quantrs2-device 0.1.3

//! Machine Learning Integration and Optimization Configuration

use std::collections::HashMap;
use std::time::Duration;

/// Machine learning optimization configuration
#[derive(Debug, Clone)]
pub struct MLOptimizationConfig {
    /// Enable ML-driven optimization
    pub enable_ml_optimization: bool,
    /// ML models to use
    pub ml_models: Vec<MLModelType>,
    /// Training configuration
    pub training_config: MLTrainingConfig,
    /// Feature engineering settings
    pub feature_engineering: FeatureEngineeringConfig,
    /// Online learning settings
    pub online_learning: OnlineLearningConfig,
    /// Transfer learning settings
    pub transfer_learning: TransferLearningConfig,
}

/// Types of ML models
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum MLModelType {
    NeuralNetwork,
    RandomForest,
    SupportVectorMachine,
    GradientBoosting,
    BayesianOptimization,
    ReinforcementLearning,
    GaussianProcess,
    EnsembleModel,
}

/// ML training configuration
#[derive(Debug, Clone, Default)]
pub struct MLTrainingConfig {
    /// Training data requirements
    pub training_data: TrainingDataConfig,
    /// Model hyperparameters
    pub hyperparameters: ModelHyperparameters,
    /// Training optimization
    pub optimization: TrainingOptimizationConfig,
    /// Regularization settings
    pub regularization: RegularizationConfig,
    /// Early stopping configuration
    pub early_stopping: EarlyStoppingConfig,
    /// Cross-validation settings
    pub cross_validation: CrossValidationConfig,
}

/// Training data configuration
#[derive(Debug, Clone)]
pub struct TrainingDataConfig {
    /// Minimum training samples
    pub min_training_samples: usize,
    /// Data collection strategy
    pub data_collection_strategy: DataCollectionStrategy,
    /// Data preprocessing settings
    pub preprocessing: DataPreprocessingConfig,
    /// Data augmentation settings
    pub augmentation: DataAugmentationConfig,
}

/// Data collection strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DataCollectionStrategy {
    Passive,
    Active,
    Adaptive,
    Balanced,
    Targeted,
}

/// Data preprocessing configuration
#[derive(Debug, Clone)]
pub struct DataPreprocessingConfig {
    /// Normalization method
    pub normalization: NormalizationMethod,
    /// Outlier handling
    pub outlier_handling: OutlierHandling,
    /// Missing value strategy
    pub missing_value_strategy: MissingValueStrategy,
    /// Data validation rules
    pub validation_rules: Vec<DataValidationRule>,
}

/// Normalization methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum NormalizationMethod {
    MinMax,
    ZScore,
    Robust,
    Quantile,
    None,
}

/// Outlier handling strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum OutlierHandling {
    Remove,
    Cap,
    Transform,
    Ignore,
}

/// Missing value handling strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum MissingValueStrategy {
    Remove,
    Impute,
    Interpolate,
    Forward,
    Backward,
}

/// Data validation rules
#[derive(Debug, Clone)]
pub struct DataValidationRule {
    /// Rule name
    pub name: String,
    /// Rule condition
    pub condition: ValidationCondition,
    /// Action on failure
    pub failure_action: ValidationFailureAction,
}

/// Validation conditions
#[derive(Debug, Clone, PartialEq)]
pub enum ValidationCondition {
    RangeCheck(f64, f64),
    NotNull,
    UniqueValues,
    Custom(String),
}

/// Actions to take on validation failure
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ValidationFailureAction {
    Reject,
    Warn,
    Transform,
    Ignore,
}

/// Data augmentation configuration
#[derive(Debug, Clone)]
pub struct DataAugmentationConfig {
    /// Enable data augmentation
    pub enable_augmentation: bool,
    /// Augmentation techniques
    pub techniques: Vec<AugmentationTechnique>,
    /// Augmentation ratio
    pub augmentation_ratio: f64,
}

/// Data augmentation techniques
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum AugmentationTechnique {
    NoiseInjection,
    Rotation,
    Scaling,
    Permutation,
    Interpolation,
    Synthetic,
}

