scirs2_datasets/

adaptive_streaming_engine.rs

//! Adaptive Streaming Data Processing Engine
//!
//! This module provides advanced-sophisticated real-time data processing capabilities
//! with adaptive algorithms, intelligent buffering, and ML-based stream optimization.

use crate::error::{DatasetsError, Result};
use crate::utils::Dataset;
use scirs2_core::ndarray::{Array1, Array2, Axis};
use scirs2_core::random::prelude::*;
// Use rayon directly for parallel operations to avoid feature flag issues
use scirs2_core::parallel_ops::*;
use statrs::statistics::Statistics;
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};

/// Advanced-advanced adaptive streaming processor
pub struct AdaptiveStreamingEngine {
    /// Stream configuration
    config: AdaptiveStreamConfig,
    /// Adaptive buffer manager
    buffer_manager: AdaptiveBufferManager,
    /// ML-based pattern detector
    pattern_detector: PatternDetector,
    /// Performance optimizer
    performance_optimizer: StreamPerformanceOptimizer,
    /// Quality monitor
    quality_monitor: StreamQualityMonitor,
}

/// Stream processing configuration
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct AdaptiveStreamConfig {
    /// Maximum buffer size in bytes
    max_buffer_size: usize,
    /// Processing batch size
    batch_size: usize,
    /// Adaptive threshold for buffer management
    adaptive_threshold: f64,
    /// Enable ML-based optimization
    ml_optimization: bool,
    /// Quality monitoring interval
    quality_check_interval: Duration,
}

/// Adaptive buffer management system
#[allow(dead_code)]
pub struct AdaptiveBufferManager {
    /// Primary buffer for incoming data
    primary_buffer: Arc<Mutex<VecDeque<StreamChunk>>>,
    /// Secondary buffer for overflow
    secondary_buffer: Arc<Mutex<VecDeque<StreamChunk>>>,
    /// Buffer statistics
    stats: Arc<Mutex<BufferStatistics>>,
    /// Adaptive sizing parameters
    adaptive_params: AdaptiveParameters,
}

/// Stream data chunk
#[derive(Debug, Clone)]
pub struct StreamChunk {
    /// Chunk data
    pub data: Array2<f64>,
    /// Timestamp
    pub timestamp: Instant,
    /// Chunk metadata
    pub metadata: ChunkMetadata,
    /// Quality score
    pub quality_score: f64,
}

/// Chunk metadata
#[derive(Debug, Clone)]
pub struct ChunkMetadata {
    /// Source identifier
    pub source_id: String,
    /// Chunk sequence number
    pub sequence_number: u64,
    /// Data characteristics
    pub characteristics: DataCharacteristics,
}

/// Data characteristics analysis
#[derive(Debug, Clone)]
pub struct DataCharacteristics {
    /// Statistical moments
    pub moments: StatisticalMoments,
    /// Entropy measure
    pub entropy: f64,
    /// Trend indicators
    pub trend: TrendIndicators,
    /// Anomaly score
    pub anomaly_score: f64,
}

/// Statistical moments
#[derive(Debug, Clone)]
pub struct StatisticalMoments {
    /// Mean
    pub mean: f64,
    /// Variance
    pub variance: f64,
    /// Skewness
    pub skewness: f64,
    /// Kurtosis
    pub kurtosis: f64,
}

/// Trend analysis indicators
#[derive(Debug, Clone)]
pub struct TrendIndicators {
    /// Linear trend slope
    pub linear_slope: f64,
    /// Trend strength (0-1)
    pub trend_strength: f64,
    /// Trend direction
    pub direction: TrendDirection,
    /// Seasonality indicator
    pub seasonality: f64,
}

/// Trend direction
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum TrendDirection {
    /// Increasing trend
    Increasing,
    /// Decreasing trend
    Decreasing,
    /// No significant trend
    Stable,
    /// Oscillating pattern
    Oscillating,
}

/// Buffer statistics
#[derive(Debug, Clone)]
pub struct BufferStatistics {
    /// Current buffer utilization
    pub utilization: f64,
    /// Average processing latency
    pub avg_latency: Duration,
    /// Throughput (chunks per second)
    pub throughput: f64,
    /// Memory usage
    pub memory_usage: usize,
    /// Overflow events count
    pub overflow_count: u64,
}

/// Adaptive parameters for buffer management
#[derive(Debug, Clone)]
pub struct AdaptiveParameters {
    /// Learning rate for adaptation
    pub learning_rate: f64,
    /// Minimum buffer size
    pub min_buffer_size: usize,
    /// Maximum buffer size
    pub max_buffer_size: usize,
    /// Adaptation window size
    pub adaptation_window: usize,
}

/// ML-based pattern detection system
#[allow(dead_code)]
pub struct PatternDetector {
    /// Pattern history
    pattern_history: Arc<Mutex<VecDeque<PatternSignature>>>,
    /// Known patterns database
    known_patterns: Arc<Mutex<HashMap<String, PatternTemplate>>>,
    /// Detection parameters
    detection_params: DetectionParameters,
}

/// Pattern signature for ML recognition
#[derive(Debug, Clone)]
pub struct PatternSignature {
    /// Feature vector
    pub features: Array1<f64>,
    /// Pattern type
    pub pattern_type: PatternType,
    /// Confidence score
    pub confidence: f64,
    /// Timestamp
    pub timestamp: Instant,
}

/// Pattern types
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum PatternType {
    /// Periodic pattern
    Periodic,
    /// Anomalous pattern
    Anomalous,
    /// Trending pattern
    Trending,
    /// Seasonal pattern
    Seasonal,
    /// Chaotic pattern
    Chaotic,
    /// Unknown pattern
    Unknown,
}

/// Pattern template for recognition
#[derive(Debug, Clone)]
pub struct PatternTemplate {
    /// Template features
    pub features: Array1<f64>,
    /// Pattern characteristics
    pub characteristics: PatternCharacteristics,
    /// Usage count
    pub usage_count: u64,
    /// Accuracy history
    pub accuracy_history: VecDeque<f64>,
}

/// Pattern characteristics
#[derive(Debug, Clone)]
pub struct PatternCharacteristics {
    /// Typical duration
    pub duration: Duration,
    /// Frequency of occurrence
    pub frequency: f64,
    /// Variability measure
    pub variability: f64,
    /// Impact on processing
    pub processing_impact: f64,
}

/// Detection parameters
#[derive(Debug, Clone)]
pub struct DetectionParameters {
    /// Similarity threshold
    pub similarity_threshold: f64,
    /// Window size for pattern detection
    pub window_size: usize,
    /// Update frequency
    pub update_frequency: usize,
    /// Minimum confidence for pattern recognition
    pub min_confidence: f64,
}

/// Stream performance optimizer
#[allow(dead_code)]
pub struct StreamPerformanceOptimizer {
    /// Performance history
    performance_history: Arc<Mutex<VecDeque<PerformanceMetrics>>>,
    /// Optimization strategies
    strategies: OptimizationStrategies,
    /// Current configuration
    current_config: Arc<Mutex<OptimizationConfig>>,
}

/// Performance metrics
#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    /// Processing latency
    pub latency: Duration,
    /// Throughput
    pub throughput: f64,
    /// Memory efficiency
    pub memory_efficiency: f64,
    /// CPU utilization
    pub cpu_utilization: f64,
    /// Timestamp
    pub timestamp: Instant,
}

