optirs-learned 0.3.1

// Knowledge base system for optimization patterns and insights

use super::config::*;
use super::ensemble::{EnsembleOptimizationResults, AdaptationEvent};
use crate::OptimizerError as OptimError;
use scirs2_core::ndarray::Array1;
use scirs2_core::numeric::Float;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;
use std::time::SystemTime;

/// Result type for knowledge base operations
type Result<T> = std::result::Result<T, OptimError>;

/// Optimization experience record
#[derive(Debug, Clone)]
pub struct OptimizationExperience<T: Float + Debug + Send + Sync + 'static> {
    /// Optimizer used
    pub optimizer_name: String,
    /// Problem characteristics
    pub problem_characteristics: Array1<T>,
    /// Performance achieved
    pub performance: T,
    /// Optimization settings
    pub settings: HashMap<String, String>,
}

/// Comprehensive optimization knowledge base
#[derive(Debug)]
pub struct OptimizationKnowledgeBase<T: Float + Debug + Send + Sync + Debug> {
    /// Database of optimization patterns
    pub optimization_patterns: HashMap<String, OptimizationPattern<T>>,

    /// Best practices database
    pub best_practices: BestPracticesDatabase,

    /// Failure analysis database
    pub failure_analysis: FailureAnalysisDatabase<T>,

    /// Research insights database
    pub research_insights: ResearchInsightsDatabase,

    /// Dynamic learning system
    pub learning_system: DynamicLearningSystem<T>,

    /// Knowledge base statistics
    pub statistics: KnowledgeBaseStatistics,

    /// Configuration for knowledge management
    pub config: KnowledgeConfig,
}

/// Optimization pattern
#[derive(Debug, Clone)]
pub struct OptimizationPattern<T: Float + Debug + Send + Sync + 'static> {
    /// Unique pattern identifier
    pub pattern_id: String,

    /// Pattern characteristics
    pub characteristics: PatternCharacteristics<T>,

    /// Recommended optimizers for this pattern
    pub recommended_optimizers: Vec<String>,

    /// Historical success probability
    pub success_probability: T,

    /// Expected performance gain
    pub performance_expectation: T,

    /// Pattern usage frequency
    pub usage_frequency: usize,

    /// Last updated timestamp
    pub last_updated: SystemTime,

    /// Pattern metadata
    pub metadata: HashMap<String, String>,
}

/// Pattern characteristics
#[derive(Debug, Clone)]
pub struct PatternCharacteristics<T: Float + Debug + Send + Sync + 'static> {
    /// Type of pattern
    pub pattern_type: PatternType,

    /// Complexity measure
    pub complexity: T,

    /// Frequency of occurrence
    pub frequency: T,

    /// Effectiveness measure
    pub effectiveness: T,

    /// Problem domain
    pub domain: ProblemDomain,

    /// Contextual features
    pub contextual_features: Array1<T>,
}

/// Pattern types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum PatternType {
    /// Convergence patterns
    ConvergencePattern,
    /// Performance optimization patterns
    PerformancePattern,
    /// Resource utilization patterns
    ResourcePattern,
    /// Failure patterns
    FailurePattern,
    /// Adaptation patterns
    AdaptationPattern,
    /// Transfer learning patterns
    TransferPattern,
}

/// Problem domains
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ProblemDomain {
    /// Computer vision
    ComputerVision,
    /// Natural language processing
    NaturalLanguageProcessing,
    /// Reinforcement learning
    ReinforcementLearning,
    /// Optimization
    Optimization,
    /// Scientific computing
    ScientificComputing,
    /// General machine learning
    GeneralML,
    /// Time series
    TimeSeries,
}

/// Best practices database
#[derive(Debug)]
pub struct BestPracticesDatabase {
    /// Practices organized by domain
    pub practices_by_domain: HashMap<String, Vec<BestPractice>>,

    /// Evidence quality scores
    pub evidence_quality: HashMap<String, f64>,

    /// Last updated timestamp
    pub last_updated: SystemTime,

    /// Practice effectiveness tracking
    pub effectiveness_tracking: HashMap<String, Vec<EffectivenessRecord>>,
}

/// Best practice record
#[derive(Debug, Clone)]
pub struct BestPractice {
    /// Unique practice identifier
    pub practice_id: String,

    /// Practice description
    pub description: String,

    /// Domain of applicability
    pub domain: String,

    /// Effectiveness score
    pub effectiveness: f64,

    /// Evidence level
    pub evidence_level: EvidenceLevel,

    /// Conditions for applicability
    pub conditions: Vec<String>,

    /// Implementation details
    pub implementation: String,

    /// References and citations
    pub references: Vec<String>,
}

/// Evidence levels for best practices
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum EvidenceLevel {
    /// Anecdotal evidence
    Anecdotal,
    /// Empirical evidence from experiments
    Empirical,
    /// Theoretical foundation
    Theoretical,
    /// Peer-reviewed research
    PeerReviewed,
    /// Extensively validated
    Validated,
}

/// Effectiveness tracking record
#[derive(Debug, Clone)]
pub struct EffectivenessRecord {
    /// Timestamp of measurement
    pub timestamp: SystemTime,

