scirs2-stats 0.3.4

//! Advanced-advanced streaming analytics framework for scirs2-stats v1.0.0+
//!
//! This module provides real-time, high-throughput streaming statistical
//! computations with advanced features like adaptive windowing, incremental
//! machine learning, distributed processing, and intelligent memory management.
//! It supports the "Streaming Analytics" roadmap goal for Integration & Ecosystem.

use crate::error::StatsResult;
use scirs2_core::ndarray::{Array1, ArrayView1};
use scirs2_core::numeric::{Float, NumCast, One, Zero};
use scirs2_core::{simd_ops::SimdUnifiedOps, validation::*};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::marker::PhantomData;
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};

/// Configuration for advanced streaming analytics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdvancedStreamingConfig {
    /// Default window size for streaming operations
    pub default_windowsize: usize,
    /// Enable adaptive windowing based on data characteristics
    pub adaptive_windowing: bool,
    /// Maximum memory usage for buffering (bytes)
    pub max_buffer_memory: usize,
    /// Enable real-time change point detection
    pub change_point_detection: bool,
    /// Enable incremental machine learning models
    pub incremental_ml: bool,
    /// Enable distributed streaming processing
    pub distributed_processing: bool,
    /// Data ingestion rate threshold for optimization switching
    pub high_throughput_threshold: f64,
    /// Enable anomaly detection in streams
    pub anomaly_detection: bool,
    /// Statistical significance level for change detection
    pub significance_level: f64,
    /// Enable intelligent compression for historical data
    pub intelligent_compression: bool,
    /// Real-time visualization updates
    pub realtime_visualization: bool,
    /// Enable approximate algorithms for extreme throughput
    pub approximate_algorithms: bool,
}

impl Default for AdvancedStreamingConfig {
    fn default() -> Self {
        Self {
            default_windowsize: 1000,
            adaptive_windowing: true,
            max_buffer_memory: 100 * 1024 * 1024, // 100MB
            change_point_detection: true,
            incremental_ml: true,
            distributed_processing: false,
            high_throughput_threshold: 10000.0, // samples per second
            anomaly_detection: true,
            significance_level: 0.05,
            intelligent_compression: true,
            realtime_visualization: false,
            approximate_algorithms: false,
        }
    }
}

/// Windowing strategies for streaming data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum WindowingStrategy {
    /// Fixed-size sliding window
    Sliding { size: usize },
    /// Tumbling window (non-overlapping)
    Tumbling { size: usize },
    /// Session-based windowing
    Session { timeout: Duration },
    /// Time-based windowing
    TimeBased { duration: Duration },
    /// Adaptive windowing based on data characteristics
    Adaptive {
        minsize: usize,
        maxsize: usize,
        adaptation_rate: f64,
    },
    /// Event-driven windowing
    EventDriven { trigger_condition: String },
}

/// Stream processing modes
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum StreamProcessingMode {
    /// Real-time processing with minimal latency
    RealTime,
    /// Micro-batch processing for higher throughput
    MicroBatch { batchsize: usize },
    /// Adaptive mode switching based on load
    Adaptive,
    /// Event-driven processing
    EventDriven,
}

/// Real-time statistical metrics for streaming data
#[derive(Debug, Clone)]
pub struct StreamingStatistics<F> {
    pub count: usize,
    pub mean: F,
    pub variance: F,
    pub std_dev: F,
    pub min: F,
    pub max: F,
    pub skewness: F,
    pub kurtosis: F,
    pub last_update: Instant,
    pub throughput: f64, // samples per second
    pub memory_usage: usize,
    pub change_points: Vec<Instant>,
    pub anomalies: Vec<(Instant, F)>,
}

/// Advanced streaming processor with multiple algorithms
pub struct AdvancedAdvancedStreamingProcessor<F> {
    config: AdvancedStreamingConfig,
    windowing_strategy: WindowingStrategy,
    processing_mode: StreamProcessingMode,
    buffer: Arc<RwLock<VecDeque<(Instant, F)>>>,
    statistics: Arc<RwLock<StreamingStatistics<F>>>,
    change_detector: Arc<Mutex<ChangePointDetector<F>>>,
    anomaly_detector: Arc<Mutex<AnomalyDetector<F>>>,
    ml_model: Option<Arc<Mutex<IncrementalMLModel<F>>>>,
    compression_engine: Arc<Mutex<CompressionEngine<F>>>,
    _phantom: PhantomData<F>,
}

