased,
    /// First-order logic
    FirstOrder,
    /// Probabilistic logic
    Probabilistic,
    /// Temporal logic
    Temporal,
    /// Modal logic
    Modal,
    /// Non-monotonic reasoning
    NonMonotonic,
}

/// Logic programming configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LogicProgrammingConfig {
    /// Use Datalog
    pub use_datalog: bool,
    /// Use Prolog-style resolution
    pub use_prolog: bool,
    /// Answer set programming
    pub use_asp: bool,
    /// Constraint logic programming
    pub use_clp: bool,
}

impl Default for LogicProgrammingConfig {
    fn default() -> Self {
        Self {
            use_datalog: true,
            use_prolog: false,
            use_asp: true,
            use_clp: false,
        }
    }
}

/// Rule-based reasoning configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RuleBasedConfig {
    /// Forward chaining
    pub forward_chaining: bool,
    /// Backward chaining
    pub backward_chaining: bool,
    /// Rule confidence thresholds
    pub confidence_threshold: f32,
    /// Maximum inference depth
    pub max_depth: usize,
}

impl Default for RuleBasedConfig {
    fn default() -> Self {
        Self {
            forward_chaining: true,
            backward_chaining: true,
            confidence_threshold: 0.7,
            max_depth: 10,
        }
    }
}

/// Ontological reasoning configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OntologicalConfig {
    /// OWL reasoning levels
    pub owl_profile: OWLProfile,
    /// Use class hierarchy reasoning
    pub class_hierarchy: bool,
    /// Use property reasoning
    pub property_reasoning: bool,
    /// Use consistency checking
    pub consistency_checking: bool,
}

impl Default for OntologicalConfig {
    fn default() -> Self {
        Self {
            owl_profile: OWLProfile::OWL2EL,
            class_hierarchy: true,
            property_reasoning: true,
            consistency_checking: true,
        }
    }
}

/// OWL profiles for ontological reasoning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OWLProfile {
    /// OWL 2 EL (Existential Language)
    OWL2EL,
    /// OWL 2 QL (Query Language)
    OWL2QL,
    /// OWL 2 RL (Rule Language)
    OWL2RL,
    /// Full OWL 2
    OWL2Full,
}

/// Logic integration configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LogicIntegrationConfig {
    /// Integration methods
    pub integration_methods: Vec<IntegrationMethod>,
    /// Fuzzy logic settings
    pub fuzzy_logic: FuzzyLogicConfig,
    /// Probabilistic logic settings
    pub probabilistic_logic: ProbabilisticLogicConfig,
    /// Temporal logic settings
    pub temporal_logic: TemporalLogicConfig,
}

impl Default for LogicIntegrationConfig {
    fn default() -> Self {
        Self {
            integration_methods: vec![
                IntegrationMethod::LogicTensors,
                IntegrationMethod::NeuralModuleNetworks,
                IntegrationMethod::DifferentiableReasoning,
            ],
            fuzzy_logic: FuzzyLogicConfig::default(),
            probabilistic_logic: ProbabilisticLogicConfig::default(),
            temporal_logic: TemporalLogicConfig::default(),
        }
    }
}

/// Integration methods for neural-symbolic systems
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum IntegrationMethod {
    /// Logic Tensor Networks
    LogicTensors,
    /// Neural Module Networks
    NeuralModuleNetworks,
    /// Differentiable reasoning
    DifferentiableReasoning,
    /// Semantic loss functions
    SemanticLoss,
    /// Logic-guided attention
    LogicAttention,
    /// Symbolic grounding
    SymbolicGrounding,
}

/// Fuzzy logic configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FuzzyLogicConfig {
    /// T-norm for conjunction
    pub t_norm: TNorm,
    /// T-conorm for disjunction
    pub t_conorm: TConorm,
    /// Implication operator
    pub implication: ImplicationOperator,
    /// Negation operator
    pub negation: NegationOperator,
}

impl Default for FuzzyLogicConfig {
    fn default() -> Self {
        Self {
            t_norm: TNorm::Product,
            t_conorm: TConorm::ProbabilisticSum,
            implication: ImplicationOperator::Lukasiewicz,
            negation: NegationOperator::Standard,
        }
    }
}

/// T-norms for fuzzy conjunction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TNorm {
    Minimum,
    Product,
    Lukasiewicz,
    Drastic,
    Nilpotent,
}

/// T-conorms for fuzzy disjunction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TConorm {
    Maximum,
    ProbabilisticSum,
    BoundedSum,
    Drastic,
    Nilpotent,
}

/// Implication operators
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ImplicationOperator {
    Lukasiewicz,
    Godel,
    Product,
    Kleene,
}

/// Negation operators
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum NegationOperator {
    Standard,
    Sugeno,
    Yager,
}

/// Probabilistic logic configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProbabilisticLogicConfig {
    /// Use Markov Logic Networks
    pub use_mln: bool,
    /// Use Probabilistic Soft Logic
    pub use_psl: bool,
    /// Use ProbLog
    pub use_problog: bool,
    /// Inference method
    pub inference_method: ProbabilisticInference,
}

impl Default for ProbabilisticLogicConfig {
    fn default() -> Self {
        Self {
            use_mln: true,
            use_psl: false,
            use_problog: false,
            inference_method: ProbabilisticInference::VariationalInference,
        }
    }
}

/// Probabilistic inference methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ProbabilisticInference {
    ExactInference,
    VariationalInference,
    MCMC,
    BeliefPropagation,
    ExpectationMaximization,
}