    /// Effectiveness score
    pub effectiveness: f64,

    /// Context information
    pub context: String,

    /// Measurement methodology
    pub methodology: String,
}

/// Failure analysis database
#[derive(Debug)]
pub struct FailureAnalysisDatabase<T: Float + Debug + Send + Sync + Debug> {
    /// Failure patterns by type
    pub failure_patterns: HashMap<FailureType, Vec<FailurePattern<T>>>,

    /// Root cause analysis
    pub root_causes: HashMap<String, RootCause<T>>,

    /// Mitigation strategies
    pub mitigation_strategies: HashMap<FailureType, Vec<MitigationStrategy>>,

    /// Failure prediction models
    pub prediction_models: Vec<FailurePredictionModel<T>>,
}

/// Failure types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum FailureType {
    /// Convergence failure
    ConvergenceFailure,
    /// Resource exhaustion
    ResourceExhaustion,
    /// Numerical instability
    NumericalInstability,
    /// Performance degradation
    PerformanceDegradation,
    /// Memory overflow
    MemoryOverflow,
    /// Timeout
    Timeout,
}

/// Failure pattern
#[derive(Debug, Clone)]
pub struct FailurePattern<T: Float + Debug + Send + Sync + 'static> {
    /// Pattern identifier
    pub pattern_id: String,

    /// Failure type
    pub failure_type: FailureType,

    /// Preconditions
    pub preconditions: Vec<String>,

    /// Failure indicators
    pub indicators: Array1<T>,

    /// Frequency of occurrence
    pub frequency: usize,

    /// Severity score
    pub severity: T,
}

/// Root cause analysis
#[derive(Debug, Clone)]
pub struct RootCause<T: Float + Debug + Send + Sync + 'static> {
    /// Cause identifier
    pub cause_id: String,

    /// Description
    pub description: String,

    /// Contributing factors
    pub contributing_factors: Vec<String>,

    /// Probability of occurrence
    pub probability: T,

    /// Impact severity
    pub impact: T,
}

/// Mitigation strategy
#[derive(Debug, Clone)]
pub struct MitigationStrategy {
    /// Strategy identifier
    pub strategy_id: String,

    /// Description
    pub description: String,

    /// Implementation steps
    pub implementation_steps: Vec<String>,

    /// Effectiveness rating
    pub effectiveness: f64,

    /// Cost assessment
    pub cost: CostLevel,
}

/// Cost levels for mitigation strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum CostLevel {
    /// Low cost/effort
    Low,
    /// Medium cost/effort
    Medium,
    /// High cost/effort
    High,
    /// Very high cost/effort
    VeryHigh,
}

/// Prediction model types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum PredictionModelType {
    /// Neural network
    Neural,
    /// Gaussian process
    Gaussian,
    /// Tree-based model
    TreeBased,
    /// Ensemble model
    Ensemble,
}

/// Failure prediction model
#[derive(Debug)]
pub struct FailurePredictionModel<T: Float + Debug + Send + Sync + Debug> {
    /// Model identifier
    pub model_id: String,

    /// Model type
    pub model_type: PredictionModelType,

    /// Feature extraction function
    pub feature_extractor: Box<dyn FeatureExtractor<T>>,

    /// Prediction accuracy
    pub accuracy: T,

    /// Training history
    pub training_history: Vec<TrainingRecord<T>>,
}

/// Research insights database
#[derive(Debug)]
pub struct ResearchInsightsDatabase {
    /// Insights by category
    pub insights_by_category: HashMap<String, Vec<ResearchInsight>>,

    /// Citation network
    pub citation_network: CitationNetwork,

    /// Emerging trends
    pub emerging_trends: Vec<EmergingTrend>,

    /// Research impact tracking
    pub impact_tracking: HashMap<String, ImpactMetrics>,
}

/// Research insight
#[derive(Debug, Clone)]
pub struct ResearchInsight {
    /// Insight identifier
    pub insight_id: String,

    /// Title
    pub title: String,

    /// Abstract or summary
    pub summary: String,

    /// Key findings
    pub key_findings: Vec<String>,

    /// Practical implications
    pub implications: Vec<String>,

    /// Confidence level
    pub confidence: f64,

    /// Publication date
    pub publication_date: SystemTime,

    /// Authors
    pub authors: Vec<String>,

    /// DOI or reference
    pub reference: String,
}

/// Citation network
#[derive(Debug)]
pub struct CitationNetwork {
    /// Research nodes
    pub nodes: Vec<ResearchNode>,

    /// Citation edges
    pub edges: Vec<CitationEdge>,

    /// Network metrics
    pub metrics: NetworkMetrics,
}

/// Research node
#[derive(Debug, Clone)]
pub struct ResearchNode {
    /// Paper identifier
    pub paper_id: String,

    /// Title
    pub title: String,

    /// Authors
    pub authors: Vec<String>,

    /// Publication year
    pub publication_year: u32,

    /// Citation count
    pub citation_count: usize,

    /// Impact factor
    pub impact_factor: f64,
}

/// Citation edge
#[derive(Debug, Clone)]
pub struct CitationEdge {
    /// Citing paper
    pub citing_paper: String,