/// Optimization strategies
#[derive(Debug, Clone)]
pub struct OptimizationStrategies {
    /// Batch size optimization
    pub batch_optimization: bool,
    /// Buffer size optimization
    pub buffer_optimization: bool,
    /// Parallel processing optimization
    pub parallel_optimization: bool,
    /// Memory optimization
    pub memory_optimization: bool,
}

/// Optimization configuration
#[derive(Debug, Clone)]
pub struct OptimizationConfig {
    /// Optimal batch size
    pub optimal_batch_size: usize,
    /// Optimal buffer size
    pub optimal_buffer_size: usize,
    /// Number of parallel workers
    pub num_workers: usize,
    /// Memory allocation strategy
    pub memory_strategy: MemoryStrategy,
}

/// Memory allocation strategies
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum MemoryStrategy {
    /// Conservative memory usage
    Conservative,
    /// Balanced memory usage
    Balanced,
    /// Aggressive memory usage for performance
    Aggressive,
    /// Adaptive based on available memory
    Adaptive,
}

/// Stream quality monitoring system
#[allow(dead_code)]
pub struct StreamQualityMonitor {
    /// Quality history
    quality_history: Arc<Mutex<VecDeque<QualityMetrics>>>,
    /// Quality thresholds
    thresholds: QualityThresholds,
    /// Alert system
    alert_system: AlertSystem,
}

/// Quality metrics
#[derive(Debug, Clone)]
pub struct QualityMetrics {
    /// Data integrity score
    pub integrity_score: f64,
    /// Completeness score
    pub completeness_score: f64,
    /// Timeliness score
    pub timeliness_score: f64,
    /// Consistency score
    pub consistency_score: f64,
    /// Overall quality score
    pub overall_score: f64,
    /// Timestamp
    pub timestamp: Instant,
}

/// Quality thresholds
#[derive(Debug, Clone)]
pub struct QualityThresholds {
    /// Minimum acceptable integrity
    pub min_integrity: f64,
    /// Minimum acceptable completeness
    pub min_completeness: f64,
    /// Maximum acceptable latency
    pub max_latency: Duration,
    /// Minimum consistency
    pub min_consistency: f64,
}

/// Alert callback function type alias
type AlertCallback = Box<dyn Fn(&QualityAlert) + Send + Sync>;

/// Alert system for quality issues
#[allow(dead_code)]
pub struct AlertSystem {
    /// Active alerts
    active_alerts: Arc<Mutex<Vec<QualityAlert>>>,
    /// Alert callbacks
    callbacks: Arc<Mutex<Vec<AlertCallback>>>,
}

/// Quality alert
#[derive(Debug, Clone)]
pub struct QualityAlert {
    /// Alert type
    pub alert_type: AlertType,
    /// Severity level
    pub severity: AlertSeverity,
    /// Description
    pub description: String,
    /// Timestamp
    pub timestamp: Instant,
    /// Affected metrics
    pub affected_metrics: Vec<String>,
}

/// Alert types
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum AlertType {
    /// Data quality degradation
    QualityDegradation,
    /// Performance degradation
    PerformanceDegradation,
    /// Buffer overflow
    BufferOverflow,
    /// Pattern anomaly
    PatternAnomaly,
    /// System error
    SystemError,
}

/// Alert severity levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum AlertSeverity {
    /// Information only
    Info,
    /// Warning level
    Warning,
    /// Critical issue
    Critical,
    /// Emergency requiring immediate attention
    Emergency,
}

impl Default for AdaptiveStreamConfig {
    fn default() -> Self {
        Self {
            max_buffer_size: 100 * 1024 * 1024, // 100MB
            batch_size: 1000,
            adaptive_threshold: 0.8,
            ml_optimization: true,
            quality_check_interval: Duration::from_secs(10),
        }
    }
}

impl AdaptiveStreamingEngine {
    /// Create a new adaptive streaming engine
    pub fn new(config: AdaptiveStreamConfig) -> Self {
        let buffer_manager = AdaptiveBufferManager::new(&config);
        let pattern_detector = PatternDetector::new();
        let performance_optimizer = StreamPerformanceOptimizer::new();
        let quality_monitor = StreamQualityMonitor::new();

        Self {
            config,
            buffer_manager,
            pattern_detector,
            performance_optimizer,
            quality_monitor,
        }
    }

    /// Process incoming data stream
    pub fn process_stream(&mut self, chunk: StreamChunk) -> Result<Vec<Dataset>> {
        // Add chunk to buffer
        self.buffer_manager.add_chunk(chunk)?;

        // Check if we have enough data to process
        if self.buffer_manager.should_process()? {
            // Get batch for processing
            let batch = self.buffer_manager.get_batch(self.config.batch_size)?;

            // Detect patterns in the batch
            let patterns = self.pattern_detector.detect_patterns(&batch)?;

            // Optimize processing based on patterns
            let optimized_config = self
                .performance_optimizer
                .optimize_for_patterns(&patterns)?;

            // Process the batch with optimized configuration
            let results = self.process_batch_optimized(batch, &optimized_config)?;

            // Monitor quality
            self.quality_monitor.assess_quality(&results)?;

            Ok(results)
        } else {
            Ok(Vec::new())
        }
    }

    /// Process batch with optimized configuration
    fn process_batch_optimized(
        &self,
        batch: Vec<StreamChunk>,
        config: &OptimizationConfig,
    ) -> Result<Vec<Dataset>> {
        // Process chunks in parallel based on optimization config
        let results: Vec<Dataset> = batch
            .into_par_iter()
            .chunks(config.optimal_batch_size)
            .map(|chunk_group| self.process_chunk_group(chunk_group.into_iter().collect()))
            .collect::<Result<Vec<_>>>()?
            .into_iter()
            .flatten()
            .collect();

        Ok(results)
    }

    /// Process a group of chunks
    fn process_chunk_group(&self, chunks: Vec<StreamChunk>) -> Result<Vec<Dataset>> {
        let mut results = Vec::new();

        for chunk in chunks {
            // Analyze chunk characteristics
            let characteristics = self.analyze_chunk_characteristics(&chunk)?;

            // Create dataset from chunk
            let dataset = self.chunk_to_dataset(chunk, characteristics)?;

            results.push(dataset);
        }

        Ok(results)
    }

    /// Analyze chunk characteristics
    fn analyze_chunk_characteristics(&self, chunk: &StreamChunk) -> Result<DataCharacteristics> {
        let data = &chunk.data;

        // Calculate statistical moments
        let moments = self.calculate_statistical_moments(data)?;

        // Calculate entropy
        let entropy = self.calculate_entropy(data)?;

        // Analyze trends
        let trend = self.analyze_trends(data)?;

        // Calculate anomaly score
        let anomaly_score = self.calculate_anomaly_score(data, &moments)?;

        Ok(DataCharacteristics {
            moments,
            entropy,
            trend,
            anomaly_score,
        })
    }

    /// Calculate statistical moments
    fn calculate_statistical_moments(&self, data: &Array2<f64>) -> Result<StatisticalMoments> {
        let flat_data = data.iter().cloned().collect::<Vec<_>>();
        let n = flat_data.len() as f64;

        if n < 1.0 {
            return Ok(StatisticalMoments {
                mean: 0.0,
                variance: 0.0,
                skewness: 0.0,
                kurtosis: 0.0,
            });
        }

        // Calculate mean
        let mean = flat_data.iter().sum::<f64>() / n;

        // Calculate variance
        let variance = flat_data.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / n;

        let std_dev = variance.sqrt();

        if std_dev < f64::EPSILON {
            return Ok(StatisticalMoments {
                mean,
                variance: 0.0,
                skewness: 0.0,
                kurtosis: 0.0,
            });
        }

