scirs2-optimize 0.4.2

//! Advanced-Adaptive Streaming Optimization
//!
//! This module implements next-generation streaming optimization algorithms with:
//! - Multi-scale temporal adaptation
//! - Neuromorphic-inspired learning rules  
//! - Quantum-inspired variational updates
//! - Federated learning capabilities
//! - Self-organizing memory hierarchies
//! - Meta-learning for algorithm selection

use super::{
    utils, StreamingConfig, StreamingDataPoint, StreamingObjective, StreamingOptimizer,
    StreamingStats,
};
use crate::error::OptimizeError;
use scirs2_core::ndarray::s;
use scirs2_core::ndarray::{Array1, Array2}; // Unused import: ArrayView1
                                            // Unused import
                                            // use scirs2_core::error::CoreResult;
                                            // Unused import
                                            // use scirs2_core::simd_ops::SimdUnifiedOps;
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

type Result<T> = std::result::Result<T, OptimizeError>;

/// Advanced-advanced streaming optimizer with multiple adaptation mechanisms
#[derive(Debug, Clone)]
pub struct AdvancedAdaptiveStreamingOptimizer<T: StreamingObjective> {
    /// Current parameter estimates
    parameters: Array1<f64>,
    /// Objective function
    objective: T,
    /// Configuration
    config: StreamingConfig,
    /// Statistics
    stats: StreamingStats,
    /// Multi-scale temporal memory
    multi_scale_memory: MultiScaleTemporalMemory,
    /// Neuromorphic learning system
    neuromorphic_learner: NeuromorphicLearningSystem,
    /// Quantum-inspired variational optimizer
    quantum_variational: QuantumInspiredVariational,
    /// Meta-learning algorithm selector
    meta_learning_selector: MetaLearningSelector,
    /// Federated learning coordinator
    federated_coordinator: FederatedLearningCoordinator,
    /// Self-organizing memory hierarchy
    memory_hierarchy: SelfOrganizingMemoryHierarchy,
    /// Performance tracker
    performance_tracker: AdvancedPerformanceTracker,
}

/// Multi-scale temporal memory system
#[derive(Debug, Clone)]
struct MultiScaleTemporalMemory {
    /// Short-term memory (milliseconds)
    short_term: VecDeque<TemporalSnapshot>,
    /// Medium-term memory (seconds)
    medium_term: VecDeque<TemporalSnapshot>,
    /// Long-term memory (minutes/hours)
    long_term: VecDeque<TemporalSnapshot>,
    /// Very long-term memory (days)
    very_long_term: VecDeque<TemporalSnapshot>,
    /// Temporal scales
    time_scales: [Duration; 4],
    /// Adaptive consolidation weights
    consolidation_weights: Array1<f64>,
}

/// Temporal snapshot for memory systems
#[derive(Debug, Clone)]
struct TemporalSnapshot {
    /// Timestamp
    timestamp: Instant,
    /// Parameter state
    parameters: Array1<f64>,
    /// Performance metrics
    performance: f64,
    /// Gradient information
    gradient: Array1<f64>,
    /// Context embedding
    context: Array1<f64>,
    /// Confidence score
    confidence: f64,
}

/// Neuromorphic learning system with spike-based adaptation
#[derive(Debug, Clone)]
struct NeuromorphicLearningSystem {
    /// Spike trains for each parameter
    spike_trains: Vec<VecDeque<f64>>,
    /// Synaptic weights
    synaptic_weights: Array2<f64>,
    /// Membrane potentials
    membrane_potentials: Array1<f64>,
    /// Adaptation thresholds
    adaptation_thresholds: Array1<f64>,
    /// STDP learning rates
    stdp_rates: STDPRates,
    /// Homeostatic scaling
    homeostatic_scaling: Array1<f64>,
}

/// Spike-timing dependent plasticity rates
#[derive(Debug, Clone)]
struct STDPRates {
    /// Long-term potentiation rate
    ltp_rate: f64,
    /// Long-term depression rate
    ltd_rate: f64,
    /// Temporal window
    temporal_window: Duration,
    /// Exponential decay constant
    decay_constant: f64,
}

/// Quantum-inspired variational optimizer
#[derive(Debug, Clone)]
struct QuantumInspiredVariational {
    /// Quantum state representation
    quantum_state: Array1<f64>,
    /// Variational parameters
    variational_params: Array1<f64>,
    /// Entanglement matrix
    entanglement_matrix: Array2<f64>,
    /// Measurement operators
    measurement_operators: Vec<Array2<f64>>,
    /// Quantum noise model
    noise_model: QuantumNoiseModel,
    /// Coherence time
    coherence_time: Duration,
}

