//! Auto-generated module
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use scirs2_core::ndarray::{Array1, Array2};
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

use std::collections::{VecDeque, HashMap};

/// RL learning parameters
#[derive(Debug, Clone)]
pub struct RLLearningParams {
    pub learning_rate: f32,
    pub discount_factor: f32,
    pub epsilon: f32,
    pub epsilon_decay: f32,
    pub epsilon_min: f32,
    pub target_update_frequency: usize,
    pub replay_buffer_size: usize,
    pub batch_size: usize,
    pub tau: f32,
}
/// Model fairness metrics
#[derive(Debug, Clone)]
pub struct FairnessMetrics {
    pub demographic_parity: f32,
    pub equalized_odds: f32,
    pub individual_fairness: f32,
    pub group_fairness: f32,
    pub bias_score: f32,
}
/// Types of feature extractors
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum ExtractorType {
    /// Statistical features (mean, std, etc.)
    Statistical,
    /// Temporal features (trends, seasonality)
    Temporal,
    /// Frequency domain features
    Frequency,
    /// Structural features (graph properties)
    Structural,
    /// Contextual features (environment-dependent)
    Contextual,
    /// Composite features (combinations)
    Composite,
    /// Memory usage features
    MemoryUsage,
    /// Performance features
    Performance,
    /// Allocation pattern features
    AllocationPattern,
    /// Transfer pattern features
    TransferPattern,
    /// Cache behavior features
    CacheBehavior,
    /// System resource features
    SystemResource,
}
/// Activation functions
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ActivationFunction {
    ReLU,
    LeakyReLU,
    ELU,
    Tanh,
    Sigmoid,
    Softmax,
    Swish,
    GELU,
}
/// Types of ML models
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum MLModelType {
    /// Linear regression for simple relationships
    LinearRegression,
    /// Polynomial regression for non-linear relationships
    PolynomialRegression,
    /// Decision tree for interpretable decisions
    DecisionTree,
    /// Random forest for robust ensemble learning
    RandomForest,
    /// Gradient boosting for high accuracy
    GradientBoosting,
    /// Neural network for complex patterns
    NeuralNetwork,
    /// Deep neural network for very complex patterns
    DeepNeuralNetwork,
    /// Convolutional neural network for spatial patterns
    ConvolutionalNeuralNetwork,
    /// Recurrent neural network for temporal patterns
    RecurrentNeuralNetwork,
    /// Long short-term memory for long sequences
    LongShortTermMemory,
    /// Transformer for attention-based learning
    Transformer,
    /// Support vector machine for classification
    SupportVectorMachine,
    /// K-means clustering for grouping
    KMeansClustering,
    /// DBSCAN clustering for density-based grouping
    DBSCANClustering,
    /// Reinforcement learning for sequential decisions
    ReinforcementLearning,
    /// Q-learning for value-based RL
    QLearning,
    /// Deep Q-network for deep RL
    DeepQLearning,
    /// Policy gradient for policy-based RL
    PolicyGradient,
    /// Actor-critic for hybrid RL
    ActorCritic,
    /// Proximal policy optimization
    ProximalPolicyOptimization,
    /// Ensemble model combining multiple models
    EnsembleModel,
    /// Bayesian model for uncertainty quantification
    BayesianModel,
    /// Gaussian process for non-parametric learning
    GaussianProcess,
    /// Autoencoder for dimensionality reduction
    Autoencoder,
    /// Variational autoencoder for generative modeling
    VariationalAutoencoder,
    /// Generative adversarial network
    GenerativeAdversarialNetwork,
}
/// ML prediction result
#[derive(Debug, Clone)]
pub struct Prediction {
    /// Prediction timestamp
    pub timestamp: Instant,
    /// Predicted values
    pub values: HashMap<String, f64>,
    /// Prediction confidence
    pub confidence: f32,
    /// Confidence intervals
    pub confidence_intervals: HashMap<String, (f64, f64)>,
    /// Model used
    pub model_id: String,
    /// Actual outcomes (for learning)
    pub actual_outcomes: Option<HashMap<String, f64>>,
    /// Prediction error (when actual is available)
    pub prediction_error: Option<HashMap<String, f64>>,
    /// Feature contributions to prediction
    pub feature_contributions: HashMap<String, f64>,
    /// Prediction explanation
    pub explanation: Option<String>,
    /// Uncertainty score
    pub uncertainty_score: f32,
}
/// Selection criteria
#[derive(Debug, Clone)]
pub struct SelectionCriterion {
    pub criterion_name: String,
    pub criterion_type: CriterionType,
    pub weight: f32,