/// Model hyperparameters
#[derive(Debug, Clone)]
pub struct ModelHyperparameters {
    /// Learning rate
    pub learning_rate: f64,
    /// Batch size
    pub batch_size: usize,
    /// Number of epochs
    pub epochs: usize,
    /// Model-specific parameters
    pub model_specific: HashMap<String, f64>,
    /// Hyperparameter optimization
    pub optimization: HyperparameterOptimization,
}

/// Hyperparameter optimization configuration
#[derive(Debug, Clone)]
pub struct HyperparameterOptimization {
    /// Enable hyperparameter optimization
    pub enable_optimization: bool,
    /// Optimization strategy
    pub strategy: HyperparameterStrategy,
    /// Search space definition
    pub search_space: SearchSpaceConfig,
    /// Optimization budget
    pub optimization_budget: OptimizationBudget,
}

/// Hyperparameter optimization strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum HyperparameterStrategy {
    GridSearch,
    RandomSearch,
    BayesianOptimization,
    GeneticAlgorithm,
    HalvingSearch,
}

/// Search space configuration
#[derive(Debug, Clone, Default)]
pub struct SearchSpaceConfig {
    /// Parameter ranges
    pub parameter_ranges: HashMap<String, ParameterRange>,
    /// Categorical parameters
    pub categorical_parameters: HashMap<String, Vec<String>>,
    /// Constraints between parameters
    pub constraints: Vec<ParameterConstraint>,
}

/// Parameter range definition
#[derive(Debug, Clone)]
pub struct ParameterRange {
    /// Minimum value
    pub min: f64,
    /// Maximum value
    pub max: f64,
    /// Step size (for discrete parameters)
    pub step: Option<f64>,
    /// Distribution type
    pub distribution: ParameterDistribution,
}

/// Parameter distributions
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ParameterDistribution {
    Uniform,
    LogUniform,
    Normal,
    LogNormal,
}

/// Constraints between parameters
#[derive(Debug, Clone)]
pub struct ParameterConstraint {
    /// Constraint name
    pub name: String,
    /// Constraint expression
    pub expression: String,
    /// Constraint type
    pub constraint_type: ConstraintType,
}

/// Types of parameter constraints
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ConstraintType {
    Equality,
    Inequality,
    Conditional,
}

/// Optimization budget configuration
#[derive(Debug, Clone)]
pub struct OptimizationBudget {
    /// Maximum evaluations
    pub max_evaluations: usize,
    /// Maximum time
    pub max_time: Duration,
    /// Early stopping criteria
    pub early_stopping: EarlyStoppingCriteria,
}

/// Early stopping criteria for hyperparameter optimization
#[derive(Debug, Clone)]
pub struct EarlyStoppingCriteria {
    /// Patience (evaluations without improvement)
    pub patience: usize,
    /// Minimum improvement threshold
    pub min_improvement: f64,
    /// Improvement metric
    pub improvement_metric: String,
}

/// Training optimization configuration
#[derive(Debug, Clone)]
pub struct TrainingOptimizationConfig {
    /// Optimizer type
    pub optimizer: OptimizerType,
    /// Learning rate scheduling
    pub lr_scheduling: LearningRateScheduling,
    /// Gradient clipping
    pub gradient_clipping: GradientClippingConfig,
    /// Loss function configuration
    pub loss_function: LossFunctionConfig,
}

/// Types of optimizers
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum OptimizerType {
    SGD,
    Adam,
    RMSprop,
    AdaGrad,
    LBFGS,
}

/// Learning rate scheduling configuration
#[derive(Debug, Clone)]
pub struct LearningRateScheduling {
    /// Enable learning rate scheduling
    pub enable_scheduling: bool,
    /// Scheduling strategy
    pub strategy: LRSchedulingStrategy,
    /// Schedule parameters
    pub parameters: HashMap<String, f64>,
}

