quantrs2-device 0.1.3

//! Configuration types for advanced crosstalk mitigation

use std::collections::HashMap;
use std::time::{Duration, SystemTime};
use serde::{Deserialize, Serialize};

use crate::crosstalk::CrosstalkConfig;

/// Advanced crosstalk mitigation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdvancedCrosstalkConfig {
    /// Base crosstalk configuration
    pub base_config: CrosstalkConfig,
    /// Machine learning configuration
    pub ml_config: CrosstalkMLConfig,
    /// Real-time adaptation configuration
    pub realtime_config: RealtimeMitigationConfig,
    /// Predictive modeling configuration
    pub prediction_config: CrosstalkPredictionConfig,
    /// Advanced signal processing configuration
    pub signal_processing_config: SignalProcessingConfig,
    /// Adaptive compensation configuration
    pub adaptive_compensation_config: AdaptiveCompensationConfig,
    /// Multi-level mitigation configuration
    pub multilevel_mitigation_config: MultilevelMitigationConfig,
}

/// Machine learning configuration for crosstalk analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CrosstalkMLConfig {
    /// Enable ML-based crosstalk prediction
    pub enable_prediction: bool,
    /// Enable clustering of crosstalk patterns
    pub enable_clustering: bool,
    /// Enable anomaly detection
    pub enable_anomaly_detection: bool,
    /// ML models to use
    pub model_types: Vec<CrosstalkMLModel>,
    /// Feature engineering configuration
    pub feature_config: CrosstalkFeatureConfig,
    /// Training configuration
    pub training_config: CrosstalkTrainingConfig,
    /// Model selection configuration
    pub model_selection_config: ModelSelectionConfig,
}

/// ML models for crosstalk analysis
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum CrosstalkMLModel {
    LinearRegression,
    RandomForest { n_estimators: usize, max_depth: Option<usize> },
    GradientBoosting { n_estimators: usize, learning_rate: f64 },
    SupportVectorMachine { kernel: String, c: f64 },
    NeuralNetwork { hidden_layers: Vec<usize>, activation: String },
    GaussianProcess { kernel: String, alpha: f64 },
    TimeSeriesForecaster { model_type: String, window_size: usize },
}

/// Feature engineering for crosstalk analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CrosstalkFeatureConfig {
    /// Enable temporal features
    pub enable_temporal_features: bool,
    /// Enable spectral features
    pub enable_spectral_features: bool,
    /// Enable spatial features
    pub enable_spatial_features: bool,
    /// Enable statistical features
    pub enable_statistical_features: bool,
    /// Window size for temporal features
    pub temporal_window_size: usize,
    /// Number of frequency bins for spectral features
    pub spectral_bins: usize,
    /// Spatial neighborhood size
    pub spatial_neighborhood: usize,
    /// Feature selection method
    pub feature_selection: FeatureSelectionMethod,
}

/// Feature selection methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum FeatureSelectionMethod {
    None,
    VarianceThreshold { threshold: f64 },
    UnivariateSelection { k: usize },
    RecursiveFeatureElimination { n_features: usize },
    LassoSelection { alpha: f64 },
    MutualInformation { k: usize },
}

/// Training configuration for crosstalk models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CrosstalkTrainingConfig {
    /// Training data split ratio
    pub train_test_split: f64,
    /// Cross-validation folds
    pub cv_folds: usize,
    /// Random seed for reproducibility
    pub random_state: Option<u64>,
    /// Early stopping configuration
    pub early_stopping: EarlyStoppingConfig,
    /// Data augmentation configuration
    pub data_augmentation: DataAugmentationConfig,
    /// Online learning configuration
    pub online_learning: OnlineLearningConfig,
}

/// Early stopping configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EarlyStoppingConfig {
    /// Enable early stopping
    pub enable: bool,
    /// Patience (epochs without improvement)
    pub patience: usize,
    /// Minimum improvement threshold
    pub min_delta: f64,
    /// Metric to monitor
    pub monitor_metric: String,
}

/// Data augmentation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataAugmentationConfig {
    /// Enable data augmentation
    pub enable: bool,
    /// Noise injection level
    pub noise_level: f64,
    /// Time warping factor
    pub time_warping: f64,
    /// Frequency shifting range
    pub frequency_shift_range: f64,
    /// Augmentation ratio
    pub augmentation_ratio: f64,
}

