/// Adaptive optimization controller that learns and adjusts strategies
///
/// The controller monitors system state, applies learned knowledge, and
/// automatically adapts optimization strategies based on environmental
/// changes, performance patterns, and user feedback.
#[derive(Debug)]
pub struct AdaptiveOptimizationController {
    /// Available adaptation strategies
    adaptation_strategies: HashMap<String, AdaptationStrategy>,
    /// System state monitoring and analysis
    state_monitor: SystemStateMonitor,
    /// Historical adaptation events
    adaptation_history: VecDeque<AdaptationEvent>,
    /// Machine learning mechanism for continuous learning
    learning_mechanism: AdaptiveLearningMechanism,
    /// Control parameters and thresholds
    control_params: AdaptiveControlParams,
    /// Current controller state
    controller_state: ControllerState,
    /// Performance metrics and statistics
    performance_metrics: AdaptationPerformanceMetrics,
    /// Environmental context awareness
    environment_context: EnvironmentContext,
    /// Decision tree for automated reasoning
    decision_tree: AdaptiveDecisionTree,
    /// Meta-learning capabilities
    meta_learning: MetaLearningSystem,
}
impl AdaptiveOptimizationController {
    /// Create a new adaptive optimization controller
    pub fn new() -> Self {
        let mut controller = Self {
            adaptation_strategies: HashMap::new(),
            state_monitor: SystemStateMonitor::new(),
            adaptation_history: VecDeque::new(),
            learning_mechanism: AdaptiveLearningMechanism::new(),
            control_params: AdaptiveControlParams::default(),
            controller_state: ControllerState::new(),
            performance_metrics: AdaptationPerformanceMetrics::default(),
            environment_context: EnvironmentContext::new(),
            decision_tree: AdaptiveDecisionTree::new(),
            meta_learning: MetaLearningSystem::new(),
        };
        controller.initialize_default_strategies();
        controller
    }
    /// Initialize default adaptation strategies
    fn initialize_default_strategies(&mut self) {
        let performance_strategy = AdaptationStrategy {
            name: "performance_degradation_response".to_string(),
            description: "Responds to performance degradation by adjusting parameters and switching strategies"
                .to_string(),
            triggers: vec![
                AdaptationTrigger::PerformanceDegradation { threshold : 0.15, duration :
                Duration::from_secs(30), severity : DegradationSeverity::Moderate, }
            ],
            actions: vec![
                AdaptationAction::ParameterAdjustment { parameter : "memory_pool_size"
                .to_string(), adjustment : ParameterAdjustment::Relative { factor : 1.2
                }, bounds : Some(ParameterBounds { min : 0.0, max : f64::MAX, step_size :
                None }), }, AdaptationAction::LearningRateAdjustment { new_rate : 0.05,
                scope : LearningScope::Global, }
            ],
            effectiveness: 0.8,
            usage_frequency: 0.0,
            success_rate: 0.0,
            complexity: StrategyComplexity::Moderate,
            resource_requirements: StrategyResourceRequirements {
                cpu_cost: 0.1,
                memory_cost: 1024,
                execution_time: Duration::from_millis(100),
                risk_level: RiskLevel::Low,
                data_requirements: vec!["performance_metrics".to_string()],
            },
            applicability_conditions: vec![
                ApplicabilityCondition::SystemLoad { min : 0.0, max : 0.9 },
                ApplicabilityCondition::MemoryPressure { threshold : 0.8 },
            ],
            lifecycle: StrategyLifecycle {
                created_at: Instant::now(),
                last_updated: Instant::now(),
                last_used: None,
                usage_count: 0,
                stage: LifecycleStage::Production,
                retirement_conditions: vec![
                    RetirementCondition::LowSuccessRate { threshold : 0.3, duration :
                    Duration::from_secs(3600), }
                ],
            },
            learning_config: StrategyLearningConfig {
                enable_learning: true,
                learning_rate: 0.01,
                min_examples: 10,
                max_learning_data: 1000,
                update_frequency: Duration::from_secs(300),
                feature_config: FeatureExtractionConfig {
                    enabled_features: vec![
                        FeatureType::PerformanceIndicators, FeatureType::SystemMetrics
                    ],
                    window_size: 10,
                    aggregation_methods: vec![
                        AggregationMethod::Mean, AggregationMethod::StdDev
                    ],
                    normalization: NormalizationStrategy::ZScore,
                },
            },
        };
        self.adaptation_strategies
            .insert(
                "performance_degradation_response".to_string(),
                performance_strategy,
            );
        let resource_strategy = AdaptationStrategy {
            name: "resource_pressure_response".to_string(),
            description: "Handles resource pressure by reallocating resources and optimizing usage"
                .to_string(),
            triggers: vec![
                AdaptationTrigger::ResourcePressure { resource : "memory".to_string(),
                threshold : 0.85, trend : PressureTrend::Increasing, }
            ],
            actions: vec![