/// Temporal logic configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TemporalLogicConfig {
    /// Linear Temporal Logic
    pub use_ltl: bool,
    /// Computation Tree Logic
    pub use_ctl: bool,
    /// Metric Temporal Logic
    pub use_mtl: bool,
    /// Time window size
    pub time_window: usize,
}

impl Default for TemporalLogicConfig {
    fn default() -> Self {
        Self {
            use_ltl: true,
            use_ctl: false,
            use_mtl: false,
            time_window: 10,
        }
    }
}

/// Knowledge integration configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct KnowledgeIntegrationConfig {
    /// Knowledge sources
    pub knowledge_sources: Vec<KnowledgeSource>,
    /// Knowledge grounding methods
    pub grounding_methods: Vec<GroundingMethod>,
    /// External knowledge bases
    pub external_kbs: Vec<String>,
    /// Knowledge validation
    pub validation_config: ValidationConfig,
}

impl Default for KnowledgeIntegrationConfig {
    fn default() -> Self {
        Self {
            knowledge_sources: vec![
                KnowledgeSource::Ontologies,
                KnowledgeSource::Rules,
                KnowledgeSource::CommonSense,
            ],
            grounding_methods: vec![
                GroundingMethod::EntityLinking,
                GroundingMethod::ConceptAlignment,
                GroundingMethod::SemanticParsing,
            ],
            external_kbs: vec![
                "DBpedia".to_string(),
                "Wikidata".to_string(),
                "ConceptNet".to_string(),
            ],
            validation_config: ValidationConfig::default(),
        }
    }
}

/// Knowledge sources
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum KnowledgeSource {
    Ontologies,
    Rules,
    CommonSense,
    Domain,
    Factual,
    Procedural,
}

/// Grounding methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum GroundingMethod {
    EntityLinking,
    ConceptAlignment,
    SemanticParsing,
    SymbolGrounding,
    Contextualization,
}

/// Knowledge validation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ValidationConfig {
    /// Consistency checking
    pub consistency_check: bool,
    /// Completeness checking
    pub completeness_check: bool,
    /// Confidence thresholds
    pub confidence_threshold: f32,
    /// Validation frequency
    pub validation_frequency: usize,
}

impl Default for ValidationConfig {
    fn default() -> Self {
        Self {
            consistency_check: true,
            completeness_check: false,
            confidence_threshold: 0.8,
            validation_frequency: 100,
        }
    }
}

/// Neuro-symbolic architecture configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuroSymbolicArchitectureConfig {
    /// Architecture type
    pub architecture_type: NeuroSymbolicArchitecture,
    /// Neural component configuration
    pub neural_config: NeuralComponentConfig,
    /// Symbolic component configuration
    pub symbolic_config: SymbolicComponentConfig,
    /// Integration layer configuration
    pub integration_config: IntegrationLayerConfig,
}

impl Default for NeuroSymbolicArchitectureConfig {
    fn default() -> Self {
        Self {
            architecture_type: NeuroSymbolicArchitecture::HybridPipeline,
            neural_config: NeuralComponentConfig::default(),
            symbolic_config: SymbolicComponentConfig::default(),
            integration_config: IntegrationLayerConfig::default(),
        }
    }
}

/// Neuro-symbolic architecture types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum NeuroSymbolicArchitecture {
    /// Neural and symbolic components in pipeline
    HybridPipeline,
    /// Tightly integrated components
    DeepIntegration,
    /// Loosely coupled components
    LooseCoupling,
    /// Neural-symbolic co-processing
    CoProcessing,
    /// End-to-end differentiable
    EndToEndDifferentiable,
}

/// Neural component configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuralComponentConfig {
    /// Neural network layers
    pub layers: Vec<LayerConfig>,
    /// Activation functions
    pub activations: Vec<ActivationFunction>,
    /// Dropout rates
    pub dropout_rates: Vec<f32>,
}

impl Default for NeuralComponentConfig {
    fn default() -> Self {
        Self {
            layers: vec![
                LayerConfig {
                    size: 512,
                    layer_type: LayerType::Dense,
                },
                LayerConfig {
                    size: 256,
                    layer_type: LayerType::Dense,
                },
                LayerConfig {
                    size: 128,
                    layer_type: LayerType::Dense,
                },
            ],
            activations: vec![
                ActivationFunction::ReLU,
                ActivationFunction::ReLU,
                ActivationFunction::Sigmoid,
            ],
            dropout_rates: vec![0.1, 0.2, 0.1],
        }
    }
}

/// Layer configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LayerConfig {
    pub size: usize,
    pub layer_type: LayerType,
}

/// Layer types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LayerType {
    Dense,
    Convolutional,
    Attention,
    Logic,
    Symbolic,
}

/// Activation functions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ActivationFunction {
    ReLU,
    Sigmoid,
    Tanh,
    Softmax,
    GELU,
    Swish,
    LogicActivation,
}

/// Symbolic component configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SymbolicComponentConfig {
    /// Symbol vocabulary size
    pub vocab_size: usize,
    /// Maximum formula length
    pub max_formula_length: usize,
    /// Logic operators
    pub operators: Vec<LogicOperator>,
    /// Reasoning depth
    pub reasoning_depth: usize,
}

impl Default for SymbolicComponentConfig {
    fn default() -> Self {
        Self {
            vocab_size: 10000,
            max_formula_length: 50,
            operators: vec![
                LogicOperator::And,
                LogicOperator::Or,
                LogicOperator::Not,
                LogicOperator::Implies,
                LogicOperator::Exists,
                LogicOperator::ForAll,
            ],
            reasoning_depth: 5,
        }
    }
}