    /// Cited paper
    pub cited_paper: String,

    /// Citation context
    pub citation_context: String,

    /// Citation type
    pub citation_type: CitationType,
}

/// Citation types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum CitationType {
    /// Supporting evidence
    Supporting,
    /// Contrasting view
    Contrasting,
    /// Methodology reference
    Methodology,
    /// Background information
    Background,
    /// Comparative analysis
    Comparative,
}

/// Network metrics
#[derive(Debug, Clone)]
pub struct NetworkMetrics {
    /// Total nodes
    pub total_nodes: usize,

    /// Total edges
    pub total_edges: usize,

    /// Average degree
    pub average_degree: f64,

    /// Clustering coefficient
    pub clustering_coefficient: f64,

    /// Diameter
    pub diameter: usize,
}

/// Emerging trend
#[derive(Debug, Clone)]
pub struct EmergingTrend {
    /// Trend identifier
    pub trend_id: String,

    /// Description
    pub description: String,

    /// Growth rate
    pub growth_rate: f64,

    /// Confidence level
    pub confidence: f64,

    /// Supporting evidence
    pub evidence: Vec<String>,

    /// Time horizon
    pub time_horizon: TrendTimeHorizon,
}

/// Time horizons for trends
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TrendTimeHorizon {
    /// Short term (months)
    ShortTerm,
    /// Medium term (1-2 years)
    MediumTerm,
    /// Long term (3+ years)
    LongTerm,
}

/// Impact metrics
#[derive(Debug, Clone)]
pub struct ImpactMetrics {
    /// Citation impact
    pub citation_impact: f64,

    /// Practical adoption
    pub practical_adoption: f64,

    /// Academic influence
    pub academic_influence: f64,

    /// Industry relevance
    pub industry_relevance: f64,
}

/// Dynamic learning system
#[derive(Debug)]
pub struct DynamicLearningSystem<T: Float + Debug + Send + Sync + Debug> {
    /// Learning algorithms
    pub learning_algorithms: Vec<Box<dyn LearningAlgorithm<T>>>,

    /// Knowledge integration engine
    pub integration_engine: KnowledgeIntegrationEngine<T>,

    /// Validation system
    pub validation_system: KnowledgeValidationSystem<T>,

    /// Active learning component
    pub active_learning: ActiveLearningSystem<T>,
}

/// Learning algorithm trait
pub trait LearningAlgorithm<T: Float + Debug + Send + Sync + 'static>: Send + Sync + Debug {
    /// Learn from data
    fn learn(&mut self, data: &Array1<T>) -> Result<()>;

    /// Make predictions
    fn predict(&self, input: &Array1<T>) -> Result<Array1<T>>;

    /// Get confidence in predictions
    fn get_confidence(&self, input: &Array1<T>) -> Result<T>;

    /// Update model incrementally
    fn incremental_update(&mut self, data: &Array1<T>, target: T) -> Result<()>;

    /// Get algorithm name
    fn algorithm_name(&self) -> &str;
}

/// Feature extractor trait
pub trait FeatureExtractor<T: Float + Debug + Send + Sync + 'static>: Send + Sync + Debug {
    /// Extract features from context
    fn extract_features(&self, context: &OptimizationContext<T>) -> Result<Array1<T>>;

    /// Get feature names
    fn feature_names(&self) -> Vec<String>;

    /// Get feature importance
    fn feature_importance(&self) -> Vec<T>;
}

/// Knowledge integration engine
#[derive(Debug)]
pub struct KnowledgeIntegrationEngine<T: Float + Debug + Send + Sync + Debug> {
    /// Integration algorithms
    pub integration_algorithms: Vec<String>,

    /// Confidence threshold
    pub confidence_threshold: T,

    /// Consensus mechanisms
    pub consensus_mechanisms: Vec<ConsensusMechanism>,

    /// Integration history
    pub integration_history: VecDeque<IntegrationEvent>,
}

/// Consensus mechanisms
#[derive(Debug, Clone)]
pub enum ConsensusMechanism {
    /// Majority voting
    MajorityVoting,
    /// Weighted voting
    WeightedVoting,
    /// Expert system
    ExpertSystem,
    /// Bayesian consensus
    BayesianConsensus,
}

/// Integration event
#[derive(Debug, Clone)]
pub struct IntegrationEvent {
    /// Event timestamp
    pub timestamp: SystemTime,

    /// Source of knowledge
    pub source: String,

    /// Integration method
    pub method: String,

    /// Confidence score
    pub confidence: f64,

    /// Success status
    pub success: bool,
}

/// Knowledge validation system
#[derive(Debug)]
pub struct KnowledgeValidationSystem<T: Float + Debug + Send + Sync + Debug> {
    /// Validation rules
    pub validation_rules: Vec<ValidationRule>,