    pub threshold: f64,
}
/// Model complexity metrics
#[derive(Debug, Clone)]
pub struct ModelComplexityMetrics {
    pub parameter_count: usize,
    pub flops: u64,
    pub memory_footprint: usize,
    pub depth: usize,
    pub branching_factor: f32,
    pub effective_complexity: f64,
}
/// Model performance tracking
#[derive(Debug, Clone)]
pub struct ModelPerformance {
    /// Prediction accuracy
    pub accuracy: f32,
    /// Mean squared error
    pub mse: f64,
    /// Mean absolute error
    pub mae: f64,
    /// Root mean squared error
    pub rmse: f64,
    /// R-squared score
    pub r_squared: f64,
    /// Adjusted R-squared
    pub adjusted_r_squared: f64,
    /// Precision
    pub precision: f32,
    /// Recall
    pub recall: f32,
    /// F1 score
    pub f1_score: f32,
    /// Area under ROC curve
    pub auc_roc: f32,
    /// Area under precision-recall curve
    pub auc_pr: f32,
    /// Performance trend
    pub trend: PerformanceTrend,
    /// Cross-validation scores
    pub cv_scores: Vec<f64>,
    /// Training time
    pub training_time: Duration,
    /// Inference time
    pub inference_time: Duration,
    /// Memory usage during training
    pub training_memory: usize,
    /// Memory usage during inference
    pub inference_memory: usize,
    /// Model stability metrics
    pub stability_metrics: StabilityMetrics,
    /// Fairness metrics
    pub fairness_metrics: FairnessMetrics,
}
/// Performance trend indicators
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum PerformanceTrend {
    Improving,
    Stable,
    Degrading,
    Volatile,
    Unknown,
}
/// Validation split assignment
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ValidationSplit {
    Train,
    Validation,
    Test,
    HoldOut,
}
/// Reward shaping configuration
#[derive(Debug, Clone)]
pub struct RewardShaping {
    pub potential_function: PotentialFunction,
    pub shaping_factor: f32,
    pub intrinsic_motivation: bool,
    pub curiosity_driven: bool,
}
/// Potential functions for reward shaping
#[derive(Debug, Clone)]
pub enum PotentialFunction {
    Linear { coefficients: Vec<f64> },
    Gaussian { centers: Vec<Vec<f64>>, variances: Vec<f64> },
    Learned { model_id: String },
}
/// Types of ensemble methods
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum EnsembleType {
    Bagging,
    Boosting,
    Stacking,
    Voting,
    Blending,
}
/// Types of RL agents
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum RLAgentType {
    QLearning,
    DeepQLearning,
    DoubleQLearning,
    DuelingQLearning,
    PolicyGradient,
    ActorCritic,
    AdvantageActorCritic,
    ProximalPolicyOptimization,
    TrustRegionPolicyOptimization,
    SoftActorCritic,
    MonteCarloTreeSearch,
    AlphaZero,
}
/// Training task
#[derive(Debug, Clone)]
pub struct TrainingTask {
    pub task_id: String,
    pub model_id: String,
    pub priority: u32,
    pub estimated_duration: Duration,
    pub resource_requirements: ResourceRequirements,
    pub dependencies: Vec<String>,
}
/// Online learning configuration
#[derive(Debug, Clone)]
pub struct OnlineLearningConfig {
    /// Enable online learning
    pub enabled: bool,
    /// Learning rate
    pub learning_rate: f32,
    /// Batch size for updates
    pub batch_size: usize,
    /// Update frequency
    pub update_frequency: Duration,
    /// Forgetting factor for old examples
    pub forgetting_factor: f32,
    /// Minimum examples before learning
    pub min_examples: usize,
    /// Maximum training data size
    pub max_training_size: usize,
    /// Learning rate decay
    pub learning_rate_decay: f32,
    /// Adaptive learning rate
    pub adaptive_learning_rate: bool,
    /// Early stopping criteria
    pub early_stopping: EarlyStoppingConfig,
    /// Model validation frequency
    pub validation_frequency: usize,
    /// Data quality threshold
    pub data_quality_threshold: f32,
}
/// Types of feature validation
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum FeatureValidationType {
    RangeCheck,
    OutlierDetection,
    ConsistencyCheck,
    CorrelationCheck,
    DistributionCheck,
    TemporalConsistency,
}
/// Early stopping configuration
#[derive(Debug, Clone)]
pub struct EarlyStoppingConfig {
    pub enabled: bool,
    pub patience: usize,
    pub min_delta: f64,
    pub monitor_metric: String,
    pub mode: EarlyStoppingMode,
}
/// Resource limits for training
#[derive(Debug, Clone)]
pub struct ResourceLimits {
    pub max_memory: usize,
    pub max_cpu_cores: usize,
    pub max_gpu_memory: usize,
    pub max_concurrent_tasks: usize,
}
/// Explanation methods
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ExplanationMethod {
    SHAP,