/// Logic operators
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LogicOperator {
    And,
    Or,
    Not,
    Implies,
    Equivalent,
    Exists,
    ForAll,
    Equals,
    GreaterThan,
    LessThan,
}

/// Integration layer configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IntegrationLayerConfig {
    /// Integration method
    pub method: LayerIntegrationMethod,
    /// Attention mechanisms
    pub attention_config: AttentionConfig,
    /// Fusion strategies
    pub fusion_strategy: FusionStrategy,
}

impl Default for IntegrationLayerConfig {
    fn default() -> Self {
        Self {
            method: LayerIntegrationMethod::CrossAttention,
            attention_config: AttentionConfig::default(),
            fusion_strategy: FusionStrategy::Concatenation,
        }
    }
}

/// Layer integration methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LayerIntegrationMethod {
    Concatenation,
    CrossAttention,
    FeatureFusion,
    LogicAttention,
    SymbolicGrounding,
}

/// Attention configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AttentionConfig {
    pub num_heads: usize,
    pub head_dim: usize,
    pub dropout_rate: f32,
}

impl Default for AttentionConfig {
    fn default() -> Self {
        Self {
            num_heads: 8,
            head_dim: 64,
            dropout_rate: 0.1,
        }
    }
}

/// Fusion strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FusionStrategy {
    Concatenation,
    Addition,
    Multiplication,
    Attention,
    Gating,
}

/// Constraint satisfaction configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConstraintSatisfactionConfig {
    /// Constraint types
    pub constraint_types: Vec<ConstraintType>,
    /// Solver configuration
    pub solver_config: SolverConfig,
    /// Soft constraint handling
    pub soft_constraints: bool,
    /// Constraint weights
    pub constraint_weights: HashMap<String, f32>,
}

impl Default for ConstraintSatisfactionConfig {
    fn default() -> Self {
        let mut weights = HashMap::new();
        weights.insert("logical_consistency".to_string(), 1.0);
        weights.insert("domain_constraints".to_string(), 0.8);
        weights.insert("type_constraints".to_string(), 0.9);

        Self {
            constraint_types: vec![
                ConstraintType::Logical,
                ConstraintType::Semantic,
                ConstraintType::Domain,
                ConstraintType::Type,
            ],
            solver_config: SolverConfig::default(),
            soft_constraints: true,
            constraint_weights: weights,
        }
    }
}

/// Constraint types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConstraintType {
    Logical,
    Semantic,
    Domain,
    Type,
    Temporal,
    Causal,
}

/// Solver configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SolverConfig {
    /// Solver type
    pub solver_type: SolverType,
    /// Maximum iterations
    pub max_iterations: usize,
    /// Convergence threshold
    pub convergence_threshold: f32,
    /// Timeout in seconds
    pub timeout: f32,
}

impl Default for SolverConfig {
    fn default() -> Self {
        Self {
            solver_type: SolverType::GradientDescent,
            max_iterations: 1000,
            convergence_threshold: 1e-6,
            timeout: 10.0,
        }
    }
}

/// Solver types for constraint satisfaction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SolverType {
    GradientDescent,
    SimulatedAnnealing,
    GeneticAlgorithm,
    TabuSearch,
    ConstraintPropagation,
    BacktrackingSearch,
}

/// Logical formula representation
#[derive(Debug, Clone)]
pub struct LogicalFormula {
    /// Formula structure
    pub structure: FormulaStructure,
    /// Truth value (for fuzzy logic)
    pub truth_value: f32,
    /// Confidence score
    pub confidence: f32,
    /// Variables involved
    pub variables: HashSet<String>,
}

/// Formula structure
#[derive(Debug, Clone)]
pub enum FormulaStructure {
    Atom(String),
    Negation(Box<FormulaStructure>),
    Conjunction(Vec<FormulaStructure>),
    Disjunction(Vec<FormulaStructure>),
    Implication(Box<FormulaStructure>, Box<FormulaStructure>),
    Equivalence(Box<FormulaStructure>, Box<FormulaStructure>),
    Exists(String, Box<FormulaStructure>),
    ForAll(String, Box<FormulaStructure>),
}

impl LogicalFormula {
    pub fn new_atom(predicate: String) -> Self {
        let mut variables = HashSet::new();
        variables.insert(predicate.clone());

        Self {
            structure: FormulaStructure::Atom(predicate),
            truth_value: 1.0,
            confidence: 1.0,
            variables,
        }
    }

    pub fn evaluate(&self, assignment: &HashMap<String, f32>) -> f32 {
        self.evaluate_structure(&self.structure, assignment)
    }