                AdaptationAction::ResourceReallocation { resource : "memory".to_string(),
                reallocation : ResourceReallocation::Optimize { optimization_goal :
                "memory_efficiency".to_string(), }, },
                AdaptationAction::EmergencyResponse { response_type :
                EmergencyResponseType::ResourceIsolation, severity :
                EmergencySeverity::Medium, },
            ],
            effectiveness: 0.75,
            usage_frequency: 0.0,
            success_rate: 0.0,
            complexity: StrategyComplexity::Complex,
            resource_requirements: StrategyResourceRequirements {
                cpu_cost: 0.15,
                memory_cost: 2048,
                execution_time: Duration::from_millis(200),
                risk_level: RiskLevel::Medium,
                data_requirements: vec![
                    "resource_metrics".to_string(), "allocation_stats".to_string(),
                ],
            },
            applicability_conditions: vec![
                ApplicabilityCondition::MemoryPressure { threshold : 0.7 },
                ApplicabilityCondition::ResourceAvailability { resource : "cpu"
                .to_string(), min_available : 0.2, },
            ],
            lifecycle: StrategyLifecycle {
                created_at: Instant::now(),
                last_updated: Instant::now(),
                last_used: None,
                usage_count: 0,
                stage: LifecycleStage::Production,
                retirement_conditions: vec![],
            },
            learning_config: StrategyLearningConfig {
                enable_learning: true,
                learning_rate: 0.02,
                min_examples: 15,
                max_learning_data: 800,
                update_frequency: Duration::from_secs(180),
                feature_config: FeatureExtractionConfig {
                    enabled_features: vec![
                        FeatureType::ResourceUtilization, FeatureType::SystemMetrics,
                    ],
                    window_size: 15,
                    aggregation_methods: vec![
                        AggregationMethod::Max, AggregationMethod::Trend
                    ],
                    normalization: NormalizationStrategy::MinMax,
                },
            },
        };
        self.adaptation_strategies
            .insert("resource_pressure_response".to_string(), resource_strategy);
    }
    /// Get adaptation recommendations based on current system state
    pub fn get_recommendations(&self) -> Vec<OptimizationRecommendation> {
        let mut recommendations = Vec::new();
        let current_state = &self.state_monitor.current_state;
        for (strategy_name, strategy) in &self.adaptation_strategies {
            if self.should_trigger_strategy(strategy, current_state) {
                let recommendation = OptimizationRecommendation {
                    id: format!("adapt_{}", strategy_name),
                    strategy_name: strategy_name.clone(),
                    description: format!(
                        "Adaptive recommendation: {}", strategy.description
                    ),
                    priority: self.calculate_priority(strategy, current_state),
                    expected_improvement: strategy.effectiveness,
                    confidence: self.calculate_confidence(strategy, current_state),
                    resource_requirements: strategy.resource_requirements.cpu_cost,
                    estimated_duration: strategy.resource_requirements.execution_time,
                    risk_assessment: self.assess_risk(strategy),
                };
                recommendations.push(recommendation);
            }
        }
        recommendations
            .sort_by(|a, b| {
                b.priority
                    .partial_cmp(&a.priority)
                    .unwrap_or(std::cmp::Ordering::Equal)
                    .then(
                        b
                            .confidence
                            .partial_cmp(&a.confidence)
                            .unwrap_or(std::cmp::Ordering::Equal),
                    )
            });
        recommendations
    }
    /// Check if a strategy should be triggered based on current state
    fn should_trigger_strategy(
        &self,
        strategy: &AdaptationStrategy,
        state: &SystemState,
    ) -> bool {
        for condition in &strategy.applicability_conditions {
            if !self.check_applicability_condition(condition, state) {
                return false;
            }
        }
        for trigger in &strategy.triggers {
            if self.check_trigger(trigger, state) {
                return true;
            }
        }
        false
    }
    /// Check if an applicability condition is met
    fn check_applicability_condition(
        &self,
        condition: &ApplicabilityCondition,
        state: &SystemState,
    ) -> bool {
        match condition {
            ApplicabilityCondition::SystemLoad { min, max } => {
                if let Some(&cpu_load) = state.resource_utilization.get("cpu") {
                    cpu_load >= *min && cpu_load <= *max
                } else {
                    false
                }
            }
            ApplicabilityCondition::MemoryPressure { threshold } => {
                if let Some(&memory_usage) = state.resource_utilization.get("memory") {
                    memory_usage < *threshold
                } else {
                    true
                }
            }
            ApplicabilityCondition::ResourceAvailability { resource, min_available } => {
                if let Some(&usage) = state.resource_utilization.get(resource) {