        // Calculate skewness
        let skewness = flat_data
            .iter()
            .map(|&x| ((x - mean) / std_dev).powi(3))
            .sum::<f64>()
            / n;

        // Calculate kurtosis
        let kurtosis = flat_data
            .iter()
            .map(|&x| ((x - mean) / std_dev).powi(4))
            .sum::<f64>()
            / n
            - 3.0;

        Ok(StatisticalMoments {
            mean,
            variance,
            skewness,
            kurtosis,
        })
    }

    /// Calculate entropy
    fn calculate_entropy(&self, data: &Array2<f64>) -> Result<f64> {
        let flat_data = data.iter().cloned().collect::<Vec<_>>();

        if flat_data.is_empty() {
            return Ok(0.0);
        }

        // Create histogram for entropy calculation
        let n_bins = (flat_data.len() as f64).sqrt() as usize + 1;
        let min_val = flat_data.iter().fold(f64::INFINITY, |a, &b| a.min(b));
        let max_val = flat_data.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));

        if (max_val - min_val).abs() < f64::EPSILON {
            return Ok(0.0);
        }

        let bin_width = (max_val - min_val) / n_bins as f64;
        let mut histogram = vec![0; n_bins];

        for &value in &flat_data {
            let bin_idx = ((value - min_val) / bin_width) as usize;
            let bin_idx = bin_idx.min(n_bins - 1);
            histogram[bin_idx] += 1;
        }

        // Calculate Shannon entropy
        let n_total = flat_data.len() as f64;
        let entropy = histogram
            .iter()
            .filter(|&&count| count > 0)
            .map(|&count| {
                let p = count as f64 / n_total;
                -p * p.ln()
            })
            .sum::<f64>();

        Ok(entropy)
    }

    /// Analyze trends
    fn analyze_trends(&self, data: &Array2<f64>) -> Result<TrendIndicators> {
        if data.is_empty() {
            return Ok(TrendIndicators {
                linear_slope: 0.0,
                trend_strength: 0.0,
                direction: TrendDirection::Stable,
                seasonality: 0.0,
            });
        }

        // Use row means as time series for trend analysis
        let time_series: Vec<f64> = data
            .axis_iter(Axis(0))
            .map(|row| {
                let mean = row.mean();
                if mean.is_nan() {
                    0.0
                } else {
                    mean
                }
            })
            .collect();

        let n = time_series.len();
        if n < 2 {
            return Ok(TrendIndicators {
                linear_slope: 0.0,
                trend_strength: 0.0,
                direction: TrendDirection::Stable,
                seasonality: 0.0,
            });
        }

        // Calculate linear trend
        let x_mean = (n - 1) as f64 / 2.0;
        let y_mean = time_series.iter().sum::<f64>() / n as f64;

        let numerator: f64 = time_series
            .iter()
            .enumerate()
            .map(|(i, &y)| (i as f64 - x_mean) * (y - y_mean))
            .sum();

        let denominator: f64 = (0..n).map(|i| (i as f64 - x_mean).powi(2)).sum();

        let linear_slope = if denominator > f64::EPSILON {
            numerator / denominator
        } else {
            0.0
        };

        // Calculate trend strength (R-squared approximation)
        let trend_strength: f64 = if denominator > f64::EPSILON {
            let ss_res: f64 = time_series
                .iter()
                .enumerate()
                .map(|(i, &y)| {
                    let predicted = y_mean + linear_slope * (i as f64 - x_mean);
                    (y - predicted).powi(2)
                })
                .sum();

            let ss_tot: f64 = time_series.iter().map(|&y| (y - y_mean).powi(2)).sum();

            if ss_tot > f64::EPSILON {
                1.0 - (ss_res / ss_tot)
            } else {
                0.0
            }
        } else {
            0.0
        };

        // Determine trend direction
        let direction = if linear_slope.abs() < 1e-6 {
            TrendDirection::Stable
        } else if linear_slope > 0.0 {
            TrendDirection::Increasing
        } else {
            TrendDirection::Decreasing
        };

        // Simple seasonality detection (placeholder)
        let seasonality = self.detect_seasonality(&time_series);

        Ok(TrendIndicators {
            linear_slope,
            trend_strength: trend_strength.clamp(0.0, 1.0),
            direction,
            seasonality,
        })
    }

    /// Detect seasonality (simplified)
    fn detect_seasonality(&self, time_series: &[f64]) -> f64 {
        if time_series.len() < 4 {
            return 0.0;
        }

        // Simple autocorrelation-based seasonality detection
        let n = time_series.len();
        let mean = time_series.iter().sum::<f64>() / n as f64;

        let mut max_autocorr: f64 = 0.0;
        for lag in 1..=n.min(10) {
            let mut numerator = 0.0;
            let mut denominator = 0.0;

            for i in lag..n {
                numerator += (time_series[i] - mean) * (time_series[i - lag] - mean);
                denominator += (time_series[i] - mean).powi(2);
            }

            if denominator > f64::EPSILON {
                let autocorr = (numerator / denominator).abs();
                max_autocorr = max_autocorr.max(autocorr);
            }
        }

        max_autocorr.min(1.0)
    }

    /// Calculate anomaly score
    fn calculate_anomaly_score(
        &self,
        data: &Array2<f64>,
        moments: &StatisticalMoments,
    ) -> Result<f64> {
        if moments.variance <= f64::EPSILON {
            return Ok(0.0);
        }

        let std_dev = moments.variance.sqrt();
        let flat_data = data.iter().cloned().collect::<Vec<_>>();

        // Count outliers using 3-sigma rule
        let outlier_count = flat_data
            .iter()
            .filter(|&&x| (x - moments.mean).abs() > 3.0 * std_dev)
            .count();

        // Anomaly score based on outlier proportion
        let anomaly_score = outlier_count as f64 / flat_data.len() as f64;

        Ok(anomaly_score.min(1.0))
    }

    /// Convert chunk to dataset
    fn chunk_to_dataset(
        &self,
        chunk: StreamChunk,
        _characteristics: DataCharacteristics,
    ) -> Result<Dataset> {
        // For now, create a simple dataset from the chunk data
        // In a real implementation, this could be more sophisticated based on _characteristics
        Ok(Dataset::new(chunk.data, None))
    }

    /// Get current performance metrics
    pub fn get_performance_metrics(&self) -> Result<PerformanceMetrics> {
        self.performance_optimizer.get_current_metrics()
    }

    /// Get current quality metrics
    pub fn get_quality_metrics(&self) -> Result<QualityMetrics> {
        self.quality_monitor.get_current_metrics()
    }

    /// Get buffer statistics
    pub fn get_buffer_statistics(&self) -> Result<BufferStatistics> {
        self.buffer_manager.get_statistics()
    }
}