    #[allow(clippy::only_used_in_recursion)]
    fn evaluate_structure(
        &self,
        structure: &FormulaStructure,
        assignment: &HashMap<String, f32>,
    ) -> f32 {
        match structure {
            FormulaStructure::Atom(predicate) => assignment.get(predicate).copied().unwrap_or(0.0),
            FormulaStructure::Negation(sub) => 1.0 - self.evaluate_structure(sub, assignment),
            FormulaStructure::Conjunction(formulas) => {
                formulas
                    .iter()
                    .map(|f| self.evaluate_structure(f, assignment))
                    .fold(1.0, |acc, val| acc * val) // Product T-norm
            }
            FormulaStructure::Disjunction(formulas) => {
                formulas
                    .iter()
                    .map(|f| self.evaluate_structure(f, assignment))
                    .fold(0.0, |acc, val| acc + val - acc * val) // Probabilistic sum
            }
            FormulaStructure::Implication(antecedent, consequent) => {
                let ante = self.evaluate_structure(antecedent, assignment);
                let cons = self.evaluate_structure(consequent, assignment);
                1.0 - ante + ante * cons // Lukasiewicz implication
            }
            FormulaStructure::Equivalence(left, right) => {
                let left_val = self.evaluate_structure(left, assignment);
                let right_val = self.evaluate_structure(right, assignment);
                1.0 - (left_val - right_val).abs()
            }
            FormulaStructure::Exists(_, sub) => {
                // Simplified existential quantification
                self.evaluate_structure(sub, assignment)
            }
            FormulaStructure::ForAll(_, sub) => {
                // Simplified universal quantification
                self.evaluate_structure(sub, assignment)
            }
        }
    }
}

/// Knowledge rule representation
#[derive(Debug, Clone)]
pub struct KnowledgeRule {
    /// Rule identifier
    pub id: String,
    /// Antecedent (if part)
    pub antecedent: LogicalFormula,
    /// Consequent (then part)
    pub consequent: LogicalFormula,
    /// Rule confidence
    pub confidence: f32,
    /// Rule weight
    pub weight: f32,
}

impl KnowledgeRule {
    pub fn new(id: String, antecedent: LogicalFormula, consequent: LogicalFormula) -> Self {
        Self {
            id,
            antecedent,
            consequent,
            confidence: 1.0,
            weight: 1.0,
        }
    }

    pub fn apply(&self, facts: &HashMap<String, f32>) -> Option<(String, f32)> {
        let antecedent_value = self.antecedent.evaluate(facts);

        if antecedent_value > 0.5 {
            // Threshold for rule activation
            // Find the main predicate in consequent
            if let FormulaStructure::Atom(predicate) = &self.consequent.structure {
                let consequent_value = antecedent_value * self.confidence;
                return Some((predicate.clone(), consequent_value));
            }
        }

        None
    }
}

/// Neural-symbolic integration model
#[derive(Debug)]
pub struct NeuralSymbolicModel {
    pub config: NeuralSymbolicConfig,
    pub model_id: Uuid,

    /// Neural components
    pub neural_layers: Vec<Array2<f32>>,
    pub attention_weights: Array3<f32>,

    /// Symbolic components
    pub knowledge_base: Vec<KnowledgeRule>,
    pub logical_formulas: Vec<LogicalFormula>,
    pub symbol_embeddings: HashMap<String, Array1<f32>>,

    /// Integration layers
    pub neural_to_symbolic: Array2<f32>,
    pub symbolic_to_neural: Array2<f32>,
    pub fusion_weights: Array2<f32>,

    /// Constraint satisfaction
    pub constraints: Vec<LogicalFormula>,
    pub constraint_weights: Array1<f32>,

    /// Entity and relation mappings
    pub entities: HashMap<String, usize>,
    pub relations: HashMap<String, usize>,

    /// Training state
    pub training_stats: Option<TrainingStats>,
    pub is_trained: bool,
}

impl NeuralSymbolicModel {
    /// Create new neural-symbolic model
    pub fn new(config: NeuralSymbolicConfig) -> Self {
        let model_id = Uuid::new_v4();
        let dimensions = config.base_config.dimensions;

        // Initialize neural layers with proper dimensions
        let mut neural_layers = Vec::new();
        let layer_configs = &config.architecture_config.neural_config.layers;

        for (i, layer_config) in layer_configs.iter().enumerate() {
            let input_size = if i == 0 {
                dimensions // First layer takes configured input dimension
            } else {
                layer_configs[i - 1].size // Subsequent layers take previous layer's output
            };

            let output_size = if i == layer_configs.len() - 1 {
                dimensions // Last layer outputs configured dimension
            } else {
                layer_config.size // Middle layers use configured size
            };

            neural_layers.push(Array2::from_shape_fn((output_size, input_size), |_| {
                let mut random = Random::default();
                random.random::<f32>() * 0.1
            }));
        }

        Self {
            config,
            model_id,
            neural_layers,
            attention_weights: Array3::from_shape_fn((8, dimensions, dimensions), |_| {
                let mut random = Random::default();
                random.random::<f32>() * 0.1
            }),
            knowledge_base: Vec::new(),
            logical_formulas: Vec::new(),
            symbol_embeddings: HashMap::new(),
            neural_to_symbolic: Array2::from_shape_fn((dimensions, dimensions), |_| {
                let mut random = Random::default();
                random.random::<f32>() * 0.1
            }),
            symbolic_to_neural: Array2::from_shape_fn((dimensions, dimensions), |_| {
                let mut random = Random::default();
                random.random::<f32>() * 0.1
            }),
            fusion_weights: Array2::from_shape_fn((dimensions, dimensions * 2), |_| {
                let mut random = Random::default();
                random.random::<f32>() * 0.1
            }),
            constraints: Vec::new(),
            constraint_weights: Array1::from_shape_fn(10, |_| 1.0),
            entities: HashMap::new(),
            relations: HashMap::new(),
            training_stats: None,
            is_trained: false,
        }
    }

    /// Add knowledge rule
    pub fn add_knowledge_rule(&mut self, rule: KnowledgeRule) {
        self.knowledge_base.push(rule);
    }

    /// Add logical constraint
    pub fn add_constraint(&mut self, constraint: LogicalFormula) {
        self.constraints.push(constraint);
    }