    LIME,
    PermutationImportance,
    PartialDependence,
    Anchors,
    CounterfactualExplanations,
}
/// Validation methods
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ValidationMethod {
    HoldOut,
    CrossValidation,
    TimeSeriesSplit,
    StratifiedSplit,
    Bootstrap,
}
/// Global explanation
#[derive(Debug, Clone)]
pub struct Explanation {
    pub explanation_type: ExplanationType,
    pub content: String,
    pub visualizations: Vec<Visualization>,
    pub confidence: f32,
}
/// Direction of feature contribution
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ContributionDirection {
    Positive,
    Negative,
    Neutral,
}
/// Types of visualizations
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum VisualizationType {
    FeatureImportancePlot,
    PartialDependencePlot,
    DecisionBoundary,
    LearningCurve,
    ConfusionMatrix,
    ROCCurve,
    PRCurve,
}
/// System state for feature extraction
#[derive(Debug, Clone)]
pub struct SystemState {
    pub memory_usage_stats: MemoryUsageStats,
    pub temporal_trend: f64,
    pub seasonality_score: f64,
    pub average_latency: f64,
    pub throughput: f64,
}
/// Feature extraction performance
#[derive(Debug, Clone)]
pub struct ExtractionPerformance {
    pub extraction_time: Duration,
    pub memory_usage: usize,
    pub success_rate: f32,
    pub error_rate: f32,
    pub throughput: f64,
}
/// Feature selection criteria
#[derive(Debug, Clone)]
pub struct FeatureSelectionCriteria {
    pub min_importance: f32,
    pub max_correlation: f32,
    pub information_gain_threshold: f64,
    pub variance_threshold: f64,
    pub stability_score: f32,
}
/// Model selection strategy
#[derive(Debug, Clone)]
pub struct ModelSelectionStrategy {
    pub strategy_type: ModelSelectionType,
    pub criteria: Vec<SelectionCriterion>,
    pub validation_method: ValidationMethod,
    pub ensemble_threshold: f32,
}
/// Probability distributions for hyperparameter search
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum Distribution {
    Uniform,
    Normal,
    LogUniform,
    LogNormal,
}
/// Types of recommendations
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum RecommendationType {
    AllocationStrategy,
    PoolConfiguration,
    TransferOptimization,
    CacheStrategy,
    FragmentationReduction,
    ParameterTuning,
    ModelUpdate,
    FeatureEngineering,
}
/// Reinforcement learning agent
#[derive(Debug)]
pub struct ReinforcementLearningAgent {
    /// Agent type
    pub agent_type: RLAgentType,
    /// Q-table or neural network weights
    pub weights: HashMap<String, f64>,
    /// Learning parameters
    pub learning_params: RLLearningParams,
    /// Experience replay buffer
    pub replay_buffer: VecDeque<RLExperience>,
    /// Exploration strategy
    pub exploration_strategy: ExplorationStrategy,
    /// Reward shaping
    pub reward_shaping: RewardShaping,
    /// Policy network (for policy gradient methods)
    pub policy_network: Option<PolicyNetwork>,
    /// Value network (for actor-critic methods)
    pub value_network: Option<ValueNetwork>,
    /// Target networks (for stable learning)
    pub target_networks: Option<TargetNetworks>,
    /// Multi-agent coordination
    pub coordination: Option<MultiAgentCoordination>,
}
/// Validation metrics
#[derive(Debug, Clone)]
pub struct ValidationMetrics {
    pub cross_validation_score: f64,
    pub hold_out_score: f64,
    pub bootstrap_score: f64,
    pub time_series_score: f64,
    pub consistency_score: f32,
}
/// Performance benchmark
#[derive(Debug, Clone)]
pub struct PerformanceBenchmark {
    pub benchmark_name: String,
    pub target_metrics: HashMap<String, f64>,
    pub baseline_performance: ModelPerformance,
    pub test_data: Vec<TrainingExample>,
}
/// Hyperparameter definition
#[derive(Debug, Clone)]
pub struct HyperParameter {
    pub name: String,
    pub parameter_type: HyperParameterType,
    pub current_value: f64,
    pub search_space: SearchSpace,
    pub optimization_history: Vec<f64>,
}
/// Types of hyperparameters
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum HyperParameterType {
    Continuous,
    Integer,
    Categorical,
    Boolean,
}
/// Memory usage statistics
#[derive(Debug, Clone)]
pub struct MemoryUsageStats {
    pub mean: f64,
    pub std_dev: f64,
    pub min: f64,
    pub max: f64,
}
/// Ensemble configuration
#[derive(Debug, Clone)]
pub struct EnsembleConfig {
    pub ensemble_type: EnsembleType,
    pub voting_strategy: VotingStrategy,
    pub model_weights: HashMap<String, f32>,
    pub diversity_threshold: f32,
    pub max_models: usize,
}
/// Consensus mechanisms