    /// Validation threshold
    pub validation_threshold: T,

    /// Cross-validation system
    pub cross_validation: CrossValidationSystem<T>,

    /// Validation history
    pub validation_history: Vec<ValidationResult>,
}

/// Validation rule
#[derive(Debug, Clone)]
pub struct ValidationRule {
    /// Rule identifier
    pub rule_id: String,

    /// Description
    pub description: String,

    /// Validation function name
    pub validation_function: String,

    /// Rule weight
    pub weight: f64,
}

/// Cross-validation system
#[derive(Debug)]
pub struct CrossValidationSystem<T: Float + Debug + Send + Sync + Debug> {
    /// Number of folds
    pub num_folds: usize,

    /// Validation metrics
    pub metrics: Vec<ValidationMetric>,

    /// Current fold
    pub current_fold: usize,

    /// Validation results
    pub results: Vec<CrossValidationResult<T>>,
}

/// Validation metrics
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ValidationMetric {
    /// Accuracy
    Accuracy,
    /// Precision
    Precision,
    /// Recall
    Recall,
    /// F1 Score
    F1Score,
    /// ROC AUC
    RocAuc,
}

/// Cross-validation result
#[derive(Debug, Clone)]
pub struct CrossValidationResult<T: Float + Debug + Send + Sync + 'static> {
    /// Fold number
    pub fold: usize,

    /// Metric scores
    pub scores: HashMap<ValidationMetric, T>,

    /// Validation time
    pub validation_time: std::time::Duration,
}

/// Validation result
#[derive(Debug, Clone)]
pub struct ValidationResult {
    /// Result identifier
    pub result_id: String,

    /// Validation timestamp
    pub timestamp: SystemTime,

    /// Overall score
    pub overall_score: f64,

    /// Individual rule scores
    pub rule_scores: HashMap<String, f64>,

    /// Validation status
    pub status: ValidationStatus,
}

/// Validation status
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ValidationStatus {
    /// Passed validation
    Passed,
    /// Failed validation
    Failed,
    /// Needs review
    NeedsReview,
    /// Incomplete
    Incomplete,
}

/// Active learning system
#[derive(Debug)]
pub struct ActiveLearningSystem<T: Float + Debug + Send + Sync + Debug> {
    /// Query strategies
    pub query_strategies: Vec<QueryStrategy>,

    /// Uncertainty estimation
    pub uncertainty_estimator: UncertaintyEstimator<T>,

    /// Active learning budget
    pub budget: ActiveLearningBudget,

    /// Learning history
    pub learning_history: Vec<ActiveLearningRecord<T>>,
}

/// Query strategies for active learning
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum QueryStrategy {
    /// Uncertainty sampling
    UncertaintySampling,
    /// Query by committee
    QueryByCommittee,
    /// Expected model change
    ExpectedModelChange,
    /// Expected error reduction
    ExpectedErrorReduction,
}

/// Uncertainty estimator
#[derive(Debug)]
pub struct UncertaintyEstimator<T: Float + Debug + Send + Sync + Debug> {
    /// Estimation models
    pub models: Vec<UncertaintyModel<T>>,

    /// Estimation method
    pub method: UncertaintyEstimationMethod,

    /// Calibration data
    pub calibration_data: CalibrationData<T>,
}

/// Uncertainty estimation methods
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum UncertaintyEstimationMethod {
    /// Monte Carlo dropout
    MonteCarloDropout,
    /// Ensemble variance
    EnsembleVariance,
    /// Bayesian neural networks
    BayesianNeuralNetworks,
    /// Deep ensembles
    DeepEnsembles,
}

/// Uncertainty model
#[derive(Debug, Clone)]
pub struct UncertaintyModel<T: Float + Debug + Send + Sync + 'static> {
    /// Model type
    pub model_type: UncertaintyModelType,

    /// Parameters
    pub parameters: HashMap<String, T>,

    /// Uncertainty estimate
    pub uncertainty_estimate: T,
}

/// Uncertainty model types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum UncertaintyModelType {
    /// Dropout-based
    Dropout,
    /// Ensemble-based
    Ensemble,
    /// Bayesian
    Bayesian,
    /// Evidential
    Evidential,
}

/// Calibration data
#[derive(Debug, Clone)]
pub struct CalibrationData<T: Float + Debug + Send + Sync + 'static> {
    /// Calibration scores
    pub calibration_scores: Vec<T>,

    /// Reliability diagram
    pub reliability_diagram: Array1<T>,

    /// Expected calibration error
    pub expected_calibration_error: T,
}

/// Active learning budget
#[derive(Debug, Clone)]
pub struct ActiveLearningBudget {
    /// Maximum queries
    pub max_queries: usize,

    /// Used queries
    pub used_queries: usize,

    /// Query cost function
    pub cost_function: CostFunction,
}

/// Cost functions for active learning
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum CostFunction {
    /// Uniform cost
    Uniform,
    /// Time-based cost
    TimeBased,
    /// Resource-based cost
    ResourceBased,
    /// Complexity-based cost
    ComplexityBased,
}

/// Active learning record
#[derive(Debug, Clone)]
pub struct ActiveLearningRecord<T: Float + Debug + Send + Sync + 'static> {
    /// Query timestamp
    pub timestamp: SystemTime,