/// Quantum noise model
#[derive(Debug, Clone)]
struct QuantumNoiseModel {
    /// Decoherence rate
    decoherence_rate: f64,
    /// Thermal noise strength
    thermal_noise: f64,
    /// Gate error rate
    gate_error_rate: f64,
}

/// Meta-learning algorithm selector
#[derive(Debug, Clone)]
struct MetaLearningSelector {
    /// Available algorithms
    available_algorithms: Vec<OptimizationAlgorithm>,
    /// Performance history per algorithm
    algorithm_performance: HashMap<String, VecDeque<f64>>,
    /// Context features
    context_features: Array1<f64>,
    /// Selection network
    selection_network: NeuralSelector,
    /// Exploration factor
    exploration_factor: f64,
}

/// Available optimization algorithms
#[derive(Debug, Clone)]
enum OptimizationAlgorithm {
    AdaptiveGradientDescent,
    RecursiveLeastSquares,
    KalmanFilter,
    ParticleFilter,
    NeuromorphicSpikes,
    QuantumVariational,
    BayesianOptimization,
    EvolutionaryStrategy,
}

/// Neural network for algorithm selection
#[derive(Debug, Clone)]
struct NeuralSelector {
    /// Hidden layers
    layers: Vec<Array2<f64>>,
    /// Activations
    activations: Vec<Array1<f64>>,
    /// Learning rate
    learning_rate: f64,
}

/// Federated learning coordinator
#[derive(Debug, Clone)]
struct FederatedLearningCoordinator {
    /// Local model
    local_model: Array1<f64>,
    /// Global model aggregate
    global_model: Array1<f64>,
    /// Peer models
    peer_models: HashMap<String, Array1<f64>>,
    /// Communication budget
    communication_budget: usize,
    /// Differential privacy parameters
    privacy_params: DifferentialPrivacyParams,
    /// Consensus mechanism
    consensus_mechanism: ConsensusType,
}

/// Differential privacy parameters
#[derive(Debug, Clone)]
struct DifferentialPrivacyParams {
    /// Privacy budget epsilon
    epsilon: f64,
    /// Sensitivity delta
    delta: f64,
    /// Noise scale
    noise_scale: f64,
}

/// Consensus mechanism type
#[derive(Debug, Clone)]
enum ConsensusType {
    FederatedAveraging,
    ByzantineFaultTolerant,
    AsyncSGD,
    SecureAggregation,
}

/// Self-organizing memory hierarchy
#[derive(Debug, Clone)]
struct SelfOrganizingMemoryHierarchy {
    /// L1 cache (fastest access)
    l1_cache: HashMap<String, Array1<f64>>,
    /// L2 cache (medium access)
    l2_cache: HashMap<String, Array1<f64>>,
    /// L3 cache (slower access)
    l3_cache: HashMap<String, Array1<f64>>,
    /// Access frequency counters
    access_counters: HashMap<String, usize>,
    /// Replacement policy
    replacement_policy: ReplacementPolicy,
    /// Cache sizes
    cache_sizes: [usize; 3],
}

/// Cache replacement policy
#[derive(Debug, Clone)]
enum ReplacementPolicy {
    LRU,
    LFU,
    AdaptiveLRU,
    NeuralPredictive,
}

/// Advanced-advanced performance tracker
#[derive(Debug, Clone)]
struct AdvancedPerformanceTracker {
    /// Performance metrics history
    metrics_history: VecDeque<PerformanceSnapshot>,
    /// Anomaly detection system
    anomaly_detector: AnomalyDetectionSystem,
    /// Predictive performance model
    predictive_model: PredictivePerformanceModel,
    /// Real-time analytics
    realtime_analytics: RealtimeAnalytics,
}

/// Performance snapshot
#[derive(Debug, Clone)]
struct PerformanceSnapshot {
    /// Timestamp
    timestamp: Instant,
    /// Loss value
    loss: f64,
    /// Convergence rate
    convergence_rate: f64,
    /// Memory usage
    memory_usage: usize,
    /// Computation time
    computation_time: Duration,
    /// Algorithm used
    algorithm_used: String,
}