// Implementation stubs for the complex subsystems
impl AdaptiveBufferManager {
    fn new(_config: &AdaptiveStreamConfig) -> Self {
        Self {
            primary_buffer: Arc::new(Mutex::new(VecDeque::new())),
            secondary_buffer: Arc::new(Mutex::new(VecDeque::new())),
            stats: Arc::new(Mutex::new(BufferStatistics {
                utilization: 0.0,
                avg_latency: Duration::from_millis(0),
                throughput: 0.0,
                memory_usage: 0,
                overflow_count: 0,
            })),
            adaptive_params: AdaptiveParameters {
                learning_rate: 0.01,
                min_buffer_size: 1000,
                max_buffer_size: 100000,
                adaptation_window: 1000,
            },
        }
    }

    fn add_chunk(&self, chunk: StreamChunk) -> Result<()> {
        if let Ok(mut buffer) = self.primary_buffer.lock() {
            buffer.push_back(chunk);
        }
        Ok(())
    }

    fn should_process(&self) -> Result<bool> {
        if let Ok(buffer) = self.primary_buffer.lock() {
            Ok(buffer.len() >= 10) // Simple threshold
        } else {
            Ok(false)
        }
    }

    fn get_batch(&self, batchsize: usize) -> Result<Vec<StreamChunk>> {
        if let Ok(mut buffer) = self.primary_buffer.lock() {
            let mut batch = Vec::new();
            for _ in 0..batchsize.min(buffer.len()) {
                if let Some(chunk) = buffer.pop_front() {
                    batch.push(chunk);
                }
            }
            Ok(batch)
        } else {
            Ok(Vec::new())
        }
    }

    fn get_statistics(&self) -> Result<BufferStatistics> {
        if let Ok(stats) = self.stats.lock() {
            Ok(stats.clone())
        } else {
            Err(DatasetsError::Other(
                "Failed to get buffer statistics".to_string(),
            ))
        }
    }
}

impl PatternDetector {
    fn new() -> Self {
        Self {
            pattern_history: Arc::new(Mutex::new(VecDeque::new())),
            known_patterns: Arc::new(Mutex::new(HashMap::new())),
            detection_params: DetectionParameters {
                similarity_threshold: 0.8,
                window_size: 100,
                update_frequency: 10,
                min_confidence: 0.7,
            },
        }
    }

    fn detect_patterns(&self, _batch: &[StreamChunk]) -> Result<Vec<PatternSignature>> {
        // Placeholder implementation
        Ok(vec![PatternSignature {
            features: Array1::zeros(10),
            pattern_type: PatternType::Unknown,
            confidence: 0.5,
            timestamp: Instant::now(),
        }])
    }
}

impl StreamPerformanceOptimizer {
    fn new() -> Self {
        Self {
            performance_history: Arc::new(Mutex::new(VecDeque::new())),
            strategies: OptimizationStrategies {
                batch_optimization: true,
                buffer_optimization: true,
                parallel_optimization: true,
                memory_optimization: true,
            },
            current_config: Arc::new(Mutex::new(OptimizationConfig {
                optimal_batch_size: 1000,
                optimal_buffer_size: 10000,
                num_workers: num_cpus::get(),
                memory_strategy: MemoryStrategy::Balanced,
            })),
        }
    }

    fn optimize_for_patterns(&self, _patterns: &[PatternSignature]) -> Result<OptimizationConfig> {
        if let Ok(config) = self.current_config.lock() {
            Ok(config.clone())
        } else {
            Err(DatasetsError::Other(
                "Failed to get optimization config".to_string(),
            ))
        }
    }

    fn get_current_metrics(&self) -> Result<PerformanceMetrics> {
        Ok(PerformanceMetrics {
            latency: Duration::from_millis(10),
            throughput: 1000.0,
            memory_efficiency: 0.8,
            cpu_utilization: 0.6,
            timestamp: Instant::now(),
        })
    }
}

impl StreamQualityMonitor {
    fn new() -> Self {
        Self {
            quality_history: Arc::new(Mutex::new(VecDeque::new())),
            thresholds: QualityThresholds {
                min_integrity: 0.95,
                min_completeness: 0.90,
                max_latency: Duration::from_millis(100),
                min_consistency: 0.85,
            },
            alert_system: AlertSystem {
                active_alerts: Arc::new(Mutex::new(Vec::new())),
                callbacks: Arc::new(Mutex::new(Vec::new())),
            },
        }
    }

    fn assess_quality(&self, _results: &[Dataset]) -> Result<()> {
        // Placeholder implementation
        Ok(())
    }

    fn get_current_metrics(&self) -> Result<QualityMetrics> {
        Ok(QualityMetrics {
            integrity_score: 0.95,
            completeness_score: 0.90,
            timeliness_score: 0.85,
            consistency_score: 0.88,
            overall_score: 0.89,
            timestamp: Instant::now(),
        })
    }
}

/// Convenience function to create a new adaptive streaming engine
#[allow(dead_code)]
pub fn create_adaptive_engine() -> AdaptiveStreamingEngine {
    AdaptiveStreamingEngine::new(AdaptiveStreamConfig::default())
}

/// Convenience function to create a streaming engine with custom config
#[allow(dead_code)]
pub fn create_adaptive_engine_with_config(
    _config: AdaptiveStreamConfig,
) -> AdaptiveStreamingEngine {
    AdaptiveStreamingEngine::new(_config)
}

/// Advanced MODE ENHANCEMENTS
/// Advanced quantum-inspired optimization, neural adaptation, and predictive analytics
/// Quantum-Inspired Optimization Engine for Advanced Stream Processing
#[derive(Debug)]
pub struct QuantumInspiredOptimizer {
    /// Quantum state superposition for optimization space exploration
    quantum_states: Vec<QuantumOptimizationState>,
    /// Entanglement matrix for parameter correlations
    entanglement_matrix: Array2<f64>,
    /// Measurement probabilities for state collapse
    measurement_probabilities: Vec<f64>,
    /// Quantum annealing parameters
    annealing_params: QuantumAnnealingParams,
}

/// Quantum optimization state representing superposition of configurations
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct QuantumOptimizationState {
    /// Configuration parameters in superposition
    config_superposition: Vec<ConfigurationAmplitude>,
    /// State energy (fitness)
    energy: f64,
    /// Coherence time
    coherence_time: Duration,
    /// Entanglement degree with other states
    entanglement_degree: f64,
}

/// Configuration amplitude in quantum superposition
#[derive(Debug, Clone)]
pub struct ConfigurationAmplitude {
    /// Configuration parameters
    config: OptimizationConfig,
    /// Quantum amplitude (complex probability)
    amplitude: (f64, f64), // (real, imaginary)
    /// Phase angle
    phase: f64,
}

/// Quantum annealing parameters
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct QuantumAnnealingParams {
    /// Initial temperature
    initial_temperature: f64,
    /// Final temperature
    final_temperature: f64,
    /// Annealing schedule
    schedule: AnnealingSchedule,
    /// Tunneling probability
    tunneling_probability: f64,
}

/// Annealing schedule types
#[derive(Debug, Clone, Copy)]
pub enum AnnealingSchedule {
    /// Linear cooling
    Linear,
    /// Exponential cooling
    Exponential,
    /// Logarithmic cooling
    Logarithmic,
    /// Adaptive cooling based on progress
    Adaptive,
}

impl Default for QuantumInspiredOptimizer {
    fn default() -> Self {
        let num_states = 16; // Quantum register size
        let quantum_states = (0..num_states)
            .map(|_| QuantumOptimizationState::random())
            .collect();

        let entanglement_matrix = Array2::zeros((num_states, num_states));
        let measurement_probabilities = vec![1.0 / num_states as f64; num_states];

        Self {
            quantum_states,
            entanglement_matrix,
            measurement_probabilities,
            annealing_params: QuantumAnnealingParams {
                initial_temperature: 1000.0,
                final_temperature: 0.1,
                schedule: AnnealingSchedule::Adaptive,
                tunneling_probability: 0.3,
            },
        }
    }
}

impl QuantumInspiredOptimizer {
    /// Create new quantum-inspired optimizer
    pub fn new() -> Self {
        Self::default()
    }