/// Online learning configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OnlineLearningConfig {
    /// Enable online learning
    pub enable: bool,
    /// Learning rate for online updates
    pub learning_rate: f64,
    /// Forgetting factor for old data
    pub forgetting_factor: f64,
    /// Batch size for mini-batch updates
    pub batch_size: usize,
    /// Update frequency
    pub update_frequency: Duration,
}

/// Model selection configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelSelectionConfig {
    /// Enable automatic model selection
    pub enable_auto_selection: bool,
    /// Ensemble method
    pub ensemble_method: EnsembleMethod,
    /// Hyperparameter optimization
    pub hyperparameter_optimization: HyperparameterOptimization,
    /// Model validation strategy
    pub validation_strategy: ValidationStrategy,
}

/// Ensemble methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum EnsembleMethod {
    None,
    Voting { strategy: String },
    Bagging { n_estimators: usize },
    Boosting { algorithm: String },
    Stacking { meta_learner: String },
}

/// Hyperparameter optimization methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum HyperparameterOptimization {
    GridSearch,
    RandomSearch { n_iter: usize },
    BayesianOptimization { n_calls: usize },
    GeneticAlgorithm { population_size: usize, generations: usize },
}

/// Validation strategies
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ValidationStrategy {
    HoldOut { test_size: f64 },
    KFold { n_splits: usize },
    StratifiedKFold { n_splits: usize },
    TimeSeriesSplit { n_splits: usize },
    LeaveOneOut,
}

/// Real-time mitigation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RealtimeMitigationConfig {
    /// Enable real-time mitigation
    pub enable_realtime: bool,
    /// Monitoring interval
    pub monitoring_interval: Duration,
    /// Adaptation threshold
    pub adaptation_threshold: f64,
    /// Maximum adaptation rate (per second)
    pub max_adaptation_rate: f64,
    /// Feedback control configuration
    pub feedback_control: FeedbackControlConfig,
    /// Alert configuration
    pub alert_config: AlertConfig,
    /// Performance tracking
    pub performance_tracking: PerformanceTrackingConfig,
}

/// Feedback control configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeedbackControlConfig {
    /// Controller type
    pub controller_type: ControllerType,
    /// Control parameters
    pub control_params: ControlParameters,
    /// Stability analysis
    pub stability_analysis: StabilityAnalysisConfig,
}

/// Controller types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ControllerType {
    PID { kp: f64, ki: f64, kd: f64 },
    LQR { q_matrix: Vec<f64>, r_matrix: Vec<f64> },
    MPC { horizon: usize, constraints: Vec<String> },
    AdaptiveControl { adaptation_rate: f64 },
    RobustControl { uncertainty_bounds: f64 },
}

/// Control parameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ControlParameters {
    /// Setpoint tracking accuracy
    pub tracking_accuracy: f64,
    /// Disturbance rejection capability
    pub disturbance_rejection: f64,
    /// Control effort limits
    pub effort_limits: (f64, f64),
    /// Response time requirements
    pub response_time: Duration,
}

/// Stability analysis configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StabilityAnalysisConfig {
    /// Enable stability monitoring
    pub enable_monitoring: bool,
    /// Stability margins
    pub stability_margins: StabilityMargins,
    /// Robustness analysis
    pub robustness_analysis: bool,
}

/// Stability margins
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StabilityMargins {
    /// Gain margin (dB)
    pub gain_margin: f64,
    /// Phase margin (degrees)
    pub phase_margin: f64,
    /// Delay margin (seconds)
    pub delay_margin: f64,
}

/// Alert configuration for crosstalk monitoring
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlertConfig {
    /// Enable alerts
    pub enable_alerts: bool,
    /// Alert thresholds
    pub thresholds: AlertThresholds,
    /// Notification channels
    pub notification_channels: Vec<NotificationChannel>,
    /// Alert escalation
    pub escalation: AlertEscalation,
}

/// Alert thresholds
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlertThresholds {
    /// Crosstalk strength threshold
    pub crosstalk_threshold: f64,
    /// Prediction error threshold
    pub prediction_error_threshold: f64,
    /// Mitigation failure threshold
    pub mitigation_failure_threshold: f64,
    /// System instability threshold
    pub instability_threshold: f64,
}

/// Notification channels
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum NotificationChannel {
    Log { level: String },
    Email { recipients: Vec<String> },
    Slack { webhook_url: String, channel: String },
    Database { table: String },
    WebSocket { endpoint: String },
}