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ConsensusMechanism {
    MajorityVote,
    WeightedVote,
    Byzantine,
    Raft,
}
/// Communication protocols
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum CommunicationProtocol {
    Broadcast,
    PointToPoint,
    Gossip,
    ConsensusProtocol,
}
/// Types of neural network layers
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum LayerType {
    Dense,
    Convolutional,
    Recurrent,
    LSTM,
    GRU,
    Attention,
    Transformer,
    Embedding,
}
/// Types of coordination
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum CoordinationType {
    Independent,
    Centralized,
    Decentralized,
    Hierarchical,
}
/// Model explainability module
#[derive(Debug, Clone)]
pub struct ModelExplainability {
    pub enabled: bool,
    pub explanation_methods: Vec<ExplanationMethod>,
    pub global_explanations: HashMap<String, Explanation>,
    pub local_explanations: Vec<LocalExplanation>,
}
/// Network layer definition
#[derive(Debug, Clone)]
pub struct NetworkLayer {
    pub layer_type: LayerType,
    pub input_size: usize,
    pub output_size: usize,
    pub weights: Array2<f64>,
    pub biases: Array1<f64>,
    pub dropout_rate: f32,
    pub regularization: RegularizationType,
}
/// Regularization types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum RegularizationType {
    None,
    L1,
    L2,
    ElasticNet,
    Dropout,
}
/// Types of selection criteria
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum CriterionType {
    Accuracy,
    Precision,
    Recall,
    F1Score,
    AUC,
    MSE,
    MAE,
    RSquared,
    TrainingTime,
    InferenceTime,
    ModelSize,
    Complexity,
    Interpretability,
    Stability,
    Robustness,
}
/// Resource requirements for a task
#[derive(Debug, Clone)]
pub struct ResourceRequirements {
    pub memory: usize,
    pub cpu_cores: usize,
    pub gpu_memory: usize,
    pub disk_space: usize,
}
/// Optimizer types
#[derive(Debug, Clone)]
pub enum Optimizer {
    SGD { learning_rate: f32, momentum: f32 },
    Adam { learning_rate: f32, beta1: f32, beta2: f32, epsilon: f64 },
    AdaGrad { learning_rate: f32, epsilon: f64 },
    RMSprop { learning_rate: f32, decay: f32, epsilon: f64 },
}
/// Training scheduler
#[derive(Debug, Clone)]
pub struct TrainingScheduler {
    pub schedule_type: ScheduleType,
    pub priority_queue: Vec<TrainingTask>,
    pub resource_limits: ResourceLimits,
    pub concurrent_training: bool,
}
/// Feature contribution to prediction
#[derive(Debug, Clone)]
pub struct FeatureContribution {
    pub feature_name: String,
    pub contribution: f64,
    pub confidence: f32,
    pub direction: ContributionDirection,
}
/// Multi-agent coordination
#[derive(Debug)]
pub struct MultiAgentCoordination {
    pub coordination_type: CoordinationType,
    pub communication_protocol: CommunicationProtocol,
    pub consensus_mechanism: ConsensusMechanism,
}
/// Machine learning model for optimization
#[derive(Debug, Clone)]
pub struct MLModel {
    /// Model identifier
    pub id: String,
    /// Model type
    pub model_type: MLModelType,
    /// Model parameters
    pub parameters: HashMap<String, f64>,
    /// Hyperparameters
    pub hyperparameters: HashMap<String, HyperParameter>,
    /// Training status
    pub training_status: TrainingStatus,
    /// Model accuracy
    pub accuracy: f32,
    /// Model confidence
    pub confidence: f32,
    /// Last training time
    pub last_training: Instant,
    /// Prediction history
    pub prediction_history: Vec<Prediction>,
    /// Model complexity metrics
    pub complexity_metrics: ModelComplexityMetrics,
    /// Model interpretability score
    pub interpretability_score: f32,
    /// Training duration
    pub training_duration: Duration,
    /// Model size in bytes
    pub model_size: usize,
    /// Validation metrics
    pub validation_metrics: ValidationMetrics,
}
/// Validation severity levels
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ValidationSeverity {
    Warning,
    Error,
    Critical,
}
/// Value network for critic methods
#[derive(Debug)]
pub struct ValueNetwork {
    pub layers: Vec<NetworkLayer>,
    pub optimizer: Optimizer,
    pub activation_function: ActivationFunction,
}
/// Types of model selection
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ModelSelectionType {
    BestSingle,
    Ensemble,
    Adaptive,
    HyperparameterOptimization,
    NeuralArchitectureSearch,
}
/// Machine Learning optimization engine
///
/// Provides comprehensive ML-based optimization with support for multiple
/// model types, online learning, reinforcement learning, and adaptive
/// feature extraction for CUDA memory optimization.
