optirs_bench/

advanced_pattern_detection.rs

// Advanced-advanced pattern detection for memory leak analysis
//
// This module implements cutting-edge pattern detection algorithms using machine learning
// techniques, statistical analysis, and advanced signal processing for memory usage patterns.

use crate::error::{OptimError, Result};
use scirs2_core::numeric::Float;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::f64::consts::PI;

/// Advanced-advanced pattern detector using ML and signal processing
#[derive(Debug)]
pub struct AdvancedPatternDetector {
    /// Configuration for pattern detection
    config: AdvancedPatternConfig,
    /// Neural network for pattern classification
    pattern_classifier: PatternClassifier,
    /// Signal processing engine
    signal_processor: SignalProcessor,
    /// Statistical analyzer
    statistical_analyzer: AdvancedStatisticalAnalyzer,
    /// Learned patterns database
    pattern_database: PatternDatabase,
    /// Real-time feature extractor
    feature_extractor: FeatureExtractor,
}

/// Configuration for advanced pattern detection
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdvancedPatternConfig {
    /// Enable machine learning classification
    pub enable_ml_classification: bool,
    /// Enable signal processing analysis
    pub enable_signal_processing: bool,
    /// Enable statistical pattern matching
    pub enable_statistical_matching: bool,
    /// Minimum pattern length for detection
    pub min_pattern_length: usize,
    /// Pattern matching threshold
    pub pattern_matching_threshold: f64,
    /// Feature extraction window size
    pub feature_window_size: usize,
    /// Maximum patterns to store
    pub max_patterns_stored: usize,
    /// Learning rate for adaptive patterns
    pub learning_rate: f64,
    /// Enable anomaly scoring
    pub enable_anomaly_scoring: bool,
    /// Enable trend forecasting
    pub enable_trend_forecasting: bool,
}

impl Default for AdvancedPatternConfig {
    fn default() -> Self {
        Self {
            enable_ml_classification: true,
            enable_signal_processing: true,
            enable_statistical_matching: true,
            min_pattern_length: 10,
            pattern_matching_threshold: 0.85,
            feature_window_size: 50,
            max_patterns_stored: 1000,
            learning_rate: 0.01,
            enable_anomaly_scoring: true,
            enable_trend_forecasting: true,
        }
    }
}

/// Advanced memory pattern with rich metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdvancedMemoryPattern {
    /// Pattern identifier
    pub id: String,
    /// Pattern type classification
    pub pattern_type: AdvancedPatternType,
    /// Confidence score (0.0 to 1.0)
    pub confidence: f64,
    /// Pattern signature (feature vector)
    pub signature: Vec<f64>,
    /// Pattern description
    pub description: String,
    /// Frequency domain characteristics
    pub frequency_characteristics: FrequencyCharacteristics,
    /// Statistical properties
    pub statistical_properties: StatisticalProperties,
    /// Anomaly score
    pub anomaly_score: f64,
    /// Pattern strength
    pub strength: f64,
    /// Periodicity information
    pub periodicity: Option<PeriodicityInfo>,
    /// Trend information
    pub trend: TrendInfo,
    /// Associated leak indicators
    pub leak_indicators: Vec<LeakIndicator>,
    /// Pattern evolution over time
    pub evolution: PatternEvolution,
}

/// Advanced pattern types with detailed classification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AdvancedPatternType {
    /// Linear growth pattern
    LinearGrowth {
        slope: f64,
        intercept: f64,
        r_squared: f64,
    },
    /// Exponential growth pattern
    ExponentialGrowth {
        growth_rate: f64,
        base_value: f64,
        doubling_time: f64,
    },
    /// Periodic pattern with harmonics
    Periodic {
        fundamental_frequency: f64,
        harmonics: Vec<f64>,
        phase_shift: f64,
        amplitude: f64,
    },
    /// Saw-tooth allocation/deallocation pattern
    SawTooth {
        peak_height: f64,
        cycle_duration: f64,
        duty_cycle: f64,
        baseline: f64,
    },
    /// Step function pattern
    StepFunction {
        step_size: f64,
        step_frequency: f64,
        plateaus: Vec<f64>,
    },
    /// Chaotic/fractal pattern
    Chaotic {
        lyapunov_exponent: f64,
        correlation_dimension: f64,
        hurst_exponent: f64,
    },
    /// Burst pattern
    Burst {
        burst_intensity: f64,
        burst_duration: f64,
        inter_burst_interval: f64,
        baseline_level: f64,
    },
    /// Memory leak signature
    LeakSignature {
        leak_rate: f64,
        leak_acceleration: f64,
        leak_confidence: f64,
    },
    /// Composite pattern (combination of multiple patterns)
    Composite {
        components: Vec<Box<AdvancedPatternType>>,
        weights: Vec<f64>,
    },
}

/// Frequency domain characteristics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FrequencyCharacteristics {
    /// Dominant frequencies
    pub dominant_frequencies: Vec<f64>,
    /// Power spectral density
    pub power_spectrum: Vec<f64>,
    /// Spectral centroid
    pub spectral_centroid: f64,
    /// Spectral bandwidth
    pub spectral_bandwidth: f64,
    /// Spectral roll-off
    pub spectral_rolloff: f64,
    /// Spectral flux
    pub spectral_flux: f64,
    /// Zero crossing rate
    pub zero_crossing_rate: f64,
}

/// Statistical properties of patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatisticalProperties {
    /// Mean value
    pub mean: f64,
    /// Standard deviation
    pub std_dev: f64,
    /// Skewness
    pub skewness: f64,
    /// Kurtosis
    pub kurtosis: f64,
    /// Entropy
    pub entropy: f64,
    /// Autocorrelation function
    pub autocorrelation: Vec<f64>,
    /// Partial autocorrelation
    pub partial_autocorrelation: Vec<f64>,
    /// Hjorth parameters
    pub hjorth_parameters: HjorthParameters,
}

/// Hjorth parameters for signal complexity analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HjorthParameters {
    /// Activity (variance)
    pub activity: f64,
    /// Mobility (mean frequency)
    pub mobility: f64,
    /// Complexity (bandwidth)
    pub complexity: f64,
}

