optirs-learned 0.3.1

// Pattern detection and analysis for optimization coordinator
//
// This module provides comprehensive pattern detection capabilities for
// optimization processes, including loss patterns, gradient patterns,
// performance patterns, and behavioral pattern recognition.

use super::config::*;
use super::performance::PerformanceSnapshot;
use crate::OptimizerError as OptimError;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::numeric::Float;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;
use std::time::{Duration, SystemTime};

/// Result type for pattern operations
type Result<T> = std::result::Result<T, OptimError>;

/// Pattern detector for optimization analysis
#[derive(Debug)]
pub struct PatternDetector<T: Float + Debug + Send + Sync + Debug> {
    /// Configuration
    config: PatternDetectionConfig,

    /// Pattern analyzers
    analyzers: Vec<PatternAnalyzer<T>>,

    /// Detected patterns history
    pattern_history: VecDeque<DetectedPattern<T>>,

    /// Pattern library
    pattern_library: PatternLibrary<T>,

    /// Pattern matcher
    pattern_matcher: PatternMatcher<T>,

    /// Sequence analyzer
    sequence_analyzer: SequenceAnalyzer<T>,
}

/// Individual pattern analyzer
#[derive(Debug)]
pub struct PatternAnalyzer<T: Float + Debug + Send + Sync + Debug> {
    /// Pattern type being analyzed
    pattern_type: PatternType,

    /// Analysis window
    analysis_window: VecDeque<T>,

    /// Pattern candidates
    pattern_candidates: Vec<PatternCandidate<T>>,

    /// Detection threshold
    detection_threshold: T,

    /// Minimum pattern length
    min_length: usize,

    /// Maximum pattern length
    max_length: usize,
}

/// Types of patterns to detect
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum PatternType {
    /// Loss function patterns
    LossPattern,

    /// Gradient norm patterns
    GradientPattern,

    /// Performance metric patterns
    PerformancePattern,

    /// Learning rate patterns
    LearningRatePattern,

    /// Oscillation patterns
    OscillationPattern,

    /// Convergence patterns
    ConvergencePattern,

    /// Plateau patterns
    PlateauPattern,

    /// Spike patterns
    SpikePattern,
}

/// Detected pattern
#[derive(Debug, Clone)]
pub struct DetectedPattern<T: Float + Debug + Send + Sync + 'static> {
    /// Pattern ID
    pub pattern_id: String,

    /// Pattern type
    pub pattern_type: PatternType,

    /// Pattern data
    pub pattern_data: Vec<T>,

    /// Pattern characteristics
    pub characteristics: PatternCharacteristics<T>,

    /// Detection confidence
    pub confidence: T,

    /// Pattern start index
    pub start_index: usize,

    /// Pattern end index
    pub end_index: usize,

    /// Detection timestamp
    pub timestamp: SystemTime,

    /// Pattern significance
    pub significance: PatternSignificance,

    /// Related patterns
    pub related_patterns: Vec<String>,
}

/// Pattern characteristics
#[derive(Debug, Clone)]
pub struct PatternCharacteristics<T: Float + Debug + Send + Sync + 'static> {
    /// Pattern length
    pub length: usize,

    /// Pattern amplitude
    pub amplitude: T,

    /// Pattern frequency
    pub frequency: T,

    /// Pattern trend
    pub trend: PatternTrend,

    /// Pattern regularity
    pub regularity: T,

    /// Pattern stability
    pub stability: T,

    /// Pattern complexity
    pub complexity: T,

    /// Statistical properties
    pub statistics: PatternStatistics<T>,
}

/// Pattern trend types
#[derive(Debug, Clone, Copy)]
pub enum PatternTrend {
    Increasing,
    Decreasing,
    Oscillating,
    Stable,
    Random,
    Periodic,
}

/// Pattern significance levels
#[derive(Debug, Clone, Copy)]
pub enum PatternSignificance {
    Low,
    Medium,
    High,
    Critical,
}

/// Pattern statistical properties
#[derive(Debug, Clone)]
pub struct PatternStatistics<T: Float + Debug + Send + Sync + 'static> {
    /// Mean value
    pub mean: T,

    /// Standard deviation
    pub std_dev: T,

    /// Variance
    pub variance: T,

    /// Skewness
    pub skewness: T,

    /// Kurtosis
    pub kurtosis: T,

    /// Autocorrelation
    pub autocorrelation: Vec<T>,

    /// Spectral density
    pub spectral_density: Vec<T>,
}

/// Pattern candidate for analysis
#[derive(Debug, Clone)]
pub struct PatternCandidate<T: Float + Debug + Send + Sync + 'static> {
    /// Candidate data
    pub data: Vec<T>,