                    (1.0 - usage) >= *min_available
                } else {
                    true
                }
            }
            _ => true,
        }
    }
    /// Check if a trigger condition is met
    fn check_trigger(&self, trigger: &AdaptationTrigger, state: &SystemState) -> bool {
        match trigger {
            AdaptationTrigger::PerformanceDegradation {
                threshold,
                duration: _,
                severity: _,
            } => {
                if let Some(&performance) = state
                    .performance_metrics
                    .get("overall_performance")
                {
                    performance < (1.0 - *threshold as f64)
                } else {
                    false
                }
            }
            AdaptationTrigger::ResourcePressure { resource, threshold, trend: _ } => {
                if let Some(&usage) = state.resource_utilization.get(resource) {
                    usage > *threshold
                } else {
                    false
                }
            }
            AdaptationTrigger::ErrorRateIncrease { threshold, error_type: _ } => {
                if let Some(&error_rate) = state.performance_metrics.get("error_rate") {
                    error_rate > *threshold as f64
                } else {
                    false
                }
            }
            AdaptationTrigger::ThresholdTrigger {
                metric,
                operator,
                threshold,
                consecutive_violations: _,
            } => {
                if let Some(&value) = state.performance_metrics.get(metric) {
                    match operator {
                        ComparisonOperator::GreaterThan => value > *threshold,
                        ComparisonOperator::LessThan => value < *threshold,
                        ComparisonOperator::GreaterThanOrEqual => value >= *threshold,
                        ComparisonOperator::LessThanOrEqual => value <= *threshold,
                        ComparisonOperator::Equal => (value - threshold).abs() < 1e-10,
                        ComparisonOperator::NotEqual => {
                            (value - threshold).abs() >= 1e-10
                        }
                    }
                } else {
                    false
                }
            }
            _ => false,
        }
    }
    /// Calculate priority for a recommendation
    fn calculate_priority(
        &self,
        strategy: &AdaptationStrategy,
        _state: &SystemState,
    ) -> f32 {
        let effectiveness_weight = 0.4;
        let urgency_weight = 0.4;
        let risk_weight = 0.2;
        let effectiveness_score = strategy.effectiveness;
        let urgency_score = 1.0 - strategy.usage_frequency;
        let risk_score = match strategy.resource_requirements.risk_level {
            RiskLevel::VeryLow => 1.0,
            RiskLevel::Low => 0.8,
            RiskLevel::Medium => 0.6,
            RiskLevel::High => 0.4,
            RiskLevel::Critical => 0.2,
        };
        effectiveness_weight * effectiveness_score + urgency_weight * urgency_score
            + risk_weight * risk_score
    }
    /// Calculate confidence in a strategy recommendation
    fn calculate_confidence(
        &self,
        strategy: &AdaptationStrategy,
        _state: &SystemState,
    ) -> f32 {
        let success_weight = 0.6;
        let usage_weight = 0.4;
        let success_score = strategy.success_rate;
        let usage_score = (strategy.usage_frequency * 10.0).min(1.0);
        success_weight * success_score + usage_weight * usage_score
    }
    /// Assess risk for a strategy
    fn assess_risk(&self, strategy: &AdaptationStrategy) -> f32 {
        let complexity_risk = match strategy.complexity {
            StrategyComplexity::Simple => 0.1,
            StrategyComplexity::Moderate => 0.3,
            StrategyComplexity::Complex => 0.5,
            StrategyComplexity::Advanced => 0.7,
            StrategyComplexity::Expert => 0.9,
        };
        let resource_risk = match strategy.resource_requirements.risk_level {
            RiskLevel::VeryLow => 0.05,
            RiskLevel::Low => 0.2,
            RiskLevel::Medium => 0.4,
            RiskLevel::High => 0.6,
            RiskLevel::Critical => 0.8,
        };
        (complexity_risk + resource_risk) / 2.0
    }
    /// Apply an adaptation strategy
    pub fn apply_strategy(
        &mut self,
        strategy_name: &str,
        context: &SystemState,
    ) -> Result<AdaptationEvent, String> {
        let strategy = self
            .adaptation_strategies
            .get(strategy_name)
            .ok_or_else(|| format!("Strategy '{}' not found", strategy_name))?
            .clone();
        let start_time = Instant::now();
        let pre_state = context.clone();
        let mut executed_actions = Vec::new();
        let mut success_count = 0;
        for action in &strategy.actions {
            match self.execute_action(action) {
                Ok(action_desc) => {
                    executed_actions.push(action_desc);
                    success_count += 1;
                }
                Err(error) => {
                    executed_actions.push(format!("Failed: {}", error));
                }
            }
        }
        let outcome = if success_count == strategy.actions.len() {
            AdaptationOutcome::Success
        } else if success_count > 0 {
            AdaptationOutcome::PartialSuccess
        } else {
            AdaptationOutcome::Failure
        };
        let duration = start_time.elapsed();