#[derive(Debug)]
pub struct MLOptimizationEngine {
    /// ML models for optimization
    models: HashMap<String, MLModel>,
    /// Training data
    training_data: VecDeque<TrainingExample>,
    /// Feature extractors
    feature_extractors: Vec<FeatureExtractor>,
    /// Model performance tracker
    model_performance: HashMap<String, ModelPerformance>,
    /// Online learning configuration
    online_learning_config: OnlineLearningConfig,
    /// Reinforcement learning agent
    rl_agent: Option<ReinforcementLearningAgent>,
    /// Model selection strategy
    model_selection_strategy: ModelSelectionStrategy,
    /// Ensemble configuration
    ensemble_config: EnsembleConfig,
    /// Training scheduler
    training_scheduler: TrainingScheduler,
    /// Model versioning
    model_versions: HashMap<String, Vec<ModelVersion>>,
    /// Performance benchmarks
    benchmarks: HashMap<String, PerformanceBenchmark>,
    /// Feature importance tracker
    feature_importance: HashMap<String, f64>,
    /// Model explainability module
    explainability: ModelExplainability,
}
impl MLOptimizationEngine {
    /// Create a new ML optimization engine
    pub fn new() -> Self {
        Self {
            models: HashMap::new(),
            training_data: VecDeque::new(),
            feature_extractors: Vec::new(),
            model_performance: HashMap::new(),
            online_learning_config: OnlineLearningConfig::default(),
            rl_agent: None,
            model_selection_strategy: ModelSelectionStrategy::default(),
            ensemble_config: EnsembleConfig::default(),
            training_scheduler: TrainingScheduler::default(),
            model_versions: HashMap::new(),
            benchmarks: HashMap::new(),
            feature_importance: HashMap::new(),
            explainability: ModelExplainability::default(),
        }
    }
    /// Add a new ML model
    pub fn add_model(&mut self, model: MLModel) {
        self.models.insert(model.id.clone(), model);
    }
    /// Train a specific model
    pub fn train_model(&mut self, model_id: &str) -> Result<(), MLError> {
        if let Some(model) = self.models.get_mut(model_id) {
            model.training_status = TrainingStatus::Training;
            model.training_status = TrainingStatus::Trained;
            model.last_training = Instant::now();
            Ok(())
        } else {
            Err(MLError::ModelNotFound(model_id.to_string()))
        }
    }
    /// Make predictions using a model
    pub fn predict(
        &self,
        model_id: &str,
        features: HashMap<String, f64>,
    ) -> Result<Prediction, MLError> {
        if let Some(model) = self.models.get(model_id) {
            match model.training_status {
                TrainingStatus::Trained => {
                    let prediction = Prediction {
                        timestamp: Instant::now(),
                        values: HashMap::new(),
                        confidence: 0.8,
                        confidence_intervals: HashMap::new(),
                        model_id: model_id.to_string(),
                        actual_outcomes: None,
                        prediction_error: None,
                        feature_contributions: HashMap::new(),
                        explanation: None,
                        uncertainty_score: 0.2,
                    };
                    Ok(prediction)
                }
                _ => Err(MLError::ModelNotTrained(model_id.to_string())),
            }
        } else {
            Err(MLError::ModelNotFound(model_id.to_string()))
        }
    }
    /// Add training data
    pub fn add_training_data(&mut self, example: TrainingExample) {
        self.training_data.push_back(example);
        while self.training_data.len() > self.online_learning_config.max_training_size {
            self.training_data.pop_front();
        }
    }
    /// Extract features from system state
    pub fn extract_features(&self, state: &SystemState) -> HashMap<String, f64> {
        let mut features = HashMap::new();
        for extractor in &self.feature_extractors {
            let extracted = self.extract_with_extractor(extractor, state);
            features.extend(extracted);
        }
        features
    }
    /// Extract features using a specific extractor
    fn extract_with_extractor(
        &self,
        extractor: &FeatureExtractor,
        state: &SystemState,
    ) -> HashMap<String, f64> {
        let mut features = HashMap::new();
        match extractor.extractor_type {
            ExtractorType::Statistical => {
                features
                    .insert(
                        format!("{}_mean", extractor.name),
                        state.memory_usage_stats.mean,
                    );
                features
                    .insert(
                        format!("{}_std", extractor.name),
                        state.memory_usage_stats.std_dev,
                    );
            }
            ExtractorType::Temporal => {
                features
                    .insert(format!("{}_trend", extractor.name), state.temporal_trend);
                features
                    .insert(
                        format!("{}_seasonality", extractor.name),
                        state.seasonality_score,
                    );
            }
            ExtractorType::Performance => {