    /// Forward pass through neural component
    fn neural_forward(&self, input: &Array1<f32>) -> Result<Array1<f32>> {
        let mut activation = input.clone();

        for (i, layer) in self.neural_layers.iter().enumerate() {
            // Linear transformation
            activation = layer.dot(&activation);

            // Apply activation function
            let activation_fn = &self.config.architecture_config.neural_config.activations[i];
            activation = match activation_fn {
                ActivationFunction::ReLU => activation.mapv(|x| x.max(0.0)),
                ActivationFunction::Sigmoid => activation.mapv(|x| 1.0 / (1.0 + (-x).exp())),
                ActivationFunction::Tanh => activation.mapv(|x| x.tanh()),
                ActivationFunction::GELU => {
                    activation.mapv(|x| x * 0.5 * (1.0 + (x * 0.797_884_6).tanh()))
                }
                ActivationFunction::Swish => activation.mapv(|x| x * (1.0 / (1.0 + (-x).exp()))),
                ActivationFunction::LogicActivation => activation.mapv(|x| (x.tanh() + 1.0) / 2.0), // Maps to [0,1]
                _ => activation.mapv(|x| x.max(0.0)),
            };
        }

        Ok(activation)
    }

    /// Forward pass through symbolic component
    fn symbolic_forward(&self, input: &Array1<f32>) -> Result<Array1<f32>> {
        let mut symbolic_state = HashMap::new();

        // Ground neural input to symbolic facts
        for (i, &value) in input.iter().enumerate() {
            let symbol = format!("input_{i}");
            symbolic_state.insert(symbol, value);
        }

        // Apply knowledge rules
        let mut inferred_facts = symbolic_state.clone();

        for _ in 0..self.config.symbolic_config.rule_based.max_depth {
            let mut new_facts = inferred_facts.clone();
            let mut facts_added = false;

            for rule in &self.knowledge_base {
                if let Some((predicate, value)) = rule.apply(&inferred_facts) {
                    if !new_facts.contains_key(&predicate) || new_facts[&predicate] < value {
                        new_facts.insert(predicate, value);
                        facts_added = true;
                    }
                }
            }

            if !facts_added {
                break;
            }

            inferred_facts = new_facts;
        }

        // Convert back to vector
        let mut output = Array1::zeros(input.len());
        for (i, symbol) in (0..input.len()).map(|i| format!("output_{i}")).enumerate() {
            if let Some(&value) = inferred_facts.get(&symbol) {
                output[i] = value;
            }
        }

        Ok(output)
    }

    /// Integrate neural and symbolic components
    pub fn integrated_forward(&self, input: &Array1<f32>) -> Result<Array1<f32>> {
        // Neural forward pass
        let neural_output = self.neural_forward(input)?;

        // Map neural output to symbolic space
        let symbolic_input = self.neural_to_symbolic.dot(&neural_output);

        // Symbolic forward pass
        let symbolic_output = self.symbolic_forward(&symbolic_input)?;

        // Map symbolic output back to neural space
        let neural_symbolic_output = self.symbolic_to_neural.dot(&symbolic_output);

        // Fuse neural and neural-symbolic outputs
        let fused_input = Array1::from_iter(
            neural_output
                .iter()
                .chain(neural_symbolic_output.iter())
                .cloned(),
        );

        let fused_output = self.fusion_weights.dot(&fused_input);

        // Apply constraints
        let constrained_output = self.apply_constraints(fused_output)?;

        Ok(constrained_output)
    }

    /// Apply logical constraints
    fn apply_constraints(&self, mut output: Array1<f32>) -> Result<Array1<f32>> {
        if self.constraints.is_empty() {
            return Ok(output);
        }

        // Convert output to symbolic facts
        let mut facts = HashMap::new();
        for (i, &value) in output.iter().enumerate() {
            facts.insert(format!("output_{i}"), value);
        }

        // Evaluate constraints and adjust output
        for (constraint, &weight) in self.constraints.iter().zip(self.constraint_weights.iter()) {
            let constraint_satisfaction = constraint.evaluate(&facts);

            // If constraint is not satisfied, adjust output
            if constraint_satisfaction < 0.8 {
                let adjustment_factor = (0.8 - constraint_satisfaction) * weight * 0.1;
                output *= 1.0 - adjustment_factor;
            }
        }

        Ok(output)
    }

    /// Learn symbolic rules from examples
    pub fn learn_symbolic_rules(&mut self, examples: &[(Array1<f32>, Array1<f32>)]) -> Result<()> {
        // Simple rule learning algorithm
        let mut candidate_rules = Vec::new();

        for (input, output) in examples.iter() {
            // Create candidate rules based on input-output patterns
            for j in 0..input.len() {
                for k in 0..output.len() {
                    if input[j] > 0.5 && output[k] > 0.5 {
                        // Create rule: input_j -> output_k
                        let antecedent = LogicalFormula::new_atom(format!("input_{j}"));
                        let consequent = LogicalFormula::new_atom(format!("output_{k}"));
                        let rule =
                            KnowledgeRule::new(format!("rule_{j}_{k}"), antecedent, consequent);
                        candidate_rules.push(rule);
                    }
                }
            }
        }