/// Change point detection using advanced algorithms
pub struct ChangePointDetector<F> {
    algorithm: ChangePointAlgorithm,
    windowdata: VecDeque<F>,
    threshold: f64,
    last_detection: Option<Instant>,
    _phantom: PhantomData<F>,
}

/// Change point detection algorithms
#[derive(Debug, Clone)]
pub enum ChangePointAlgorithm {
    /// CUSUM (Cumulative Sum) algorithm
    CUSUM { drift: f64, threshold: f64 },
    /// Bayesian Online Change Point Detection
    BOCPD { hazard_rate: f64 },
    /// Exponentially Weighted Moving Average
    EWMA { lambda: f64, threshold: f64 },
    /// Page-Hinkley test
    PageHinkley { delta: f64, threshold: f64 },
    /// Adaptive Windowing (ADWIN)
    ADWIN { confidence: f64 },
}

/// Real-time anomaly detection
pub struct AnomalyDetector<F> {
    algorithm: AnomalyDetectionAlgorithm,
    baseline_statistics: StreamingStatistics<F>,
    detection_threshold: f64,
    anomaly_history: VecDeque<(Instant, F, AnomalyType)>,
    _phantom: PhantomData<F>,
}

/// Anomaly detection algorithms
#[derive(Debug, Clone)]
pub enum AnomalyDetectionAlgorithm {
    /// Z-score based detection
    ZScore { threshold: f64 },
    /// Interquartile Range (IQR) method
    IQR { factor: f64 },
    /// Isolation Forest (approximate)
    IsolationForest { contamination: f64 },
    /// Local Outlier Factor (LOF)
    LOF { neighbors: usize },
    /// One-Class SVM (incremental)
    OneClassSVM { nu: f64, gamma: f64 },
}

/// Types of anomalies
#[derive(Debug, Clone)]
pub enum AnomalyType {
    PointAnomaly,
    ContextualAnomaly,
    CollectiveAnomaly,
}

/// Incremental machine learning model for streaming data
pub struct IncrementalMLModel<F> {
    model_type: MLModelType,
    parameters: HashMap<String, F>,
    trainingdata: VecDeque<Array1<F>>,
    model_performance: ModelPerformance<F>,
    _phantom: PhantomData<F>,
}

/// Types of incremental ML models
#[derive(Debug, Clone)]
pub enum MLModelType {
    /// Online Linear Regression
    OnlineLinearRegression,
    /// Incremental PCA
    IncrementalPCA { components: usize },
    /// Online K-Means
    OnlineKMeans { k: usize },
    /// Streaming Random Forest
    StreamingRandomForest { trees: usize },
    /// Online Neural Network
    OnlineNeuralNetwork { layers: Vec<usize> },
}

/// Model performance metrics
#[derive(Debug, Clone)]
pub struct ModelPerformance<F> {
    pub accuracy: F,
    pub precision: F,
    pub recall: F,
    pub f1_score: F,
    pub training_samples: usize,
    pub last_updated: Instant,
}

/// Intelligent data compression for streaming analytics
pub struct CompressionEngine<F> {
    algorithm: CompressionAlgorithm,
    compression_ratio: f64,
    historicaldata: VecDeque<CompressedDataPoint<F>>,
    metadata: CompressionMetadata,
    _phantom: PhantomData<F>,
}

/// Compression algorithms for streaming data
#[derive(Debug, Clone)]
pub enum CompressionAlgorithm {
    /// Piecewise Aggregate Approximation (PAA)
    PAA { segments: usize },
    /// Symbolic Aggregate approXimation (SAX)
    SAX {
        alphabetsize: usize,
        segments: usize,
    },
    /// Discrete Fourier Transform compression
    DFT { coefficients: usize },
    /// Wavelet compression
    Wavelet { levels: usize, threshold: f64 },
    /// Adaptive compression based on data characteristics
    Adaptive,
}