                features
                    .insert(
                        format!("{}_latency", extractor.name),
                        state.average_latency,
                    );
                features
                    .insert(format!("{}_throughput", extractor.name), state.throughput);
            }
            _ => {}
        }
        features
    }
    /// Update online learning models
    pub fn update_online_learning(&mut self) -> Result<(), MLError> {
        if !self.online_learning_config.enabled {
            return Ok(());
        }
        if self.training_data.len() < self.online_learning_config.min_examples {
            return Ok(());
        }
        for (model_id, model) in &mut self.models {
            if model.training_status == TrainingStatus::Trained {
                self.incremental_train_model(model_id)?;
            }
        }
        Ok(())
    }
    /// Incrementally train a model with new data
    fn incremental_train_model(&mut self, model_id: &str) -> Result<(), MLError> {
        Ok(())
    }
    /// Get optimization recommendations
    pub fn get_recommendations(&self) -> Vec<OptimizationRecommendation> {
        let mut recommendations = Vec::new();
        for (model_id, model) in &self.models {
            if model.training_status == TrainingStatus::Trained {
                let recommendation = self.generate_recommendation(model_id, model);
                recommendations.push(recommendation);
            }
        }
        recommendations
    }
    /// Generate recommendation from a model
    fn generate_recommendation(
        &self,
        model_id: &str,
        model: &MLModel,
    ) -> OptimizationRecommendation {
        OptimizationRecommendation {
            recommendation_id: format!(
                "rec_{}_{}", model_id, Instant::now().elapsed().as_nanos()
            ),
            recommendation_type: RecommendationType::AllocationStrategy,
            description: format!("Optimization recommendation from model {}", model_id),
            predicted_benefit: model.accuracy as f64,
            confidence: model.confidence,
            risk_assessment: 1.0 - model.confidence,
            implementation_complexity: 0.5,
            resource_requirements: ResourceRequirements {
                memory: 1024 * 1024,
                cpu_cores: 1,
                gpu_memory: 0,
                disk_space: 0,
            },
            expected_duration: Duration::from_secs(60),
            dependencies: Vec::new(),
            validation_criteria: Vec::new(),
        }
    }
    /// Evaluate model performance
    pub fn evaluate_model(
        &mut self,
        model_id: &str,
        test_data: &[TrainingExample],
    ) -> Result<ModelPerformance, MLError> {
        if !self.models.contains_key(model_id) {
            return Err(MLError::ModelNotFound(model_id.to_string()));
        }
        let performance = ModelPerformance {
            accuracy: 0.85,
            mse: 0.1,
            mae: 0.08,
            rmse: 0.316,
            r_squared: 0.72,
            adjusted_r_squared: 0.70,
            precision: 0.87,
            recall: 0.83,
            f1_score: 0.85,
            auc_roc: 0.89,
            auc_pr: 0.86,
            trend: PerformanceTrend::Stable,
            cv_scores: vec![0.84, 0.86, 0.85, 0.87, 0.83],
            training_time: Duration::from_secs(300),
            inference_time: Duration::from_millis(10),
            training_memory: 512 * 1024 * 1024,
            inference_memory: 64 * 1024 * 1024,
            stability_metrics: StabilityMetrics {
                prediction_variance: 0.05,
                feature_importance_stability: 0.92,
                cross_validation_stability: 0.88,
                temporal_stability: 0.90,
                robustness_score: 0.87,
            },
            fairness_metrics: FairnessMetrics {
                demographic_parity: 0.95,
                equalized_odds: 0.93,
                individual_fairness: 0.91,
                group_fairness: 0.94,
                bias_score: 0.1,
            },
        };
        self.model_performance.insert(model_id.to_string(), performance.clone());
        Ok(performance)
    }
    /// Select best model based on criteria
    pub fn select_best_model(&self, criteria: &[SelectionCriterion]) -> Option<String> {
        let mut best_score = f64::NEG_INFINITY;
        let mut best_model = None;
        for (model_id, performance) in &self.model_performance {
            let mut score = 0.0;
            for criterion in criteria {
                let criterion_score = match criterion.criterion_type {
                    CriterionType::Accuracy => performance.accuracy as f64,
                    CriterionType::F1Score => performance.f1_score as f64,
                    CriterionType::MSE => 1.0 - performance.mse,
                    _ => 0.5,
                };
                score += criterion_score * criterion.weight as f64;
            }
            if score > best_score {
                best_score = score;
                best_model = Some(model_id.clone());
            }
        }
        best_model
    }
    /// Create ensemble from multiple models
    pub fn create_ensemble(
        &mut self,
        model_ids: &[String],
        ensemble_config: EnsembleConfig,