/// Learning rate scheduling strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum LRSchedulingStrategy {
    StepDecay,
    ExponentialDecay,
    CosineAnnealing,
    ReduceOnPlateau,
    Cyclical,
}

/// Gradient clipping configuration
#[derive(Debug, Clone)]
pub struct GradientClippingConfig {
    /// Enable gradient clipping
    pub enable_clipping: bool,
    /// Clipping method
    pub method: ClippingMethod,
    /// Clipping threshold
    pub threshold: f64,
}

/// Gradient clipping methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ClippingMethod {
    Norm,
    Value,
    Global,
}

/// Loss function configuration
#[derive(Debug, Clone)]
pub struct LossFunctionConfig {
    /// Primary loss function
    pub primary_loss: LossFunction,
    /// Auxiliary losses
    pub auxiliary_losses: Vec<AuxiliaryLoss>,
    /// Loss weighting scheme
    pub weighting_scheme: LossWeightingScheme,
}

/// Loss function types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum LossFunction {
    MeanSquaredError,
    MeanAbsoluteError,
    Huber,
    CrossEntropy,
    FocalLoss,
    Custom(String),
}

/// Auxiliary loss functions
#[derive(Debug, Clone)]
pub struct AuxiliaryLoss {
    /// Loss function
    pub loss_function: LossFunction,
    /// Weight in total loss
    pub weight: f64,
    /// Application scope
    pub scope: String,
}

/// Loss weighting schemes
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum LossWeightingScheme {
    Static,
    Dynamic,
    Adaptive,
    Uncertainty,
}

/// Regularization configuration
#[derive(Debug, Clone)]
pub struct RegularizationConfig {
    /// L1 regularization strength
    pub l1_strength: f64,
    /// L2 regularization strength
    pub l2_strength: f64,
    /// Dropout rate
    pub dropout_rate: f64,
    /// Batch normalization
    pub batch_normalization: bool,
    /// Additional regularization techniques
    pub additional_techniques: Vec<RegularizationTechnique>,
}

/// Additional regularization techniques
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum RegularizationTechnique {
    Dropout,
    BatchNorm,
    LayerNorm,
    WeightDecay,
    EarlyStop,
    DataAugmentation,
}

/// Early stopping configuration
#[derive(Debug, Clone)]
pub struct EarlyStoppingConfig {
    /// Enable early stopping
    pub enable_early_stopping: bool,
    /// Patience (epochs without improvement)
    pub patience: usize,
    /// Minimum improvement delta
    pub min_delta: f64,
    /// Metric to monitor
    pub monitor_metric: String,
    /// Improvement direction
    pub improvement_direction: ImprovementDirection,
}

/// Direction of improvement for monitored metric
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ImprovementDirection {
    Maximize,
    Minimize,
}

/// Cross-validation configuration
#[derive(Debug, Clone)]
pub struct CrossValidationConfig {
    /// Enable cross-validation
    pub enable_cv: bool,
    /// Number of folds
    pub folds: usize,
    /// Cross-validation strategy
    pub strategy: CVStrategy,
    /// Stratification settings
    pub stratification: StratificationConfig,
}

/// Cross-validation strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum CVStrategy {
    KFold,
    StratifiedKFold,
    TimeSeriesSplit,
    GroupKFold,
    LeaveOneOut,
}

/// Stratification configuration
#[derive(Debug, Clone)]
pub struct StratificationConfig {
    /// Enable stratification
    pub enable_stratification: bool,
    /// Stratification variable
    pub stratification_variable: String,
    /// Balance strategy
    pub balance_strategy: BalanceStrategy,
}

/// Strategies for balancing stratified samples
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum BalanceStrategy {
    None,
    Oversample,
    Undersample,
    SMOTE,
    Adaptive,
}

/// Feature engineering configuration
#[derive(Debug, Clone)]
pub struct FeatureEngineeringConfig {
    /// Enable automatic feature engineering
    pub automatic_feature_engineering: bool,
    /// Feature selection methods
    pub feature_selection: Vec<FeatureSelectionMethod>,
    /// Feature scaling method
    pub feature_scaling: FeatureScalingMethod,
    /// Dimensionality reduction
    pub dimensionality_reduction: DimensionalityReductionConfig,
    /// Feature interaction detection
    pub interaction_detection: InteractionDetectionConfig,
}