    /// Starting position
    pub start_pos: usize,

    /// Confidence score
    pub confidence: T,

    /// Pattern template match
    pub template_match: Option<PatternTemplate<T>>,

    /// Similarity score
    pub similarity_score: T,
}

/// Pattern template
#[derive(Debug, Clone)]
pub struct PatternTemplate<T: Float + Debug + Send + Sync + 'static> {
    /// Template name
    pub name: String,

    /// Template data
    pub template_data: Vec<T>,

    /// Template characteristics
    pub characteristics: PatternCharacteristics<T>,

    /// Template tolerance
    pub tolerance: T,

    /// Usage count
    pub usage_count: usize,

    /// Success rate
    pub success_rate: T,
}

/// Pattern library for storing known patterns
#[derive(Debug)]
pub struct PatternLibrary<T: Float + Debug + Send + Sync + Debug> {
    /// Template patterns
    templates: HashMap<String, PatternTemplate<T>>,

    /// Pattern categories
    categories: HashMap<PatternType, Vec<String>>,

    /// Pattern relationships
    relationships: HashMap<String, Vec<String>>,

    /// Pattern metadata
    metadata: HashMap<String, PatternMetadata>,
}

/// Pattern metadata
#[derive(Debug, Clone)]
pub struct PatternMetadata {
    /// Creation timestamp
    pub created: SystemTime,

    /// Last used timestamp
    pub last_used: SystemTime,

    /// Usage frequency
    pub usage_frequency: f64,

    /// Pattern description
    pub description: String,

    /// Pattern tags
    pub tags: Vec<String>,

    /// Pattern source
    pub source: PatternSource,
}

/// Source of pattern
#[derive(Debug, Clone)]
pub enum PatternSource {
    UserDefined,
    MachineLearned,
    HistoricalData,
    Literature,
    Heuristic,
}

/// Pattern matcher for finding similar patterns
#[derive(Debug)]
pub struct PatternMatcher<T: Float + Debug + Send + Sync + Debug> {
    /// Matching algorithms
    algorithms: Vec<MatchingAlgorithm>,

    /// Similarity threshold
    similarity_threshold: T,

    /// Match cache
    match_cache: HashMap<String, Vec<PatternMatch<T>>>,

    /// Matching statistics
    matching_stats: MatchingStatistics<T>,
}

/// Pattern matching algorithms
#[derive(Debug, Clone)]
pub enum MatchingAlgorithm {
    /// Dynamic Time Warping
    DTW,

    /// Euclidean distance
    Euclidean,

    /// Correlation-based
    Correlation,

    /// Frequency domain
    FrequencyDomain,

    /// Statistical comparison
    Statistical,

    /// Machine learning based
    MLBased,
}

/// Pattern match result
#[derive(Debug, Clone)]
pub struct PatternMatch<T: Float + Debug + Send + Sync + 'static> {
    /// Template name
    pub template_name: String,

    /// Similarity score
    pub similarity_score: T,

    /// Match confidence
    pub confidence: T,

    /// Alignment offset
    pub alignment_offset: i32,

    /// Scale factor
    pub scale_factor: T,

    /// Match quality
    pub match_quality: MatchQuality,
}

/// Quality of pattern match
#[derive(Debug, Clone, Copy)]
pub enum MatchQuality {
    Excellent,
    Good,
    Fair,
    Poor,
}

/// Matching statistics
#[derive(Debug, Clone)]
pub struct MatchingStatistics<T: Float + Debug + Send + Sync + 'static> {
    /// Total matches performed
    pub total_matches: usize,

    /// Successful matches
    pub successful_matches: usize,

    /// Average matching time
    pub avg_matching_time: Duration,

    /// Average similarity score
    pub avg_similarity_score: T,

    /// Match accuracy
    pub match_accuracy: T,
}

/// Sequence analyzer for temporal patterns
#[derive(Debug)]
pub struct SequenceAnalyzer<T: Float + Debug + Send + Sync + Debug> {
    /// Sequence buffer
    sequence_buffer: VecDeque<SequenceElement<T>>,

    /// Sequence patterns
    sequence_patterns: Vec<SequencePattern<T>>,

    /// Temporal windows
    temporal_windows: Vec<Duration>,

    /// Anomaly detector
    anomaly_detector: SequenceAnomalyDetector<T>,
}

/// Element in a sequence
#[derive(Debug, Clone)]
pub struct SequenceElement<T: Float + Debug + Send + Sync + 'static> {
    /// Timestamp
    pub timestamp: SystemTime,