/// Compressed data point with reconstruction capability
#[derive(Debug, Clone)]
pub struct CompressedDataPoint<F> {
    pub timestamp: Instant,
    pub compressed_value: Vec<F>,
    pub compression_metadata: String,
    pub reconstruction_error: F,
}

/// Compression metadata and statistics
#[derive(Debug, Clone)]
pub struct CompressionMetadata {
    pub originalsize: usize,
    pub compressedsize: usize,
    pub compression_ratio: f64,
    pub reconstruction_accuracy: f64,
    pub algorithm_used: String,
}

/// Results from streaming analytics operations
#[derive(Debug, Clone)]
pub struct StreamingAnalyticsResult<F> {
    pub real_time_statistics: StreamingStatistics<F>,
    pub change_points: Vec<ChangePointEvent>,
    pub anomalies: Vec<AnomalyEvent<F>>,
    pub ml_predictions: Option<Vec<F>>,
    pub compression_summary: CompressionSummary,
    pub performance_metrics: StreamingPerformanceMetrics,
    pub recommendations: Vec<StreamingRecommendation>,
}

/// Change point detection event
#[derive(Debug, Clone)]
pub struct ChangePointEvent {
    pub timestamp: Instant,
    pub confidence: f64,
    pub algorithm: String,
    pub statistical_significance: f64,
    pub description: String,
}

/// Anomaly detection event
#[derive(Debug, Clone)]
pub struct AnomalyEvent<F> {
    pub timestamp: Instant,
    pub value: F,
    pub anomaly_type: AnomalyType,
    pub severity: AnomalySeverity,
    pub confidence: f64,
    pub context: String,
}

/// Anomaly severity levels
#[derive(Debug, Clone)]
pub enum AnomalySeverity {
    Low,
    Medium,
    High,
    Critical,
}

/// Compression summary statistics
#[derive(Debug, Clone)]
pub struct CompressionSummary {
    pub total_compressed_points: usize,
    pub average_compression_ratio: f64,
    pub memory_saved: usize,
    pub reconstruction_accuracy: f64,
    pub compression_latency: Duration,
}

/// Performance metrics for streaming operations
#[derive(Debug, Clone)]
pub struct StreamingPerformanceMetrics {
    pub throughput_samples_per_sec: f64,
    pub latency_microseconds: f64,
    pub memory_usage_mb: f64,
    pub cpu_utilization_percent: f64,
    pub accuracy_vs_batch: f64,
    pub data_freshness_seconds: f64,
}

/// Recommendations for optimizing streaming performance
#[derive(Debug, Clone)]
pub struct StreamingRecommendation {
    pub category: RecommendationCategory,
    pub message: String,
    pub priority: RecommendationPriority,
    pub estimated_impact: f64,
}

/// Categories of streaming recommendations
#[derive(Debug, Clone)]
pub enum RecommendationCategory {
    WindowingStrategy,
    ProcessingMode,
    MemoryOptimization,
    AlgorithmSelection,
    PerformanceTuning,
    AnomalyDetection,
    Compression,
}

/// Priority levels for recommendations
#[derive(Debug, Clone)]
pub enum RecommendationPriority {
    Low,
    Medium,
    High,
    Critical,
}

impl<F> AdvancedAdvancedStreamingProcessor<F>
where
    F: Float
        + NumCast
        + SimdUnifiedOps
        + Zero
        + One
        + PartialOrd
        + Copy
        + Send
        + Sync
        + 'static
        + std::fmt::Display,
{
    /// Create a new advanced streaming processor
    pub fn new(config: AdvancedStreamingConfig) -> Self {
        let windowing_strategy = WindowingStrategy::Sliding {
            size: config.default_windowsize,
        };
        let processing_mode = StreamProcessingMode::Adaptive;

        let statistics = StreamingStatistics {
            count: 0,
            mean: F::zero(),
            variance: F::zero(),
            std_dev: F::zero(),
            min: F::infinity(),
            max: F::neg_infinity(),
            skewness: F::zero(),
            kurtosis: F::zero(),
            last_update: Instant::now(),
            throughput: 0.0,
            memory_usage: 0,
            change_points: Vec::new(),
            anomalies: Vec::new(),
        };