/// Periodicity information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PeriodicityInfo {
    /// Period length
    pub period: f64,
    /// Periodicity strength
    pub strength: f64,
    /// Phase coherence
    pub phase_coherence: f64,
    /// Period stability
    pub stability: f64,
}

/// Trend information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrendInfo {
    /// Trend direction (-1: decreasing, 0: stable, 1: increasing)
    pub direction: f64,
    /// Trend strength (0.0 to 1.0)
    pub strength: f64,
    /// Trend acceleration
    pub acceleration: f64,
    /// Trend stability
    pub stability: f64,
    /// Change points
    pub change_points: Vec<usize>,
}

/// Leak indicator from pattern analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LeakIndicator {
    /// Indicator type
    pub indicator_type: LeakIndicatorType,
    /// Strength of indicator (0.0 to 1.0)
    pub strength: f64,
    /// Time to critical threshold
    pub time_to_critical: Option<f64>,
    /// Confidence in indicator
    pub confidence: f64,
}

/// Types of leak indicators
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LeakIndicatorType {
    /// Monotonic increase in memory
    MonotonicIncrease,
    /// Accelerating growth
    AcceleratingGrowth,
    /// Irregular spikes
    IrregularSpikes,
    /// Baseline drift
    BaselineDrift,
    /// Fragmentation signature
    FragmentationSignature,
    /// Cache thrashing pattern
    CacheThrashing,
    /// Resource exhaustion pattern
    ResourceExhaustion,
}

/// Pattern evolution tracking
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PatternEvolution {
    /// Pattern stability over time
    pub stability: f64,
    /// Evolution rate
    pub evolution_rate: f64,
    /// Adaptation score
    pub adaptation_score: f64,
    /// Historical states
    pub historical_states: Vec<PatternState>,
}

/// State of pattern at specific time
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PatternState {
    /// Timestamp
    pub timestamp: u64,
    /// Pattern parameters at this time
    pub parameters: HashMap<String, f64>,
    /// Confidence at this time
    pub confidence: f64,
}

/// Neural network-inspired pattern classifier
#[derive(Debug)]
pub struct PatternClassifier {
    /// Network weights for pattern classification
    weights: Vec<Vec<f64>>,
    /// Network biases
    biases: Vec<f64>,
    /// Learning rate
    learning_rate: f64,
    /// Training data
    training_data: Vec<TrainingExample>,
}

/// Training example for pattern classifier
#[derive(Debug, Clone)]
pub struct TrainingExample {
    /// Input features
    pub features: Vec<f64>,
    /// Expected pattern type
    pub pattern_type: AdvancedPatternType,
    /// Confidence weight
    pub weight: f64,
}

/// Signal processing engine for memory analysis
#[derive(Debug)]
pub struct SignalProcessor {
    /// FFT processor
    fft_processor: FFTProcessor,
    /// Wavelet processor
    wavelet_processor: WaveletProcessor,
    /// Kalman filter for noise reduction
    kalman_filter: KalmanFilter,
}

/// FFT processor for frequency analysis
#[derive(Debug)]
pub struct FFTProcessor {
    /// Window function type
    window_type: WindowType,
    /// FFT size
    fft_size: usize,
    /// Overlap factor
    overlap_factor: f64,
}

/// Window function types for FFT
#[derive(Debug, Clone)]
pub enum WindowType {
    Rectangular,
    Hamming,
    Hanning,
    Blackman,
    Kaiser { beta: f64 },
}

/// Wavelet processor for time-frequency analysis
#[derive(Debug)]
pub struct WaveletProcessor {
    /// Wavelet type
    wavelet_type: WaveletType,
    /// Number of decomposition levels
    levels: usize,
}

/// Wavelet types
#[derive(Debug, Clone)]
pub enum WaveletType {
    Daubechies { order: usize },
    Biorthogonal { order: (usize, usize) },
    Coiflets { order: usize },
    Haar,
    Morlet { sigma: f64 },
}

/// Kalman filter for signal denoising
#[derive(Debug)]
pub struct KalmanFilter {
    /// State estimate
    state: Vec<f64>,
    /// Covariance matrix
    covariance: Vec<Vec<f64>>,
    /// Process noise
    process_noise: f64,
    /// Measurement noise
    measurement_noise: f64,
}

/// Advanced statistical analyzer
#[derive(Debug)]
pub struct AdvancedStatisticalAnalyzer {
    /// Configuration
    config: StatisticalConfig,
    /// Hypothesis test engine
    hypothesis_tester: HypothesisTestEngine,
    /// Time series analyzer
    time_series_analyzer: TimeSeriesAnalyzer,
}

/// Statistical analysis configuration
#[derive(Debug, Clone)]
pub struct StatisticalConfig {
    /// Significance level for tests
    pub significance_level: f64,
    /// Bootstrap iterations
    pub bootstrap_iterations: usize,
    /// Confidence interval level
    pub confidence_interval: f64,
}

/// Hypothesis test engine
#[derive(Debug)]
pub struct HypothesisTestEngine {
    /// Available test types
    test_types: Vec<HypothesisTestType>,
}

/// Types of hypothesis tests
#[derive(Debug, Clone)]
pub enum HypothesisTestType {
    /// Kolmogorov-Smirnov test
    KolmogorovSmirnov,
    /// Anderson-Darling test
    AndersonDarling,
    /// Mann-Kendall trend test
    MannKendall,
    /// Ljung-Box test for autocorrelation
    LjungBox,
    /// Augmented Dickey-Fuller test
    AugmentedDickeyFuller,
    /// KPSS test for stationarity
    KPSS,
}

/// Time series analyzer
#[derive(Debug)]
pub struct TimeSeriesAnalyzer {
    /// ARIMA model fitter
    arima_fitter: ARIMAFitter,
    /// Seasonal decomposer
    seasonal_decomposer: SeasonalDecomposer,
    /// Change point detector
    change_point_detector: ChangePointDetector,
}

/// ARIMA model fitter
#[derive(Debug)]
pub struct ARIMAFitter {
    /// Model parameters
    parameters: ARIMAParameters,
}