    /// Query strategy used
    pub strategy: QueryStrategy,

    /// Query features
    pub query_features: Array1<T>,

    /// Oracle response
    pub oracle_response: T,

    /// Model improvement
    pub model_improvement: T,

    /// Query cost
    pub query_cost: f64,
}

/// Knowledge base statistics
#[derive(Debug, Clone)]
pub struct KnowledgeBaseStatistics {
    /// Total patterns
    pub total_patterns: usize,

    /// Total best practices
    pub total_best_practices: usize,

    /// Total failure cases
    pub total_failure_cases: usize,

    /// Total research insights
    pub total_research_insights: usize,

    /// Knowledge utilization rate
    pub utilization_rate: f64,

    /// Knowledge accuracy
    pub accuracy: f64,

    /// Last update time
    pub last_update: SystemTime,
}

/// Knowledge configuration
#[derive(Debug, Clone)]
pub struct KnowledgeConfig {
    /// Maximum patterns to store
    pub max_patterns: usize,

    /// Pattern pruning threshold
    pub pruning_threshold: f64,

    /// Validation frequency
    pub validation_frequency: std::time::Duration,

    /// Auto-update enabled
    pub auto_update_enabled: bool,

    /// Learning rate for dynamic updates
    pub learning_rate: f64,
}

/// Training record
#[derive(Debug, Clone)]
pub struct TrainingRecord<T: Float + Debug + Send + Sync + 'static> {
    /// Epoch number
    pub epoch: usize,

    /// Loss value
    pub loss: T,

    /// Accuracy value
    pub accuracy: T,

    /// Training timestamp
    pub timestamp: SystemTime,
}

// Implementation of OptimizationKnowledgeBase
impl<T: Float + Debug + Send + Sync + 'static> OptimizationKnowledgeBase<T> {
    /// Create a new knowledge base
    pub fn new() -> Result<Self> {
        let mut knowledge_base = Self {
            optimization_patterns: HashMap::new(),
            best_practices: BestPracticesDatabase::new(),
            failure_analysis: FailureAnalysisDatabase::new(),
            research_insights: ResearchInsightsDatabase::new(),
            learning_system: DynamicLearningSystem::new(),
            statistics: KnowledgeBaseStatistics::default(),
            config: KnowledgeConfig::default(),
        };

        // Load default knowledge
        knowledge_base.load_default_patterns()?;
        knowledge_base.load_default_best_practices()?;

        Ok(knowledge_base)
    }

    /// Update the pattern database with a new pattern
    pub fn update_pattern_database(&mut self, pattern: &OptimizationPattern<T>) -> Result<()> {
        // Check if pattern already exists
        if let Some(existing_pattern) = self.optimization_patterns.get_mut(&pattern.pattern_id) {
            // Update existing pattern with new evidence
            existing_pattern.success_probability = (existing_pattern.success_probability
                + pattern.success_probability)
                / scirs2_core::numeric::NumCast::from(2.0).unwrap_or_else(|| T::zero());
            existing_pattern.performance_expectation = (existing_pattern.performance_expectation
                + pattern.performance_expectation)
                / scirs2_core::numeric::NumCast::from(2.0).unwrap_or_else(|| T::zero());

            // Merge recommended optimizers
            for optimizer in &pattern.recommended_optimizers {
                if !existing_pattern.recommended_optimizers.contains(optimizer) {
                    existing_pattern
                        .recommended_optimizers
                        .push(optimizer.clone());
                }
            }

            existing_pattern.usage_frequency += 1;
            existing_pattern.last_updated = SystemTime::now();
        } else {
            // Add new pattern
            self.optimization_patterns
                .insert(pattern.pattern_id.clone(), pattern.clone());
        }

        // Update statistics
        self.statistics.total_patterns = self.optimization_patterns.len();
        self.statistics.last_update = SystemTime::now();

        Ok(())
    }

    /// Record a successful optimization pattern
    pub fn record_success_pattern(&mut self, pattern: &OptimizationPattern<T>) -> Result<()> {
        // Increase success probability for this pattern type
        if let Some(existing_pattern) = self.optimization_patterns.get_mut(&pattern.pattern_id) {
            existing_pattern.success_probability = (existing_pattern.success_probability
                + scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()))
            .min(scirs2_core::numeric::NumCast::from(0.95).unwrap_or_else(|| T::zero()));
        }

        // Update best practices based on successful pattern
        self.extract_best_practices_from_success(pattern)?;

        Ok(())
    }

    /// Record a failed optimization pattern
    pub fn record_failure_pattern(
        &mut self,
        pattern: &OptimizationPattern<T>,
        results: &EnsembleOptimizationResults<T>,
    ) -> Result<()> {
        // Decrease success probability
        if let Some(existing_pattern) = self.optimization_patterns.get_mut(&pattern.pattern_id) {
            existing_pattern.success_probability = (existing_pattern.success_probability
                - scirs2_core::numeric::NumCast::from(0.05).unwrap_or_else(|| T::zero()))
            .max(scirs2_core::numeric::NumCast::from(0.05).unwrap_or_else(|| T::zero()));
        }