    /// Value
    pub value: T,

    /// Element type
    pub element_type: String,

    /// Context
    pub context: HashMap<String, String>,
}

/// Sequence pattern
#[derive(Debug, Clone)]
pub struct SequencePattern<T: Float + Debug + Send + Sync + 'static> {
    /// Pattern name
    pub name: String,

    /// Sequence elements
    pub elements: Vec<SequenceElement<T>>,

    /// Pattern duration
    pub duration: Duration,

    /// Repetition count
    pub repetition_count: usize,

    /// Pattern confidence
    pub confidence: T,

    /// Predictive power
    pub predictive_power: T,
}

/// Sequence anomaly detector
#[derive(Debug)]
pub struct SequenceAnomalyDetector<T: Float + Debug + Send + Sync + Debug> {
    /// Normal sequence patterns
    normal_patterns: Vec<SequencePattern<T>>,

    /// Anomaly threshold
    anomaly_threshold: T,

    /// Detection window
    detection_window: Duration,

    /// Anomaly history
    anomaly_history: VecDeque<SequenceAnomaly<T>>,
}

/// Sequence anomaly
#[derive(Debug, Clone)]
pub struct SequenceAnomaly<T: Float + Debug + Send + Sync + 'static> {
    /// Anomaly timestamp
    pub timestamp: SystemTime,

    /// Anomaly score
    pub anomaly_score: T,

    /// Anomaly type
    pub anomaly_type: AnomalyType,

    /// Context
    pub context: SequenceElement<T>,

    /// Explanation
    pub explanation: String,
}

/// Types of sequence anomalies
#[derive(Debug, Clone, Copy)]
pub enum AnomalyType {
    OutOfPattern,
    UnexpectedValue,
    TimingAnomaly,
    FrequencyAnomaly,
    StructuralAnomaly,
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> PatternDetector<T> {
    /// Create new pattern detector
    pub fn new(config: PatternDetectionConfig) -> Result<Self> {
        let mut analyzers = Vec::new();

        // Create analyzers for enabled pattern types
        if config.enable_loss_patterns {
            analyzers.push(PatternAnalyzer::new(
                PatternType::LossPattern,
                config.detection_sensitivity,
                config.min_pattern_length,
                config.max_pattern_length,
            )?);
        }

        if config.enable_gradient_patterns {
            analyzers.push(PatternAnalyzer::new(
                PatternType::GradientPattern,
                config.detection_sensitivity,
                config.min_pattern_length,
                config.max_pattern_length,
            )?);
        }

        if config.enable_performance_patterns {
            analyzers.push(PatternAnalyzer::new(
                PatternType::PerformancePattern,
                config.detection_sensitivity,
                config.min_pattern_length,
                config.max_pattern_length,
            )?);
        }

        Ok(Self {
            config,
            analyzers,
            pattern_history: VecDeque::new(),
            pattern_library: PatternLibrary::new()?,
            pattern_matcher: PatternMatcher::new(scirs2_core::numeric::NumCast::from(config.pattern_tolerance).unwrap_or_else(|| T::zero()))?,
            sequence_analyzer: SequenceAnalyzer::new()?,
        })
    }

    /// Detect patterns in performance data
    pub fn detect_patterns(&mut self, snapshot: &PerformanceSnapshot<T>) -> Result<Vec<DetectedPattern<T>>> {
        let mut detected_patterns = Vec::new();

        // Run each analyzer
        for analyzer in &mut self.analyzers {
            let value = match analyzer.pattern_type {
                PatternType::LossPattern => snapshot.loss,
                PatternType::GradientPattern => snapshot.gradient_norm.unwrap_or(T::zero()),
                PatternType::PerformancePattern => snapshot.accuracy.unwrap_or(T::zero()),
                _ => T::zero(),
            };

            if let Some(pattern) = analyzer.analyze_value(value, snapshot.step)? {
                detected_patterns.push(pattern);
            }
        }

        // Update pattern history
        for pattern in &detected_patterns {
            if self.pattern_history.len() >= 1000 {
                self.pattern_history.pop_front();
            }
            self.pattern_history.push_back(pattern.clone());

            // Update pattern library
            self.pattern_library.update_with_pattern(pattern)?;
        }

        Ok(detected_patterns)
    }

    /// Get pattern history
    pub fn get_pattern_history(&self) -> &VecDeque<DetectedPattern<T>> {
        &self.pattern_history
    }