    /// Perform quantum optimization step
    pub fn quantum_optimize_step(
        &mut self,
        performance_feedback: &PerformanceMetrics,
    ) -> OptimizationConfig {
        // Update quantum states based on performance _feedback
        self.update_quantum_states(performance_feedback);

        // Apply quantum tunneling for exploration
        self.apply_quantum_tunneling();

        // Entangle states based on correlation
        self.update_entanglement_matrix();

        // Perform quantum measurement to collapse to optimal configuration
        self.quantum_measurement()
    }

    /// Update quantum states based on performance feedback
    fn update_quantum_states(&mut self, performance: &PerformanceMetrics) {
        let performance_score = self.calculate_performance_score(performance);

        for state in &mut self.quantum_states {
            // Update energy based on performance
            state.energy = state.energy * 0.9 + performance_score * 0.1;

            // Update amplitudes based on energy
            for config_amp in &mut state.config_superposition {
                let energy_factor =
                    (-state.energy / self.annealing_params.initial_temperature).exp();
                config_amp.amplitude.0 *= energy_factor;
                config_amp.phase += performance_score * 0.1;
            }
        }
    }

    /// Apply quantum tunneling for exploration
    fn apply_quantum_tunneling(&mut self) {
        for state in &mut self.quantum_states {
            if thread_rng().random::<f64>() < self.annealing_params.tunneling_probability {
                // Quantum tunneling: randomly perturb configuration
                for config_amp in &mut state.config_superposition {
                    if thread_rng().random::<f64>() < 0.1 {
                        // Tunnel to nearby configuration space
                        config_amp.config.optimal_batch_size = (config_amp.config.optimal_batch_size
                            as f64
                            * (1.0 + (thread_rng().random::<f64>() - 0.5) * 0.2))
                            as usize;
                        config_amp.config.optimal_buffer_size =
                            (config_amp.config.optimal_buffer_size as f64
                                * (1.0 + (thread_rng().random::<f64>() - 0.5) * 0.2))
                                as usize;
                    }
                }
            }
        }
    }

    /// Update entanglement matrix based on state correlations
    fn update_entanglement_matrix(&mut self) {
        let n = self.quantum_states.len();
        for i in 0..n {
            for j in i + 1..n {
                let correlation = self.calculate_state_correlation(i, j);
                self.entanglement_matrix[[i, j]] = correlation;
                self.entanglement_matrix[[j, i]] = correlation;
            }
        }
    }

    /// Calculate correlation between quantum states
    fn calculate_state_correlation(&self, i: usize, j: usize) -> f64 {
        let state_i = &self.quantum_states[i];
        let state_j = &self.quantum_states[j];

        // Calculate energy correlation
        let energy_diff = (state_i.energy - state_j.energy).abs();
        let energy_correlation = (-energy_diff / 10.0).exp();

        // Calculate configuration similarity
        let config_similarity = if !state_i.config_superposition.is_empty()
            && !state_j.config_superposition.is_empty()
        {
            let config_i = &state_i.config_superposition[0].config;
            let config_j = &state_j.config_superposition[0].config;

            let batch_similarity = 1.0
                - (config_i.optimal_batch_size as f64 - config_j.optimal_batch_size as f64).abs()
                    / 10000.0;
            let buffer_similarity = 1.0
                - (config_i.optimal_buffer_size as f64 - config_j.optimal_buffer_size as f64).abs()
                    / 100000.0;

            (batch_similarity + buffer_similarity) / 2.0
        } else {
            0.0
        };

        (energy_correlation + config_similarity) / 2.0
    }

    /// Perform quantum measurement to collapse superposition
    fn quantum_measurement(&mut self) -> OptimizationConfig {
        // Update measurement probabilities based on state energies
        let total_energy: f64 = self.quantum_states.iter().map(|s| (-s.energy).exp()).sum();

        for (i, state) in self.quantum_states.iter().enumerate() {
            self.measurement_probabilities[i] = (-state.energy).exp() / total_energy;
        }

        // Quantum measurement - probabilistic state selection
        let random_value = thread_rng().random::<f64>();
        let mut cumulative_prob = 0.0;

        for (i, &prob) in self.measurement_probabilities.iter().enumerate() {
            cumulative_prob += prob;
            if random_value <= cumulative_prob {
                // State collapsed - return corresponding configuration
                return if !self.quantum_states[i].config_superposition.is_empty() {
                    self.quantum_states[i].config_superposition[0]
                        .config
                        .clone()
                } else {
                    OptimizationConfig::default()
                };
            }
        }

        // Fallback
        OptimizationConfig::default()
    }

    /// Calculate performance score from metrics
    fn calculate_performance_score(&self, performance: &PerformanceMetrics) -> f64 {
        let latency_score = 1.0 / (1.0 + performance.latency.as_millis() as f64 / 1000.0);
        let throughput_score = (performance.throughput / 10000.0).min(1.0);
        let efficiency_score = performance.memory_efficiency;
        let cpu_score = 1.0 - performance.cpu_utilization; // Lower CPU usage is better

        (latency_score + throughput_score + efficiency_score + cpu_score) / 4.0
    }
}

impl QuantumOptimizationState {
    /// Create random quantum state
    fn random() -> Self {
        let config_superposition = (0..4)
            .map(|_| ConfigurationAmplitude {
                config: OptimizationConfig {
                    optimal_batch_size: thread_rng().gen_range(500..2000),
                    optimal_buffer_size: thread_rng().gen_range(5000..20000),
                    num_workers: thread_rng().gen_range(1..9),
                    memory_strategy: match thread_rng().gen_range(0..4) {
                        0 => MemoryStrategy::Conservative,
                        1 => MemoryStrategy::Balanced,
                        2 => MemoryStrategy::Aggressive,
                        _ => MemoryStrategy::Adaptive,
                    },
                },
                amplitude: (thread_rng().random::<f64>(), thread_rng().random::<f64>()),
                phase: thread_rng().random::<f64>() * 2.0 * std::f64::consts::PI,
            })
            .collect();

        Self {
            config_superposition,
            energy: thread_rng().random::<f64>() * 10.0,
            coherence_time: Duration::from_millis(thread_rng().gen_range(100..1000)),
            entanglement_degree: thread_rng().random::<f64>(),
        }
    }
}

impl Default for OptimizationConfig {
    fn default() -> Self {
        Self {
            optimal_batch_size: 1000,
            optimal_buffer_size: 10000,
            num_workers: num_cpus::get(),
            memory_strategy: MemoryStrategy::Balanced,
        }
    }
}

/// Neural Adaptive Learning System for Stream Optimization
#[derive(Debug)]
pub struct NeuralAdaptiveSystem {
    /// Multi-layer neural network for pattern recognition
    neural_network: AdaptiveNeuralNetwork,
    /// Learning history
    learning_history: VecDeque<LearningEpisode>,
    /// Adaptation parameters
    adaptation_params: AdaptationParameters,
    /// Performance prediction model
    prediction_model: PerformancePredictionModel,
}

/// Adaptive neural network with dynamic architecture
#[derive(Debug)]
pub struct AdaptiveNeuralNetwork {
    /// Network layers
    layers: Vec<NeuralLayer>,
    /// Learning rate schedule
    learning_rate_schedule: LearningRateSchedule,
    /// Dropout rates for regularization
    dropout_rates: Vec<f64>,
    /// Architecture modification history
    architecture_history: VecDeque<ArchitectureChange>,
}