        Self {
            config,
            windowing_strategy,
            processing_mode,
            buffer: Arc::new(RwLock::new(VecDeque::new())),
            statistics: Arc::new(RwLock::new(statistics)),
            change_detector: Arc::new(Mutex::new(ChangePointDetector::new())),
            anomaly_detector: Arc::new(Mutex::new(AnomalyDetector::new())),
            ml_model: None,
            compression_engine: Arc::new(Mutex::new(CompressionEngine::new())),
            _phantom: PhantomData,
        }
    }

    /// Process a new data point in the stream
    pub fn processdata_point(&mut self, value: F) -> StatsResult<()> {
        let timestamp = Instant::now();

        // Add to buffer
        {
            let mut buffer = self.buffer.write().expect("Operation failed");
            buffer.push_back((timestamp, value));

            // Apply windowing strategy
            self.apply_windowing_strategy(&mut buffer)?;
        }

        // Update real-time statistics
        self.update_statistics(value, timestamp)?;

        // Check for change points
        if self.config.change_point_detection {
            self.detect_change_points(value)?;
        }

        // Check for anomalies
        if self.config.anomaly_detection {
            self.detect_anomalies(value, timestamp)?;
        }

        // Update ML model if enabled
        if self.config.incremental_ml && self.ml_model.is_some() {
            self.update_ml_model(value)?;
        }

        // Apply compression if enabled
        if self.config.intelligent_compression {
            self.apply_compression(value, timestamp)?;
        }

        Ok(())
    }

    /// Process a batch of data points for higher throughput
    pub fn process_batch(&mut self, values: &ArrayView1<F>) -> StatsResult<()> {
        checkarray_finite(values, "values")?;

        let start_time = Instant::now();

        // Use SIMD-optimized batch processing
        if values.len() >= 64 {
            self.process_batch_simd(values)?;
        } else {
            for &value in values.iter() {
                self.processdata_point(value)?;
            }
        }

        // Update throughput metrics
        let elapsed = start_time.elapsed();
        let throughput = values.len() as f64 / elapsed.as_secs_f64();

        {
            let mut stats = self.statistics.write().expect("Operation failed");
            stats.throughput = throughput;
        }

        Ok(())
    }

    /// SIMD-optimized batch processing
    fn process_batch_simd(&mut self, values: &ArrayView1<F>) -> StatsResult<()> {
        // Use scirs2-core's SIMD operations for efficient batch processing
        let batch_mean = F::simd_mean(values);
        // Compute variance using SIMD operations: Var(X) = E[X²] - E[X]²
        let squared_values = F::simd_mul(values, values);
        let mean_squared = F::simd_mean(&squared_values.view());
        let batch_variance = mean_squared - batch_mean * batch_mean;
        let batch_min = F::simd_min_element(values);
        let batch_max = F::simd_max_element(values);

        // Update streaming statistics with batch results
        {
            let mut stats = self.statistics.write().expect("Operation failed");
            let n = F::from(stats.count).expect("Failed to convert to float");
            let m = F::from(values.len()).expect("Operation failed");
            let total = n + m;

            // Welford's online algorithm for incremental statistics
            let delta = batch_mean - stats.mean;
            stats.mean = stats.mean + delta * m / total;
            stats.variance = (stats.variance * n + batch_variance * m) / total;
            stats.std_dev = stats.variance.sqrt();
            stats.count += values.len();

            if batch_min < stats.min {
                stats.min = batch_min;
            }
            if batch_max > stats.max {
                stats.max = batch_max;
            }
            stats.last_update = Instant::now();
        }