/// Alert escalation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlertEscalation {
    /// Enable escalation
    pub enable_escalation: bool,
    /// Escalation levels
    pub escalation_levels: Vec<EscalationLevel>,
    /// Time to escalate
    pub escalation_time: Duration,
}

/// Escalation level
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EscalationLevel {
    /// Level name
    pub level: String,
    /// Severity threshold
    pub severity_threshold: f64,
    /// Actions to take
    pub actions: Vec<String>,
    /// Notification channels for this level
    pub channels: Vec<NotificationChannel>,
}

/// Performance tracking configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceTrackingConfig {
    /// Enable performance tracking
    pub enable_tracking: bool,
    /// Metrics to track
    pub tracked_metrics: Vec<String>,
    /// Historical data retention
    pub data_retention: Duration,
    /// Performance analysis interval
    pub analysis_interval: Duration,
}

/// Crosstalk prediction configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CrosstalkPredictionConfig {
    /// Enable predictive modeling
    pub enable_prediction: bool,
    /// Prediction horizon
    pub prediction_horizon: Duration,
    /// Prediction interval
    pub prediction_interval: Duration,
    /// Uncertainty quantification
    pub uncertainty_quantification: UncertaintyQuantificationConfig,
    /// Time series modeling
    pub time_series_config: TimeSeriesConfig,
}

/// Uncertainty quantification configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UncertaintyQuantificationConfig {
    /// Enable uncertainty quantification
    pub enable: bool,
    /// Confidence levels to compute
    pub confidence_levels: Vec<f64>,
    /// Uncertainty estimation method
    pub estimation_method: UncertaintyEstimationMethod,
    /// Monte Carlo samples
    pub monte_carlo_samples: usize,
}

/// Uncertainty estimation methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum UncertaintyEstimationMethod {
    Bootstrap { n_bootstrap: usize },
    Bayesian { prior_type: String },
    Ensemble { n_models: usize },
    DropoutMonteCarlo { dropout_rate: f64, n_samples: usize },
}

/// Time series configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimeSeriesConfig {
    /// Model type
    pub model_type: TimeSeriesModel,
    /// Seasonality configuration
    pub seasonality: SeasonalityConfig,
    /// Trend analysis
    pub trend_analysis: TrendAnalysisConfig,
    /// Changepoint detection
    pub changepoint_detection: ChangepointDetectionConfig,
}

/// Time series models
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum TimeSeriesModel {
    ARIMA { p: usize, d: usize, q: usize },
    ExponentialSmoothing { trend: String, seasonal: String },
    Prophet { growth: String, seasonality_mode: String },
    LSTM { hidden_size: usize, num_layers: usize },
    Transformer { d_model: usize, n_heads: usize, n_layers: usize },
}

/// Seasonality configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SeasonalityConfig {
    /// Enable seasonality detection
    pub enable_detection: bool,
    /// Seasonal periods to test
    pub periods: Vec<usize>,
    /// Seasonal strength threshold
    pub strength_threshold: f64,
}

/// Trend analysis configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrendAnalysisConfig {
    /// Enable trend analysis
    pub enable_analysis: bool,
    /// Trend detection method
    pub detection_method: TrendDetectionMethod,
    /// Significance threshold
    pub significance_threshold: f64,
}

/// Trend detection methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum TrendDetectionMethod {
    MannKendall,
    LinearRegression,
    TheilSen,
    LOWESS { frac: f64 },
}

/// Changepoint detection configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChangepointDetectionConfig {
    /// Enable changepoint detection
    pub enable_detection: bool,
    /// Detection method
    pub detection_method: ChangepointDetectionMethod,
    /// Minimum segment length
    pub min_segment_length: usize,
    /// Detection threshold
    pub detection_threshold: f64,
}

/// Changepoint detection methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ChangepointDetectionMethod {
    PELT { penalty: f64 },
    BinarySegmentation { max_changepoints: usize },
    WindowBased { window_size: usize },
    BayesianChangepoint { prior_prob: f64 },
}

/// Signal processing configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SignalProcessingConfig {
    /// Enable advanced signal processing
    pub enable_advanced_processing: bool,
    /// Filtering configuration
    pub filtering_config: FilteringConfig,
    /// Spectral analysis configuration
    pub spectral_config: SpectralAnalysisConfig,
    /// Time-frequency analysis configuration
    pub timefreq_config: TimeFrequencyConfig,
    /// Wavelet analysis configuration
    pub wavelet_config: WaveletConfig,
}