/// Neural network layer
#[derive(Debug, Clone)]
pub struct NeuralLayer {
    /// Weight matrix
    weights: Array2<f64>,
    /// Bias vector
    bias: Array1<f64>,
    /// Activation function
    activation: ActivationFunction,
    /// Layer type
    layer_type: LayerType,
}

/// Activation function types
#[derive(Debug, Clone, Copy)]
pub enum ActivationFunction {
    /// Rectified Linear Unit
    ReLU,
    /// Leaky ReLU
    LeakyReLU,
    /// Sigmoid
    Sigmoid,
    /// Hyperbolic tangent
    Tanh,
    /// Swish activation
    Swish,
    /// GELU activation
    GELU,
}

/// Neural layer types
#[derive(Debug, Clone, Copy)]
pub enum LayerType {
    /// Dense/fully connected layer
    Dense,
    /// Convolutional layer
    Convolutional,
    /// Recurrent layer
    Recurrent,
    /// Attention layer
    Attention,
}

/// Learning rate schedule
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct LearningRateSchedule {
    /// Initial learning rate
    initial_rate: f64,
    /// Current learning rate
    current_rate: f64,
    /// Decay factor
    decay_factor: f64,
    /// Schedule type
    schedule_type: ScheduleType,
}

/// Learning rate schedule types
#[derive(Debug, Clone, Copy)]
pub enum ScheduleType {
    /// Constant learning rate
    Constant,
    /// Exponential decay
    ExponentialDecay,
    /// Step decay
    StepDecay,
    /// Cosine annealing
    CosineAnnealing,
    /// Adaptive based on performance
    Adaptive,
}

/// Architecture change record
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct ArchitectureChange {
    /// Change type
    change_type: ChangeType,
    /// Performance before change
    performance_before: f64,
    /// Performance after change
    performance_after: f64,
    /// Timestamp
    timestamp: Instant,
}

/// Architecture change types
#[derive(Debug, Clone, Copy)]
pub enum ChangeType {
    /// Add layer
    AddLayer,
    /// Remove layer
    RemoveLayer,
    /// Modify layer size
    ModifyLayerSize,
    /// Change activation function
    ChangeActivation,
    /// Adjust learning rate
    AdjustLearningRate,
}

/// Learning episode record
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct LearningEpisode {
    /// Input features
    input_features: Array1<f64>,
    /// Target output
    target_output: Array1<f64>,
    /// Predicted output
    predicted_output: Array1<f64>,
    /// Prediction error
    prediction_error: f64,
    /// Learning timestamp
    timestamp: Instant,
}

/// Adaptation parameters
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct AdaptationParameters {
    /// Learning rate
    learning_rate: f64,
    /// Momentum factor
    momentum: f64,
    /// Regularization strength
    regularization: f64,
    /// Architecture adaptation threshold
    adaptation_threshold: f64,
    /// Maximum network size
    max_network_size: usize,
}

/// Performance prediction model
#[derive(Debug)]
#[allow(dead_code)]
pub struct PerformancePredictionModel {
    /// Historical performance data
    performance_history: VecDeque<PerformancePredictionPoint>,
    /// Prediction horizon
    prediction_horizon: Duration,
    /// Model parameters
    model_params: PredictionModelParams,
}

/// Performance prediction data point
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct PerformancePredictionPoint {
    /// Input features
    features: Array1<f64>,
    /// Actual performance
    actual_performance: f64,
    /// Predicted performance
    predicted_performance: f64,
    /// Confidence interval
    confidence_interval: (f64, f64),
    /// Timestamp
    timestamp: Instant,
}

/// Prediction model parameters
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct PredictionModelParams {
    /// Time series model order
    model_order: usize,
    /// Trend component weight
    trend_weight: f64,
    /// Seasonal component weight
    seasonal_weight: f64,
    /// Noise variance
    noise_variance: f64,
}

impl Default for NeuralAdaptiveSystem {
    fn default() -> Self {
        Self {
            neural_network: AdaptiveNeuralNetwork::new(),
            learning_history: VecDeque::with_capacity(10000),
            adaptation_params: AdaptationParameters {
                learning_rate: 0.001,
                momentum: 0.9,
                regularization: 0.001,
                adaptation_threshold: 0.05,
                max_network_size: 1000,
            },
            prediction_model: PerformancePredictionModel::new(),
        }
    }
}

impl NeuralAdaptiveSystem {
    /// Create new neural adaptive system
    pub fn new() -> Self {
        Self::default()
    }

    /// Learn from streaming data and adapt
    pub fn learn_and_adapt(
        &mut self,
        input: &Array1<f64>,
        target: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        // Forward pass
        let prediction = self.neural_network.forward(input)?;

        // Calculate prediction error
        let error = self.calculate_prediction_error(&prediction, target);

        // Record learning episode
        self.learning_history.push_back(LearningEpisode {
            input_features: input.clone(),
            target_output: target.clone(),
            predicted_output: prediction.clone(),
            prediction_error: error,
            timestamp: Instant::now(),
        });

        // Backward pass and learning
        self.neural_network
            .backward_and_update(&prediction, target, &self.adaptation_params)?;

        // Adapt architecture if needed
        if error > self.adaptation_params.adaptation_threshold {
            self.adapt_architecture(error)?;
        }

        // Update prediction model
        self.prediction_model.update(input, error);

        Ok(prediction)
    }

    /// Predict future performance
    pub fn predict_performance(&self, horizon: Duration) -> Result<PerformancePredictionPoint> {
        self.prediction_model.predict(horizon)
    }

    /// Calculate prediction error
    fn calculate_prediction_error(&self, prediction: &Array1<f64>, target: &Array1<f64>) -> f64 {
        prediction
            .iter()
            .zip(target.iter())
            .map(|(p, t)| (p - t).powi(2))
            .sum::<f64>()
            / prediction.len() as f64
    }

    /// Adapt neural network architecture based on performance
    fn adapt_architecture(&mut self, error: f64) -> Result<()> {
        let performance_before = 1.0 / (1.0 + error);

        // Decide on architecture change based on recent performance
        let change_type = if self.learning_history.len() > 100 {
            let recent_errors: Vec<f64> = self
                .learning_history
                .iter()
                .rev()
                .take(100)
                .map(|episode| episode.prediction_error)
                .collect();

            let avg_error = recent_errors.iter().sum::<f64>() / recent_errors.len() as f64;

            if avg_error > self.adaptation_params.adaptation_threshold * 2.0 {
                ChangeType::AddLayer
            } else if avg_error < self.adaptation_params.adaptation_threshold * 0.5 {
                ChangeType::ModifyLayerSize
            } else {
                ChangeType::AdjustLearningRate
            }
        } else {
            ChangeType::AdjustLearningRate
        };

        // Apply architecture change
        match change_type {
            ChangeType::AddLayer => {
                if self.neural_network.layers.len() < 10 {
                    self.neural_network
                        .add_layer(64, ActivationFunction::ReLU, LayerType::Dense);
                }
            }
            ChangeType::ModifyLayerSize => {
                if !self.neural_network.layers.is_empty() {
                    let layer_idx = thread_rng().gen_range(0..self.neural_network.layers.len());
                    self.neural_network.modify_layer_size(layer_idx, 32);
                }
            }
            ChangeType::AdjustLearningRate => {
                self.neural_network.learning_rate_schedule.current_rate *= 0.95;
            }
            _ => {} // Other changes not implemented yet
        }

        // Record architecture change
        self.neural_network
            .architecture_history
            .push_back(ArchitectureChange {
                change_type,
                performance_before,
                performance_after: 1.0 / (1.0 + error), // Will be updated later
                timestamp: Instant::now(),
            });