/// ARIMA model parameters
#[derive(Debug, Clone)]
pub struct ARIMAParameters {
    /// Autoregressive order
    pub p: usize,
    /// Differencing order
    pub d: usize,
    /// Moving average order
    pub q: usize,
    /// Seasonal parameters
    pub seasonal: Option<SeasonalParameters>,
}

/// Seasonal ARIMA parameters
#[derive(Debug, Clone)]
pub struct SeasonalParameters {
    /// Seasonal autoregressive order
    pub p: usize,
    /// Seasonal differencing order
    pub d: usize,
    /// Seasonal moving average order
    pub q: usize,
    /// Seasonal period
    pub period: usize,
}

/// Seasonal decomposer
#[derive(Debug)]
pub struct SeasonalDecomposer {
    /// Decomposition method
    method: DecompositionMethod,
}

/// Seasonal decomposition methods
#[derive(Debug, Clone)]
pub enum DecompositionMethod {
    /// Additive decomposition
    Additive,
    /// Multiplicative decomposition
    Multiplicative,
    /// STL decomposition
    STL,
    /// X-13ARIMA-SEATS
    X13ARIMA,
}

/// Change point detector
#[derive(Debug)]
pub struct ChangePointDetector {
    /// Detection algorithms
    algorithms: Vec<ChangePointAlgorithm>,
}

/// Change point detection algorithms
#[derive(Debug, Clone)]
pub enum ChangePointAlgorithm {
    /// CUSUM algorithm
    CUSUM { threshold: f64 },
    /// PELT (Pruned Exact Linear Time)
    PELT { penalty: f64 },
    /// Binary segmentation
    BinarySegmentation { min_size: usize },
    /// Bayesian change point detection
    Bayesian { prior_scale: f64 },
}

/// Pattern database for storing learned patterns
#[derive(Debug)]
pub struct PatternDatabase {
    /// Stored patterns
    patterns: HashMap<String, AdvancedMemoryPattern>,
    /// Pattern similarity matrix
    similarity_matrix: HashMap<(String, String), f64>,
    /// Pattern frequency statistics
    frequency_stats: HashMap<String, PatternFrequencyStats>,
}

/// Pattern frequency statistics
#[derive(Debug, Clone)]
pub struct PatternFrequencyStats {
    /// Occurrence count
    pub count: usize,
    /// Average confidence
    pub avg_confidence: f64,
    /// Last seen timestamp
    pub last_seen: u64,
    /// Context information
    pub contexts: Vec<String>,
}

/// Feature extractor for pattern analysis
#[derive(Debug)]
pub struct FeatureExtractor {
    /// Feature types to extract
    feature_types: Vec<FeatureType>,
    /// Feature scaling parameters
    scaling_params: HashMap<String, (f64, f64)>, // (mean, std)
}

/// Types of features to extract
#[derive(Debug, Clone)]
pub enum FeatureType {
    /// Statistical moments
    StatisticalMoments,
    /// Frequency domain features
    FrequencyDomain,
    /// Time domain features
    TimeDomain,
    /// Wavelet features
    WaveletFeatures,
    /// Fractal features
    FractalFeatures,
    /// Information theoretic features
    InformationTheoretic,
    /// Shape features
    ShapeFeatures,
}

impl AdvancedPatternDetector {
    /// Create a new advanced pattern detector
    pub fn new(config: AdvancedPatternConfig) -> Result<Self> {
        Ok(Self {
            config: config.clone(),
            pattern_classifier: PatternClassifier::new(config.learning_rate)?,
            signal_processor: SignalProcessor::new()?,
            statistical_analyzer: AdvancedStatisticalAnalyzer::new()?,
            pattern_database: PatternDatabase::new(),
            feature_extractor: FeatureExtractor::new(),
        })
    }

    /// Detect patterns in memory usage data using advanced algorithms
    pub fn detect_patterns(&mut self, memorydata: &[f64]) -> Result<Vec<AdvancedMemoryPattern>> {
        if memorydata.len() < self.config.min_pattern_length {
            return Ok(Vec::new());
        }

        let mut detected_patterns = Vec::new();

        // Extract features from memory _data
        let features = self.feature_extractor.extract_features(memorydata)?;

        // Signal processing analysis
        if self.config.enable_signal_processing {
            let signal_patterns = self.signal_processor.analyze_signal(memorydata)?;
            detected_patterns.extend(signal_patterns);
        }

        // Statistical pattern matching
        if self.config.enable_statistical_matching {
            let statistical_patterns = self.statistical_analyzer.analyze_patterns(memorydata)?;
            detected_patterns.extend(statistical_patterns);
        }

        // Machine learning classification
        if self.config.enable_ml_classification {
            let ml_patterns = self.pattern_classifier.classify_patterns(&features)?;
            detected_patterns.extend(ml_patterns);
        }

        // Pattern fusion and refinement
        let refined_patterns = self.fuse_and_refine_patterns(detected_patterns)?;

        // Update pattern database
        self.update_pattern_database(&refined_patterns)?;

        // Generate anomaly scores
        if self.config.enable_anomaly_scoring {
            self.compute_anomaly_scores(&mut refined_patterns.clone(), memorydata)?;
        }

        // Trend forecasting
        if self.config.enable_trend_forecasting {
            self.add_trend_forecasts(&mut refined_patterns.clone(), memorydata)?;
        }

        Ok(refined_patterns)
    }

    /// Fuse and refine overlapping patterns
    fn fuse_and_refine_patterns(
        &self,
        patterns: Vec<AdvancedMemoryPattern>,
    ) -> Result<Vec<AdvancedMemoryPattern>> {
        let mut refined_patterns = Vec::new();
        let mut used_patterns = vec![false; patterns.len()];

        for i in 0..patterns.len() {
            if used_patterns[i] {
                continue;
            }

            let mut pattern_group = vec![&patterns[i]];
            used_patterns[i] = true;

            // Find similar patterns to fuse
            for j in (i + 1)..patterns.len() {
                if used_patterns[j] {
                    continue;
                }

                let similarity = self.calculate_pattern_similarity(&patterns[i], &patterns[j])?;
                if similarity > self.config.pattern_matching_threshold {
                    pattern_group.push(&patterns[j]);
                    used_patterns[j] = true;
                }
            }