        let impact = self.calculate_impact(&pre_state, context);
        let event = AdaptationEvent {
            timestamp: start_time,
            strategy: strategy_name.to_string(),
            trigger: "manual_trigger".to_string(),
            actions: executed_actions,
            outcome,
            impact,
            metadata: AdaptationMetadata {
                pre_state,
                post_state: context.clone(),
                resource_usage: ResourceUsage {
                    cpu_utilization: strategy.resource_requirements.cpu_cost,
                    memory_usage_mb: strategy.resource_requirements.memory_cost,
                    gpu_utilization: 0.0,
                    network_mbps: 0.0,
                    disk_iops: 0.0,
                },
                duration,
                confidence: self.calculate_confidence(&strategy, context),
            },
            success_metrics: HashMap::new(),
        };
        if let Some(strategy_mut) = self.adaptation_strategies.get_mut(strategy_name) {
            strategy_mut.usage_frequency += 1.0;
            strategy_mut.lifecycle.usage_count += 1;
            strategy_mut.lifecycle.last_used = Some(start_time);
            if outcome == AdaptationOutcome::Success {
                strategy_mut.success_rate = (strategy_mut.success_rate
                    * (strategy_mut.lifecycle.usage_count - 1) as f32 + 1.0)
                    / strategy_mut.lifecycle.usage_count as f32;
            }
        }
        self.adaptation_history.push_back(event.clone());
        if self.adaptation_history.len() > 1000 {
            self.adaptation_history.pop_front();
        }
        Ok(event)
    }
    /// Execute a single adaptation action
    fn execute_action(&self, action: &AdaptationAction) -> Result<String, String> {
        match action {
            AdaptationAction::ParameterAdjustment { parameter, adjustment, bounds } => {
                let description = match adjustment {
                    ParameterAdjustment::Absolute { value } => {
                        format!("Set {} to {}", parameter, value)
                    }
                    ParameterAdjustment::Relative { factor } => {
                        format!("Multiply {} by factor {}", parameter, factor)
                    }
                    ParameterAdjustment::Increment { step } => {
                        format!("Increment {} by {}", parameter, step)
                    }
                    ParameterAdjustment::Adaptive { target, learning_rate } => {
                        format!(
                            "Adaptively adjust {} towards {} with rate {}", parameter,
                            target, learning_rate
                        )
                    }
                };
                if let Some(_bounds) = bounds {}
                Ok(description)
            }
            AdaptationAction::StrategySwitch {
                from_strategy,
                to_strategy,
                transition_mode,
            } => {
                Ok(
                    format!(
                        "Switch from {} to {} with {:?} transition", from_strategy,
                        to_strategy, transition_mode
                    ),
                )
            }
            AdaptationAction::ResourceReallocation { resource, reallocation } => {
                let description = match reallocation {
                    ResourceReallocation::Increase { amount, unit } => {
                        format!("Increase {} by {} {}", resource, amount, unit)
                    }
                    ResourceReallocation::Decrease { amount, unit } => {
                        format!("Decrease {} by {} {}", resource, amount, unit)
                    }
                    ResourceReallocation::Redistribute { source, target, amount } => {
                        format!("Redistribute {} from {} to {}", amount, source, target)
                    }
                    ResourceReallocation::Optimize { optimization_goal } => {
                        format!("Optimize {} for {}", resource, optimization_goal)
                    }
                };
                Ok(description)
            }
            AdaptationAction::AlertGeneration { alert_type, message, recipients } => {
                Ok(
                    format!(
                        "Generate {:?} alert '{}' for {} recipients", alert_type,
                        message, recipients.len()
                    ),
                )
            }
            AdaptationAction::EmergencyResponse { response_type, severity } => {
                Ok(
                    format!(
                        "Execute {:?} emergency response with {:?} severity",
                        response_type, severity
                    ),
                )
            }
            _ => Ok("Action executed successfully".to_string()),
        }
    }
    /// Calculate the impact of an adaptation
    fn calculate_impact(
        &self,
        pre_state: &SystemState,
        post_state: &SystemState,
    ) -> f32 {
        let mut impact = 0.0;
        let mut metric_count = 0;
        for (metric, post_value) in &post_state.performance_metrics {
            if let Some(&pre_value) = pre_state.performance_metrics.get(metric) {
                let change = (post_value - pre_value) / pre_value.max(1e-10);
                impact += change as f32;
                metric_count += 1;
            }
        }
        if metric_count > 0 { impact / metric_count as f32 } else { 0.0 }
    }
    /// Update the learning mechanism with new experience
    pub fn learn_from_experience(&mut self, experience: AdaptiveExperience) {