/// Anomaly detection system
#[derive(Debug, Clone)]
struct AnomalyDetectionSystem {
    /// Statistical thresholds
    statistical_thresholds: HashMap<String, (f64, f64)>,
    /// Machine learning detector
    ml_detector: MLAnomalyDetector,
    /// Ensemble detectors
    ensemble_detectors: Vec<AnomalyDetectorType>,
}

/// ML-based anomaly detector
#[derive(Debug, Clone)]
struct MLAnomalyDetector {
    /// Feature extractor
    feature_extractor: Array2<f64>,
    /// Anomaly scoring model
    scoring_model: Array2<f64>,
    /// Threshold
    threshold: f64,
}

/// Types of anomaly detectors
#[derive(Debug, Clone)]
enum AnomalyDetectorType {
    IsolationForest,
    OneClassSVM,
    LocalOutlierFactor,
    EllipticEnvelope,
    StatisticalControl,
}

/// Predictive performance model
#[derive(Debug, Clone)]
struct PredictivePerformanceModel {
    /// Time series forecaster
    forecaster: TimeSeriesForecaster,
    /// Performance predictor
    performance_predictor: Array2<f64>,
    /// Uncertainty quantification
    uncertainty_quantifier: UncertaintyModel,
}

/// Time series forecaster
#[derive(Debug, Clone)]
struct TimeSeriesForecaster {
    /// LSTM-like recurrent weights
    recurrent_weights: Array2<f64>,
    /// Input weights
    input_weights: Array2<f64>,
    /// Hidden state
    hidden_state: Array1<f64>,
    /// Cell state
    cell_state: Array1<f64>,
}

/// Uncertainty quantification model
#[derive(Debug, Clone)]
struct UncertaintyModel {
    /// Epistemic uncertainty
    epistemic_uncertainty: f64,
    /// Aleatoric uncertainty
    aleatoric_uncertainty: f64,
    /// Confidence intervals
    confidence_intervals: Array1<f64>,
}

/// Real-time analytics system
#[derive(Debug, Clone)]
struct RealtimeAnalytics {
    /// Streaming statistics
    streaming_stats: StreamingStatistics,
    /// Dashboard metrics
    dashboard_metrics: DashboardMetrics,
    /// Alert system
    alert_system: AlertSystem,
}

/// Streaming statistics
#[derive(Debug, Clone)]
struct StreamingStatistics {
    /// Running mean
    running_mean: f64,
    /// Running variance
    running_variance: f64,
    /// Skewness
    skewness: f64,
    /// Kurtosis
    kurtosis: f64,
    /// Sample count
    sample_count: usize,
}

/// Dashboard metrics
#[derive(Debug, Clone)]
struct DashboardMetrics {
    /// Key performance indicators
    kpis: HashMap<String, f64>,
    /// Visualization data
    visualization_data: HashMap<String, Vec<f64>>,
    /// Real-time plots
    realtime_plots: Vec<PlotData>,
}

/// Plot data for visualization
#[derive(Debug, Clone)]
struct PlotData {
    /// X values
    x_values: Vec<f64>,
    /// Y values
    y_values: Vec<f64>,
    /// Plot type
    plot_type: PlotType,
}

/// Types of plots
#[derive(Debug, Clone)]
enum PlotType {
    Line,
    Scatter,
    Histogram,
    Heatmap,
    Surface3D,
}

/// Alert system for monitoring
#[derive(Debug, Clone)]
struct AlertSystem {
    /// Alert rules
    alert_rules: Vec<AlertRule>,
    /// Alert history
    alert_history: VecDeque<Alert>,
    /// Notification channels
    notification_channels: Vec<NotificationChannel>,
}

/// Alert rule definition
#[derive(Debug, Clone)]
struct AlertRule {
    /// Rule name
    name: String,
    /// Condition
    condition: AlertCondition,
    /// Severity level
    severity: AlertSeverity,
    /// Cooldown period
    cooldown: Duration,
}

/// Alert condition
#[derive(Debug, Clone)]
enum AlertCondition {
    ThresholdExceeded(f64),
    AnomalyDetected,
    ConvergenceStalled,
    PerformanceDegraded,
    ResourceExhausted,
}

/// Alert severity levels
#[derive(Debug, Clone)]
enum AlertSeverity {
    Info,
    Warning,
    Error,
    Critical,
}

/// Alert instance
#[derive(Debug, Clone)]
struct Alert {
    /// Timestamp
    timestamp: Instant,
    /// Alert rule triggered
    rule_name: String,
    /// Message
    message: String,
    /// Severity
    severity: AlertSeverity,
    /// Context data
    context: HashMap<String, String>,
}