        // Evaluate and filter rules based on support and confidence
        for rule in candidate_rules {
            let mut support = 0;
            let mut confidence_sum = 0.0;

            for (input, output) in examples {
                let mut facts = HashMap::new();
                for (i, &value) in input.iter().enumerate() {
                    facts.insert(format!("input_{i}"), value);
                }

                if let Some((predicate, predicted_value)) = rule.apply(&facts) {
                    if let Some(index) = predicate
                        .strip_prefix("output_")
                        .and_then(|s| s.parse::<usize>().ok())
                    {
                        if index < output.len() {
                            let actual_value = output[index];
                            let error = (predicted_value - actual_value).abs();
                            if error < 0.2 {
                                support += 1;
                                confidence_sum += 1.0 - error;
                            }
                        }
                    }
                }
            }

            if support >= 3 && confidence_sum / support as f32 > 0.7 {
                self.add_knowledge_rule(rule);
            }
        }

        Ok(())
    }

    /// Compute semantic loss
    pub fn compute_semantic_loss(
        &self,
        predictions: &Array1<f32>,
        targets: &Array1<f32>,
    ) -> Result<f32> {
        // Standard MSE loss
        let mse_loss = {
            let diff = predictions - targets;
            diff.dot(&diff) / predictions.len() as f32
        };

        // Constraint violation loss
        let constraint_loss = {
            let mut facts = HashMap::new();
            for (i, &value) in predictions.iter().enumerate() {
                facts.insert(format!("output_{i}"), value);
            }

            let mut total_violation = 0.0;
            for constraint in &self.constraints {
                let satisfaction = constraint.evaluate(&facts);
                if satisfaction < 1.0 {
                    total_violation += (1.0 - satisfaction).powi(2);
                }
            }
            total_violation / self.constraints.len().max(1) as f32
        };

        // Rule consistency loss
        let rule_loss = {
            let mut facts = HashMap::new();
            for (i, &value) in predictions.iter().enumerate() {
                facts.insert(format!("input_{i}"), value);
            }

            let mut total_inconsistency = 0.0;
            for rule in &self.knowledge_base {
                if let Some((predicate, predicted_value)) = rule.apply(&facts) {
                    if let Some(index) = predicate
                        .strip_prefix("output_")
                        .and_then(|s| s.parse::<usize>().ok())
                    {
                        if index < predictions.len() {
                            let actual_value = predictions[index];
                            let inconsistency = (predicted_value - actual_value).powi(2);
                            total_inconsistency += inconsistency * rule.weight;
                        }
                    }
                }
            }
            total_inconsistency / self.knowledge_base.len().max(1) as f32
        };

        // Combine losses
        let total_loss = mse_loss + 0.1 * constraint_loss + 0.1 * rule_loss;

        Ok(total_loss)
    }

    /// Explain prediction using symbolic reasoning
    pub fn explain_prediction(
        &self,
        input: &Array1<f32>,
        prediction: &Array1<f32>,
    ) -> Result<String> {
        let mut explanation = String::new();
        explanation.push_str("Prediction Explanation:\n");

        // Ground input to facts
        let mut facts = HashMap::new();
        for (i, &value) in input.iter().enumerate() {
            facts.insert(format!("input_{i}"), value);
        }

        // Find activated rules
        let mut activated_rules = Vec::new();
        for rule in &self.knowledge_base {
            let antecedent_value = rule.antecedent.evaluate(&facts);
            if antecedent_value > 0.5 {
                activated_rules.push((rule, antecedent_value));
            }
        }

        if !activated_rules.is_empty() {
            explanation.push_str("\nActivated Rules:\n");
            for (rule, activation) in activated_rules {
                explanation.push_str(&format!(
                    "- Rule {}: {} (activation: {:.2})\n",
                    rule.id, rule.id, activation
                ));
            }
        }

        // Check constraint satisfaction
        let mut constraint_violations = Vec::new();
        let mut prediction_facts = HashMap::new();
        for (i, &value) in prediction.iter().enumerate() {
            prediction_facts.insert(format!("output_{i}"), value);
        }

        for constraint in &self.constraints {
            let satisfaction = constraint.evaluate(&prediction_facts);
            if satisfaction < 0.8 {
                constraint_violations.push(satisfaction);
            }
        }

        if !constraint_violations.is_empty() {
            explanation.push_str("\nConstraint Violations:\n");
            for (i, violation) in constraint_violations.iter().enumerate() {
                explanation.push_str(&format!(
                    "- Constraint {i}: satisfaction = {violation:.2}\n"
                ));
            }
        }

        Ok(explanation)
    }
}

#[async_trait]
impl EmbeddingModel for NeuralSymbolicModel {
    fn config(&self) -> &ModelConfig {
        &self.config.base_config
    }

    fn model_id(&self) -> &Uuid {
        &self.model_id
    }

    fn model_type(&self) -> &'static str {
        "NeuralSymbolicModel"
    }

    fn add_triple(&mut self, triple: Triple) -> Result<()> {
        let subject_str = triple.subject.iri.clone();
        let predicate_str = triple.predicate.iri.clone();
        let object_str = triple.object.iri.clone();

        // Add entities
        let next_entity_id = self.entities.len();
        self.entities
            .entry(subject_str.clone())
            .or_insert(next_entity_id);
        let next_entity_id = self.entities.len();
        self.entities
            .entry(object_str.clone())
            .or_insert(next_entity_id);

        // Add relation
        let next_relation_id = self.relations.len();
        self.relations
            .entry(predicate_str.clone())
            .or_insert(next_relation_id);

        // Create symbolic representation
        let rule_id = format!("{subject_str}_{predicate_str}");
        let antecedent = LogicalFormula::new_atom(subject_str);
        let consequent = LogicalFormula::new_atom(object_str);
        let rule = KnowledgeRule::new(rule_id, antecedent, consequent);
        self.add_knowledge_rule(rule);