/// Feature selection methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum FeatureSelectionMethod {
    VarianceThreshold,
    UnivariateSelection,
    RecursiveFeatureElimination,
    FeatureImportance,
    LassoRegularization,
    MutualInformation,
}

/// Feature scaling methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum FeatureScalingMethod {
    StandardScaler,
    MinMaxScaler,
    RobustScaler,
    Normalizer,
    QuantileTransformer,
    PowerTransformer,
}

/// Dimensionality reduction configuration
#[derive(Debug, Clone)]
pub struct DimensionalityReductionConfig {
    /// Enable dimensionality reduction
    pub enable_reduction: bool,
    /// Reduction methods
    pub methods: Vec<DimensionalityReductionMethod>,
    /// Target dimensionality
    pub target_dimensions: Option<usize>,
    /// Variance explained threshold
    pub variance_threshold: f64,
}

/// Dimensionality reduction methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DimensionalityReductionMethod {
    PCA,
    ICA,
    LDA,
    TSNE,
    UMAP,
    FactorAnalysis,
}

/// Feature interaction detection configuration
#[derive(Debug, Clone)]
pub struct InteractionDetectionConfig {
    /// Enable interaction detection
    pub enable_detection: bool,
    /// Detection methods
    pub methods: Vec<InteractionDetectionMethod>,
    /// Interaction order (2-way, 3-way, etc.)
    pub interaction_order: usize,
    /// Significance threshold
    pub significance_threshold: f64,
}

/// Feature interaction detection methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum InteractionDetectionMethod {
    Correlation,
    MutualInformation,
    ANOVA,
    TreeBased,
    Statistical,
}

/// Online learning configuration
#[derive(Debug, Clone)]
pub struct OnlineLearningConfig {
    /// Enable online learning
    pub enable_online_learning: bool,
    /// Learning rate adaptation
    pub learning_rate_adaptation: AdaptiveLearningRate,
    /// Model update frequency
    pub update_frequency: UpdateFrequency,
    /// Concept drift detection
    pub concept_drift: ConceptDriftConfig,
    /// Memory management
    pub memory_management: MemoryManagementConfig,
}

/// Adaptive learning rate configuration
#[derive(Debug, Clone)]
pub struct AdaptiveLearningRate {
    /// Initial learning rate
    pub initial_rate: f64,
    /// Adaptation strategy
    pub adaptation_strategy: LRAdaptationStrategy,
    /// Adaptation parameters
    pub parameters: HashMap<String, f64>,
}

/// Learning rate adaptation strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum LRAdaptationStrategy {
    Constant,
    InverseScaling,
    Adaptive,
    Performance,
}

/// Model update frequency configuration
#[derive(Debug, Clone)]
pub struct UpdateFrequency {
    /// Update trigger
    pub trigger: UpdateTrigger,
    /// Minimum update interval
    pub min_interval: Duration,
    /// Maximum update interval
    pub max_interval: Duration,
}

/// Triggers for model updates
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum UpdateTrigger {
    TimeInterval,
    DataVolume,
    PerformanceDrift,
    Manual,
    Adaptive,
}

/// Concept drift detection configuration
#[derive(Debug, Clone)]
pub struct ConceptDriftConfig {
    /// Enable drift detection
    pub enable_detection: bool,
    /// Detection methods
    pub detection_methods: Vec<DriftDetectionMethod>,
    /// Detection sensitivity
    pub sensitivity: f64,
    /// Response strategy
    pub response_strategy: DriftResponseStrategy,
}

/// Concept drift detection methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DriftDetectionMethod {
    StatisticalTest,
    PerformanceMonitoring,
    DistributionComparison,
    EnsembleBased,
}

/// Response strategies for concept drift
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DriftResponseStrategy {
    Retrain,
    Adapt,
    EnsembleUpdate,
    ModelSwitch,
}