        self.learning_mechanism.knowledge_base.experiences.push_back(experience.clone());
        if self.learning_mechanism.knowledge_base.experiences.len()
            > self.learning_mechanism.online_config.max_examples
        {
            self.learning_mechanism.knowledge_base.experiences.pop_front();
        }
        self.update_learning_performance(&experience);
        self.extract_rules_from_experience(&experience);
    }
    /// Update learning performance metrics
    fn update_learning_performance(&mut self, experience: &AdaptiveExperience) {
        let performance = &mut self.learning_mechanism.performance;
        let success_score = if experience.result.success { 1.0 } else { 0.0 };
        performance.accuracy = performance.accuracy * 0.9 + success_score * 0.1;
        performance.adaptation_speed = performance.adaptation_speed * 0.9
            + experience.learning_value * 0.1;
        performance.stability = performance.stability * 0.95
            + experience.result.confidence * 0.05;
    }
    /// Extract new rules from experience
    fn extract_rules_from_experience(&mut self, experience: &AdaptiveExperience) {
        if experience.result.success && experience.result.confidence > 0.7 {
            let rule_id = format!(
                "rule_{}", self.learning_mechanism.knowledge_base.rules.len()
            );
            let condition = RuleCondition::StateMatch {
                state_pattern: format!(
                    "performance_{:.2}", experience.result.performance_change
                ),
                tolerance: 0.1,
            };
            let action = RuleAction::ParameterUpdate {
                parameter: "adaptation_rate".to_string(),
                update: ParameterUpdate::SetValue {
                    value: experience.learning_value as f64,
                },
            };
            let rule = AdaptiveRule {
                id: rule_id.clone(),
                condition,
                action,
                confidence: experience.result.confidence,
                usage_count: 0,
                success_rate: 1.0,
                created_at: Instant::now(),
                last_success: None,
                priority: RulePriority::Medium,
                validity_conditions: vec![],
            };
            self.learning_mechanism.knowledge_base.rules.insert(rule_id, rule);
        }
    }
    /// Get current learning performance
    pub fn get_learning_performance(&self) -> &AdaptiveLearningPerformance {
        &self.learning_mechanism.performance
    }
    /// Get adaptation history
    pub fn get_adaptation_history(&self) -> &VecDeque<AdaptationEvent> {
        &self.adaptation_history
    }
    /// Get current controller state
    pub fn get_controller_state(&self) -> &ControllerState {
        &self.controller_state
    }
    /// Update controller state
    pub fn update_controller_state(&mut self, new_state: ControllerState) {
        self.controller_state = new_state;
        self.controller_state.last_adaptation = Some(Instant::now());
    }
    /// Add custom adaptation strategy
    pub fn add_strategy(&mut self, name: String, strategy: AdaptationStrategy) {
        self.adaptation_strategies.insert(name, strategy);
    }
    /// Remove adaptation strategy
    pub fn remove_strategy(&mut self, name: &str) -> Option<AdaptationStrategy> {
        self.adaptation_strategies.remove(name)
    }
    /// List available strategies
    pub fn list_strategies(&self) -> Vec<String> {
        self.adaptation_strategies.keys().cloned().collect()
    }
    /// Get strategy by name
    pub fn get_strategy(&self, name: &str) -> Option<&AdaptationStrategy> {
        self.adaptation_strategies.get(name)
    }
}
/// Adaptive learning mechanism
#[derive(Debug)]
pub struct AdaptiveLearningMechanism {
    /// Learning algorithm used
    pub algorithm: AdaptiveLearningAlgorithm,
    /// Algorithm parameters
    pub parameters: HashMap<String, f64>,
    /// Knowledge base for learned information
    pub knowledge_base: AdaptiveKnowledgeBase,
    /// Learning performance metrics
    pub performance: AdaptiveLearningPerformance,
    /// Online learning configuration
    pub online_config: OnlineLearningConfig,
    /// Meta-learning capabilities
    pub meta_learning: MetaLearningCapabilities,
}
impl AdaptiveLearningMechanism {
    fn new() -> Self {
        Self {
            algorithm: AdaptiveLearningAlgorithm::OnlineGradientDescent,
            parameters: HashMap::new(),
            knowledge_base: AdaptiveKnowledgeBase {
                rules: HashMap::new(),
                experiences: VecDeque::new(),
                confidence_scores: HashMap::new(),
                freshness_scores: HashMap::new(),
                knowledge_graph: KnowledgeGraph {
                    nodes: HashMap::new(),
                    edges: Vec::new(),
                    metrics: GraphMetrics {
                        node_count: 0,
                        edge_count: 0,
                        average_degree: 0.0,
                        clustering_coefficient: 0.0,
                        density: 0.0,
                    },
                },
                semantic_search: SemanticSearch {
                    index: SearchIndex {
                        terms: HashMap::new(),
                        term_frequencies: HashMap::new(),
                        document_frequencies: HashMap::new(),
                        vectors: HashMap::new(),
                    },
                    similarity_metrics: vec![SimilarityMetric::Cosine],
                    query_processor: QueryProcessor {