            // Fuse the pattern group
            let fused_pattern = self.fuse_pattern_group(pattern_group)?;
            refined_patterns.push(fused_pattern);
        }

        Ok(refined_patterns)
    }

    /// Calculate similarity between two patterns
    fn calculate_pattern_similarity(
        &self,
        pattern1: &AdvancedMemoryPattern,
        pattern2: &AdvancedMemoryPattern,
    ) -> Result<f64> {
        // Compare signatures using cosine similarity
        let dot_product: f64 = pattern1
            .signature
            .iter()
            .zip(pattern2.signature.iter())
            .map(|(a, b)| a * b)
            .sum();

        let norm1: f64 = pattern1.signature.iter().map(|x| x * x).sum::<f64>().sqrt();
        let norm2: f64 = pattern2.signature.iter().map(|x| x * x).sum::<f64>().sqrt();

        if norm1 == 0.0 || norm2 == 0.0 {
            return Ok(0.0);
        }

        let cosine_similarity = dot_product / (norm1 * norm2);

        // Weight by pattern type similarity
        let type_similarity =
            self.calculate_type_similarity(&pattern1.pattern_type, &pattern2.pattern_type);

        // Combine similarities
        Ok((cosine_similarity + type_similarity) / 2.0)
    }

    /// Calculate similarity between pattern types
    fn calculate_type_similarity(
        &self,
        type1: &AdvancedPatternType,
        type2: &AdvancedPatternType,
    ) -> f64 {
        match (type1, type2) {
            (
                AdvancedPatternType::LinearGrowth { .. },
                AdvancedPatternType::LinearGrowth { .. },
            ) => 1.0,
            (
                AdvancedPatternType::ExponentialGrowth { .. },
                AdvancedPatternType::ExponentialGrowth { .. },
            ) => 1.0,
            (AdvancedPatternType::Periodic { .. }, AdvancedPatternType::Periodic { .. }) => 1.0,
            (AdvancedPatternType::SawTooth { .. }, AdvancedPatternType::SawTooth { .. }) => 1.0,
            (
                AdvancedPatternType::StepFunction { .. },
                AdvancedPatternType::StepFunction { .. },
            ) => 1.0,
            (AdvancedPatternType::Chaotic { .. }, AdvancedPatternType::Chaotic { .. }) => 1.0,
            (AdvancedPatternType::Burst { .. }, AdvancedPatternType::Burst { .. }) => 1.0,
            (
                AdvancedPatternType::LeakSignature { .. },
                AdvancedPatternType::LeakSignature { .. },
            ) => 1.0,
            // Partial similarities for related types
            (
                AdvancedPatternType::LinearGrowth { .. },
                AdvancedPatternType::ExponentialGrowth { .. },
            ) => 0.7,
            (
                AdvancedPatternType::ExponentialGrowth { .. },
                AdvancedPatternType::LinearGrowth { .. },
            ) => 0.7,
            (AdvancedPatternType::SawTooth { .. }, AdvancedPatternType::Periodic { .. }) => 0.6,
            (AdvancedPatternType::Periodic { .. }, AdvancedPatternType::SawTooth { .. }) => 0.6,
            _ => 0.0,
        }
    }

    /// Fuse a group of similar patterns
    fn fuse_pattern_group(
        &self,
        pattern_group: Vec<&AdvancedMemoryPattern>,
    ) -> Result<AdvancedMemoryPattern> {
        if pattern_group.is_empty() {
            return Err(std::io::Error::new(
                std::io::ErrorKind::InvalidInput,
                "Empty pattern group",
            )
            .into());
        }

        if pattern_group.len() == 1 {
            return Ok(pattern_group[0].clone());
        }

        // Use the pattern with highest confidence as base
        let base_pattern = pattern_group
            .iter()
            .max_by(|a, b| {
                a.confidence
                    .partial_cmp(&b.confidence)
                    .unwrap_or(std::cmp::Ordering::Equal)
            })
            .expect("unwrap failed");

        let mut fused_pattern = (*base_pattern).clone();

        // Average confidence scores
        fused_pattern.confidence =
            pattern_group.iter().map(|p| p.confidence).sum::<f64>() / pattern_group.len() as f64;

        // Average signatures
        let signature_len = fused_pattern.signature.len();
        let mut averaged_signature = vec![0.0; signature_len];

        for pattern in &pattern_group {
            for (i, &val) in pattern.signature.iter().enumerate() {
                if i < signature_len {
                    averaged_signature[i] += val;
                }
            }
        }

        for val in &mut averaged_signature {
            *val /= pattern_group.len() as f64;
        }

        fused_pattern.signature = averaged_signature;

        // Combine descriptions
        fused_pattern.description = format!(
            "Fused pattern from {} similar patterns: {}",
            pattern_group.len(),
            pattern_group
                .iter()
                .map(|p| p.description.as_str())
                .collect::<Vec<_>>()
                .join(", ")
        );

        Ok(fused_pattern)
    }

    /// Update pattern database with new patterns
    fn update_pattern_database(&mut self, patterns: &[AdvancedMemoryPattern]) -> Result<()> {
        for pattern in patterns {
            // Check if pattern already exists
            if let Some(existing_pattern) = self.pattern_database.patterns.get_mut(&pattern.id) {
                // Update existing pattern with new information
                existing_pattern.confidence =
                    (existing_pattern.confidence + pattern.confidence) / 2.0;
                // Update frequency stats
                if let Some(freq_stats) = self.pattern_database.frequency_stats.get_mut(&pattern.id)
                {
                    freq_stats.count += 1;
                    freq_stats.avg_confidence =
                        (freq_stats.avg_confidence + pattern.confidence) / 2.0;
                    freq_stats.last_seen = std::time::SystemTime::now()
                        .duration_since(std::time::UNIX_EPOCH)
                        .expect("unwrap failed")
                        .as_secs();
                }
            } else {
                // Add new pattern
                self.pattern_database
                    .patterns
                    .insert(pattern.id.clone(), pattern.clone());
                self.pattern_database.frequency_stats.insert(
                    pattern.id.clone(),
                    PatternFrequencyStats {
                        count: 1,
                        avg_confidence: pattern.confidence,
                        last_seen: std::time::SystemTime::now()
                            .duration_since(std::time::UNIX_EPOCH)
                            .expect("unwrap failed")
                            .as_secs(),
                        contexts: Vec::new(),
                    },
                );
            }
        }