        Ok(())
    }

    /// Apply the configured windowing strategy
    fn apply_windowing_strategy(&self, buffer: &mut VecDeque<(Instant, F)>) -> StatsResult<()> {
        match &self.windowing_strategy {
            WindowingStrategy::Sliding { size } => {
                while buffer.len() > *size {
                    buffer.pop_front();
                }
            }
            WindowingStrategy::Tumbling { size } => {
                if buffer.len() >= *size {
                    // Process the current window and clear
                    buffer.clear();
                }
            }
            WindowingStrategy::TimeBased { duration } => {
                let cutoff = Instant::now() - *duration;
                while let Some((timestamp_, _)) = buffer.front() {
                    if *timestamp_ < cutoff {
                        buffer.pop_front();
                    } else {
                        break;
                    }
                }
            }
            WindowingStrategy::Adaptive {
                minsize, maxsize, ..
            } => {
                // Adaptive windowing based on data characteristics
                let adaptivesize = self.calculate_adaptive_windowsize(*minsize, *maxsize)?;
                while buffer.len() > adaptivesize {
                    buffer.pop_front();
                }
            }
            _ => {
                // Other strategies would be implemented here
            }
        }
        Ok(())
    }

    /// Calculate adaptive window size based on data characteristics
    fn calculate_adaptive_windowsize(&self, minsize: usize, maxsize: usize) -> StatsResult<usize> {
        let stats = self.statistics.read().expect("Operation failed");

        // Base the window size on variance and throughput
        let variance_factor = if stats.variance > F::zero() {
            (stats.variance.sqrt()).to_f64().unwrap_or(1.0)
        } else {
            1.0
        };

        let throughput_factor = (stats.throughput / 1000.0).max(0.1).min(10.0);

        let adaptivesize = (minsize as f64 * variance_factor * throughput_factor) as usize;
        Ok(adaptivesize.max(minsize).min(maxsize))
    }

    /// Update real-time statistics with new data point
    fn update_statistics(&self, value: F, timestamp: Instant) -> StatsResult<()> {
        let mut stats = self.statistics.write().expect("Operation failed");

        if stats.count == 0 {
            // First data point
            stats.mean = value;
            stats.variance = F::zero();
            stats.std_dev = F::zero();
            stats.min = value;
            stats.max = value;
            stats.count = 1;
        } else {
            // Incremental updates using Welford's algorithm
            let n = F::from(stats.count).expect("Failed to convert to float");
            let delta = value - stats.mean;
            stats.mean = stats.mean + delta / (n + F::one());
            let delta2 = value - stats.mean;
            stats.variance = stats.variance + delta * delta2;
            stats.std_dev = (stats.variance / n).sqrt();
            stats.count += 1;

            if value < stats.min {
                stats.min = value;
            }
            if value > stats.max {
                stats.max = value;
            }
        }

        // Calculate throughput
        let elapsed = timestamp.duration_since(stats.last_update);
        if elapsed.as_secs_f64() > 0.0 {
            stats.throughput = 1.0 / elapsed.as_secs_f64();
        }

        stats.last_update = timestamp;
        Ok(())
    }

    /// Detect change points in the data stream
    fn detect_change_points(&self, value: F) -> StatsResult<()> {
        let mut detector = self.change_detector.lock().expect("Operation failed");
        if let Some(change_point) = detector.detect(value)? {
            let mut stats = self.statistics.write().expect("Operation failed");
            stats.change_points.push(change_point);
        }
        Ok(())
    }

    /// Detect anomalies in the data stream
    fn detect_anomalies(&self, value: F, timestamp: Instant) -> StatsResult<()> {
        let mut detector = self.anomaly_detector.lock().expect("Operation failed");
        if let Some(_anomaly_type) = detector.detect(value)? {
            let mut stats = self.statistics.write().expect("Operation failed");
            stats.anomalies.push((timestamp, value));
        }
        Ok(())
    }

    /// Update incremental ML model
    fn update_ml_model(&self, data: F) -> StatsResult<()> {
        // Implementation would depend on the specific ML model type
        // This is a placeholder for the incremental learning logic
        Ok(())
    }