                        pipeline: vec![
                            QueryProcessingStep::Tokenization,
                            QueryProcessingStep::Normalization,
                        ],
                        expansion_rules: Vec::new(),
                        ranking_algorithm: RankingAlgorithm::TfIdf,
                    },
                },
            },
            performance: AdaptiveLearningPerformance::default(),
            online_config: OnlineLearningConfig::default(),
            meta_learning: MetaLearningCapabilities {
                enabled: true,
                algorithms: vec![MetaLearningAlgorithm::MAML],
                meta_knowledge: MetaKnowledgeBase {
                    learning_strategies: HashMap::new(),
                    problem_solutions: HashMap::new(),
                    performance_patterns: Vec::new(),
                    success_factors: HashMap::new(),
                },
                transfer_learning: TransferLearningConfig {
                    enabled: true,
                    source_domains: vec!["memory_optimization".to_string()],
                    target_domain: "cuda_optimization".to_string(),
                    transfer_methods: vec![TransferMethod::FineTuning],
                    similarity_threshold: 0.7,
                },
            },
        }
    }
}
/// Result ranking algorithms
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum RankingAlgorithm {
    TfIdf,
    BM25,
    Semantic,
    Hybrid,
    LearningToRank,
}
/// Adaptive decision tree for automated reasoning
#[derive(Debug, Clone)]
pub struct AdaptiveDecisionTree {
    /// Root node of the decision tree
    pub root: DecisionNode,
    /// Tree depth
    pub depth: usize,
    /// Number of nodes
    pub node_count: usize,
    /// Tree performance metrics
    pub performance: TreePerformance,
    /// Tree learning configuration
    pub learning_config: TreeLearningConfig,
}
impl AdaptiveDecisionTree {
    fn new() -> Self {
        Self {
            root: DecisionNode {
                id: "root".to_string(),
                condition: None,
                left: None,
                right: None,
                action: None,
                stats: NodeStatistics {
                    sample_count: 0,
                    accuracy: 0.0,
                    information_gain: 0.0,
                    impurity: 0.0,
                },
            },
            depth: 0,
            node_count: 1,
            performance: TreePerformance {
                accuracy: 0.0,
                precision: 0.0,
                recall: 0.0,
                f1_score: 0.0,
                complexity: 0.0,
            },
            learning_config: TreeLearningConfig {
                max_depth: 10,
                min_samples_leaf: 5,
                min_samples_split: 10,
                pruning_strategy: PruningStrategy::PostPruning,
                split_criterion: SplitCriterion::Entropy,
            },
        }
    }
}
/// Decision action for leaf nodes
#[derive(Debug, Clone)]
pub struct DecisionAction {
    /// Action type
    pub action_type: String,
    /// Action parameters
    pub parameters: HashMap<String, f64>,
    /// Expected outcome
    pub expected_outcome: f32,
    /// Action confidence
    pub confidence: f32,
}
/// State predictor for forecasting
#[derive(Debug)]
pub struct StatePredictor {
    /// Prediction models
    pub models: HashMap<String, PredictionModel>,
    /// Prediction horizon
    pub horizon: Duration,
    /// Prediction accuracy tracking
    pub accuracy_tracker: PredictionAccuracyTracker,
    /// Feature engineering pipeline
    pub feature_pipeline: FeatureEngineeringPipeline,
}
impl StatePredictor {
    fn new() -> Self {
        Self {
            models: HashMap::new(),
            horizon: Duration::from_secs(3600),
            accuracy_tracker: PredictionAccuracyTracker {
                model_accuracy: HashMap::new(),
                accuracy_history: VecDeque::new(),
                overall_accuracy: 0.0,
                best_model: None,
            },
            feature_pipeline: FeatureEngineeringPipeline {
                extractors: Vec::new(),
                transformers: Vec::new(),
                selectors: Vec::new(),
                config: PipelineConfig {
                    enable_caching: true,
                    parallel: true,
                    memory_limit: 1024 * 1024 * 1024,
                    timeout: Duration::from_secs(300),
                },
            },
        }
    }
}
/// Adaptation event record
#[derive(Debug, Clone)]
pub struct AdaptationEvent {
    /// Event timestamp
    pub timestamp: Instant,
    /// Adaptation strategy used
    pub strategy: String,
    /// Trigger that caused adaptation
    pub trigger: String,
    /// Actions taken during adaptation
    pub actions: Vec<String>,
    /// Adaptation outcome
    pub outcome: AdaptationOutcome,
    /// Performance impact measurement
    pub impact: f32,
    /// Event metadata
    pub metadata: AdaptationMetadata,
    /// Success metrics
    pub success_metrics: HashMap<String, f64>,
}
/// User preference model
#[derive(Debug, Clone)]
pub struct UserPreferenceModel {
    /// Preference weights
    pub preference_weights: HashMap<String, f32>,
    /// User interaction history
    pub interaction_history: Vec<UserInteraction>,
    /// Implicit preferences
    pub implicit_preferences: HashMap<String, f32>,
    /// Explicit preferences
    pub explicit_preferences: HashMap<String, f32>,
}
/// Strategy transition modes
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TransitionMode {
    Immediate,
    Gradual,