        // Limit database size
        if self.pattern_database.patterns.len() > self.config.max_patterns_stored {
            self.prune_pattern_database()?;
        }

        Ok(())
    }

    /// Prune pattern database to maintain size limits
    fn prune_pattern_database(&mut self) -> Result<()> {
        // Remove patterns with lowest frequency and confidence
        let mut patterns_to_remove = Vec::new();

        for (id, freq_stats) in &self.pattern_database.frequency_stats {
            if let Some(pattern) = self.pattern_database.patterns.get(id) {
                let score = freq_stats.count as f64 * pattern.confidence;
                patterns_to_remove.push((id.clone(), score));
            }
        }

        // Sort by score and remove the lowest scoring patterns
        patterns_to_remove
            .sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));

        let patterns_to_remove_count =
            self.pattern_database.patterns.len() - self.config.max_patterns_stored;
        for (id_, _) in patterns_to_remove.iter().take(patterns_to_remove_count) {
            self.pattern_database.patterns.remove(id_);
            self.pattern_database.frequency_stats.remove(id_);
        }

        Ok(())
    }

    /// Compute anomaly scores for patterns
    fn compute_anomaly_scores(
        &self,
        patterns: &mut Vec<AdvancedMemoryPattern>,
        memorydata: &[f64],
    ) -> Result<()> {
        for pattern in patterns {
            // Compute anomaly score based on historical _data
            let historical_mean = self.get_historical_pattern_mean(&pattern.pattern_type)?;
            let deviation = (pattern.strength - historical_mean).abs();
            pattern.anomaly_score = (deviation / historical_mean.max(1.0)).min(1.0);
        }
        Ok(())
    }

    /// Add trend forecasts to patterns
    fn add_trend_forecasts(
        &self,
        patterns: &mut Vec<AdvancedMemoryPattern>,
        memorydata: &[f64],
    ) -> Result<()> {
        for pattern in patterns {
            // Simple linear trend forecasting
            if memorydata.len() >= 2 {
                let n = memorydata.len();
                let last_values = &memorydata[n.saturating_sub(10)..];

                if let Some((slope_, _)) = self.calculate_linear_trend(last_values) {
                    pattern.trend.direction = slope_.signum();
                    pattern.trend.strength = slope_.abs().min(1.0);
                    pattern.trend.acceleration = 0.0; // Would be computed from second derivative
                }
            }
        }
        Ok(())
    }

    /// Calculate linear trend from data
    fn calculate_linear_trend(&self, data: &[f64]) -> Option<(f64, f64)> {
        if data.len() < 2 {
            return None;
        }

        let n = data.len() as f64;
        let sum_x = (0..data.len()).map(|i| i as f64).sum::<f64>();
        let sum_y = data.iter().sum::<f64>();
        let sum_xy = data
            .iter()
            .enumerate()
            .map(|(i, &y)| i as f64 * y)
            .sum::<f64>();
        let sum_x2 = (0..data.len()).map(|i| (i * i) as f64).sum::<f64>();

        let denominator = n * sum_x2 - sum_x * sum_x;
        if denominator.abs() < f64::EPSILON {
            return None;
        }

        let slope = (n * sum_xy - sum_x * sum_y) / denominator;
        let intercept = (sum_y - slope * sum_x) / n;

        Some((slope, intercept))
    }

    /// Get historical mean for pattern type
    fn get_historical_pattern_mean(&self, patterntype: &AdvancedPatternType) -> Result<f64> {
        // Simplified implementation - would use actual historical data
        match patterntype {
            AdvancedPatternType::LinearGrowth { .. } => Ok(0.5),
            AdvancedPatternType::ExponentialGrowth { .. } => Ok(0.3),
            AdvancedPatternType::Periodic { .. } => Ok(0.7),
            AdvancedPatternType::LeakSignature { .. } => Ok(0.2),
            _ => Ok(0.5),
        }
    }
}

// Implementation stubs for the various components

impl PatternClassifier {
    fn new(_learningrate: f64) -> Result<Self> {
        Ok(Self {
            weights: vec![vec![0.0; 50]; 10], // 10 output classes, 50 input features
            biases: vec![0.0; 10],
            learning_rate: _learningrate,
            training_data: Vec::new(),
        })
    }

    fn classify_patterns(&self, features: &[f64]) -> Result<Vec<AdvancedMemoryPattern>> {
        // Simplified neural network classification
        let mut patterns = Vec::new();

        // Forward pass through network
        let outputs = self.forward_pass(features);

        // Convert outputs to patterns
        for (i, &output) in outputs.iter().enumerate() {
            if output > 0.5 {
                patterns.push(self.create_pattern_from_class(i, output, features)?);
            }
        }

        Ok(patterns)
    }

    fn forward_pass(&self, features: &[f64]) -> Vec<f64> {
        let mut outputs = vec![0.0; self.weights.len()];

        for (i, weights_row) in self.weights.iter().enumerate() {
            let mut sum = self.biases[i];
            for (j, &weight) in weights_row.iter().enumerate() {
                if j < features.len() {
                    sum += weight * features[j];
                }
            }
            outputs[i] = 1.0 / (1.0 + (-sum).exp()); // Sigmoid activation
        }

        outputs
    }

    fn create_pattern_from_class(
        &self,
        class_id: usize,
        confidence: f64,
        features: &[f64],
    ) -> Result<AdvancedMemoryPattern> {
        let pattern_type = match class_id {
            0 => AdvancedPatternType::LinearGrowth {
                slope: features.first().copied().unwrap_or(0.0),
                intercept: features.get(1).copied().unwrap_or(0.0),
                r_squared: 0.8,
            },
            1 => AdvancedPatternType::ExponentialGrowth {
                growth_rate: features.get(2).copied().unwrap_or(0.1),
                base_value: features.get(3).copied().unwrap_or(1.0),
                doubling_time: 10.0,
            },
            2 => AdvancedPatternType::Periodic {
                fundamental_frequency: features.get(4).copied().unwrap_or(0.1),
                harmonics: vec![1.0, 0.5, 0.25],
                phase_shift: 0.0,
                amplitude: features.get(5).copied().unwrap_or(1.0),
            },
            _ => AdvancedPatternType::LeakSignature {
                leak_rate: features.get(6).copied().unwrap_or(0.01),
                leak_acceleration: features.get(7).copied().unwrap_or(0.001),
                leak_confidence: confidence,
            },
        };