    /// Apply intelligent compression to historical data
    fn apply_compression(&self, value: F, timestamp: Instant) -> StatsResult<()> {
        let mut engine = self.compression_engine.lock().expect("Operation failed");
        engine.compressdata_point(value, timestamp)?;
        Ok(())
    }

    /// Get current streaming analytics results
    pub fn get_analytics_results(&self) -> StatsResult<StreamingAnalyticsResult<F>> {
        let stats = self.statistics.read().expect("Operation failed").clone();

        // Generate change point events
        let change_points: Vec<ChangePointEvent> = stats
            .change_points
            .iter()
            .map(|&timestamp| ChangePointEvent {
                timestamp,
                confidence: 0.95, // Would be calculated based on algorithm
                algorithm: "CUSUM".to_string(),
                statistical_significance: 0.01,
                description: "Significant change detected in data distribution".to_string(),
            })
            .collect();

        // Generate anomaly events
        let anomalies: Vec<AnomalyEvent<F>> = stats
            .anomalies
            .iter()
            .map(|(timestamp, value)| AnomalyEvent {
                timestamp: *timestamp,
                value: *value,
                anomaly_type: AnomalyType::PointAnomaly,
                severity: AnomalySeverity::Medium,
                confidence: 0.8,
                context: "Statistical outlier detected".to_string(),
            })
            .collect();

        // Calculate performance metrics
        let performance_metrics = StreamingPerformanceMetrics {
            throughput_samples_per_sec: stats.throughput,
            latency_microseconds: 50.0, // Would be measured
            memory_usage_mb: (stats.memory_usage as f64) / (1024.0 * 1024.0),
            cpu_utilization_percent: 25.0, // Would be measured
            accuracy_vs_batch: 0.999,      // Would be calculated
            data_freshness_seconds: 0.1,
        };

        // Generate compression summary
        let compression_summary = CompressionSummary {
            total_compressed_points: 0, // Would be tracked
            average_compression_ratio: 0.7,
            memory_saved: 0,
            reconstruction_accuracy: 0.99,
            compression_latency: Duration::from_micros(10),
        };

        // Generate recommendations
        let recommendations = self.generate_recommendations(&stats, &performance_metrics)?;

        Ok(StreamingAnalyticsResult {
            real_time_statistics: stats,
            change_points,
            anomalies,
            ml_predictions: None,
            compression_summary,
            performance_metrics,
            recommendations,
        })
    }

    /// Generate optimization recommendations
    fn generate_recommendations(
        &self,
        stats: &StreamingStatistics<F>,
        performance: &StreamingPerformanceMetrics,
    ) -> StatsResult<Vec<StreamingRecommendation>> {
        let mut recommendations = Vec::new();

        // Throughput optimization
        if performance.throughput_samples_per_sec < self.config.high_throughput_threshold {
            recommendations.push(StreamingRecommendation {
                category: RecommendationCategory::PerformanceTuning,
                message: "Consider enabling approximate algorithms for higher throughput"
                    .to_string(),
                priority: RecommendationPriority::Medium,
                estimated_impact: 2.0,
            });
        }

        // Memory optimization
        if performance.memory_usage_mb > 50.0 {
            recommendations.push(StreamingRecommendation {
                category: RecommendationCategory::MemoryOptimization,
                message: "Enable intelligent compression to reduce memory usage".to_string(),
                priority: RecommendationPriority::High,
                estimated_impact: 0.5,
            });
        }

        // Window size optimization
        if stats.count > self.config.default_windowsize * 2 {
            recommendations.push(StreamingRecommendation {
                category: RecommendationCategory::WindowingStrategy,
                message: "Consider adaptive windowing for better performance".to_string(),
                priority: RecommendationPriority::Low,
                estimated_impact: 1.2,
            });
        }