/// Notification channels
#[derive(Debug, Clone)]
enum NotificationChannel {
    Email(String),
    Slack(String),
    Discord(String),
    Webhook(String),
    Console,
}

impl<T: StreamingObjective> AdvancedAdaptiveStreamingOptimizer<T> {
    /// Create a new advanced-adaptive streaming optimizer
    pub fn new(_initialparameters: Array1<f64>, objective: T, config: StreamingConfig) -> Self {
        let param_size = _initialparameters.len();

        Self {
            parameters: _initialparameters,
            objective,
            config,
            stats: StreamingStats::default(),
            multi_scale_memory: MultiScaleTemporalMemory::new(param_size),
            neuromorphic_learner: NeuromorphicLearningSystem::new(param_size),
            quantum_variational: QuantumInspiredVariational::new(param_size),
            meta_learning_selector: MetaLearningSelector::new(),
            federated_coordinator: FederatedLearningCoordinator::new(param_size),
            memory_hierarchy: SelfOrganizingMemoryHierarchy::new(),
            performance_tracker: AdvancedPerformanceTracker::new(),
        }
    }

    /// Advanced-advanced parameter update using multiple adaptation mechanisms
    fn advanced_adaptive_update(&mut self, datapoint: &StreamingDataPoint) -> Result<()> {
        let start_time = Instant::now();

        // 1. Multi-scale temporal analysis
        let temporal_context = self.analyze_temporal_context()?;

        // 2. Neuromorphic spike-based learning
        let neuromorphic_update = self.neuromorphic_learner.process_spike_update(
            &self.parameters,
            datapoint,
            &temporal_context,
        )?;

        // 3. Quantum-inspired variational optimization
        let quantum_update = self.quantum_variational.variational_update(
            &self.parameters,
            datapoint,
            &temporal_context,
        )?;

        // 4. Meta-learning algorithm selection
        let selected_algorithm = self.meta_learning_selector.select_algorithm(
            &temporal_context,
            &self.performance_tracker.get_current_metrics(),
        )?;

        // 5. Federated learning update
        let federated_update = self
            .federated_coordinator
            .aggregate_update(&neuromorphic_update, &quantum_update)?;

        // 6. Self-organizing memory consolidation
        self.memory_hierarchy
            .consolidate_updates(&federated_update, &temporal_context)?;

        // 7. Adaptive fusion of all updates
        let fused_update = self.adaptive_fusion(
            &neuromorphic_update,
            &quantum_update,
            &federated_update,
            &selected_algorithm,
        )?;

        // 8. Apply update with advanced regularization
        self.apply_advanced_regularized_update(&fused_update, datapoint)?;

        // 9. Update performance tracking and anomaly detection
        self.performance_tracker.update_metrics(
            &self.parameters,
            datapoint,
            start_time.elapsed(),
        )?;

        // 10. Adaptive hyperparameter tuning
        self.adaptive_hyperparameter_tuning(&temporal_context)?;

        Ok(())
    }

    /// Analyze temporal context across multiple scales
    fn analyze_temporal_context(&mut self) -> Result<Array1<f64>> {
        let mut context = Array1::zeros(64); // Rich context representation

        // Short-term patterns
        if let Some(short_term_pattern) = self.multi_scale_memory.analyze_short_term() {
            context.slice_mut(s![0..16]).assign(&short_term_pattern);
        }

        // Medium-term trends
        if let Some(medium_term_trend) = self.multi_scale_memory.analyze_medium_term() {
            context.slice_mut(s![16..32]).assign(&medium_term_trend);
        }

        // Long-term dynamics
        if let Some(long_term_dynamics) = self.multi_scale_memory.analyze_long_term() {
            context.slice_mut(s![32..48]).assign(&long_term_dynamics);
        }

        // Very long-term structure
        if let Some(structure) = self.multi_scale_memory.analyze_very_long_term() {
            context.slice_mut(s![48..64]).assign(&structure);
        }

        Ok(context)
    }

    /// Adaptive fusion of multiple update mechanisms
    fn adaptive_fusion(
        &self,
        neuromorphic_update: &Array1<f64>,
        quantum_update: &Array1<f64>,
        federated_update: &Array1<f64>,
        selected_algorithm: &OptimizationAlgorithm,
    ) -> Result<Array1<f64>> {
        let mut fusion_weights: Array1<f64> = Array1::ones(3) / 3.0;