    Scheduled,
    ConditionalGradual,
}
/// Tree performance metrics
#[derive(Debug, Clone)]
pub struct TreePerformance {
    /// Overall accuracy
    pub accuracy: f32,
    /// Precision score
    pub precision: f32,
    /// Recall score
    pub recall: f32,
    /// F1 score
    pub f1_score: f32,
    /// Tree complexity score
    pub complexity: f32,
}
/// Rule validity conditions
#[derive(Debug, Clone)]
pub enum ValidityCondition {
    TimeRange { start: Instant, end: Instant },
    SystemState { required_state: String },
    ResourceAvailability { min_resources: HashMap<String, f32> },
    UserPermission { required_permission: String },
    ContextualCondition { context: String, value: String },
}
/// Query expansion rules
#[derive(Debug, Clone)]
pub struct ExpansionRule {
    /// Rule pattern
    pub pattern: String,
    /// Expansion terms
    pub expansions: Vec<String>,
    /// Rule weight
    pub weight: f32,
}
/// Similarity metrics for semantic search
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SimilarityMetric {
    Cosine,
    Euclidean,
    Jaccard,
    Hamming,
    Manhattan,
    Semantic,
}
/// Logical operators for combining conditions
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum LogicalOperator {
    And,
    Or,
    Not,
    Xor,
    Implies,
}
/// Change directions
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ChangeDirection {
    Increase,
    Decrease,
    Shift,
    Volatility,
}
/// Controller operational modes
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ControllerMode {
    Learning,
    Optimizing,
    Monitoring,
    Emergency,
    Maintenance,
    Offline,
}
/// Adaptive experience record
#[derive(Debug, Clone)]
pub struct AdaptiveExperience {
    /// Experience timestamp
    pub timestamp: Instant,
    /// System state at the time
    pub state: SystemState,
    /// Action taken
    pub action: String,
    /// Result achieved
    pub result: AdaptiveResult,
    /// Learning value from experience
    pub learning_value: f32,
    /// Experience context
    pub context: ExperienceContext,
    /// Feedback received
    pub feedback: Option<ExperienceFeedback>,
}
/// Adaptive learning result
#[derive(Debug, Clone)]
pub struct AdaptiveResult {
    /// Success indicator
    pub success: bool,
    /// Performance change measurement
    pub performance_change: f32,
    /// Resource impact
    pub resource_impact: f32,
    /// Side effects observed
    pub side_effects: Vec<String>,
    /// Confidence in result
    pub confidence: f32,
    /// Result quality metrics
    pub quality_metrics: ResultQualityMetrics,
}
/// Training session record
#[derive(Debug, Clone)]
pub struct TrainingSession {
    /// Session timestamp
    pub timestamp: Instant,
    /// Training data size
    pub data_size: usize,
    /// Training duration
    pub duration: Duration,
    /// Validation performance
    pub validation_performance: HashMap<String, f32>,
    /// Model version
    pub version: String,
}
/// Types of feature transformers
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum FeatureTransformerType {
    StandardScaler,
    MinMaxScaler,
    RobustScaler,
    PowerTransformer,
    QuantileTransformer,
    PCA,
    Custom,
}
/// Search index for knowledge
#[derive(Debug, Clone)]
pub struct SearchIndex {
    /// Indexed terms
    pub terms: HashMap<String, Vec<String>>,
    /// Term frequencies
    pub term_frequencies: HashMap<String, f32>,
    /// Document frequencies
    pub document_frequencies: HashMap<String, f32>,
    /// Vector representations
    pub vectors: HashMap<String, Vec<f32>>,
}
/// Context snapshot
#[derive(Debug, Clone)]
pub struct ContextSnapshot {
    /// Snapshot timestamp
    pub timestamp: Instant,
    /// Environment state
    pub environment_state: HashMap<String, f64>,
    /// Active workloads
    pub active_workloads: Vec<String>,
    /// Resource availability
    pub resource_availability: HashMap<String, f32>,
    /// Performance metrics
    pub performance_metrics: HashMap<String, f64>,
}
/// Types of context prediction models
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ContextModelType {
    TimeSeries,
    MachineLearning,
    StatisticalModel,
    HybridModel,
}
/// Transfer learning methods
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TransferMethod {
    FineTuning,
    FeatureExtraction,
    DomainAdaptation,
    TaskAdaptation,
    ParameterTransfer,
}
/// Anomaly detection result
#[derive(Debug, Clone)]
pub struct AnomalyDetection {
    /// Detection timestamp
    pub timestamp: Instant,
    /// Anomaly score
    pub score: f32,
    /// Anomaly type
    pub anomaly_type: AnomalyType,
    /// Affected features
    pub affected_features: Vec<String>,
    /// Severity level
    pub severity: f32,
    /// Detection confidence
    pub confidence: f32,
    /// Context information
    pub context: HashMap<String, String>,
}
/// Meta-learning capabilities
#[derive(Debug, Clone)]
pub struct MetaLearningCapabilities {
    /// Enable meta-learning
    pub enabled: bool,
    /// Learning to learn algorithms
    pub algorithms: Vec<MetaLearningAlgorithm>,
    /// Meta-knowledge base
    pub meta_knowledge: MetaKnowledgeBase,
    /// Transfer learning capabilities
    pub transfer_learning: TransferLearningConfig,
}
/// Action scheduling
#[derive(Debug, Clone)]
pub struct ActionSchedule {
    /// Schedule type
    pub schedule_type: ActionScheduleType,
    /// Next execution time
    pub next_execution: Instant,
    /// Repeat configuration
    pub repeat_config: Option<RepeatConfig>,
}
/// Types of detected changes
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ChangeType {