        // Analyze failure and add to failure analysis database
        self.analyze_and_record_failure(pattern, results)?;

        Ok(())
    }

    /// Find similar patterns in the knowledge base
    pub fn find_similar_patterns(
        &self,
        context: &OptimizationContext<T>,
    ) -> Result<Vec<OptimizationPattern<T>>> {
        let mut similar_patterns = Vec::new();
        let similarity_threshold = scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero());

        for pattern in self.optimization_patterns.values() {
            let similarity = self.compute_pattern_similarity(context, &pattern.characteristics)?;
            if similarity > similarity_threshold {
                similar_patterns.push(pattern.clone());
            }
        }

        // Sort by similarity
        similar_patterns.sort_by(|a, b| {
            b.success_probability
                .partial_cmp(&a.success_probability)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(similar_patterns)
    }

    /// Prune outdated or irrelevant knowledge
    pub fn prune_knowledge(&mut self) -> Result<()> {
        // Remove patterns with very low success probability
        self.optimization_patterns
            .retain(|_, pattern| pattern.success_probability > scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()));

        // Limit knowledge base size
        if self.optimization_patterns.len() > self.config.max_patterns {
            // Keep only the most successful patterns
            let mut patterns: Vec<_> = self
                .optimization_patterns
                .iter()
                .map(|(k, v)| (k.clone(), v.clone()))
                .collect();
            patterns.sort_by(|a, b| {
                b.1.success_probability
                    .partial_cmp(&a.1.success_probability)
                    .unwrap_or(std::cmp::Ordering::Equal)
            });

            self.optimization_patterns.clear();
            for (pattern_id, pattern) in patterns.into_iter().take(self.config.max_patterns * 8 / 10) {
                self.optimization_patterns.insert(pattern_id, pattern);
            }
        }

        Ok(())
    }

    /// Get knowledge base statistics
    pub fn get_statistics(&self) -> KnowledgeBaseStatistics {
        self.statistics.clone()
    }

    // Helper methods

    fn load_default_patterns(&mut self) -> Result<()> {
        // Add some default successful patterns
        let convex_pattern = OptimizationPattern {
            pattern_id: "default_convex".to_string(),
            characteristics: PatternCharacteristics {
                pattern_type: PatternType::ConvergencePattern,
                complexity: scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero()),
                frequency: scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero()),
                effectiveness: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
                domain: ProblemDomain::Optimization,
                contextual_features: Array1::zeros(5),
            },
            recommended_optimizers: vec!["lbfgs_neural".to_string(), "adam_enhanced".to_string()],
            success_probability: scirs2_core::numeric::NumCast::from(0.85).unwrap_or_else(|| T::zero()),
            performance_expectation: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
            usage_frequency: 0,
            last_updated: SystemTime::now(),
            metadata: HashMap::new(),
        };

        self.optimization_patterns
            .insert("default_convex".to_string(), convex_pattern);

        let nonconvex_pattern = OptimizationPattern {
            pattern_id: "default_nonconvex".to_string(),
            characteristics: PatternCharacteristics {
                pattern_type: PatternType::PerformancePattern,
                complexity: scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero()),
                frequency: scirs2_core::numeric::NumCast::from(0.6).unwrap_or_else(|| T::zero()),
                effectiveness: scirs2_core::numeric::NumCast::from(0.75).unwrap_or_else(|| T::zero()),
                domain: ProblemDomain::GeneralML,
                contextual_features: Array1::zeros(5),
            },
            recommended_optimizers: vec!["lstm_advanced".to_string(), "meta_learner".to_string()],
            success_probability: scirs2_core::numeric::NumCast::from(0.75).unwrap_or_else(|| T::zero()),
            performance_expectation: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
            usage_frequency: 0,
            last_updated: SystemTime::now(),
            metadata: HashMap::new(),
        };

        self.optimization_patterns
            .insert("default_nonconvex".to_string(), nonconvex_pattern);

        Ok(())
    }

    fn load_default_best_practices(&mut self) -> Result<()> {
        // Add some default best practices
        let practice = BestPractice {
            practice_id: "gradient_clipping".to_string(),
            description: "Use gradient clipping to prevent gradient explosion".to_string(),
            domain: "deep_learning".to_string(),
            effectiveness: 0.85,
            evidence_level: EvidenceLevel::Validated,
            conditions: vec!["Deep networks".to_string(), "RNN training".to_string()],
            implementation: "Clip gradients by norm or value".to_string(),
            references: vec!["Bengio et al., 2013".to_string()],
        };

        self.best_practices.add_practice(practice)?;