        // Adaptive weight calculation based on recent performance
        let _recent_performance = self.performance_tracker.get_recent_performance();

        // Algorithm-specific weight adjustment
        match selected_algorithm {
            OptimizationAlgorithm::NeuromorphicSpikes => {
                fusion_weights[0] *= 1.5; // Boost neuromorphic
            }
            OptimizationAlgorithm::QuantumVariational => {
                fusion_weights[1] *= 1.5; // Boost quantum
            }
            _ => {
                fusion_weights[2] *= 1.5; // Boost federated
            }
        }

        // Normalize weights
        let weight_sum = fusion_weights.sum();
        fusion_weights /= weight_sum;

        // Compute fused _update
        let fused = fusion_weights[0] * neuromorphic_update
            + fusion_weights[1] * quantum_update
            + fusion_weights[2] * federated_update;

        Ok(fused)
    }

    /// Apply advanced-regularized parameter update
    fn apply_advanced_regularized_update(
        &mut self,
        update: &Array1<f64>,
        data_point: &StreamingDataPoint,
    ) -> Result<()> {
        // Adaptive learning rate based on temporal context
        let adaptive_lr = self.compute_adaptive_learning_rate(data_point)?;

        // Apply update with multiple regularization techniques
        let regularized_update = self.apply_multi_regularization(update, adaptive_lr)?;

        // Update parameters
        self.parameters = &self.parameters + &regularized_update;

        // Ensure parameter constraints
        self.enforce_parameter_constraints()?;

        Ok(())
    }

    /// Compute adaptive learning rate
    fn compute_adaptive_learning_rate(&self, datapoint: &StreamingDataPoint) -> Result<f64> {
        let base_lr = self.config.learning_rate;

        // Gradient-based adaptation
        let gradient = self.objective.gradient(&self.parameters.view(), datapoint);
        let gradient_norm = gradient.mapv(|x| x * x).sum().sqrt();

        // Curvature-based adaptation
        let curvature_factor = if let Some(hessian) = T::hessian(&self.parameters.view(), datapoint)
        {
            let eigenvalues = self.approximate_eigenvalues(&hessian);
            let condition_number = eigenvalues
                .iter()
                .max_by(|a, b| a.partial_cmp(b).expect("Operation failed"))
                .unwrap_or(&1.0)
                / eigenvalues
                    .iter()
                    .min_by(|a, b| a.partial_cmp(b).expect("Operation failed"))
                    .unwrap_or(&1.0);
            1.0 / condition_number.sqrt()
        } else {
            1.0
        };

        // Performance-based adaptation
        let performance_factor = if self.performance_tracker.is_improving() {
            1.1 // Slightly increase if improving
        } else {
            0.9 // Slightly decrease if not improving
        };

        let adaptive_lr = base_lr * curvature_factor * performance_factor / (1.0 + gradient_norm);

        Ok(adaptive_lr.max(1e-8).min(1.0)) // Clamp to reasonable range
    }

    /// Apply multiple regularization techniques
    fn apply_multi_regularization(
        &self,
        update: &Array1<f64>,
        learning_rate: f64,
    ) -> Result<Array1<f64>> {
        let mut regularized = update.clone();

        // L1 regularization (sparsity)
        let l1_factor = 1e-6;
        for i in 0..regularized.len() {
            let sign = self.parameters[i].signum();
            regularized[i] -= l1_factor * sign;
        }

        // L2 regularization (weight decay)
        let l2_factor = 1e-4;
        regularized = &regularized - &(l2_factor * &self.parameters);

        // Elastic net (combination of L1 and L2)
        let alpha = 0.5;
        let _elastic_net_reg = alpha * l1_factor + (1.0 - alpha) * l2_factor;

        // Adaptive gradient clipping
        let gradient_norm = regularized.mapv(|x| x * x).sum().sqrt();
        let clip_threshold = 1.0;
        if gradient_norm > clip_threshold {
            regularized *= clip_threshold / gradient_norm;
        }

        // Apply learning _rate
        regularized *= learning_rate;

        Ok(regularized)
    }

    /// Enforce parameter constraints
    fn enforce_parameter_constraints(&mut self) -> Result<()> {
        // Project parameters onto feasible region
        for param in self.parameters.iter_mut() {
            // Example constraints (can be customized)
            *param = param.max(-10.0).min(10.0); // Box constraints
        }

        // Ensure numerical stability
        for param in self.parameters.iter_mut() {
            if !param.is_finite() {
                *param = 0.0; // Reset to safe value
            }
        }