    Mean,
    Variance,
    Distribution,
    Trend,
    Seasonality,
    Outlier,
    Structural,
}
/// Performance level indicators
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum PerformanceLevel {
    Poor,
    BelowAverage,
    Average,
    Good,
    Excellent,
}
/// Prediction accuracy tracker
#[derive(Debug, Clone)]
pub struct PredictionAccuracyTracker {
    /// Model accuracy by metric
    pub model_accuracy: HashMap<String, f32>,
    /// Accuracy history
    pub accuracy_history: VecDeque<AccuracyRecord>,
    /// Overall accuracy
    pub overall_accuracy: f32,
    /// Best performing model
    pub best_model: Option<String>,
}
/// Alert delivery channels
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum AlertChannel {
    Email,
    SMS,
    Slack,
    PagerDuty,
    Webhook,
    Log,
}
/// Split criteria for decision trees
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SplitCriterion {
    Gini,
    Entropy,
    ChiSquare,
    InformationGain,
}
/// Anomaly model for specific detection
#[derive(Debug, Clone)]
pub struct AnomalyModel {
    /// Model identifier
    pub id: String,
    /// Model type
    pub model_type: AnomalyDetectionAlgorithm,
    /// Training data characteristics
    pub training_characteristics: TrainingCharacteristics,
    /// Model performance metrics
    pub performance_metrics: AnomalyModelPerformance,
    /// Model update history
    pub update_history: Vec<ModelUpdate>,
}
/// Accuracy record for tracking
#[derive(Debug, Clone)]
pub struct AccuracyRecord {
    /// Record timestamp
    pub timestamp: Instant,
    /// Model identifier
    pub model_id: String,
    /// Predicted values
    pub predictions: Vec<f64>,
    /// Actual values
    pub actuals: Vec<f64>,
    /// Accuracy score
    pub accuracy: f32,
}
/// Rule action types
#[derive(Debug, Clone)]
pub enum RuleAction {
    ParameterUpdate { parameter: String, update: ParameterUpdate },
    StrategyActivation { strategy: String, activation_mode: ActivationMode },
    ConfigurationChange { config: String, change: ConfigChange },
    AlertGeneration { alert: AlertConfiguration },
    CombinationAction { actions: Vec<RuleAction>, execution_mode: ActionExecutionMode },
    ConditionalAction {
        condition: String,
        true_action: Box<RuleAction>,
        false_action: Option<Box<RuleAction>>,
    },
    DelayedAction { delay: Duration, action: Box<RuleAction> },
    ScheduledAction { schedule: ActionSchedule, action: Box<RuleAction> },
}
/// Decision condition for tree nodes
#[derive(Debug, Clone)]
pub struct DecisionCondition {
    /// Feature to test
    pub feature: String,
    /// Comparison operator
    pub operator: ComparisonOperator,
    /// Threshold value
    pub threshold: f64,
    /// Condition confidence
    pub confidence: f32,
}
/// Rollback scope
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum RollbackScope {
    Parameter,
    Strategy,
    Configuration,
    System,
}
/// Adaptive learning rate configuration
#[derive(Debug, Clone)]
pub struct AdaptiveLearningRateConfig {
    /// Initial learning rate
    pub initial_rate: f32,
    /// Minimum learning rate
    pub min_rate: f32,
    /// Maximum learning rate
    pub max_rate: f32,
    /// Decay factor
    pub decay_factor: f32,
    /// Patience for rate adjustment
    pub patience: usize,
    /// Performance threshold for adjustment
    pub threshold: f32,
}
/// Adaptation urgency levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum AdaptationUrgency {
    Low,
    Medium,
    High,
    Urgent,
    Immediate,
}
/// Performance prediction model for strategies
#[derive(Debug, Clone)]
pub struct PerformancePredictionModel {
    /// Model identifier
    pub id: String,
    /// Model type
    pub model_type: PredictionModelType,
    /// Model parameters
    pub parameters: HashMap<String, f64>,
    /// Feature importance scores
    pub feature_importance: HashMap<String, f32>,
    /// Model accuracy
    pub accuracy: f32,
    /// Training history
    pub training_history: Vec<TrainingSession>,
}
/// Sources of feedback
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum FeedbackSource {
    Automatic,
    User,
    System,
    External,
}
/// Feature normalization strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum NormalizationStrategy {
    None,
    ZScore,
    MinMax,
    Robust,
    Quantile,
}
/// Types of feature selectors
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum FeatureSelectorType {
    VarianceThreshold,
    UnivariateSelection,
    RecursiveFeatureElimination,
    L1Regularization,
    MutualInformation,
    Custom,
}
/// Model update record
#[derive(Debug, Clone)]
pub struct ModelUpdate {
    /// Update timestamp
    pub timestamp: Instant,
    /// Update reason
    pub reason: UpdateReason,
    /// Performance change
    pub performance_delta: HashMap<String, f32>,
    /// Updated parameters
    pub parameter_changes: HashMap<String, f64>,
}
/// Performance metrics for anomaly models
#[derive(Debug, Clone)]
pub struct AnomalyModelPerformance {
    /// Precision score
    pub precision: f32,
    /// Recall score
    pub recall: f32,
    /// F1 score
    pub f1_score: f32,
    /// False positive rate
    pub false_positive_rate: f32,
    /// True positive rate
    pub true_positive_rate: f32,
    /// Area under ROC curve
    pub auc_roc: f32,
}
/// Semantic search capabilities
#[derive(Debug, Clone)]
pub struct SemanticSearch {
    /// Search index
    pub index: SearchIndex,
    /// Similarity metrics
    pub similarity_metrics: Vec<SimilarityMetric>,
    /// Query processing
    pub query_processor: QueryProcessor,
}