        Ok(())
    }

    /// Get learning statistics
    pub fn get_learning_stats(&self) -> LearningStatistics {
        if self.learning_history.is_empty() {
            return LearningStatistics::default();
        }

        let recent_episodes = self
            .learning_history
            .iter()
            .rev()
            .take(1000)
            .collect::<Vec<_>>();

        let avg_error = recent_episodes
            .iter()
            .map(|episode| episode.prediction_error)
            .sum::<f64>()
            / recent_episodes.len() as f64;

        let error_trend = if recent_episodes.len() >= 100 {
            let first_half_error = recent_episodes
                .iter()
                .take(50)
                .map(|episode| episode.prediction_error)
                .sum::<f64>()
                / 50.0;

            let second_half_error = recent_episodes
                .iter()
                .skip(50)
                .take(50)
                .map(|episode| episode.prediction_error)
                .sum::<f64>()
                / 50.0;

            if second_half_error < first_half_error {
                LearningTrend::Improving
            } else if second_half_error > first_half_error * 1.1 {
                LearningTrend::Degrading
            } else {
                LearningTrend::Stable
            }
        } else {
            LearningTrend::Unknown
        };

        LearningStatistics {
            average_error: avg_error,
            learning_trend: error_trend,
            total_episodes: self.learning_history.len(),
            architecture_changes: self.neural_network.architecture_history.len(),
            current_learning_rate: self.neural_network.learning_rate_schedule.current_rate,
        }
    }
}

/// Learning statistics summary
#[derive(Debug, Clone)]
pub struct LearningStatistics {
    /// Average prediction error
    pub average_error: f64,
    /// Learning trend
    pub learning_trend: LearningTrend,
    /// Total learning episodes
    pub total_episodes: usize,
    /// Number of architecture changes
    pub architecture_changes: usize,
    /// Current learning rate
    pub current_learning_rate: f64,
}

impl Default for LearningStatistics {
    fn default() -> Self {
        Self {
            average_error: 0.0,
            learning_trend: LearningTrend::Unknown,
            total_episodes: 0,
            architecture_changes: 0,
            current_learning_rate: 0.001,
        }
    }
}

/// Learning trend indicators
#[derive(Debug, Clone, Copy)]
pub enum LearningTrend {
    /// Learning is improving
    Improving,
    /// Learning is degrading
    Degrading,
    /// Learning is stable
    Stable,
    /// Insufficient data
    Unknown,
}

impl AdaptiveNeuralNetwork {
    /// Create new adaptive neural network
    fn new() -> Self {
        Self {
            layers: vec![
                NeuralLayer::new(10, 64, ActivationFunction::ReLU, LayerType::Dense),
                NeuralLayer::new(64, 32, ActivationFunction::ReLU, LayerType::Dense),
                NeuralLayer::new(32, 1, ActivationFunction::Sigmoid, LayerType::Dense),
            ],
            learning_rate_schedule: LearningRateSchedule {
                initial_rate: 0.001,
                current_rate: 0.001,
                decay_factor: 0.995,
                schedule_type: ScheduleType::Adaptive,
            },
            dropout_rates: vec![0.0, 0.2, 0.1],
            architecture_history: VecDeque::with_capacity(1000),
        }
    }

    /// Forward pass through network
    fn forward(&self, input: &Array1<f64>) -> Result<Array1<f64>> {
        let mut current_output = input.clone();

        for layer in &self.layers {
            current_output = layer.forward(&current_output)?;
        }

        Ok(current_output)
    }

    /// Backward pass and parameter update
    fn backward_and_update(
        &mut self,
        prediction: &Array1<f64>,
        target: &Array1<f64>,
        params: &AdaptationParameters,
    ) -> Result<()> {
        // Simplified backward pass (in real implementation, this would be more sophisticated)
        let error = prediction
            .iter()
            .zip(target.iter())
            .map(|(p, t)| p - t)
            .collect::<Vec<_>>();

        // Update learning rate based on schedule
        self.update_learning_rate(&error);

        // Update weights (simplified gradient descent)
        for layer in &mut self.layers {
            layer.update_weights(self.learning_rate_schedule.current_rate, params.momentum);
        }

        Ok(())
    }

    /// Add new layer to network
    fn add_layer(&mut self, size: usize, activation: ActivationFunction, layertype: LayerType) {
        if self.layers.len() < 2 {
            return;
        }

        let insert_position = self.layers.len() - 1;
        let prev_layer_size = self.layers[insert_position - 1].weights.ncols();
        let _next_layer_size = self.layers[insert_position].weights.nrows();

        // Create new layer
        let new_layer = NeuralLayer::new(prev_layer_size, size, activation, layertype);

        // Modify next layer to accept new input size
        self.layers[insert_position].resize_input(size);

        // Insert new layer
        self.layers.insert(insert_position, new_layer);
        self.dropout_rates.insert(insert_position, 0.1);
    }

    /// Modify layer size
    fn modify_layer_size(&mut self, layer_idx: usize, newsize: usize) {
        if layer_idx >= self.layers.len() || layer_idx == 0 || layer_idx == self.layers.len() - 1 {
            return; // Don't modify input or output layers
        }

        let input_size = self.layers[layer_idx - 1].weights.ncols();
        let _output_size = if layer_idx + 1 < self.layers.len() {
            self.layers[layer_idx + 1].weights.nrows()
        } else {
            newsize
        };

        // Recreate layer with new _size
        self.layers[layer_idx] = NeuralLayer::new(
            input_size,
            newsize,
            self.layers[layer_idx].activation,
            self.layers[layer_idx].layer_type,
        );

        // Update next layer if exists
        if layer_idx + 1 < self.layers.len() {
            self.layers[layer_idx + 1].resize_input(newsize);
        }
    }

    /// Update learning rate based on performance
    fn update_learning_rate(&mut self, error: &[f64]) {
        let avg_error = error.iter().sum::<f64>() / error.len() as f64;

        match self.learning_rate_schedule.schedule_type {
            ScheduleType::Adaptive => {
                if avg_error > 0.1 {
                    self.learning_rate_schedule.current_rate *= 1.01; // Increase for high error
                } else if avg_error < 0.01 {
                    self.learning_rate_schedule.current_rate *= 0.99; // Decrease for low error
                }
            }
            ScheduleType::ExponentialDecay => {
                self.learning_rate_schedule.current_rate *=
                    self.learning_rate_schedule.decay_factor;
            }
            _ => {} // Other schedules not implemented
        }

        // Clamp learning rate
        self.learning_rate_schedule.current_rate =
            self.learning_rate_schedule.current_rate.clamp(1e-6, 1.0);
    }
}

impl NeuralLayer {
    /// Create new neural layer
    fn new(
        input_size: usize,
        output_size: usize,
        activation: ActivationFunction,
        layer_type: LayerType,
    ) -> Self {
        let weights = Array2::from_shape_fn((output_size, input_size), |_| {
            thread_rng().random::<f64>() * 0.01 - 0.005 // Small random initialization
        });

        let bias = Array1::zeros(output_size);

        Self {
            weights,
            bias,
            activation,
            layer_type,
        }
    }

    /// Forward pass through layer
    fn forward(&self, input: &Array1<f64>) -> Result<Array1<f64>> {
        if input.len() != self.weights.ncols() {
            return Err(DatasetsError::Other(format!(
                "Input size {} doesn't match layer input size {}",
                input.len(),
                self.weights.ncols()
            )));
        }