    /// Search for similar patterns
    pub fn find_similar_patterns(&self, pattern: &DetectedPattern<T>) -> Result<Vec<PatternMatch<T>>> {
        self.pattern_matcher.find_matches(&pattern.pattern_data)
    }

    /// Predict next values based on detected patterns
    pub fn predict_next_values(&self, steps_ahead: usize) -> Result<Vec<T>> {
        if self.pattern_history.is_empty() {
            return Ok(vec![T::zero(); steps_ahead]);
        }

        // Use most recent pattern for prediction
        let recent_pattern = self.pattern_history.back().expect("unwrap failed");
        let mut predictions = Vec::new();

        // Simple pattern-based prediction
        for i in 0..steps_ahead {
            let pattern_index = i % recent_pattern.pattern_data.len();
            let predicted_value = recent_pattern.pattern_data[pattern_index];
            predictions.push(predicted_value);
        }

        Ok(predictions)
    }

    /// Analyze sequence patterns
    pub fn analyze_sequence_patterns(&mut self, sequence: &[SequenceElement<T>]) -> Result<Vec<SequencePattern<T>>> {
        self.sequence_analyzer.analyze_sequence(sequence)
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> PatternAnalyzer<T> {
    /// Create new pattern analyzer
    pub fn new(
        pattern_type: PatternType,
        detection_threshold: f64,
        min_length: usize,
        max_length: usize,
    ) -> Result<Self> {
        Ok(Self {
            pattern_type,
            analysis_window: VecDeque::new(),
            pattern_candidates: Vec::new(),
            detection_threshold: scirs2_core::numeric::NumCast::from(detection_threshold).unwrap_or_else(|| T::zero()),
            min_length,
            max_length,
        })
    }

    /// Analyze new value for patterns
    pub fn analyze_value(&mut self, value: T, step: usize) -> Result<Option<DetectedPattern<T>>> {
        // Add to analysis window
        self.analysis_window.push_back(value);
        if self.analysis_window.len() > self.max_length * 2 {
            self.analysis_window.pop_front();
        }

        // Check if we have enough data
        if self.analysis_window.len() < self.min_length {
            return Ok(None);
        }

        // Look for patterns
        if let Some(pattern) = self.detect_pattern_in_window(step)? {
            return Ok(Some(pattern));
        }

        Ok(None)
    }

    /// Detect pattern in current window
    fn detect_pattern_in_window(&self, step: usize) -> Result<Option<DetectedPattern<T>>> {
        let data: Vec<T> = self.analysis_window.iter().cloned().collect();

        match self.pattern_type {
            PatternType::LossPattern => self.detect_loss_pattern(&data, step),
            PatternType::GradientPattern => self.detect_gradient_pattern(&data, step),
            PatternType::PerformancePattern => self.detect_performance_pattern(&data, step),
            PatternType::OscillationPattern => self.detect_oscillation_pattern(&data, step),
            PatternType::PlateauPattern => self.detect_plateau_pattern(&data, step),
            _ => Ok(None),
        }
    }

    /// Detect loss patterns
    fn detect_loss_pattern(&self, data: &[T], step: usize) -> Result<Option<DetectedPattern<T>>> {
        if data.len() < self.min_length {
            return Ok(None);
        }

        // Check for decreasing trend (good for loss)
        let trend = self.calculate_trend(data)?;
        if trend < -self.detection_threshold {
            let characteristics = self.calculate_characteristics(data)?;
            let confidence = (-trend).min(T::one());

            return Ok(Some(DetectedPattern {
                pattern_id: format!("loss_decreasing_{}", step),
                pattern_type: self.pattern_type,
                pattern_data: data.to_vec(),
                characteristics,
                confidence,
                start_index: step.saturating_sub(data.len()),
                end_index: step,
                timestamp: SystemTime::now(),
                significance: PatternSignificance::High,
                related_patterns: Vec::new(),
            }));
        }

        Ok(None)
    }

    /// Detect gradient patterns
    fn detect_gradient_pattern(&self, data: &[T], step: usize) -> Result<Option<DetectedPattern<T>>> {
        if data.len() < self.min_length {
            return Ok(None);
        }

        // Check for decreasing gradient norm (indicates convergence)
        let trend = self.calculate_trend(data)?;
        if trend < -self.detection_threshold {
            let characteristics = self.calculate_characteristics(data)?;
            let confidence = (-trend).min(T::one());

            return Ok(Some(DetectedPattern {
                pattern_id: format!("gradient_decreasing_{}", step),
                pattern_type: self.pattern_type,
                pattern_data: data.to_vec(),
                characteristics,
                confidence,
                start_index: step.saturating_sub(data.len()),
                end_index: step,
                timestamp: SystemTime::now(),
                significance: PatternSignificance::Medium,
                related_patterns: Vec::new(),
            }));
        }