        Ok(())
    }

    /// Adaptive hyperparameter tuning
    fn adaptive_hyperparameter_tuning(&mut self, context: &Array1<f64>) -> Result<()> {
        // Tune learning rate based on performance
        if self.performance_tracker.is_stagnant() {
            self.config.learning_rate *= 1.1; // Increase learning rate
        } else if self.performance_tracker.is_oscillating() {
            self.config.learning_rate *= 0.9; // Decrease learning rate
        }

        // Tune forgetting factor
        if self.performance_tracker.is_non_stationary() {
            self.config.forgetting_factor *= 0.95; // Adapt faster
        } else {
            self.config.forgetting_factor = (self.config.forgetting_factor * 1.01).min(0.999);
            // Adapt slower
        }

        // Clamp hyperparameters to reasonable ranges
        self.config.learning_rate = self.config.learning_rate.max(1e-8).min(1.0);
        self.config.forgetting_factor = self.config.forgetting_factor.max(0.1).min(0.999);

        Ok(())
    }

    /// Approximate eigenvalues of a matrix
    fn approximate_eigenvalues(&self, matrix: &Array2<f64>) -> Vec<f64> {
        // Simplified power iteration for dominant eigenvalue
        let n = matrix.nrows();
        let mut eigenvalues = Vec::new();

        if n > 0 {
            let mut v = Array1::ones(n);
            v /= v.mapv(|x: f64| -> f64 { x * x }).sum().sqrt();

            for _ in 0..10 {
                // Power iterations
                let new_v = matrix.dot(&v);
                let eigenvalue = v.dot(&new_v);
                eigenvalues.push(eigenvalue);

                let norm = new_v.mapv(|x| x * x).sum().sqrt();
                if norm > 1e-12 {
                    v = new_v / norm;
                }
            }
        }

        if eigenvalues.is_empty() {
            eigenvalues.push(1.0); // Default eigenvalue
        }

        eigenvalues
    }
}

impl<T: StreamingObjective + Clone> StreamingOptimizer for AdvancedAdaptiveStreamingOptimizer<T> {
    fn update(&mut self, datapoint: &StreamingDataPoint) -> Result<()> {
        let start_time = Instant::now();
        let old_parameters = self.parameters.clone();

        // Advanced-adaptive update
        self.advanced_adaptive_update(datapoint)?;

        // Update statistics
        self.stats.points_processed += 1;
        self.stats.updates_performed += 1;
        let loss = self.objective.evaluate(&self.parameters.view(), datapoint);
        self.stats.current_loss = loss;
        self.stats.average_loss = utils::ewma_update(
            self.stats.average_loss,
            loss,
            0.01, // Slower adaptation for advanced-optimizer
        );

        // Check convergence
        self.stats.converged = utils::check_convergence(
            &old_parameters.view(),
            &self.parameters.view(),
            self.config.tolerance,
        );

        self.stats.processing_time_ms += start_time.elapsed().as_secs_f64() * 1000.0;

        Ok(())
    }

    fn parameters(&self) -> &Array1<f64> {
        &self.parameters
    }

    fn stats(&self) -> &StreamingStats {
        &self.stats
    }

    fn reset(&mut self) {
        self.stats = StreamingStats::default();
        self.multi_scale_memory = MultiScaleTemporalMemory::new(self.parameters.len());
        self.neuromorphic_learner = NeuromorphicLearningSystem::new(self.parameters.len());
        self.quantum_variational = QuantumInspiredVariational::new(self.parameters.len());
        self.performance_tracker = AdvancedPerformanceTracker::new();
    }
}

// Placeholder implementations for the complex subsystems
// (In a real implementation, these would be fully developed)

impl MultiScaleTemporalMemory {
    fn new(_paramsize: usize) -> Self {
        Self {
            short_term: VecDeque::with_capacity(100),
            medium_term: VecDeque::with_capacity(50),
            long_term: VecDeque::with_capacity(25),
            very_long_term: VecDeque::with_capacity(10),
            time_scales: [
                Duration::from_millis(100),
                Duration::from_secs(1),
                Duration::from_secs(60),
                Duration::from_secs(3600),
            ],
            consolidation_weights: Array1::ones(4) / 4.0,
        }
    }

    fn analyze_short_term(&self) -> Option<Array1<f64>> {
        if self.short_term.len() >= 2 {
            Some(Array1::zeros(16)) // Placeholder
        } else {
            None
        }
    }