        Ok(AdvancedMemoryPattern {
            id: format!("ml_pattern_{}", class_id),
            pattern_type,
            confidence,
            signature: features.to_vec(),
            description: format!("ML-detected pattern class {}", class_id),
            frequency_characteristics: FrequencyCharacteristics::default(),
            statistical_properties: StatisticalProperties::default(),
            anomaly_score: 0.0,
            strength: confidence,
            periodicity: None,
            trend: TrendInfo::default(),
            leak_indicators: Vec::new(),
            evolution: PatternEvolution::default(),
        })
    }
}

impl SignalProcessor {
    fn new() -> Result<Self> {
        Ok(Self {
            fft_processor: FFTProcessor::new(),
            wavelet_processor: WaveletProcessor::new(),
            kalman_filter: KalmanFilter::new(),
        })
    }

    fn analyze_signal(&mut self, signal: &[f64]) -> Result<Vec<AdvancedMemoryPattern>> {
        let mut patterns = Vec::new();

        // FFT analysis
        let frequency_patterns = self.fft_processor.analyze_frequencies(signal)?;
        patterns.extend(frequency_patterns);

        // Wavelet analysis
        let wavelet_patterns = self.wavelet_processor.analyze_wavelets(signal)?;
        patterns.extend(wavelet_patterns);

        // Apply Kalman filtering for noise reduction
        let filtered_signal = self.kalman_filter.filter(signal)?;

        // Analyze filtered signal for additional patterns
        if !filtered_signal.is_empty() {
            // Additional pattern detection on filtered signal would go here
        }

        Ok(patterns)
    }
}

impl FFTProcessor {
    fn new() -> Self {
        Self {
            window_type: WindowType::Hanning,
            fft_size: 1024,
            overlap_factor: 0.5,
        }
    }

    fn analyze_frequencies(&self, signal: &[f64]) -> Result<Vec<AdvancedMemoryPattern>> {
        // Simplified FFT implementation
        let mut patterns = Vec::new();

        if signal.len() < 8 {
            return Ok(patterns);
        }

        // Apply window function
        let windowed_signal = self.apply_window(signal);

        // Compute FFT (simplified)
        let spectrum = self.compute_fft(&windowed_signal)?;

        // Find dominant frequencies
        let dominant_freqs = self.find_dominant_frequencies(&spectrum);

        // Create pattern from frequency analysis
        if !dominant_freqs.is_empty() {
            patterns.push(AdvancedMemoryPattern {
                id: "fft_pattern".to_string(),
                pattern_type: AdvancedPatternType::Periodic {
                    fundamental_frequency: dominant_freqs[0],
                    harmonics: dominant_freqs[1..].to_vec(),
                    phase_shift: 0.0,
                    amplitude: spectrum.iter().sum::<f64>() / spectrum.len() as f64,
                },
                confidence: 0.8,
                signature: spectrum.clone(),
                description: "FFT-detected periodic pattern".to_string(),
                frequency_characteristics: FrequencyCharacteristics {
                    dominant_frequencies: dominant_freqs,
                    power_spectrum: spectrum,
                    spectral_centroid: 0.0,
                    spectral_bandwidth: 0.0,
                    spectral_rolloff: 0.0,
                    spectral_flux: 0.0,
                    zero_crossing_rate: 0.0,
                },
                statistical_properties: StatisticalProperties::default(),
                anomaly_score: 0.0,
                strength: 0.8,
                periodicity: None,
                trend: TrendInfo::default(),
                leak_indicators: Vec::new(),
                evolution: PatternEvolution::default(),
            });
        }

        Ok(patterns)
    }

    fn apply_window(&self, signal: &[f64]) -> Vec<f64> {
        let n = signal.len();
        match &self.window_type {
            WindowType::Hanning => signal
                .iter()
                .enumerate()
                .map(|(i, &x)| x * 0.5 * (1.0 - (2.0 * PI * i as f64 / (n - 1) as f64).cos()))
                .collect(),
            WindowType::Hamming => signal
                .iter()
                .enumerate()
                .map(|(i, &x)| x * (0.54 - 0.46 * (2.0 * PI * i as f64 / (n - 1) as f64).cos()))
                .collect(),
            _ => signal.to_vec(),
        }
    }

    fn compute_fft(&self, signal: &[f64]) -> Result<Vec<f64>> {
        // Simplified FFT - in practice would use a proper FFT library
        let n = signal.len();
        let mut spectrum = vec![0.0; n / 2];

        for (k, spectrum_k) in spectrum.iter_mut().enumerate() {
            let mut real = 0.0;
            let mut imag = 0.0;

            for (j, &signal_j) in signal.iter().enumerate() {
                let angle = -2.0 * PI * (k * j) as f64 / n as f64;
                real += signal_j * angle.cos();
                imag += signal_j * angle.sin();
            }

            *spectrum_k = (real * real + imag * imag).sqrt();
        }

        Ok(spectrum)
    }

    fn find_dominant_frequencies(&self, spectrum: &[f64]) -> Vec<f64> {
        let mut freq_mag_pairs: Vec<(usize, f64)> = spectrum
            .iter()
            .enumerate()
            .map(|(i, &mag)| (i, mag))
            .collect();

        freq_mag_pairs.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));

        freq_mag_pairs
            .iter()
            .take(5)
            .map(|(i_, _)| *i_ as f64 / spectrum.len() as f64)
            .collect()
    }
}

impl WaveletProcessor {
    fn new() -> Self {
        Self {
            wavelet_type: WaveletType::Daubechies { order: 4 },
            levels: 5,
        }
    }

    fn analyze_wavelets(&self, signal: &[f64]) -> Result<Vec<AdvancedMemoryPattern>> {
        // Simplified wavelet analysis
        let patterns = Vec::new();
        // Would implement proper wavelet transform here
        Ok(patterns)
    }
}

impl KalmanFilter {
    fn new() -> Self {
        Self {
            state: vec![0.0, 0.0], // position and velocity
            covariance: vec![vec![1.0, 0.0], vec![0.0, 1.0]],
            process_noise: 0.01,
            measurement_noise: 0.1,
        }
    }

    fn filter(&mut self, signal: &[f64]) -> Result<Vec<f64>> {
        let mut filtered = Vec::new();

        for &measurement in signal {
            // Prediction step
            self.predict();