        Ok(())
    }

    fn extract_best_practices_from_success(
        &mut self,
        _pattern: &OptimizationPattern<T>,
    ) -> Result<()> {
        // Analyze successful pattern to extract best practices
        // Implementation would involve pattern analysis and best practice extraction
        Ok(())
    }

    fn analyze_and_record_failure(
        &mut self,
        pattern: &OptimizationPattern<T>,
        _results: &EnsembleOptimizationResults<T>,
    ) -> Result<()> {
        // Analyze failure patterns and record for future reference
        let failure_pattern = FailurePattern {
            pattern_id: pattern.pattern_id.clone(),
            failure_type: FailureType::PerformanceDegradation,
            preconditions: vec!["High complexity".to_string()],
            indicators: Array1::zeros(3),
            frequency: 1,
            severity: scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()),
        };

        self.failure_analysis
            .failure_patterns
            .entry(FailureType::PerformanceDegradation)
            .or_insert_with(Vec::new)
            .push(failure_pattern);

        Ok(())
    }

    fn compute_pattern_similarity(
        &self,
        context: &OptimizationContext<T>,
        characteristics: &PatternCharacteristics<T>,
    ) -> Result<T> {
        // Compute similarity based on problem characteristics
        let complexity_similarity = scirs2_core::numeric::NumCast::from(1.0).unwrap_or_else(|| T::zero())
            - (characteristics.complexity
                - T::from(context.dimensionality as f64 / 10000.0)
                    .expect("unwrap failed"))
            .abs();

        let effectiveness_similarity = characteristics.effectiveness;

        // Simple weighted average
        let similarity = (complexity_similarity + effectiveness_similarity) / scirs2_core::numeric::NumCast::from(2.0).unwrap_or_else(|| T::zero());

        Ok(similarity)
    }

    /// Update knowledge base with optimization experience
    pub fn update_with_optimization_experience(&mut self, _experience: &OptimizationExperience<T>) -> Result<()> {
        // Placeholder implementation
        Ok(())
    }

    /// Initialize the knowledge base
    pub fn initialize(&mut self) -> Result<()> {
        // Placeholder implementation
        Ok(())
    }

    /// Reset the knowledge base
    pub fn reset(&mut self) -> Result<()> {
        self.optimization_patterns.clear();
        self.statistics.total_patterns = 0;
        self.statistics.last_update = SystemTime::now();
        Ok(())
    }
}

// Implementations for supporting structures

impl BestPracticesDatabase {
    pub fn new() -> Self {
        Self {
            practices_by_domain: HashMap::new(),
            evidence_quality: HashMap::new(),
            last_updated: SystemTime::now(),
            effectiveness_tracking: HashMap::new(),
        }
    }

    pub fn add_practice(&mut self, practice: BestPractice) -> Result<()> {
        let domain_practices = self
            .practices_by_domain
            .entry(practice.domain.clone())
            .or_insert_with(Vec::new);

        domain_practices.push(practice.clone());

        self.evidence_quality
            .insert(practice.practice_id.clone(), practice.evidence_level.into());

        self.last_updated = SystemTime::now();
        Ok(())
    }
}

impl<T: Float + Debug + Send + Sync + 'static> FailureAnalysisDatabase<T> {
    pub fn new() -> Self {
        Self {
            failure_patterns: HashMap::new(),
            root_causes: HashMap::new(),
            mitigation_strategies: HashMap::new(),
            prediction_models: Vec::new(),
        }
    }
}

impl ResearchInsightsDatabase {
    pub fn new() -> Self {
        Self {
            insights_by_category: HashMap::new(),
            citation_network: CitationNetwork::new(),
            emerging_trends: Vec::new(),
            impact_tracking: HashMap::new(),
        }
    }

    pub fn add_insight(&mut self, insight: ResearchInsight) -> Result<()> {
        let category = self.categorize_insight(&insight);
        let category_insights = self
            .insights_by_category
            .entry(category)
            .or_insert_with(Vec::new);

        category_insights.push(insight);
        Ok(())
    }

    fn categorize_insight(&self, insight: &ResearchInsight) -> String {
        // Simple categorization based on title keywords
        if insight.title.to_lowercase().contains("optimization") {
            "optimization".to_string()
        } else if insight.title.to_lowercase().contains("learning") {
            "learning".to_string()
        } else if insight.title.to_lowercase().contains("neural") {
            "neural_networks".to_string()
        } else {
            "general".to_string()
        }
    }
}

impl CitationNetwork {
    pub fn new() -> Self {
        Self {
            nodes: Vec::new(),
            edges: Vec::new(),
            metrics: NetworkMetrics::default(),
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static> DynamicLearningSystem<T> {
    pub fn new() -> Self {
        Self {
            learning_algorithms: Vec::new(),
            integration_engine: KnowledgeIntegrationEngine::new(),
            validation_system: KnowledgeValidationSystem::new(),
            active_learning: ActiveLearningSystem::new(),
        }
    }

    pub fn incremental_learn(&mut self, features: &Array1<T>, target: T) -> Result<()> {
        // Perform incremental learning on all available algorithms
        for algorithm in &mut self.learning_algorithms {
            algorithm.incremental_update(features, target)?;
        }

        // Update integration engine with new knowledge
        self.integration_engine
            .integrate_new_knowledge(features, target)?;