    fn analyze_medium_term(&self) -> Option<Array1<f64>> {
        if self.medium_term.len() >= 2 {
            Some(Array1::zeros(16)) // Placeholder
        } else {
            None
        }
    }

    fn analyze_long_term(&self) -> Option<Array1<f64>> {
        if self.long_term.len() >= 2 {
            Some(Array1::zeros(16)) // Placeholder
        } else {
            None
        }
    }

    fn analyze_very_long_term(&self) -> Option<Array1<f64>> {
        if self.very_long_term.len() >= 2 {
            Some(Array1::zeros(16)) // Placeholder
        } else {
            None
        }
    }
}

impl NeuromorphicLearningSystem {
    fn new(paramsize: usize) -> Self {
        Self {
            spike_trains: vec![VecDeque::with_capacity(100); paramsize],
            synaptic_weights: Array2::eye(paramsize),
            membrane_potentials: Array1::zeros(paramsize),
            adaptation_thresholds: Array1::ones(paramsize),
            stdp_rates: STDPRates {
                ltp_rate: 0.01,
                ltd_rate: 0.005,
                temporal_window: Duration::from_millis(20),
                decay_constant: 0.95,
            },
            homeostatic_scaling: Array1::ones(paramsize),
        }
    }

    fn process_spike_update(
        &mut self,
        parameters: &Array1<f64>,
        _data_point: &StreamingDataPoint,
        _context: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        // Placeholder for neuromorphic update
        Ok(Array1::zeros(parameters.len()))
    }
}

impl QuantumInspiredVariational {
    fn new(_paramsize: usize) -> Self {
        Self {
            quantum_state: Array1::ones(_paramsize) / (_paramsize as f64).sqrt(),
            variational_params: Array1::zeros(_paramsize),
            entanglement_matrix: Array2::eye(_paramsize),
            measurement_operators: vec![Array2::eye(_paramsize)],
            noise_model: QuantumNoiseModel {
                decoherence_rate: 0.01,
                thermal_noise: 0.001,
                gate_error_rate: 0.0001,
            },
            coherence_time: Duration::from_millis(1),
        }
    }

    fn variational_update(
        &mut self,
        parameters: &Array1<f64>,
        _data_point: &StreamingDataPoint,
        _context: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        // Placeholder for quantum variational update
        Ok(Array1::zeros(parameters.len()))
    }
}

impl MetaLearningSelector {
    fn new() -> Self {
        Self {
            available_algorithms: vec![
                OptimizationAlgorithm::AdaptiveGradientDescent,
                OptimizationAlgorithm::RecursiveLeastSquares,
                OptimizationAlgorithm::KalmanFilter,
                OptimizationAlgorithm::NeuromorphicSpikes,
                OptimizationAlgorithm::QuantumVariational,
            ],
            algorithm_performance: HashMap::new(),
            context_features: Array1::zeros(32),
            selection_network: NeuralSelector {
                layers: vec![Array2::zeros((32, 16)), Array2::zeros((16, 8))],
                activations: vec![Array1::zeros(16), Array1::zeros(8)],
                learning_rate: 0.001,
            },
            exploration_factor: 0.1,
        }
    }

    fn select_algorithm(
        &mut self,
        context: &Array1<f64>,
        _metrics: &HashMap<String, f64>,
    ) -> Result<OptimizationAlgorithm> {
        // Placeholder for meta-learning selection
        Ok(OptimizationAlgorithm::AdaptiveGradientDescent)
    }
}

impl FederatedLearningCoordinator {
    fn new(_paramsize: usize) -> Self {
        Self {
            local_model: Array1::zeros(_paramsize),
            global_model: Array1::zeros(_paramsize),
            peer_models: HashMap::new(),
            communication_budget: 100,
            privacy_params: DifferentialPrivacyParams {
                epsilon: 1.0,
                delta: 1e-5,
                noise_scale: 0.1,
            },
            consensus_mechanism: ConsensusType::FederatedAveraging,
        }
    }

    fn aggregate_update(
        &mut self,
        update1: &Array1<f64>,
        _update2: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        // Placeholder for federated aggregation
        Ok(Array1::zeros(update1.len()))
    }
}

impl SelfOrganizingMemoryHierarchy {
    fn new() -> Self {
        Self {
            l1_cache: HashMap::new(),
            l2_cache: HashMap::new(),
            l3_cache: HashMap::new(),
            access_counters: HashMap::new(),
            replacement_policy: ReplacementPolicy::AdaptiveLRU,
            cache_sizes: [16, 64, 256],
        }
    }