/// Memory management for online learning
#[derive(Debug, Clone)]
pub struct MemoryManagementConfig {
    /// Memory window size
    pub window_size: usize,
    /// Forgetting factor
    pub forgetting_factor: f64,
    /// Memory strategy
    pub strategy: MemoryStrategy,
}

/// Memory management strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum MemoryStrategy {
    FixedWindow,
    SlidingWindow,
    FadingMemory,
    Adaptive,
}

/// Transfer learning configuration
#[derive(Debug, Clone)]
pub struct TransferLearningConfig {
    /// Enable transfer learning
    pub enable_transfer_learning: bool,
    /// Transfer strategies
    pub transfer_strategies: Vec<TransferStrategy>,
    /// Source domain configuration
    pub source_domain: SourceDomainConfig,
    /// Domain adaptation
    pub domain_adaptation: DomainAdaptationConfig,
}

/// Transfer learning strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum TransferStrategy {
    FeatureExtraction,
    FineTuning,
    DomainAdaptation,
    TaskSpecificLayers,
    MetaLearning,
}

/// Source domain configuration
#[derive(Debug, Clone)]
pub struct SourceDomainConfig {
    /// Source domain identifier
    pub domain_id: String,
    /// Similarity metrics
    pub similarity_metrics: Vec<SimilarityMetric>,
    /// Transfer eligibility criteria
    pub eligibility_criteria: EligibilityCriteria,
}

/// Similarity metrics for domain comparison
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum SimilarityMetric {
    Statistical,
    Distributional,
    Performance,
    Structural,
}

/// Criteria for transfer learning eligibility
#[derive(Debug, Clone)]
pub struct EligibilityCriteria {
    /// Minimum similarity threshold
    pub min_similarity: f64,
    /// Performance requirements
    pub performance_requirements: PerformanceRequirements,
    /// Data requirements
    pub data_requirements: DataRequirements,
}

/// Performance requirements for transfer learning
#[derive(Debug, Clone)]
pub struct PerformanceRequirements {
    /// Minimum source model accuracy
    pub min_source_accuracy: f64,
    /// Expected transfer benefit
    pub expected_benefit: f64,
}

/// Data requirements for transfer learning
#[derive(Debug, Clone)]
pub struct DataRequirements {
    /// Minimum source data size
    pub min_source_size: usize,
    /// Minimum target data size
    pub min_target_size: usize,
    /// Data quality requirements
    pub quality_requirements: DataQualityRequirements,
}

/// Data quality requirements
#[derive(Debug, Clone)]
pub struct DataQualityRequirements {
    /// Minimum data completeness
    pub min_completeness: f64,
    /// Maximum noise level
    pub max_noise_level: f64,
    /// Consistency requirements
    pub consistency_requirements: Vec<String>,
}

/// Domain adaptation configuration
#[derive(Debug, Clone)]
pub struct DomainAdaptationConfig {
    /// Adaptation methods
    pub methods: Vec<DomainAdaptationMethod>,
    /// Adaptation strength
    pub adaptation_strength: f64,
    /// Validation strategy
    pub validation_strategy: AdaptationValidationStrategy,
}

/// Domain adaptation methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DomainAdaptationMethod {
    FeatureAlignment,
    DistributionMatching,
    AdversarialTraining,
    CorrectionModels,
}

/// Validation strategies for domain adaptation
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum AdaptationValidationStrategy {
    TargetValidation,
    SourceValidation,
    CombinedValidation,
    UnsupervisedMetrics,
}