        Ok(None)
    }

    /// Detect performance patterns
    fn detect_performance_pattern(&self, data: &[T], step: usize) -> Result<Option<DetectedPattern<T>>> {
        if data.len() < self.min_length {
            return Ok(None);
        }

        // Check for increasing trend (good for accuracy)
        let trend = self.calculate_trend(data)?;
        if trend > self.detection_threshold {
            let characteristics = self.calculate_characteristics(data)?;
            let confidence = trend.min(T::one());

            return Ok(Some(DetectedPattern {
                pattern_id: format!("performance_improving_{}", step),
                pattern_type: self.pattern_type,
                pattern_data: data.to_vec(),
                characteristics,
                confidence,
                start_index: step.saturating_sub(data.len()),
                end_index: step,
                timestamp: SystemTime::now(),
                significance: PatternSignificance::High,
                related_patterns: Vec::new(),
            }));
        }

        Ok(None)
    }

    /// Detect oscillation patterns
    fn detect_oscillation_pattern(&self, data: &[T], step: usize) -> Result<Option<DetectedPattern<T>>> {
        if data.len() < self.min_length {
            return Ok(None);
        }

        // Count direction changes
        let mut direction_changes = 0;
        for window in data.windows(3) {
            let trend1 = window[1] - window[0];
            let trend2 = window[2] - window[1];
            if trend1 * trend2 < T::zero() {
                direction_changes += 1;
            }
        }

        let oscillation_ratio = scirs2_core::numeric::NumCast::from(direction_changes).unwrap_or_else(|| T::zero()) / T::from(data.len().saturating_sub(2)).expect("unwrap failed");

        if oscillation_ratio > self.detection_threshold {
            let characteristics = self.calculate_characteristics(data)?;
            let confidence = oscillation_ratio.min(T::one());

            return Ok(Some(DetectedPattern {
                pattern_id: format!("oscillation_{}", step),
                pattern_type: PatternType::OscillationPattern,
                pattern_data: data.to_vec(),
                characteristics,
                confidence,
                start_index: step.saturating_sub(data.len()),
                end_index: step,
                timestamp: SystemTime::now(),
                significance: PatternSignificance::Medium,
                related_patterns: Vec::new(),
            }));
        }

        Ok(None)
    }

    /// Detect plateau patterns
    fn detect_plateau_pattern(&self, data: &[T], step: usize) -> Result<Option<DetectedPattern<T>>> {
        if data.len() < self.min_length {
            return Ok(None);
        }

        // Calculate variance - low variance indicates plateau
        let mean = data.iter().fold(T::zero(), |acc, &x| acc + x) / T::from(data.len()).expect("unwrap failed");
        let variance = data.iter()
            .map(|&x| (x - mean) * (x - mean))
            .fold(T::zero(), |acc, x| acc + x) / T::from(data.len()).expect("unwrap failed");

        let stability = T::one() / (T::one() + variance.sqrt());

        if stability > self.detection_threshold {
            let characteristics = self.calculate_characteristics(data)?;
            let confidence = stability;

            return Ok(Some(DetectedPattern {
                pattern_id: format!("plateau_{}", step),
                pattern_type: PatternType::PlateauPattern,
                pattern_data: data.to_vec(),
                characteristics,
                confidence,
                start_index: step.saturating_sub(data.len()),
                end_index: step,
                timestamp: SystemTime::now(),
                significance: PatternSignificance::Low,
                related_patterns: Vec::new(),
            }));
        }

        Ok(None)
    }

    /// Calculate trend in data
    fn calculate_trend(&self, data: &[T]) -> Result<T> {
        if data.len() < 2 {
            return Ok(T::zero());
        }

        // Simple linear regression slope
        let n = T::from(data.len()).expect("unwrap failed");
        let sum_x = (0..data.len()).map(|i| scirs2_core::numeric::NumCast::from(i).unwrap_or_else(|| T::zero())).fold(T::zero(), |acc, x| acc + x);
        let sum_y = data.iter().fold(T::zero(), |acc, &y| acc + y);
        let sum_xy = data.iter().enumerate()
            .map(|(i, &y)| scirs2_core::numeric::NumCast::from(i).unwrap_or_else(|| T::zero()) * y)
            .fold(T::zero(), |acc, xy| acc + xy);
        let sum_x2 = (0..data.len())
            .map(|i| scirs2_core::numeric::NumCast::from(i).unwrap_or_else(|| T::zero()) * scirs2_core::numeric::NumCast::from(i).unwrap_or_else(|| T::zero()))
            .fold(T::zero(), |acc, x2| acc + x2);

        let numerator = n * sum_xy - sum_x * sum_y;
        let denominator = n * sum_x2 - sum_x * sum_x;

        if denominator == T::zero() {
            Ok(T::zero())
        } else {
            Ok(numerator / denominator)
        }
    }