/// Filtering configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FilteringConfig {
    /// Enable adaptive filtering
    pub enable_adaptive: bool,
    /// Filter types to use
    pub filter_types: Vec<FilterType>,
    /// Filter parameters
    pub filter_params: FilterParameters,
    /// Noise reduction configuration
    pub noise_reduction: NoiseReductionConfig,
}

/// Filter types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum FilterType {
    Butterworth { order: usize, cutoff: f64 },
    Chebyshev1 { order: usize, rp: f64, cutoff: f64 },
    Chebyshev2 { order: usize, rs: f64, cutoff: f64 },
    Elliptic { order: usize, rp: f64, rs: f64, cutoff: f64 },
    Kalman { process_noise: f64, measurement_noise: f64 },
    Wiener { noise_estimate: f64 },
}

/// Filter parameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FilterParameters {
    /// Sampling frequency
    pub sampling_frequency: f64,
    /// Passband frequencies
    pub passband: (f64, f64),
    /// Stopband frequencies
    pub stopband: (f64, f64),
    /// Passband ripple (dB)
    pub passband_ripple: f64,
    /// Stopband attenuation (dB)
    pub stopband_attenuation: f64,
}

/// Noise reduction configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NoiseReductionConfig {
    /// Enable noise reduction
    pub enable: bool,
    /// Noise reduction method
    pub method: NoiseReductionMethod,
    /// Noise level estimation
    pub noise_estimation: NoiseEstimationMethod,
}

/// Noise reduction methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum NoiseReductionMethod {
    SpectralSubtraction { over_subtraction_factor: f64 },
    WienerFiltering { noise_estimate: f64 },
    WaveletDenoising { wavelet: String, threshold_method: String },
    AdaptiveFiltering { step_size: f64, filter_length: usize },
}

/// Noise estimation methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum NoiseEstimationMethod {
    VoiceActivityDetection,
    MinimumStatistics,
    MCRA { alpha: f64 },
    IMCRA { alpha_s: f64, alpha_d: f64 },
}

/// Spectral analysis configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SpectralAnalysisConfig {
    /// Window function
    pub window_function: WindowFunction,
    /// FFT size
    pub fft_size: usize,
    /// Overlap percentage
    pub overlap: f64,
    /// Zero padding factor
    pub zero_padding: usize,
    /// Spectral estimation method
    pub estimation_method: SpectralEstimationMethod,
}

/// Window functions
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum WindowFunction {
    Rectangular,
    Hanning,
    Hamming,
    Blackman,
    Kaiser { beta: f64 },
    Tukey { alpha: f64 },
}

/// Spectral estimation methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum SpectralEstimationMethod {
    Periodogram,
    Welch { nperseg: usize, noverlap: usize },
    Bartlett,
    Multitaper { nw: f64, k: usize },
}

/// Time-frequency analysis configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimeFrequencyConfig {
    /// Enable time-frequency analysis
    pub enable: bool,
    /// STFT configuration
    pub stft_config: STFTConfig,
    /// Continuous wavelet transform configuration
    pub cwt_config: CWTConfig,
    /// Hilbert-Huang transform configuration
    pub hht_config: HHTConfig,
}

/// Short-time Fourier transform configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct STFTConfig {
    /// Window size
    pub window_size: usize,
    /// Hop size
    pub hop_size: usize,
    /// Window function
    pub window_function: WindowFunction,
    /// Zero padding
    pub zero_padding: usize,
}

/// Continuous wavelet transform configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CWTConfig {
    /// Wavelet type
    pub wavelet_type: WaveletType,
    /// Scales
    pub scales: Vec<f64>,
    /// Sampling period
    pub sampling_period: f64,
}

/// Wavelet types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum WaveletType {
    Morlet { omega: f64 },
    MexicanHat,
    Daubechies { order: usize },
    Biorthogonal { nr: usize, nd: usize },
    Coiflets { order: usize },
}

/// Hilbert-Huang transform configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HHTConfig {
    /// EMD configuration
    pub emd_config: EMDConfig,
    /// Instantaneous frequency method
    pub if_method: InstantaneousFrequencyMethod,
}

/// Empirical mode decomposition configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EMDConfig {
    /// Maximum number of IMFs
    pub max_imfs: usize,
    /// Stopping criterion
    pub stopping_criterion: f64,
    /// Ensemble EMD
    pub ensemble_emd: bool,
    /// Noise standard deviation (for EEMD)
    pub noise_std: f64,
}