        // Linear transformation: output = weights * input + bias
        let linear_output = self.weights.dot(input) + &self.bias;

        // Apply activation function
        let activated_output = self.apply_activation(&linear_output);

        Ok(activated_output)
    }

    /// Apply activation function
    fn apply_activation(&self, input: &Array1<f64>) -> Array1<f64> {
        input.mapv(|x| match self.activation {
            ActivationFunction::ReLU => x.max(0.0),
            ActivationFunction::LeakyReLU => {
                if x > 0.0 {
                    x
                } else {
                    0.01 * x
                }
            }
            ActivationFunction::Sigmoid => 1.0 / (1.0 + (-x).exp()),
            ActivationFunction::Tanh => x.tanh(),
            ActivationFunction::Swish => x / (1.0 + (-x).exp()),
            ActivationFunction::GELU => {
                // GELU approximation: 0.5 * x * (1 + tanh(sqrt(2/π) * (x + 0.044715 * x^3)))
                let sqrt_2_pi = (2.0 / std::f64::consts::PI).sqrt();
                let approx = sqrt_2_pi * (x + 0.044715 * x.powi(3));
                0.5 * x * (1.0 + approx.tanh())
            }
        })
    }

    /// Update layer weights
    fn update_weights(&mut self, learning_rate: f64, _momentum: f64) {
        // Simplified weight update (in real implementation, this would use gradients)
        let weight_update = Array2::from_shape_fn(self.weights.dim(), |_| {
            (thread_rng().random::<f64>() - 0.5) * learning_rate * 0.001
        });

        self.weights = &self.weights - &weight_update;

        // Simple bias update
        let bias_update = Array1::from_shape_fn(self.bias.len(), |_| {
            (thread_rng().random::<f64>() - 0.5) * learning_rate * 0.001
        });

        self.bias = &self.bias - &bias_update;
    }

    /// Resize input dimension
    fn resize_input(&mut self, new_inputsize: usize) {
        let output_size = self.weights.nrows();

        // Create new weights matrix with different input _size
        self.weights = Array2::from_shape_fn((output_size, new_inputsize), |_| {
            thread_rng().random::<f64>() * 0.01 - 0.005
        });
    }
}

impl PerformancePredictionModel {
    /// Create new performance prediction model
    fn new() -> Self {
        Self {
            performance_history: VecDeque::with_capacity(10000),
            prediction_horizon: Duration::from_secs(60),
            model_params: PredictionModelParams {
                model_order: 10,
                trend_weight: 0.3,
                seasonal_weight: 0.2,
                noise_variance: 0.01,
            },
        }
    }

    /// Update model with new performance data
    fn update(&mut self, features: &Array1<f64>, performance: f64) {
        let prediction_point = PerformancePredictionPoint {
            features: features.clone(),
            actual_performance: performance,
            predicted_performance: 0.0, // Will be updated when prediction is made
            confidence_interval: (0.0, 0.0),
            timestamp: Instant::now(),
        };

        if self.performance_history.len() >= 10000 {
            self.performance_history.pop_front();
        }

        self.performance_history.push_back(prediction_point);
    }

    /// Predict future performance
    fn predict(&self, _horizon: Duration) -> Result<PerformancePredictionPoint> {
        if self.performance_history.is_empty() {
            return Ok(PerformancePredictionPoint {
                features: Array1::zeros(1),
                actual_performance: 0.0,
                predicted_performance: 0.5,
                confidence_interval: (0.0, 1.0),
                timestamp: Instant::now(),
            });
        }

        // Simple trend-based prediction
        let recent_performance: Vec<f64> = self
            .performance_history
            .iter()
            .rev()
            .take(self.model_params.model_order)
            .map(|point| point.actual_performance)
            .collect();

        let prediction = if recent_performance.len() >= 2 {
            let trend = (recent_performance[0] - recent_performance[recent_performance.len() - 1])
                / (recent_performance.len() - 1) as f64;

            recent_performance[0] + trend * self.model_params.trend_weight
        } else {
            recent_performance.first().copied().unwrap_or(0.5)
        };

        let confidence_width = self.model_params.noise_variance.sqrt() * 2.0;

        Ok(PerformancePredictionPoint {
            features: Array1::zeros(1),
            actual_performance: 0.0,
            predicted_performance: prediction.clamp(0.0, 1.0),
            confidence_interval: (
                (prediction - confidence_width).clamp(0.0, f64::MAX),
                (prediction + confidence_width).clamp(0.0, 1.0),
            ),
            timestamp: Instant::now(),
        })
    }
}

/// Enhanced Adaptive Streaming Engine with Quantum and Neural Optimization
impl AdaptiveStreamingEngine {
    /// Create advanced streaming engine with quantum and neural optimization
    pub fn with_quantum_neural_optimization(config: AdaptiveStreamConfig) -> Self {
        // In a full implementation, this would integrate:
        // - QuantumInspiredOptimizer for parameter optimization
        // - NeuralAdaptiveSystem for pattern learning
        // - Advanced prediction models

        Self::new(config)
    }

    /// Optimize using quantum-inspired algorithms
    pub fn quantum_optimize(
        &mut self,
        performance_metrics: &PerformanceMetrics,
    ) -> Result<OptimizationConfig> {
        let mut quantum_optimizer = QuantumInspiredOptimizer::new();
        let optimized_config = quantum_optimizer.quantum_optimize_step(performance_metrics);
        Ok(optimized_config)
    }

    /// Learn and adapt using neural system
    pub fn neural_adapt(
        &mut self,
        features: &Array1<f64>,
        targets: &Array1<f64>,
    ) -> Result<LearningStatistics> {
        let mut neural_system = NeuralAdaptiveSystem::new();
        neural_system.learn_and_adapt(features, targets)?;
        Ok(neural_system.get_learning_stats())
    }

    /// Predict future performance using advanced models
    pub fn predict_future_performance(
        &self,
        horizon: Duration,
    ) -> Result<PerformancePredictionPoint> {
        let prediction_model = PerformancePredictionModel::new();
        prediction_model.predict(horizon)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[allow(dead_code)]
    fn create_test_chunk() -> StreamChunk {
        let data = Array2::from_shape_vec((10, 5), (0..50).map(|x| x as f64).collect()).unwrap();
        StreamChunk {
            data,
            timestamp: Instant::now(),
            metadata: ChunkMetadata {
                source_id: "test".to_string(),
                sequence_number: 1,
                characteristics: DataCharacteristics {
                    moments: StatisticalMoments {
                        mean: 25.0,
                        variance: 100.0,
                        skewness: 0.0,
                        kurtosis: 0.0,
                    },
                    entropy: 1.0,
                    trend: TrendIndicators {
                        linear_slope: 1.0,
                        trend_strength: 0.8,
                        direction: TrendDirection::Increasing,
                        seasonality: 0.2,
                    },
                    anomaly_score: 0.1,
                },
            },
            quality_score: 0.9,
        }
    }

    #[test]
    fn test_adaptive_engine_creation() {
        let engine = create_adaptive_engine();
        assert!(engine.config.ml_optimization);
    }

    #[test]
    fn test_statistical_moments_calculation() {
        let engine = create_adaptive_engine();
        let data = Array2::from_shape_vec(
            (5, 3),
            vec![
                1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0,
            ],
        )
        .unwrap();
        let moments = engine.calculate_statistical_moments(&data);
        assert!(moments.is_ok());
        let moments = moments.unwrap();
        assert!(moments.mean > 0.0);
        assert!(moments.variance >= 0.0);
    }
}