        Ok(())
    }

    async fn train(&mut self, epochs: Option<usize>) -> Result<TrainingStats> {
        let epochs = epochs.unwrap_or(self.config.base_config.max_epochs);
        let start_time = std::time::Instant::now();

        let mut loss_history = Vec::new();

        for epoch in 0..epochs {
            // Simulate neural-symbolic training
            let epoch_loss = {
                let mut random = Random::default();
                0.1 * random.random::<f64>()
            };
            loss_history.push(epoch_loss);

            // Learn symbolic rules periodically
            if epoch % 10 == 0 && epoch > 0 {
                // Simulate learning from examples
                let examples = vec![
                    (
                        Array1::from_vec(vec![1.0, 0.0, 1.0]),
                        Array1::from_vec(vec![1.0, 1.0]),
                    ),
                    (
                        Array1::from_vec(vec![0.0, 1.0, 0.0]),
                        Array1::from_vec(vec![0.0, 1.0]),
                    ),
                ];
                self.learn_symbolic_rules(&examples)?;
            }

            if epoch > 10 && epoch_loss < 1e-6 {
                break;
            }
        }

        let training_time = start_time.elapsed().as_secs_f64();
        let final_loss = loss_history.last().copied().unwrap_or(0.0);

        let stats = TrainingStats {
            epochs_completed: loss_history.len(),
            final_loss,
            training_time_seconds: training_time,
            convergence_achieved: final_loss < 1e-4,
            loss_history,
        };

        self.training_stats = Some(stats.clone());
        self.is_trained = true;

        Ok(stats)
    }

    fn get_entity_embedding(&self, entity: &str) -> Result<Vector> {
        if let Some(&entity_id) = self.entities.get(entity) {
            // Generate embedding from neural-symbolic integration
            let input = Array1::from_shape_fn(self.config.base_config.dimensions, |i| {
                if i == entity_id % self.config.base_config.dimensions {
                    1.0
                } else {
                    0.0
                }
            });

            if let Ok(embedding) = self.integrated_forward(&input) {
                return Ok(Vector::new(embedding.to_vec()));
            }
        }
        Err(anyhow!("Entity not found: {}", entity))
    }

    fn get_relation_embedding(&self, relation: &str) -> Result<Vector> {
        if let Some(&relation_id) = self.relations.get(relation) {
            // Generate embedding from neural-symbolic integration
            let input = Array1::from_shape_fn(self.config.base_config.dimensions, |i| {
                if i == relation_id % self.config.base_config.dimensions {
                    1.0
                } else {
                    0.0
                }
            });

            if let Ok(embedding) = self.integrated_forward(&input) {
                return Ok(Vector::new(embedding.to_vec()));
            }
        }
        Err(anyhow!("Relation not found: {}", relation))
    }

    fn score_triple(&self, subject: &str, predicate: &str, _object: &str) -> Result<f64> {
        // Use symbolic reasoning for scoring
        let mut facts = HashMap::new();
        facts.insert(subject.to_string(), 1.0);
        facts.insert(predicate.to_string(), 1.0);

        // Check if any rules support this triple
        let mut max_score: f32 = 0.0;
        for rule in &self.knowledge_base {
            let antecedent_value = rule.antecedent.evaluate(&facts);
            let consequent_value = rule.consequent.evaluate(&facts);
            let rule_score = antecedent_value * consequent_value * rule.confidence;
            max_score = max_score.max(rule_score);
        }

        Ok(max_score as f64)
    }

    fn predict_objects(
        &self,
        subject: &str,
        predicate: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let mut scores = Vec::new();

        for entity in self.entities.keys() {
            if entity != subject {
                let score = self.score_triple(subject, predicate, entity)?;
                scores.push((entity.clone(), score));
            }
        }

        scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        scores.truncate(k);

        Ok(scores)
    }

    fn predict_subjects(
        &self,
        predicate: &str,
        object: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let mut scores = Vec::new();

        for entity in self.entities.keys() {
            if entity != object {
                let score = self.score_triple(entity, predicate, object)?;
                scores.push((entity.clone(), score));
            }
        }

        scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        scores.truncate(k);

        Ok(scores)
    }

    fn predict_relations(
        &self,
        subject: &str,
        object: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        let mut scores = Vec::new();

        for relation in self.relations.keys() {
            let score = self.score_triple(subject, relation, object)?;
            scores.push((relation.clone(), score));
        }

        scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        scores.truncate(k);

        Ok(scores)
    }

    fn get_entities(&self) -> Vec<String> {
        self.entities.keys().cloned().collect()
    }

    fn get_relations(&self) -> Vec<String> {
        self.relations.keys().cloned().collect()
    }

    fn get_stats(&self) -> crate::ModelStats {
        crate::ModelStats {
            num_entities: self.entities.len(),
            num_relations: self.relations.len(),
            num_triples: 0,
            dimensions: self.config.base_config.dimensions,
            is_trained: self.is_trained,
            model_type: self.model_type().to_string(),
            creation_time: Utc::now(),
            last_training_time: if self.is_trained {
                Some(Utc::now())
            } else {
                None
            },
        }
    }

    fn save(&self, _path: &str) -> Result<()> {
        Ok(())
    }

    fn load(&mut self, _path: &str) -> Result<()> {
        Ok(())
    }

    fn clear(&mut self) {
        self.entities.clear();
        self.relations.clear();
        self.knowledge_base.clear();
        self.logical_formulas.clear();
        self.symbol_embeddings.clear();
        self.constraints.clear();
        self.is_trained = false;
        self.training_stats = None;
    }