/// Instantaneous frequency methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum InstantaneousFrequencyMethod {
    HilbertTransform,
    TeagerKaiser,
    DirectQuadrature,
}

/// Wavelet analysis configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WaveletConfig {
    /// Enable wavelet analysis
    pub enable: bool,
    /// Wavelet decomposition levels
    pub decomposition_levels: usize,
    /// Wavelet type
    pub wavelet_type: WaveletType,
    /// Boundary conditions
    pub boundary_condition: BoundaryCondition,
    /// Thresholding configuration
    pub thresholding: WaveletThresholdingConfig,
}

/// Boundary conditions for wavelet analysis
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum BoundaryCondition {
    Zero,
    Symmetric,
    Periodic,
    Constant,
}

/// Wavelet thresholding configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WaveletThresholdingConfig {
    /// Thresholding method
    pub method: ThresholdingMethod,
    /// Threshold selection
    pub threshold_selection: ThresholdSelection,
    /// Threshold value (if manual)
    pub threshold_value: Option<f64>,
}

/// Thresholding methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ThresholdingMethod {
    Soft,
    Hard,
    Greater,
    Less,
}

/// Threshold selection methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ThresholdSelection {
    Manual,
    SURE,
    Minimax,
    BayesThresh,
}

/// Adaptive compensation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdaptiveCompensationConfig {
    /// Enable adaptive compensation
    pub enable_adaptive: bool,
    /// Compensation algorithms
    pub compensation_algorithms: Vec<CompensationAlgorithm>,
    /// Learning configuration
    pub learning_config: CompensationLearningConfig,
    /// Performance optimization
    pub optimization_config: CompensationOptimizationConfig,
}

/// Compensation algorithms
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum CompensationAlgorithm {
    LinearCompensation { gain_matrix: Vec<f64> },
    NonlinearCompensation { polynomial_order: usize },
    NeuralNetworkCompensation { architecture: Vec<usize> },
    AdaptiveFilterCompensation { filter_type: String, order: usize },
    FeedforwardCompensation { delay: f64 },
    FeedbackCompensation { controller: ControllerType },
}

/// Compensation learning configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompensationLearningConfig {
    /// Learning algorithm
    pub algorithm: LearningAlgorithm,
    /// Learning rate
    pub learning_rate: f64,
    /// Forgetting factor
    pub forgetting_factor: f64,
    /// Convergence criterion
    pub convergence_criterion: f64,
    /// Maximum iterations
    pub max_iterations: usize,
}

/// Learning algorithms for compensation
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum LearningAlgorithm {
    LMS { step_size: f64 },
    RLS { forgetting_factor: f64 },
    GradientDescent { momentum: f64 },
    Adam { beta1: f64, beta2: f64, epsilon: f64 },
    KalmanFilter { process_noise: f64, measurement_noise: f64 },
}

/// Compensation optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompensationOptimizationConfig {
    /// Optimization objective
    pub objective: OptimizationObjective,
    /// Constraints
    pub constraints: Vec<OptimizationConstraint>,
    /// Optimization algorithm
    pub algorithm: OptimizationAlgorithm,
    /// Convergence tolerance
    pub tolerance: f64,
}

/// Optimization objectives
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum OptimizationObjective {
    MinimizeCrosstalk,
    MaximizeFidelity,
    MinimizeEnergy,
    MaximizeRobustness,
    MultiObjective { weights: Vec<f64> },
}

/// Optimization constraints
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationConstraint {
    /// Constraint type
    pub constraint_type: ConstraintType,
    /// Constraint value
    pub value: f64,
    /// Tolerance
    pub tolerance: f64,
}

/// Constraint types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ConstraintType {
    MaxCrosstalk,
    MinFidelity,
    MaxEnergy,
    MaxCompensationEffort,
    StabilityMargin,
}

/// Optimization algorithms
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum OptimizationAlgorithm {
    GradientDescent,
    ConjugateGradient,
    BFGS,
    ParticleSwarm,
    GeneticAlgorithm,
    DifferentialEvolution,
    SimulatedAnnealing,
    BayesianOptimization,
}