            // Update step
            self.update(measurement);

            filtered.push(self.state[0]);
        }

        Ok(filtered)
    }

    fn predict(&mut self) {
        // Simple constant velocity model
        self.state[0] += self.state[1]; // position += velocity

        // Update covariance
        self.covariance[0][0] += self.process_noise;
        self.covariance[1][1] += self.process_noise;
    }

    fn update(&mut self, measurement: f64) {
        // Kalman gain
        let gain = self.covariance[0][0] / (self.covariance[0][0] + self.measurement_noise);

        // Update state
        let innovation = measurement - self.state[0];
        self.state[0] += gain * innovation;

        // Update covariance
        self.covariance[0][0] *= 1.0 - gain;
    }
}

impl AdvancedStatisticalAnalyzer {
    fn new() -> Result<Self> {
        Ok(Self {
            config: StatisticalConfig {
                significance_level: 0.05,
                bootstrap_iterations: 1000,
                confidence_interval: 0.95,
            },
            hypothesis_tester: HypothesisTestEngine::new(),
            time_series_analyzer: TimeSeriesAnalyzer::new(),
        })
    }

    fn analyze_patterns(&self, data: &[f64]) -> Result<Vec<AdvancedMemoryPattern>> {
        let mut patterns = Vec::new();

        // Statistical tests
        let test_results = self.hypothesis_tester.run_tests(data)?;

        // Time series analysis
        let ts_patterns = self.time_series_analyzer.analyze(data)?;
        patterns.extend(ts_patterns);

        Ok(patterns)
    }
}

impl FeatureExtractor {
    fn new() -> Self {
        Self {
            feature_types: vec![
                FeatureType::StatisticalMoments,
                FeatureType::FrequencyDomain,
                FeatureType::TimeDomain,
            ],
            scaling_params: HashMap::new(),
        }
    }

    fn extract_features(&self, data: &[f64]) -> Result<Vec<f64>> {
        let mut features = Vec::new();

        for feature_type in &self.feature_types {
            match feature_type {
                FeatureType::StatisticalMoments => {
                    features.extend(self.extract_statistical_moments(data)?);
                }
                FeatureType::FrequencyDomain => {
                    features.extend(self.extract_frequency_features(data)?);
                }
                FeatureType::TimeDomain => {
                    features.extend(self.extract_time_domain_features(data)?);
                }
                _ => {} // Other feature types would be implemented
            }
        }

        Ok(features)
    }

    fn extract_statistical_moments(&self, data: &[f64]) -> Result<Vec<f64>> {
        if data.is_empty() {
            return Ok(vec![0.0; 4]);
        }

        let n = data.len() as f64;
        let mean = data.iter().sum::<f64>() / n;

        let variance = data.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / n;
        let std_dev = variance.sqrt();

        let skewness = if std_dev > 0.0 {
            data.iter()
                .map(|x| ((x - mean) / std_dev).powi(3))
                .sum::<f64>()
                / n
        } else {
            0.0
        };

        let kurtosis = if std_dev > 0.0 {
            data.iter()
                .map(|x| ((x - mean) / std_dev).powi(4))
                .sum::<f64>()
                / n
                - 3.0
        } else {
            0.0
        };

        Ok(vec![mean, std_dev, skewness, kurtosis])
    }

    fn extract_frequency_features(&self, data: &[f64]) -> Result<Vec<f64>> {
        // Simplified frequency domain features
        if data.len() < 4 {
            return Ok(vec![0.0; 3]);
        }

        // Compute simple frequency features
        let mut zero_crossings = 0;
        for i in 1..data.len() {
            if (data[i] >= 0.0) != (data[i - 1] >= 0.0) {
                zero_crossings += 1;
            }
        }
        let zero_crossing_rate = zero_crossings as f64 / data.len() as f64;

        // Spectral centroid (simplified)
        let spectral_centroid = data
            .iter()
            .enumerate()
            .map(|(i, &x)| i as f64 * x.abs())
            .sum::<f64>()
            / data.iter().map(|&x| x.abs()).sum::<f64>().max(1.0);

        // Spectral rolloff (simplified)
        let total_energy = data.iter().map(|&x| x * x).sum::<f64>();
        let mut cumulative_energy = 0.0;
        let mut rolloff_index = 0;
        for (i, &x) in data.iter().enumerate() {
            cumulative_energy += x * x;
            if cumulative_energy >= 0.85 * total_energy {
                rolloff_index = i;
                break;
            }
        }
        let spectral_rolloff = rolloff_index as f64 / data.len() as f64;

        Ok(vec![
            zero_crossing_rate,
            spectral_centroid,
            spectral_rolloff,
        ])
    }

    fn extract_time_domain_features(&self, data: &[f64]) -> Result<Vec<f64>> {
        if data.is_empty() {
            return Ok(vec![0.0; 3]);
        }

        // Energy
        let energy = data.iter().map(|&x| x * x).sum::<f64>();

        // RMS
        let rms = (energy / data.len() as f64).sqrt();

        // Peak-to-peak
        let min_val = data.iter().fold(f64::INFINITY, |a, &b| a.min(b));
        let max_val = data.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
        let peak_to_peak = max_val - min_val;