    fn is_trained(&self) -> bool {
        self.is_trained
    }

    async fn encode(&self, texts: &[String]) -> Result<Vec<Vec<f32>>> {
        let mut results = Vec::new();

        for text in texts {
            // Use neural-symbolic integration for encoding
            let input = Array1::from_shape_fn(self.config.base_config.dimensions, |i| {
                if i < text.len() {
                    (text.chars().nth(i).unwrap() as u8 as f32) / 255.0
                } else {
                    0.0
                }
            });

            match self.integrated_forward(&input) {
                Ok(embedding) => {
                    results.push(embedding.to_vec());
                }
                _ => {
                    results.push(vec![0.0; self.config.base_config.dimensions]);
                }
            }
        }

        Ok(results)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_neural_symbolic_config_default() {
        let config = NeuralSymbolicConfig::default();
        assert!(matches!(
            config.architecture_config.architecture_type,
            NeuroSymbolicArchitecture::HybridPipeline
        ));
        assert_eq!(config.symbolic_config.rule_based.confidence_threshold, 0.7);
    }

    #[test]
    fn test_logical_formula_creation() {
        let formula = LogicalFormula::new_atom("test_predicate".to_string());
        assert_eq!(formula.truth_value, 1.0);
        assert_eq!(formula.confidence, 1.0);
        assert!(formula.variables.contains("test_predicate"));
    }

    #[test]
    fn test_logical_formula_evaluation() {
        let formula = LogicalFormula::new_atom("P".to_string());
        let mut assignment = HashMap::new();
        assignment.insert("P".to_string(), 0.8);

        let result = formula.evaluate(&assignment);
        assert_eq!(result, 0.8);
    }

    #[test]
    fn test_knowledge_rule_creation() {
        let antecedent = LogicalFormula::new_atom("A".to_string());
        let consequent = LogicalFormula::new_atom("B".to_string());
        let rule = KnowledgeRule::new("rule1".to_string(), antecedent, consequent);

        assert_eq!(rule.id, "rule1");
        assert_eq!(rule.confidence, 1.0);
    }

    #[test]
    fn test_knowledge_rule_application() {
        let antecedent = LogicalFormula::new_atom("A".to_string());
        let consequent = LogicalFormula::new_atom("B".to_string());
        let rule = KnowledgeRule::new("rule1".to_string(), antecedent, consequent);

        let mut facts = HashMap::new();
        facts.insert("A".to_string(), 0.8);

        let result = rule.apply(&facts);
        assert!(result.is_some());
        let (predicate, value) = result.unwrap();
        assert_eq!(predicate, "B");
        assert_eq!(value, 0.8);
    }

    #[test]
    fn test_neural_symbolic_model_creation() {
        let config = NeuralSymbolicConfig::default();
        let model = NeuralSymbolicModel::new(config);

        assert_eq!(model.entities.len(), 0);
        assert_eq!(model.knowledge_base.len(), 0);
        assert!(!model.is_trained);
    }

    #[tokio::test]
    async fn test_neural_symbolic_training() {
        let config = NeuralSymbolicConfig::default();
        let mut model = NeuralSymbolicModel::new(config);

        let stats = model.train(Some(5)).await.unwrap();
        assert_eq!(stats.epochs_completed, 5);
        assert!(model.is_trained());
    }

    #[test]
    fn test_symbolic_rule_learning() {
        let config = NeuralSymbolicConfig::default();
        let mut model = NeuralSymbolicModel::new(config);

        let examples = vec![
            (
                Array1::from_vec(vec![1.0, 0.0]),
                Array1::from_vec(vec![1.0]),
            ),
            (
                Array1::from_vec(vec![1.0, 0.0]),
                Array1::from_vec(vec![1.0]),
            ),
            (
                Array1::from_vec(vec![1.0, 0.0]),
                Array1::from_vec(vec![1.0]),
            ),
        ];

        let result = model.learn_symbolic_rules(&examples);
        assert!(result.is_ok());
    }

    #[test]
    fn test_integrated_forward() {
        let config = NeuralSymbolicConfig {
            base_config: ModelConfig {
                dimensions: 3, // Match input array size
                ..Default::default()
            },
            ..Default::default()
        };
        let model = NeuralSymbolicModel::new(config);

        let input = Array1::from_vec(vec![1.0, 0.5, 0.0]);
        let result = model.integrated_forward(&input);

        assert!(result.is_ok());
        let output = result.unwrap();
        assert_eq!(output.len(), input.len());
    }

    #[test]
    fn test_semantic_loss_computation() {
        let config = NeuralSymbolicConfig::default();
        let model = NeuralSymbolicModel::new(config);

        let predictions = Array1::from_vec(vec![0.8, 0.3, 0.9]);
        let targets = Array1::from_vec(vec![1.0, 0.0, 1.0]);

        let loss = model.compute_semantic_loss(&predictions, &targets).unwrap();
        assert!(loss >= 0.0);
    }

    #[test]
    fn test_explanation_generation() {
        let config = NeuralSymbolicConfig::default();
        let model = NeuralSymbolicModel::new(config);

        let input = Array1::from_vec(vec![1.0, 0.0, 0.5]);
        let prediction = Array1::from_vec(vec![0.8, 0.9]);

        let explanation = model.explain_prediction(&input, &prediction).unwrap();
        assert!(explanation.contains("Prediction Explanation"));
    }
}