    /// Calculate pattern characteristics
    fn calculate_characteristics(&self, data: &[T]) -> Result<PatternCharacteristics<T>> {
        let length = data.len();
        let mean = data.iter().fold(T::zero(), |acc, &x| acc + x) / scirs2_core::numeric::NumCast::from(length).unwrap_or_else(|| T::zero());

        let variance = data.iter()
            .map(|&x| (x - mean) * (x - mean))
            .fold(T::zero(), |acc, x| acc + x) / scirs2_core::numeric::NumCast::from(length).unwrap_or_else(|| T::zero());

        let std_dev = variance.sqrt();

        let max_val = data.iter().fold(T::zero(), |acc, &x| if x > acc { x } else { acc });
        let min_val = data.iter().fold(T::one(), |acc, &x| if x < acc { x } else { acc });
        let amplitude = max_val - min_val;

        // Calculate autocorrelation (simplified)
        let autocorr = self.calculate_autocorrelation(data, 5)?;

        // Estimate frequency (simplified)
        let frequency = T::one() / scirs2_core::numeric::NumCast::from(length).unwrap_or_else(|| T::zero());

        // Calculate trend
        let trend_val = self.calculate_trend(data)?;
        let trend = if trend_val > scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()) {
            PatternTrend::Increasing
        } else if trend_val < scirs2_core::numeric::NumCast::from(-0.01).unwrap_or_else(|| T::zero()) {
            PatternTrend::Decreasing
        } else {
            PatternTrend::Stable
        };

        // Calculate regularity (inverse of variance)
        let regularity = T::one() / (T::one() + variance);

        // Calculate stability (similar to regularity)
        let stability = regularity;

        // Calculate complexity (number of direction changes)
        let mut direction_changes = 0;
        for window in data.windows(2) {
            if (window[1] - window[0]).abs() > std_dev / scirs2_core::numeric::NumCast::from(2.0).unwrap_or_else(|| T::zero()) {
                direction_changes += 1;
            }
        }
        let complexity = scirs2_core::numeric::NumCast::from(direction_changes).unwrap_or_else(|| T::zero()) / scirs2_core::numeric::NumCast::from(length).unwrap_or_else(|| T::zero());

        Ok(PatternCharacteristics {
            length,
            amplitude,
            frequency,
            trend,
            regularity,
            stability,
            complexity,
            statistics: PatternStatistics {
                mean,
                std_dev,
                variance,
                skewness: T::zero(), // Simplified
                kurtosis: T::zero(), // Simplified
                autocorrelation: autocorr,
                spectral_density: Vec::new(), // Simplified
            },
        })
    }

    /// Calculate autocorrelation
    fn calculate_autocorrelation(&self, data: &[T], max_lag: usize) -> Result<Vec<T>> {
        let mut autocorr = Vec::new();

        for lag in 0..=max_lag.min(data.len() / 2) {
            if lag >= data.len() {
                autocorr.push(T::zero());
                continue;
            }

            let mut sum = T::zero();
            let mut count = 0;

            for i in 0..(data.len() - lag) {
                sum = sum + data[i] * data[i + lag];
                count += 1;
            }

            autocorr.push(if count > 0 { sum / scirs2_core::numeric::NumCast::from(count).unwrap_or_else(|| T::zero()) } else { T::zero() });
        }

        Ok(autocorr)
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> PatternLibrary<T> {
    /// Create new pattern library
    pub fn new() -> Result<Self> {
        Ok(Self {
            templates: HashMap::new(),
            categories: HashMap::new(),
            relationships: HashMap::new(),
            metadata: HashMap::new(),
        })
    }

    /// Add pattern template
    pub fn add_template(&mut self, template: PatternTemplate<T>) -> Result<()> {
        let template_name = template.name.clone();

        // Add to templates
        self.templates.insert(template_name.clone(), template.clone());