    fn consolidate_updates(&mut self, update: &Array1<f64>, context: &Array1<f64>) -> Result<()> {
        // Placeholder for memory consolidation
        Ok(())
    }
}

impl AdvancedPerformanceTracker {
    fn new() -> Self {
        Self {
            metrics_history: VecDeque::with_capacity(1000),
            anomaly_detector: AnomalyDetectionSystem {
                statistical_thresholds: HashMap::new(),
                ml_detector: MLAnomalyDetector {
                    feature_extractor: Array2::zeros((32, 16)),
                    scoring_model: Array2::zeros((16, 1)),
                    threshold: 0.5,
                },
                ensemble_detectors: vec![
                    AnomalyDetectorType::IsolationForest,
                    AnomalyDetectorType::StatisticalControl,
                ],
            },
            predictive_model: PredictivePerformanceModel {
                forecaster: TimeSeriesForecaster {
                    recurrent_weights: Array2::zeros((32, 32)),
                    input_weights: Array2::zeros((16, 32)),
                    hidden_state: Array1::zeros(32),
                    cell_state: Array1::zeros(32),
                },
                performance_predictor: Array2::zeros((32, 1)),
                uncertainty_quantifier: UncertaintyModel {
                    epistemic_uncertainty: 0.1,
                    aleatoric_uncertainty: 0.05,
                    confidence_intervals: Array1::zeros(2),
                },
            },
            realtime_analytics: RealtimeAnalytics {
                streaming_stats: StreamingStatistics {
                    running_mean: 0.0,
                    running_variance: 0.0,
                    skewness: 0.0,
                    kurtosis: 0.0,
                    sample_count: 0,
                },
                dashboard_metrics: DashboardMetrics {
                    kpis: HashMap::new(),
                    visualization_data: HashMap::new(),
                    realtime_plots: Vec::new(),
                },
                alert_system: AlertSystem {
                    alert_rules: Vec::new(),
                    alert_history: VecDeque::new(),
                    notification_channels: vec![NotificationChannel::Console],
                },
            },
        }
    }

    fn update_metrics(
        &mut self,
        parameters: &Array1<f64>,
        _data_point: &StreamingDataPoint,
        _time: Duration,
    ) -> Result<()> {
        // Placeholder for metrics update
        Ok(())
    }

    fn get_current_metrics(&self) -> HashMap<String, f64> {
        // Placeholder for current metrics
        HashMap::new()
    }

    fn get_recent_performance(&self) -> f64 {
        // Placeholder for recent performance
        1.0
    }

    fn is_improving(&self) -> bool {
        // Placeholder for improvement detection
        true
    }

    fn is_stagnant(&self) -> bool {
        // Placeholder for stagnation detection
        false
    }

    fn is_oscillating(&self) -> bool {
        // Placeholder for oscillation detection
        false
    }

    fn is_non_stationary(&self) -> bool {
        // Placeholder for non-stationarity detection
        false
    }
}

/// Create advanced-adaptive streaming optimizer
#[allow(dead_code)]
pub fn create_advanced_adaptive_optimizer<T: StreamingObjective>(
    initial_parameters: Array1<f64>,
    objective: T,
    config: Option<StreamingConfig>,
) -> AdvancedAdaptiveStreamingOptimizer<T> {
    let config = config.unwrap_or_default();
    AdvancedAdaptiveStreamingOptimizer::new(initial_parameters, objective, config)
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::streaming::{LinearRegressionObjective, StreamingDataPoint};

    #[test]
    fn test_advanced_adaptive_creation() {
        let optimizer =
            create_advanced_adaptive_optimizer(Array1::zeros(2), LinearRegressionObjective, None);

        assert_eq!(optimizer.parameters().len(), 2);
        assert_eq!(optimizer.stats().points_processed, 0);
    }

    #[test]
    fn test_advanced_adaptive_update() {
        let mut optimizer =
            create_advanced_adaptive_optimizer(Array1::zeros(2), LinearRegressionObjective, None);

        let data_point = StreamingDataPoint::new(Array1::from(vec![1.0, 2.0]), 3.0);

        assert!(optimizer.update(&data_point).is_ok());
        assert_eq!(optimizer.stats().points_processed, 1);
    }
}

#[allow(dead_code)]
pub fn placeholder() {
    // Placeholder function to prevent unused module warnings
}