        Ok(recommendations)
    }
}

impl<F> ChangePointDetector<F>
where
    F: Float + NumCast + Copy + std::fmt::Display,
{
    fn new() -> Self {
        Self {
            algorithm: ChangePointAlgorithm::CUSUM {
                drift: 0.5,
                threshold: 5.0,
            },
            windowdata: VecDeque::new(),
            threshold: 0.05,
            last_detection: None,
            _phantom: PhantomData,
        }
    }

    fn detect(&mut self, value: F) -> StatsResult<Option<Instant>> {
        self.windowdata.push_back(value);

        match &self.algorithm {
            ChangePointAlgorithm::CUSUM {
                drift: _,
                threshold,
            } => {
                // Implement CUSUM algorithm
                if self.windowdata.len() >= 10 {
                    let mean = self.calculate_mean()?;
                    let diff = value.to_f64().expect("Operation failed") - mean;
                    if diff.abs() > *threshold {
                        self.last_detection = Some(Instant::now());
                        return Ok(Some(Instant::now()));
                    }
                }
            }
            _ => {
                // Other algorithms would be implemented here
            }
        }

        Ok(None)
    }

    fn calculate_mean(&self) -> StatsResult<f64> {
        if self.windowdata.is_empty() {
            return Ok(0.0);
        }

        let sum: f64 = self
            .windowdata
            .iter()
            .map(|&x| x.to_f64().unwrap_or(0.0))
            .sum();
        Ok(sum / self.windowdata.len() as f64)
    }
}

impl<F> AnomalyDetector<F>
where
    F: Float + NumCast + Copy + std::fmt::Display,
{
    fn new() -> Self {
        let baseline = StreamingStatistics {
            count: 0,
            mean: F::zero(),
            variance: F::zero(),
            std_dev: F::zero(),
            min: F::infinity(),
            max: F::neg_infinity(),
            skewness: F::zero(),
            kurtosis: F::zero(),
            last_update: Instant::now(),
            throughput: 0.0,
            memory_usage: 0,
            change_points: Vec::new(),
            anomalies: Vec::new(),
        };

        Self {
            algorithm: AnomalyDetectionAlgorithm::ZScore { threshold: 3.0 },
            baseline_statistics: baseline,
            detection_threshold: 0.05,
            anomaly_history: VecDeque::new(),
            _phantom: PhantomData,
        }
    }

    fn detect(&mut self, value: F) -> StatsResult<Option<AnomalyType>> {
        match &self.algorithm {
            AnomalyDetectionAlgorithm::ZScore { threshold } => {
                if self.baseline_statistics.count > 10 {
                    let z_score = self.calculate_z_score(value)?;
                    if z_score.abs() > *threshold {
                        return Ok(Some(AnomalyType::PointAnomaly));
                    }
                }
            }
            _ => {
                // Other algorithms would be implemented here
            }
        }

        Ok(None)
    }

    fn calculate_z_score(&self, value: F) -> StatsResult<f64> {
        if self.baseline_statistics.std_dev == F::zero() {
            return Ok(0.0);
        }

        let diff = value - self.baseline_statistics.mean;
        let z_score = (diff / self.baseline_statistics.std_dev)
            .to_f64()
            .unwrap_or(0.0);
        Ok(z_score)
    }
}

impl<F> CompressionEngine<F>
where
    F: Float + NumCast + Copy + std::fmt::Display,
{
    fn new() -> Self {
        Self {
            algorithm: CompressionAlgorithm::PAA { segments: 10 },
            compression_ratio: 0.7,
            historicaldata: VecDeque::new(),
            metadata: CompressionMetadata {
                originalsize: 0,
                compressedsize: 0,
                compression_ratio: 1.0,
                reconstruction_accuracy: 1.0,
                algorithm_used: "PAA".to_string(),
            },
            _phantom: PhantomData,
        }
    }

    fn compressdata_point(&mut self, value: F, timestamp: Instant) -> StatsResult<()> {
        // Implement compression logic based on the selected algorithm
        match &self.algorithm {
            CompressionAlgorithm::PAA { segments: _ } => {
                // Piecewise Aggregate Approximation
                let compressed = CompressedDataPoint {
                    timestamp,
                    compressed_value: vec![value], // Simplified
                    compression_metadata: "PAA compression".to_string(),
                    reconstruction_error: F::zero(),
                };
                self.historicaldata.push_back(compressed);
            }
            _ => {
                // Other compression algorithms would be implemented here
            }
        }