        Ok(())
    }
}

impl<T: Float + Debug + Send + Sync + 'static> KnowledgeIntegrationEngine<T> {
    pub fn new() -> Self {
        Self {
            integration_algorithms: vec!["consensus".to_string(), "weighted_voting".to_string()],
            confidence_threshold: scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero()),
            consensus_mechanisms: vec![ConsensusMechanism::WeightedVoting],
            integration_history: VecDeque::new(),
        }
    }

    pub fn integrate_new_knowledge(&mut self, _features: &Array1<T>, _target: T) -> Result<()> {
        // Integrate new knowledge into existing knowledge base
        let event = IntegrationEvent {
            timestamp: SystemTime::now(),
            source: "incremental_learning".to_string(),
            method: "weighted_consensus".to_string(),
            confidence: 0.8,
            success: true,
        };

        self.integration_history.push_back(event);

        // Limit history size
        if self.integration_history.len() > 1000 {
            self.integration_history.pop_front();
        }

        Ok(())
    }
}

impl<T: Float + Debug + Send + Sync + 'static> KnowledgeValidationSystem<T> {
    pub fn new() -> Self {
        Self {
            validation_rules: Vec::new(),
            validation_threshold: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
            cross_validation: CrossValidationSystem::new(),
            validation_history: Vec::new(),
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static> CrossValidationSystem<T> {
    pub fn new() -> Self {
        Self {
            num_folds: 5,
            metrics: vec![ValidationMetric::Accuracy, ValidationMetric::F1Score],
            current_fold: 0,
            results: Vec::new(),
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static> ActiveLearningSystem<T> {
    pub fn new() -> Self {
        Self {
            query_strategies: vec![QueryStrategy::UncertaintySampling],
            uncertainty_estimator: UncertaintyEstimator::new(),
            budget: ActiveLearningBudget::default(),
            learning_history: Vec::new(),
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static> UncertaintyEstimator<T> {
    pub fn new() -> Self {
        Self {
            models: Vec::new(),
            method: UncertaintyEstimationMethod::EnsembleVariance,
            calibration_data: CalibrationData::default(),
        }
    }
}

// Default implementations

impl Default for KnowledgeBaseStatistics {
    fn default() -> Self {
        Self {
            total_patterns: 0,
            total_best_practices: 0,
            total_failure_cases: 0,
            total_research_insights: 0,
            utilization_rate: 0.0,
            accuracy: 0.0,
            last_update: SystemTime::now(),
        }
    }
}

impl Default for KnowledgeConfig {
    fn default() -> Self {
        Self {
            max_patterns: 10000,
            pruning_threshold: 0.1,
            validation_frequency: std::time::Duration::from_secs(3600), // 1 hour
            auto_update_enabled: true,
            learning_rate: 0.01,
        }
    }
}

impl Default for NetworkMetrics {
    fn default() -> Self {
        Self {
            total_nodes: 0,
            total_edges: 0,
            average_degree: 0.0,
            clustering_coefficient: 0.0,
            diameter: 0,
        }
    }
}

impl Default for ActiveLearningBudget {
    fn default() -> Self {
        Self {
            max_queries: 100,
            used_queries: 0,
            cost_function: CostFunction::Uniform,
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static> Default for CalibrationData<T> {
    fn default() -> Self {
        Self {
            calibration_scores: Vec::new(),
            reliability_diagram: Array1::zeros(0),
            expected_calibration_error: T::zero(),
        }
    }
}

impl From<EvidenceLevel> for f64 {
    fn from(evidence: EvidenceLevel) -> Self {
        match evidence {
            EvidenceLevel::Anecdotal => 0.2,
            EvidenceLevel::Empirical => 0.5,
            EvidenceLevel::Theoretical => 0.7,
            EvidenceLevel::PeerReviewed => 0.8,
            EvidenceLevel::Validated => 0.95,
        }
    }
}

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

    #[test]
    fn test_knowledge_base_creation() {
        let kb = OptimizationKnowledgeBase::<f32>::new();
        assert!(kb.is_ok());

        let kb = kb.expect("unwrap failed");
        assert!(!kb.optimization_patterns.is_empty());
    }

    #[test]
    fn test_pattern_similarity() {
        let kb = OptimizationKnowledgeBase::<f32>::new().expect("unwrap failed");
        let context = OptimizationContext::default();

        let characteristics = PatternCharacteristics {
            pattern_type: PatternType::ConvergencePattern,
            complexity: 0.5,
            frequency: 0.7,
            effectiveness: 0.8,
            domain: ProblemDomain::Optimization,
            contextual_features: Array1::zeros(5),
        };

        let similarity = kb.compute_pattern_similarity(&context, &characteristics);
        assert!(similarity.is_ok());
    }

    #[test]
    fn test_best_practices_database() {
        let mut db = BestPracticesDatabase::new();

        let practice = BestPractice {
            practice_id: "test_practice".to_string(),
            description: "Test practice".to_string(),
            domain: "test".to_string(),
            effectiveness: 0.8,
            evidence_level: EvidenceLevel::Empirical,
            conditions: vec![],
            implementation: "Test implementation".to_string(),
            references: vec![],
        };

        assert!(db.add_practice(practice).is_ok());
        assert_eq!(db.practices_by_domain.len(), 1);
    }
}