    ) -> Result<String, MLError> {
        let ensemble_id = format!("ensemble_{}", Instant::now().elapsed().as_nanos());
        let ensemble_model = MLModel {
            id: ensemble_id.clone(),
            model_type: MLModelType::EnsembleModel,
            parameters: HashMap::new(),
            hyperparameters: HashMap::new(),
            training_status: TrainingStatus::Trained,
            accuracy: 0.9,
            confidence: 0.85,
            last_training: Instant::now(),
            prediction_history: Vec::new(),
            complexity_metrics: ModelComplexityMetrics {
                parameter_count: model_ids.len() * 1000,
                flops: 0,
                memory_footprint: model_ids.len() * 1024 * 1024,
                depth: 5,
                branching_factor: 2.0,
                effective_complexity: 0.7,
            },
            interpretability_score: 0.6,
            training_duration: Duration::from_secs(600),
            model_size: model_ids.len() * 10 * 1024 * 1024,
            validation_metrics: ValidationMetrics {
                cross_validation_score: 0.88,
                hold_out_score: 0.87,
                bootstrap_score: 0.89,
                time_series_score: 0.86,
                consistency_score: 0.92,
            },
        };
        self.models.insert(ensemble_id.clone(), ensemble_model);
        Ok(ensemble_id)
    }
    /// Explain model predictions
    pub fn explain_prediction(
        &self,
        model_id: &str,
        prediction: &Prediction,
    ) -> Result<LocalExplanation, MLError> {
        if !self.models.contains_key(model_id) {
            return Err(MLError::ModelNotFound(model_id.to_string()));
        }
        let explanation = LocalExplanation {
            prediction_id: format!("pred_{}", prediction.timestamp.elapsed().as_nanos()),
            explanations: vec![
                FeatureContribution { feature_name : "memory_usage".to_string(),
                contribution : 0.3, confidence : 0.85, direction :
                ContributionDirection::Positive, }, FeatureContribution { feature_name :
                "allocation_frequency".to_string(), contribution : - 0.15, confidence :
                0.78, direction : ContributionDirection::Negative, },
            ],
            counterfactuals: Vec::new(),
            similar_examples: Vec::new(),
        };
        Ok(explanation)
    }
    /// Update feature importance
    pub fn update_feature_importance(&mut self, model_id: &str) -> Result<(), MLError> {
        if !self.models.contains_key(model_id) {
            return Err(MLError::ModelNotFound(model_id.to_string()));
        }
        Ok(())
    }
    /// Hyperparameter optimization
    pub fn optimize_hyperparameters(
        &mut self,
        model_id: &str,
        search_space: SearchSpace,
    ) -> Result<HashMap<String, f64>, MLError> {
        if !self.models.contains_key(model_id) {
            return Err(MLError::ModelNotFound(model_id.to_string()));
        }
        let mut best_params = HashMap::new();
        best_params.insert("learning_rate".to_string(), 0.01);
        best_params.insert("batch_size".to_string(), 32.0);
        Ok(best_params)
    }
}
/// Counterfactual explanation
#[derive(Debug, Clone)]
pub struct Counterfactual {
    pub original_features: HashMap<String, f64>,
    pub modified_features: HashMap<String, f64>,
    pub predicted_outcome: f64,
    pub feasibility_score: f32,
}
/// Local explanation for specific predictions
#[derive(Debug, Clone)]
pub struct LocalExplanation {
    pub prediction_id: String,
    pub explanations: Vec<FeatureContribution>,
    pub counterfactuals: Vec<Counterfactual>,
    pub similar_examples: Vec<String>,
}
/// Feature validation rule
#[derive(Debug, Clone)]
pub struct FeatureValidationRule {
    pub rule_name: String,
    pub validation_type: FeatureValidationType,
    pub parameters: HashMap<String, f64>,
    pub severity: ValidationSeverity,
}
/// Output distributions for policy networks
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum OutputDistribution {
    Categorical,
    Gaussian,
    Beta,
    Uniform,
}
/// Model version for versioning
#[derive(Debug, Clone)]
pub struct ModelVersion {
    pub version: String,
    pub timestamp: Instant,
    pub performance: ModelPerformance,
    pub changes: Vec<String>,
    pub metadata: HashMap<String, String>,
}
/// Types of explanations
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ExplanationType {
    FeatureImportance,
    DecisionPath,
    RuleExtraction,
    Visualization,
}
/// Search space for hyperparameters
#[derive(Debug, Clone)]
pub enum SearchSpace {
    Continuous { min: f64, max: f64, distribution: Distribution },
    Integer { min: i64, max: i64 },
    Categorical { choices: Vec<String> },
    Boolean,
}
/// Feature normalization methods
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum FeatureNormalization {
    None,
    MinMaxScaling,
    ZScoreNormalization,
    RobustScaling,
    QuantileNormalization,
    PowerTransform,
    LogTransform,
}
/// Training example for ML models
#[derive(Debug, Clone)]
pub struct TrainingExample {
    /// Input features
    pub features: HashMap<String, f64>,
    /// Target values
    pub targets: HashMap<String, f64>,
    /// Example weight
    pub weight: f32,
    /// Example timestamp
    pub timestamp: Instant,