        // Add metadata
        let metadata = PatternMetadata {
            created: SystemTime::now(),
            last_used: SystemTime::now(),
            usage_frequency: 0.0,
            description: format!("Pattern template: {}", template_name),
            tags: Vec::new(),
            source: PatternSource::UserDefined,
        };
        self.metadata.insert(template_name, metadata);

        Ok(())
    }

    /// Update library with detected pattern
    pub fn update_with_pattern(&mut self, pattern: &DetectedPattern<T>) -> Result<()> {
        // Check if pattern matches existing templates
        for (template_name, template) in &mut self.templates {
            if self.patterns_similar(&pattern.pattern_data, &template.template_data)? {
                template.usage_count += 1;

                // Update metadata
                if let Some(metadata) = self.metadata.get_mut(template_name) {
                    metadata.last_used = SystemTime::now();
                    metadata.usage_frequency += 1.0;
                }
            }
        }

        Ok(())
    }

    /// Check if patterns are similar
    fn patterns_similar(&self, pattern1: &[T], pattern2: &[T]) -> Result<bool> {
        if pattern1.len() != pattern2.len() {
            return Ok(false);
        }

        let mut sum_squared_diff = T::zero();
        for (a, b) in pattern1.iter().zip(pattern2.iter()) {
            let diff = *a - *b;
            sum_squared_diff = sum_squared_diff + diff * diff;
        }

        let rmse = (sum_squared_diff / T::from(pattern1.len()).expect("unwrap failed")).sqrt();
        Ok(rmse < scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero())) // Threshold for similarity
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> PatternMatcher<T> {
    /// Create new pattern matcher
    pub fn new(similarity_threshold: T) -> Result<Self> {
        Ok(Self {
            algorithms: vec![
                MatchingAlgorithm::Correlation,
                MatchingAlgorithm::Euclidean,
            ],
            similarity_threshold,
            match_cache: HashMap::new(),
            matching_stats: MatchingStatistics::default(),
        })
    }

    /// Find matches for pattern
    pub fn find_matches(&self, pattern_data: &[T]) -> Result<Vec<PatternMatch<T>>> {
        let mut matches = Vec::new();

        // Use correlation-based matching (simplified)
        let match_result = PatternMatch {
            template_name: "example_template".to_string(),
            similarity_score: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
            confidence: scirs2_core::numeric::NumCast::from(0.75).unwrap_or_else(|| T::zero()),
            alignment_offset: 0,
            scale_factor: T::one(),
            match_quality: MatchQuality::Good,
        };

        matches.push(match_result);
        Ok(matches)
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> SequenceAnalyzer<T> {
    /// Create new sequence analyzer
    pub fn new() -> Result<Self> {
        Ok(Self {
            sequence_buffer: VecDeque::new(),
            sequence_patterns: Vec::new(),
            temporal_windows: vec![
                Duration::from_secs(60),
                Duration::from_secs(300),
                Duration::from_secs(900),
            ],
            anomaly_detector: SequenceAnomalyDetector::new()?,
        })
    }

    /// Analyze sequence for patterns
    pub fn analyze_sequence(&mut self, sequence: &[SequenceElement<T>]) -> Result<Vec<SequencePattern<T>>> {
        let mut patterns = Vec::new();

        // Simple pattern detection
        if sequence.len() >= 3 {
            let pattern = SequencePattern {
                name: format!("sequence_pattern_{}", SystemTime::now().duration_since(SystemTime::UNIX_EPOCH).expect("unwrap failed").as_secs()),
                elements: sequence.to_vec(),
                duration: Duration::from_secs(sequence.len() as u64),
                repetition_count: 1,
                confidence: scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero()),
                predictive_power: scirs2_core::numeric::NumCast::from(0.6).unwrap_or_else(|| T::zero()),
            };
            patterns.push(pattern);
        }

        Ok(patterns)
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> SequenceAnomalyDetector<T> {
    /// Create new sequence anomaly detector
    pub fn new() -> Result<Self> {
        Ok(Self {
            normal_patterns: Vec::new(),
            anomaly_threshold: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
            detection_window: Duration::from_secs(300),
            anomaly_history: VecDeque::new(),
        })
    }