// Default implementations for the main configuration types

impl Default for MLOptimizationConfig {
    fn default() -> Self {
        Self {
            enable_ml_optimization: true,
            ml_models: vec![
                MLModelType::NeuralNetwork,
                MLModelType::BayesianOptimization,
            ],
            training_config: MLTrainingConfig::default(),
            feature_engineering: FeatureEngineeringConfig::default(),
            online_learning: OnlineLearningConfig::default(),
            transfer_learning: TransferLearningConfig::default(),
        }
    }
}

impl Default for FeatureEngineeringConfig {
    fn default() -> Self {
        Self {
            automatic_feature_engineering: true,
            feature_selection: vec![
                FeatureSelectionMethod::VarianceThreshold,
                FeatureSelectionMethod::FeatureImportance,
            ],
            feature_scaling: FeatureScalingMethod::StandardScaler,
            dimensionality_reduction: DimensionalityReductionConfig::default(),
            interaction_detection: InteractionDetectionConfig::default(),
        }
    }
}

impl Default for OnlineLearningConfig {
    fn default() -> Self {
        Self {
            enable_online_learning: true,
            learning_rate_adaptation: AdaptiveLearningRate::default(),
            update_frequency: UpdateFrequency::default(),
            concept_drift: ConceptDriftConfig::default(),
            memory_management: MemoryManagementConfig::default(),
        }
    }
}

impl Default for TransferLearningConfig {
    fn default() -> Self {
        Self {
            enable_transfer_learning: true,
            transfer_strategies: vec![TransferStrategy::FeatureExtraction],
            source_domain: SourceDomainConfig::default(),
            domain_adaptation: DomainAdaptationConfig::default(),
        }
    }
}

// Additional default implementations for supporting types would go here
// (truncated for brevity)

impl Default for TrainingDataConfig {
    fn default() -> Self {
        Self {
            min_training_samples: 1000,
            data_collection_strategy: DataCollectionStrategy::Adaptive,
            preprocessing: DataPreprocessingConfig::default(),
            augmentation: DataAugmentationConfig::default(),
        }
    }
}

impl Default for DataPreprocessingConfig {
    fn default() -> Self {
        Self {
            normalization: NormalizationMethod::ZScore,
            outlier_handling: OutlierHandling::Cap,
            missing_value_strategy: MissingValueStrategy::Impute,
            validation_rules: vec![],
        }
    }
}

impl Default for DataAugmentationConfig {
    fn default() -> Self {
        Self {
            enable_augmentation: false,
            techniques: vec![],
            augmentation_ratio: 0.1,
        }
    }
}

impl Default for ModelHyperparameters {
    fn default() -> Self {
        Self {
            learning_rate: 0.001,
            batch_size: 32,
            epochs: 100,
            model_specific: HashMap::new(),
            optimization: HyperparameterOptimization::default(),
        }
    }
}

impl Default for HyperparameterOptimization {
    fn default() -> Self {
        Self {
            enable_optimization: false,
            strategy: HyperparameterStrategy::RandomSearch,
            search_space: SearchSpaceConfig::default(),
            optimization_budget: OptimizationBudget::default(),
        }
    }
}

impl Default for OptimizationBudget {
    fn default() -> Self {
        Self {
            max_evaluations: 100,
            max_time: Duration::from_secs(3600),
            early_stopping: EarlyStoppingCriteria::default(),
        }
    }
}

impl Default for EarlyStoppingCriteria {
    fn default() -> Self {
        Self {
            patience: 20,
            min_improvement: 0.001,
            improvement_metric: "validation_loss".to_string(),
        }
    }
}

impl Default for TrainingOptimizationConfig {
    fn default() -> Self {
        Self {
            optimizer: OptimizerType::Adam,
            lr_scheduling: LearningRateScheduling::default(),
            gradient_clipping: GradientClippingConfig::default(),
            loss_function: LossFunctionConfig::default(),
        }
    }
}

impl Default for LearningRateScheduling {
    fn default() -> Self {
        Self {
            enable_scheduling: false,
            strategy: LRSchedulingStrategy::ReduceOnPlateau,
            parameters: HashMap::new(),
        }
    }
}

impl Default for GradientClippingConfig {
    fn default() -> Self {
        Self {
            enable_clipping: true,
            method: ClippingMethod::Norm,
            threshold: 1.0,
        }
    }
}

impl Default for LossFunctionConfig {
    fn default() -> Self {
        Self {
            primary_loss: LossFunction::MeanSquaredError,
            auxiliary_losses: vec![],
            weighting_scheme: LossWeightingScheme::Static,
        }
    }
}