/// Multi-level mitigation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MultilevelMitigationConfig {
    /// Enable multi-level mitigation
    pub enable_multilevel: bool,
    /// Mitigation levels
    pub mitigation_levels: Vec<MitigationLevel>,
    /// Level selection strategy
    pub level_selection: LevelSelectionStrategy,
    /// Coordination strategy
    pub coordination_strategy: CoordinationStrategy,
}

/// Mitigation level configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MitigationLevel {
    /// Level name
    pub name: String,
    /// Priority (lower number = higher priority)
    pub priority: usize,
    /// Mitigation strategies for this level
    pub strategies: Vec<String>,
    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
    /// Performance targets
    pub performance_targets: PerformanceTargets,
}

/// Resource requirements for mitigation level
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceRequirements {
    /// Computational complexity
    pub computational_complexity: f64,
    /// Memory requirements (MB)
    pub memory_mb: usize,
    /// Real-time constraints
    pub realtime_constraints: Duration,
    /// Hardware requirements
    pub hardware_requirements: Vec<String>,
}

/// Performance targets for mitigation level
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceTargets {
    /// Target crosstalk reduction (0-1)
    pub crosstalk_reduction: f64,
    /// Target fidelity improvement (0-1)
    pub fidelity_improvement: f64,
    /// Maximum allowed latency
    pub max_latency: Duration,
    /// Target reliability (0-1)
    pub reliability: f64,
}

/// Level selection strategies
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum LevelSelectionStrategy {
    Priority,
    Dynamic { criteria: Vec<String> },
    Adaptive { selection_criteria: Vec<String> },
    RoundRobin,
    LoadBalanced,
}

/// Coordination strategies for multi-level mitigation
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum CoordinationStrategy {
    Sequential,
    Parallel,
    Hierarchical { control_hierarchy: Vec<String> },
    Adaptive { coordination_algorithm: String },
}