    /// Detect anomalies in sequence
    pub fn detect_anomalies(&mut self, element: &SequenceElement<T>) -> Result<Option<SequenceAnomaly<T>>> {
        // Simplified anomaly detection
        if element.value > scirs2_core::numeric::NumCast::from(10.0).unwrap_or_else(|| T::zero()) {
            let anomaly = SequenceAnomaly {
                timestamp: SystemTime::now(),
                anomaly_score: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
                anomaly_type: AnomalyType::UnexpectedValue,
                context: element.clone(),
                explanation: "Value exceeds expected range".to_string(),
            };

            self.anomaly_history.push_back(anomaly.clone());
            if self.anomaly_history.len() > 1000 {
                self.anomaly_history.pop_front();
            }

            return Ok(Some(anomaly));
        }

        Ok(None)
    }
}

// Default implementations
impl<T: Float + Debug + Send + Sync + 'static> Default for MatchingStatistics<T> {
    fn default() -> Self {
        Self {
            total_matches: 0,
            successful_matches: 0,
            avg_matching_time: Duration::from_millis(10),
            avg_similarity_score: T::from(0.5).unwrap_or(T::zero()),
            match_accuracy: T::from(0.8).unwrap_or(T::zero()),
        }
    }
}

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

    #[test]
    fn test_pattern_detector_creation() {
        let config = PatternDetectionConfig::default();
        let detector = PatternDetector::<f32>::new(config);
        assert!(detector.is_ok());
    }

    #[test]
    fn test_pattern_analyzer_creation() {
        let analyzer = PatternAnalyzer::<f32>::new(
            PatternType::LossPattern,
            0.5,
            5,
            20
        );
        assert!(analyzer.is_ok());
    }

    #[test]
    fn test_trend_calculation() {
        let analyzer = PatternAnalyzer::<f32>::new(
            PatternType::LossPattern,
            0.5,
            5,
            20
        ).expect("unwrap failed");

        let data = vec![5.0, 4.0, 3.0, 2.0, 1.0]; // Decreasing trend
        let trend = analyzer.calculate_trend(&data).expect("unwrap failed");
        assert!(trend < 0.0); // Should be negative for decreasing trend
    }

    #[test]
    fn test_pattern_characteristics_calculation() {
        let analyzer = PatternAnalyzer::<f32>::new(
            PatternType::LossPattern,
            0.5,
            5,
            20
        ).expect("unwrap failed");

        let data = vec![1.0, 2.0, 3.0, 2.0, 1.0];
        let characteristics = analyzer.calculate_characteristics(&data).expect("unwrap failed");

        assert_eq!(characteristics.length, 5);
        assert!(characteristics.amplitude > 0.0);
        assert!(characteristics.regularity > 0.0);
    }

    #[test]
    fn test_autocorrelation_calculation() {
        let analyzer = PatternAnalyzer::<f32>::new(
            PatternType::LossPattern,
            0.5,
            5,
            20
        ).expect("unwrap failed");

        let data = vec![1.0, 2.0, 1.0, 2.0, 1.0]; // Periodic pattern
        let autocorr = analyzer.calculate_autocorrelation(&data, 3).expect("unwrap failed");
        assert!(!autocorr.is_empty());
    }

    #[test]
    fn test_pattern_library() {
        let mut library = PatternLibrary::<f32>::new().expect("unwrap failed");

        let template = PatternTemplate {
            name: "test_template".to_string(),
            template_data: vec![1.0, 2.0, 3.0],
            characteristics: PatternCharacteristics {
                length: 3,
                amplitude: 2.0,
                frequency: 0.5,
                trend: PatternTrend::Increasing,
                regularity: 0.8,
                stability: 0.9,
                complexity: 0.3,
                statistics: PatternStatistics {
                    mean: 2.0,
                    std_dev: 0.8,
                    variance: 0.67,
                    skewness: 0.0,
                    kurtosis: 0.0,
                    autocorrelation: vec![1.0, 0.5, 0.0],
                    spectral_density: Vec::new(),
                },
            },
            tolerance: 0.1,
            usage_count: 0,
            success_rate: 0.0,
        };

        let result = library.add_template(template);
        assert!(result.is_ok());
        assert_eq!(library.templates.len(), 1);
    }

    #[test]
    fn test_sequence_analyzer() {
        let mut analyzer = SequenceAnalyzer::<f32>::new().expect("unwrap failed");

        let sequence = vec![
            SequenceElement {
                timestamp: SystemTime::now(),
                value: 1.0,
                element_type: "test".to_string(),
                context: HashMap::new(),
            },
            SequenceElement {
                timestamp: SystemTime::now(),
                value: 2.0,
                element_type: "test".to_string(),
                context: HashMap::new(),
            },
            SequenceElement {
                timestamp: SystemTime::now(),
                value: 3.0,
                element_type: "test".to_string(),
                context: HashMap::new(),
            },
        ];

        let patterns = analyzer.analyze_sequence(&sequence).expect("unwrap failed");
        assert!(!patterns.is_empty());
    }
}