impl Default for RegularizationConfig {
    fn default() -> Self {
        Self {
            l1_strength: 0.0,
            l2_strength: 0.001,
            dropout_rate: 0.1,
            batch_normalization: true,
            additional_techniques: vec![],
        }
    }
}

impl Default for EarlyStoppingConfig {
    fn default() -> Self {
        Self {
            enable_early_stopping: true,
            patience: 10,
            min_delta: 0.001,
            monitor_metric: "validation_loss".to_string(),
            improvement_direction: ImprovementDirection::Minimize,
        }
    }
}

impl Default for CrossValidationConfig {
    fn default() -> Self {
        Self {
            enable_cv: true,
            folds: 5,
            strategy: CVStrategy::KFold,
            stratification: StratificationConfig::default(),
        }
    }
}

impl Default for StratificationConfig {
    fn default() -> Self {
        Self {
            enable_stratification: false,
            stratification_variable: String::new(),
            balance_strategy: BalanceStrategy::None,
        }
    }
}

impl Default for DimensionalityReductionConfig {
    fn default() -> Self {
        Self {
            enable_reduction: false,
            methods: vec![DimensionalityReductionMethod::PCA],
            target_dimensions: None,
            variance_threshold: 0.95,
        }
    }
}

impl Default for InteractionDetectionConfig {
    fn default() -> Self {
        Self {
            enable_detection: false,
            methods: vec![InteractionDetectionMethod::Correlation],
            interaction_order: 2,
            significance_threshold: 0.05,
        }
    }
}

impl Default for AdaptiveLearningRate {
    fn default() -> Self {
        Self {
            initial_rate: 0.001,
            adaptation_strategy: LRAdaptationStrategy::Adaptive,
            parameters: HashMap::new(),
        }
    }
}

impl Default for UpdateFrequency {
    fn default() -> Self {
        Self {
            trigger: UpdateTrigger::TimeInterval,
            min_interval: Duration::from_secs(300),
            max_interval: Duration::from_secs(3600),
        }
    }
}

impl Default for ConceptDriftConfig {
    fn default() -> Self {
        Self {
            enable_detection: true,
            detection_methods: vec![DriftDetectionMethod::PerformanceMonitoring],
            sensitivity: 0.05,
            response_strategy: DriftResponseStrategy::Adapt,
        }
    }
}

impl Default for MemoryManagementConfig {
    fn default() -> Self {
        Self {
            window_size: 10000,
            forgetting_factor: 0.99,
            strategy: MemoryStrategy::SlidingWindow,
        }
    }
}

impl Default for SourceDomainConfig {
    fn default() -> Self {
        Self {
            domain_id: String::new(),
            similarity_metrics: vec![SimilarityMetric::Statistical],
            eligibility_criteria: EligibilityCriteria::default(),
        }
    }
}

impl Default for EligibilityCriteria {
    fn default() -> Self {
        Self {
            min_similarity: 0.7,
            performance_requirements: PerformanceRequirements::default(),
            data_requirements: DataRequirements::default(),
        }
    }
}

impl Default for PerformanceRequirements {
    fn default() -> Self {
        Self {
            min_source_accuracy: 0.8,
            expected_benefit: 0.1,
        }
    }
}

impl Default for DataRequirements {
    fn default() -> Self {
        Self {
            min_source_size: 1000,
            min_target_size: 100,
            quality_requirements: DataQualityRequirements::default(),
        }
    }
}

impl Default for DataQualityRequirements {
    fn default() -> Self {
        Self {
            min_completeness: 0.9,
            max_noise_level: 0.1,
            consistency_requirements: vec![],
        }
    }
}

impl Default for DomainAdaptationConfig {
    fn default() -> Self {
        Self {
            methods: vec![DomainAdaptationMethod::FeatureAlignment],
            adaptation_strength: 0.5,
            validation_strategy: AdaptationValidationStrategy::CombinedValidation,
        }
    }
}