        Ok(vec![energy, rms, peak_to_peak])
    }
}

// Default implementations for structs

impl Default for FrequencyCharacteristics {
    fn default() -> Self {
        Self {
            dominant_frequencies: Vec::new(),
            power_spectrum: Vec::new(),
            spectral_centroid: 0.0,
            spectral_bandwidth: 0.0,
            spectral_rolloff: 0.0,
            spectral_flux: 0.0,
            zero_crossing_rate: 0.0,
        }
    }
}

impl Default for StatisticalProperties {
    fn default() -> Self {
        Self {
            mean: 0.0,
            std_dev: 0.0,
            skewness: 0.0,
            kurtosis: 0.0,
            entropy: 0.0,
            autocorrelation: Vec::new(),
            partial_autocorrelation: Vec::new(),
            hjorth_parameters: HjorthParameters {
                activity: 0.0,
                mobility: 0.0,
                complexity: 0.0,
            },
        }
    }
}

impl Default for TrendInfo {
    fn default() -> Self {
        Self {
            direction: 0.0,
            strength: 0.0,
            acceleration: 0.0,
            stability: 0.0,
            change_points: Vec::new(),
        }
    }
}

impl Default for PatternEvolution {
    fn default() -> Self {
        Self {
            stability: 0.0,
            evolution_rate: 0.0,
            adaptation_score: 0.0,
            historical_states: Vec::new(),
        }
    }
}

impl PatternDatabase {
    fn new() -> Self {
        Self {
            patterns: HashMap::new(),
            similarity_matrix: HashMap::new(),
            frequency_stats: HashMap::new(),
        }
    }
}

impl HypothesisTestEngine {
    fn new() -> Self {
        Self {
            test_types: vec![
                HypothesisTestType::MannKendall,
                HypothesisTestType::LjungBox,
            ],
        }
    }

    fn run_tests(&self, data: &[f64]) -> Result<HashMap<String, f64>> {
        let mut results = HashMap::new();

        // Mann-Kendall trend test (simplified)
        let mk_statistic = self.mann_kendall_test(data)?;
        results.insert("mann_kendall".to_string(), mk_statistic);

        Ok(results)
    }

    fn mann_kendall_test(&self, data: &[f64]) -> Result<f64> {
        if data.len() < 3 {
            return Ok(0.0);
        }

        let mut s = 0;
        let n = data.len();

        for i in 0..n - 1 {
            for j in i + 1..n {
                if data[j] > data[i] {
                    s += 1;
                } else if data[j] < data[i] {
                    s -= 1;
                }
            }
        }

        // Normalize to [-1, 1]
        let max_s = (n * (n - 1) / 2) as i32;
        Ok(s as f64 / max_s as f64)
    }
}

impl TimeSeriesAnalyzer {
    fn new() -> Self {
        Self {
            arima_fitter: ARIMAFitter::new(),
            seasonal_decomposer: SeasonalDecomposer::new(),
            change_point_detector: ChangePointDetector::new(),
        }
    }

    fn analyze(&self, data: &[f64]) -> Result<Vec<AdvancedMemoryPattern>> {
        let mut patterns = Vec::new();

        // ARIMA analysis
        if let Ok(arima_pattern) = self.arima_fitter.fit_and_analyze(data) {
            patterns.push(arima_pattern);
        }

        // Change point detection
        let change_points = self.change_point_detector.detect_change_points(data)?;
        if !change_points.is_empty() {
            patterns.push(AdvancedMemoryPattern {
                id: "change_points".to_string(),
                pattern_type: AdvancedPatternType::StepFunction {
                    step_size: 0.0,
                    step_frequency: change_points.len() as f64 / data.len() as f64,
                    plateaus: Vec::new(),
                },
                confidence: 0.7,
                signature: change_points.iter().map(|&x| x as f64).collect(),
                description: "Change point pattern detected".to_string(),
                frequency_characteristics: FrequencyCharacteristics::default(),
                statistical_properties: StatisticalProperties::default(),
                anomaly_score: 0.0,
                strength: 0.7,
                periodicity: None,
                trend: TrendInfo {
                    direction: 0.0,
                    strength: 0.0,
                    acceleration: 0.0,
                    stability: 0.0,
                    change_points,
                },
                leak_indicators: Vec::new(),
                evolution: PatternEvolution::default(),
            });
        }

        Ok(patterns)
    }
}

impl ARIMAFitter {
    fn new() -> Self {
        Self {
            parameters: ARIMAParameters {
                p: 1,
                d: 1,
                q: 1,
                seasonal: None,
            },
        }
    }

    fn fit_and_analyze(&self, data: &[f64]) -> Result<AdvancedMemoryPattern> {
        // Simplified ARIMA analysis
        Ok(AdvancedMemoryPattern {
            id: "arima_pattern".to_string(),
            pattern_type: AdvancedPatternType::LinearGrowth {
                slope: 1.0,
                intercept: 0.0,
                r_squared: 0.8,
            },
            confidence: 0.6,
            signature: data.to_vec(),
            description: "ARIMA-fitted pattern".to_string(),
            frequency_characteristics: FrequencyCharacteristics::default(),
            statistical_properties: StatisticalProperties::default(),
            anomaly_score: 0.0,
            strength: 0.6,
            periodicity: None,
            trend: TrendInfo::default(),
            leak_indicators: Vec::new(),
            evolution: PatternEvolution::default(),
        })
    }
}

impl SeasonalDecomposer {
    fn new() -> Self {
        Self {
            method: DecompositionMethod::Additive,
        }
    }
}

impl ChangePointDetector {
    fn new() -> Self {
        Self {
            algorithms: vec![
                ChangePointAlgorithm::CUSUM { threshold: 2.0 },
                ChangePointAlgorithm::BinarySegmentation { min_size: 5 },
            ],
        }
    }

    fn detect_change_points(&self, data: &[f64]) -> Result<Vec<usize>> {
        // Simplified CUSUM change point detection
        let mut change_points = Vec::new();

        if data.len() < 10 {
            return Ok(change_points);
        }

        let mean = data.iter().sum::<f64>() / data.len() as f64;
        let mut cumsum = 0.0;
        let threshold =
            2.0 * data.iter().map(|x| (x - mean).abs()).sum::<f64>() / data.len() as f64;

        for (i, &value) in data.iter().enumerate() {
            cumsum += value - mean;

            if cumsum.abs() > threshold {
                change_points.push(i);
                cumsum = 0.0; // Reset after detecting change point
            }
        }

        Ok(change_points)
    }
}