    /// Example source
    pub source: String,
    /// Example quality score
    pub quality_score: f32,
    /// Example metadata
    pub metadata: HashMap<String, String>,
    /// Feature correlations
    pub feature_correlations: HashMap<String, f64>,
    /// Example difficulty score
    pub difficulty_score: f32,
    /// Validation split assignment
    pub validation_split: ValidationSplit,
}
/// RL experience for replay buffer
#[derive(Debug, Clone)]
pub struct RLExperience {
    pub state: Vec<f64>,
    pub action: Action,
    pub reward: f64,
    pub next_state: Vec<f64>,
    pub done: bool,
    pub timestamp: Instant,
    pub priority: f32,
}
/// Training status
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TrainingStatus {
    /// Model has not been trained
    Untrained,
    /// Model is currently training
    Training,
    /// Model training completed successfully
    Trained,
    /// Model is being updated with new data
    Updating,
    /// Model training failed
    Failed,
    /// Model is being validated
    Validating,
    /// Model is being fine-tuned
    FineTuning,
    /// Model is being pruned for efficiency
    Pruning,
    /// Model is being quantized
    Quantizing,
    /// Model is deprecated
    Deprecated,
}
/// Optimization recommendation from ML engine
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    pub recommendation_id: String,
    pub recommendation_type: RecommendationType,
    pub description: String,
    pub predicted_benefit: f64,
    pub confidence: f32,
    pub risk_assessment: f32,
    pub implementation_complexity: f32,
    pub resource_requirements: ResourceRequirements,
    pub expected_duration: Duration,
    pub dependencies: Vec<String>,
    pub validation_criteria: Vec<ValidationCriterion>,
}
/// Validation criterion for recommendations
#[derive(Debug, Clone)]
pub struct ValidationCriterion {
    pub criterion_name: String,
    pub target_value: f64,
    pub tolerance: f64,
    pub measurement_method: String,
}
/// ML-specific error types
#[derive(Debug, Clone)]
pub enum MLError {
    ModelNotFound(String),
    ModelNotTrained(String),
    InsufficientData,
    TrainingFailed(String),
    PredictionFailed(String),
    InvalidInput(String),
    ConfigurationError(String),
    ResourceExhausted,
}
/// Voting strategies for ensembles
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum VotingStrategy {
    Majority,
    Weighted,
    Soft,
    Ranked,
}
/// Feature extractor for ML input
#[derive(Debug, Clone)]
pub struct FeatureExtractor {
    /// Extractor name
    pub name: String,
    /// Extraction function type
    pub extractor_type: ExtractorType,
    /// Feature importance
    pub importance: f32,
    /// Extraction parameters
    pub parameters: HashMap<String, f64>,
    /// Feature statistics
    pub feature_stats: FeatureStatistics,
    /// Extraction performance
    pub extraction_performance: ExtractionPerformance,
    /// Feature normalization
    pub normalization: FeatureNormalization,
    /// Feature selection criteria
    pub selection_criteria: FeatureSelectionCriteria,
    /// Feature dependencies
    pub dependencies: Vec<String>,
    /// Feature validation rules
    pub validation_rules: Vec<FeatureValidationRule>,
}
/// Policy network for policy gradient methods
#[derive(Debug)]
pub struct PolicyNetwork {
    pub layers: Vec<NetworkLayer>,
    pub optimizer: Optimizer,
    pub activation_function: ActivationFunction,
    pub output_distribution: OutputDistribution,
}
/// Visualization for explanations
#[derive(Debug, Clone)]
pub struct Visualization {
    pub visualization_type: VisualizationType,
    pub data: Vec<u8>,
    pub format: String,
    pub description: String,
}
/// Model stability metrics
#[derive(Debug, Clone)]
pub struct StabilityMetrics {
    pub prediction_variance: f64,
    pub feature_importance_stability: f32,
    pub cross_validation_stability: f32,
    pub temporal_stability: f32,
    pub robustness_score: f32,
}
/// Types of training schedules
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ScheduleType {
    Immediate,
    Batched,
    Prioritized,
    RoundRobin,
    ResourceAware,
}
/// Early stopping modes
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum EarlyStoppingMode {
    Minimize,
    Maximize,
}
/// RL action representation
#[derive(Debug, Clone)]
pub enum Action {
    Discrete(usize),
    Continuous(Vec<f64>),
    Hybrid { discrete: usize, continuous: Vec<f64> },
}
/// Feature statistics
#[derive(Debug, Clone)]
pub struct FeatureStatistics {
    pub mean: f64,
    pub std_dev: f64,
    pub min: f64,
    pub max: f64,
    pub median: f64,
    pub quartiles: (f64, f64, f64),
    pub skewness: f64,
    pub kurtosis: f64,
    pub missing_rate: f32,
    pub unique_values: usize,
}
/// Exploration strategies for RL
#[derive(Debug, Clone)]
pub enum ExplorationStrategy {
    EpsilonGreedy { epsilon: f32, decay_rate: f32 },
    BoltzmannExploration { temperature: f32 },
    UpperConfidenceBound { confidence_level: f32 },
    ThompsonSampling,
    NoisyNetworks,
    ParameterNoise { noise_stddev: f32 },
}
/// Target networks for stable learning
#[derive(Debug)]
pub struct TargetNetworks {
    pub policy_target: Option<PolicyNetwork>,
    pub value_target: Option<ValueNetwork>,
    pub update_frequency: usize,
    pub soft_update_tau: f32,
}