impl Default for AdvancedCrosstalkConfig {
    fn default() -> Self {
        Self {
            base_config: CrosstalkConfig::default(),
            ml_config: CrosstalkMLConfig {
                enable_prediction: true,
                enable_clustering: true,
                enable_anomaly_detection: true,
                model_types: vec![
                    CrosstalkMLModel::RandomForest { n_estimators: 100, max_depth: Some(10) },
                    CrosstalkMLModel::GradientBoosting { n_estimators: 50, learning_rate: 0.1 },
                    CrosstalkMLModel::NeuralNetwork {
                        hidden_layers: vec![64, 32, 16],
                        activation: "relu".to_string()
                    },
                ],
                feature_config: CrosstalkFeatureConfig {
                    enable_temporal_features: true,
                    enable_spectral_features: true,
                    enable_spatial_features: true,
                    enable_statistical_features: true,
                    temporal_window_size: 100,
                    spectral_bins: 256,
                    spatial_neighborhood: 3,
                    feature_selection: FeatureSelectionMethod::UnivariateSelection { k: 20 },
                },
                training_config: CrosstalkTrainingConfig {
                    train_test_split: 0.8,
                    cv_folds: 5,
                    random_state: Some(42),
                    early_stopping: EarlyStoppingConfig {
                        enable: true,
                        patience: 10,
                        min_delta: 0.001,
                        monitor_metric: "val_loss".to_string(),
                    },
                    data_augmentation: DataAugmentationConfig {
                        enable: true,
                        noise_level: 0.01,
                        time_warping: 0.1,
                        frequency_shift_range: 0.05,
                        augmentation_ratio: 0.2,
                    },
                    online_learning: OnlineLearningConfig {
                        enable: true,
                        learning_rate: 0.001,
                        forgetting_factor: 0.95,
                        batch_size: 32,
                        update_frequency: Duration::from_secs(60),
                    },
                },
                model_selection_config: ModelSelectionConfig {
                    enable_auto_selection: true,
                    ensemble_method: EnsembleMethod::Voting { strategy: "soft".to_string() },
                    hyperparameter_optimization: HyperparameterOptimization::BayesianOptimization { n_calls: 50 },
                    validation_strategy: ValidationStrategy::KFold { n_splits: 5 },
                },
            },
            realtime_config: RealtimeMitigationConfig {
                enable_realtime: true,
                monitoring_interval: Duration::from_millis(100),
                adaptation_threshold: 0.1,
                max_adaptation_rate: 10.0,
                feedback_control: FeedbackControlConfig {
                    controller_type: ControllerType::PID { kp: 1.0, ki: 0.1, kd: 0.01 },
                    control_params: ControlParameters {
                        tracking_accuracy: 0.95,
                        disturbance_rejection: 0.8,
                        effort_limits: (-1.0, 1.0),
                        response_time: Duration::from_millis(10),
                    },
                    stability_analysis: StabilityAnalysisConfig {
                        enable_monitoring: true,
                        stability_margins: StabilityMargins {
                            gain_margin: 6.0,
                            phase_margin: 45.0,
                            delay_margin: 0.001,
                        },
                        robustness_analysis: true,
                    },
                },
                alert_config: AlertConfig {
                    enable_alerts: true,
                    thresholds: AlertThresholds {
                        crosstalk_threshold: 0.1,
                        prediction_error_threshold: 0.05,
                        mitigation_failure_threshold: 0.2,
                        instability_threshold: 0.15,
                    },
                    notification_channels: vec![
                        NotificationChannel::Log { level: "WARN".to_string() },
                    ],
                    escalation: AlertEscalation {
                        enable_escalation: true,
                        escalation_levels: vec![
                            EscalationLevel {
                                level: "WARNING".to_string(),
                                severity_threshold: 0.1,
                                actions: vec!["alert".to_string()],
                                channels: vec![NotificationChannel::Log { level: "WARN".to_string() }],
                            },
                            EscalationLevel {
                                level: "CRITICAL".to_string(),
                                severity_threshold: 0.2,
                                actions: vec!["alert".to_string(), "compensate".to_string()],
                                channels: vec![NotificationChannel::Log { level: "ERROR".to_string() }],
                            },
                        ],
                        escalation_time: Duration::from_secs(30),
                    },
                },
                performance_tracking: PerformanceTrackingConfig {
                    enable_tracking: true,
                    tracked_metrics: vec![
                        "crosstalk_strength".to_string(),
                        "fidelity".to_string(),
                        "mitigation_effectiveness".to_string(),
                    ],
                    data_retention: Duration::from_secs(3600 * 24), // 24 hours
                    analysis_interval: Duration::from_secs(300), // 5 minutes
                },
            },
            prediction_config: CrosstalkPredictionConfig {
                enable_prediction: true,
                prediction_horizon: Duration::from_secs(600), // 10 minutes
                prediction_interval: Duration::from_secs(60),  // 1 minute
                uncertainty_quantification: UncertaintyQuantificationConfig {
                    enable: true,
                    confidence_levels: vec![0.68, 0.95, 0.99],
                    estimation_method: UncertaintyEstimationMethod::Bootstrap { n_bootstrap: 1000 },
                    monte_carlo_samples: 1000,
                },
                time_series_config: TimeSeriesConfig {
                    model_type: TimeSeriesModel::ARIMA { p: 2, d: 1, q: 2 },
                    seasonality: SeasonalityConfig {
                        enable_detection: true,
                        periods: vec![24, 168, 8760], // Hourly, daily, weekly, yearly patterns
                        strength_threshold: 0.1,
                    },
                    trend_analysis: TrendAnalysisConfig {
                        enable_analysis: true,
                        detection_method: TrendDetectionMethod::MannKendall,