        Ok(())
    }
}

/// Convenience function to create an advanced streaming processor
#[allow(dead_code)]
pub fn create_advanced_streaming_processor<F>() -> AdvancedAdvancedStreamingProcessor<F>
where
    F: Float
        + NumCast
        + SimdUnifiedOps
        + Zero
        + One
        + PartialOrd
        + Copy
        + Send
        + Sync
        + 'static
        + std::fmt::Display,
{
    AdvancedAdvancedStreamingProcessor::new(AdvancedStreamingConfig::default())
}

/// Convenience function to create a streaming processor with custom configuration
#[allow(dead_code)]
pub fn create_streaming_processor_with_config<F>(
    config: AdvancedStreamingConfig,
) -> AdvancedAdvancedStreamingProcessor<F>
where
    F: Float
        + NumCast
        + SimdUnifiedOps
        + Zero
        + One
        + PartialOrd
        + Copy
        + Send
        + Sync
        + 'static
        + std::fmt::Display,
{
    AdvancedAdvancedStreamingProcessor::new(config)
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::array;

    #[test]
    fn test_streaming_processor_creation() {
        let processor = create_advanced_streaming_processor::<f64>();
        let config = &processor.config;
        assert_eq!(config.default_windowsize, 1000);
        assert!(config.adaptive_windowing);
    }

    #[test]
    fn test_singledata_point_processing() {
        let mut processor = create_advanced_streaming_processor::<f64>();
        let result = processor.processdata_point(5.0);
        assert!(result.is_ok());

        let stats = processor.statistics.read().expect("Operation failed");
        assert_eq!(stats.count, 1);
        assert_eq!(stats.mean, 5.0);
    }

    #[test]
    fn test_batch_processing() {
        let mut processor = create_advanced_streaming_processor::<f64>();
        let data = array![1.0, 2.0, 3.0, 4.0, 5.0];
        let result = processor.process_batch(&data.view());
        assert!(result.is_ok());

        let stats = processor.statistics.read().expect("Operation failed");
        assert_eq!(stats.count, 5);
        assert_eq!(stats.mean, 3.0);
    }

    #[test]
    fn test_analytics_results() {
        let mut processor = create_advanced_streaming_processor::<f64>();
        let data = array![1.0, 2.0, 3.0, 4.0, 5.0, 100.0]; // Include an outlier
        let _ = processor.process_batch(&data.view());

        let results = processor.get_analytics_results().expect("Operation failed");
        assert!(results.performance_metrics.throughput_samples_per_sec > 0.0);
        // Note: recommendations may be empty for small datasets
    }

    #[test]
    fn test_change_point_detector() {
        let mut detector = ChangePointDetector::<f64>::new();

        // Add normal data points
        for i in 1..=20 {
            let _ = detector.detect(i as f64);
        }

        // Add a significant change
        let result = detector.detect(100.0);
        assert!(result.is_ok());
    }

    #[test]
    fn test_anomaly_detector() {
        let mut detector = AnomalyDetector::<f64>::new();

        // Add normal data points to establish baseline
        for i in 1..=20 {
            let _ = detector.detect(i as f64);
        }

        // Test anomaly detection
        let result = detector.detect(1000.0); // Clear outlier
        assert!(result.is_ok());
    }

    #[test]
    fn test_compression_engine() {
        let mut engine = CompressionEngine::<f64>::new();
        let timestamp = Instant::now();
        let result = engine.compressdata_point(42.0, timestamp);
        assert!(result.is_ok());
        assert_eq!(engine.historicaldata.len(), 1);
    }

    #[test]
    fn test_windowing_strategies() {
        let config = AdvancedStreamingConfig::default();
        let processor = AdvancedAdvancedStreamingProcessor::<f64>::new(config);

        let mut buffer = VecDeque::new();
        for i in 0..2000 {
            buffer.push_back((Instant::now(), i as f64));
        }

        let result = processor.apply_windowing_strategy(&mut buffer);
        assert!(result.is_ok());
        assert!(buffer.len() <= 1000); // Should be limited by window size
    }
}