                        significance_threshold: 0.05,
                    },
                    changepoint_detection: ChangepointDetectionConfig {
                        enable_detection: true,
                        detection_method: ChangepointDetectionMethod::PELT { penalty: 1.0 },
                        min_segment_length: 10,
                        detection_threshold: 0.01,
                    },
                },
            },
            signal_processing_config: SignalProcessingConfig {
                enable_advanced_processing: true,
                filtering_config: FilteringConfig {
                    enable_adaptive: true,
                    filter_types: vec![
                        FilterType::Butterworth { order: 4, cutoff: 0.1 },
                        FilterType::Kalman { process_noise: 0.01, measurement_noise: 0.1 },
                    ],
                    filter_params: FilterParameters {
                        sampling_frequency: 1e9, // 1 GHz
                        passband: (1e6, 100e6),   // 1-100 MHz
                        stopband: (0.5e6, 200e6), // 0.5-200 MHz
                        passband_ripple: 0.1,
                        stopband_attenuation: 60.0,
                    },
                    noise_reduction: NoiseReductionConfig {
                        enable: true,
                        method: NoiseReductionMethod::WienerFiltering { noise_estimate: 0.01 },
                        noise_estimation: NoiseEstimationMethod::MinimumStatistics,
                    },
                },
                spectral_config: SpectralAnalysisConfig {
                    window_function: WindowFunction::Hanning,
                    fft_size: 1024,
                    overlap: 0.5,
                    zero_padding: 2,
                    estimation_method: SpectralEstimationMethod::Welch { nperseg: 256, noverlap: 128 },
                },
                timefreq_config: TimeFrequencyConfig {
                    enable: true,
                    stft_config: STFTConfig {
                        window_size: 256,
                        hop_size: 64,
                        window_function: WindowFunction::Hanning,
                        zero_padding: 1,
                    },
                    cwt_config: CWTConfig {
                        wavelet_type: WaveletType::Morlet { omega: 6.0 },
                        scales: (1..100).map(|i| i as f64).collect(),
                        sampling_period: 1e-9, // 1 ns
                    },
                    hht_config: HHTConfig {
                        emd_config: EMDConfig {
                            max_imfs: 10,
                            stopping_criterion: 0.01,
                            ensemble_emd: true,
                            noise_std: 0.1,
                        },
                        if_method: InstantaneousFrequencyMethod::HilbertTransform,
                    },
                },
                wavelet_config: WaveletConfig {
                    enable: true,
                    decomposition_levels: 6,
                    wavelet_type: WaveletType::Daubechies { order: 4 },
                    boundary_condition: BoundaryCondition::Symmetric,
                    thresholding: WaveletThresholdingConfig {
                        method: ThresholdingMethod::Soft,
                        threshold_selection: ThresholdSelection::SURE,
                        threshold_value: None,
                    },
                },
            },
            adaptive_compensation_config: AdaptiveCompensationConfig {
                enable_adaptive: true,
                compensation_algorithms: vec![
                    CompensationAlgorithm::LinearCompensation { gain_matrix: vec![1.0; 16] },
                    CompensationAlgorithm::AdaptiveFilterCompensation {
                        filter_type: "LMS".to_string(),
                        order: 10
                    },
                ],
                learning_config: CompensationLearningConfig {
                    algorithm: LearningAlgorithm::Adam { beta1: 0.9, beta2: 0.999, epsilon: 1e-8 },
                    learning_rate: 0.001,
                    forgetting_factor: 0.99,
                    convergence_criterion: 1e-6,
                    max_iterations: 1000,
                },
                optimization_config: CompensationOptimizationConfig {
                    objective: OptimizationObjective::MinimizeCrosstalk,
                    constraints: vec![
                        OptimizationConstraint {
                            constraint_type: ConstraintType::MaxEnergy,
                            value: 1.0,
                            tolerance: 0.1,
                        },
                    ],
                    algorithm: OptimizationAlgorithm::BayesianOptimization,
                    tolerance: 1e-6,
                },
            },
            multilevel_mitigation_config: MultilevelMitigationConfig {
                enable_multilevel: true,
                mitigation_levels: vec![
                    MitigationLevel {
                        name: "Level1_Fast".to_string(),
                        priority: 1,
                        strategies: vec![],
                        resource_requirements: ResourceRequirements {
                            computational_complexity: 0.1,
                            memory_mb: 10,
                            realtime_constraints: Duration::from_millis(1),
                            hardware_requirements: vec!["CPU".to_string()],
                        },
                        performance_targets: PerformanceTargets {
                            crosstalk_reduction: 0.5,
                            fidelity_improvement: 0.1,
                            max_latency: Duration::from_millis(1),
                            reliability: 0.9,
                        },
                    },
                    MitigationLevel {
                        name: "Level2_Accurate".to_string(),
                        priority: 2,
                        strategies: vec![],
                        resource_requirements: ResourceRequirements {
                            computational_complexity: 1.0,
                            memory_mb: 100,
                            realtime_constraints: Duration::from_millis(10),
                            hardware_requirements: vec!["CPU".to_string(), "GPU".to_string()],
                        },
                        performance_targets: PerformanceTargets {
                            crosstalk_reduction: 0.8,
                            fidelity_improvement: 0.3,
                            max_latency: Duration::from_millis(10),
                            reliability: 0.95,
                        },
                    },
                ],
                level_selection: LevelSelectionStrategy::Adaptive {
                    selection_criteria: vec!["latency".to_string(), "accuracy".to_string()]
                },
                coordination_strategy: CoordinationStrategy::Hierarchical {
                    control_hierarchy: vec!["Level1_Fast".to_string(), "Level2_Accurate".to_string()]
                },
            },
        }
    }
}