//! Enhanced Algorithms for Advanced Mode
//!
//! This module provides advanced algorithmic enhancements for the advanced coordinator,
//! including new optimization strategies, advanced pattern recognition, and self-improving
//! algorithmic components.

#![allow(dead_code)]
#![allow(clippy::too_many_arguments)]

use crate::error::{IoError, Result};
use scirs2_core::ndarray::ArrayStatCompat;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::random::{Rng, RngExt};
use statrs::statistics::Statistics;
use std::collections::{HashMap, VecDeque};
use std::time::Instant;

/// Advanced pattern recognition system with deep learning capabilities
#[derive(Debug)]
pub struct AdvancedPatternRecognizer {
    /// Multi-layer pattern detection networks
    pattern_networks: Vec<PatternNetwork>,
    /// Historical pattern database
    pattern_database: HashMap<String, PatternMetadata>,
    /// Real-time pattern analysis buffer
    analysis_buffer: VecDeque<PatternInstance>,
    /// Learning rate for pattern adaptation
    learning_rate: f32,
}

impl Default for AdvancedPatternRecognizer {
    fn default() -> Self {
        Self::new()
    }
}

impl AdvancedPatternRecognizer {
    /// Create a new advanced pattern recognizer
    pub fn new() -> Self {
        let pattern_networks = vec![
            PatternNetwork::new("repetition", 16, 8, 4),
            PatternNetwork::new("sequential", 16, 8, 4),
            PatternNetwork::new("fractal", 32, 16, 8),
            PatternNetwork::new("entropy", 16, 8, 4),
            PatternNetwork::new("compression", 24, 12, 6),
        ];

        Self {
            pattern_networks,
            pattern_database: HashMap::new(),
            analysis_buffer: VecDeque::with_capacity(1000),
            learning_rate: 0.001,
        }
    }

    /// Analyze data for advanced patterns using deep learning
    pub fn analyze_patterns(&mut self, data: &[u8]) -> Result<AdvancedPatternAnalysis> {
        let mut pattern_scores = HashMap::new();
        let mut emergent_patterns = Vec::new();

        // Extract multi-scale features
        let features = self.extract_multiscale_features(data)?;

        // Pre-compute data characteristics to avoid borrow conflicts
        let data_characteristics = self.characterize_data(data);

        // Collect network analysis results first
        let mut network_results = Vec::new();
        for network in &mut self.pattern_networks {
            let score = network.analyze(&features)?;
            let pattern_type = network.pattern_type.clone();
            network_results.push((pattern_type, score));
        }

        // Now process results without mutable borrow conflicts
        for (pattern_type, score) in network_results {
            // Check if pattern is novel
            let is_novel = self.is_novel_pattern(&pattern_type, score);

            pattern_scores.insert(pattern_type.clone(), score);

            // Detect emergent patterns
            if score > 0.8 && is_novel {
                emergent_patterns.push(EmergentPattern {
                    pattern_type,
                    confidence: score,
                    discovered_at: Instant::now(),
                    data_characteristics: data_characteristics.clone(),
                });
            }
        }

        // Update pattern database
        self.update_pattern_database(data, &pattern_scores)?;

        // Cross-correlate patterns for meta-patterns
        let meta_patterns = self.detect_meta_patterns(&pattern_scores)?;
        let optimization_recommendations =
            self.generate_optimization_recommendations(&pattern_scores);

        Ok(AdvancedPatternAnalysis {
            pattern_scores,
            emergent_patterns,
            meta_patterns,
            complexity_index: self.calculate_complexity_index(&features),
            predictability_score: self.calculate_predictability(data),
            optimization_recommendations,
        })
    }

    /// Extract multi-scale features from data
    fn extract_multiscale_features(&self, data: &[u8]) -> Result<Array2<f32>> {
        // Extract features for each scale separately to ensure consistent dimensions
        let byte_features = self.extract_byte_level_features(data);
        let local_features_4 = self.extract_local_structure_features(data, 4);
        let local_features_16 = self.extract_local_structure_features(data, 16);
        let global_features = self.extract_global_structure_features(data);

        // Find the maximum number of features to ensure consistent dimensions
        let max_features = [
            byte_features.len(),
            local_features_4.len(),
            local_features_16.len(),
            global_features.len(),
        ]
        .into_iter()
        .max()
        .unwrap_or(0);

        // Create padded feature vectors and build the 2D array
        let mut padded_features = Vec::with_capacity(4 * max_features);

        // Helper function to pad a feature vector
        let pad_features = |mut features: Vec<f32>, target_len: usize| {
            features.resize(target_len, 0.0);
            features
        };

        // Add all feature scales with consistent padding
        padded_features.extend(pad_features(byte_features, max_features));
        padded_features.extend(pad_features(local_features_4, max_features));
        padded_features.extend(pad_features(local_features_16, max_features));
        padded_features.extend(pad_features(global_features, max_features));

        // Convert to 2D array (4 scales x max_features)
        let feature_array = Array2::from_shape_vec((4, max_features), padded_features)
            .map_err(|e| IoError::Other(format!("Feature extraction error: {e}")))?;

        Ok(feature_array)
    }

    /// Extract byte-level statistical features
    fn extract_byte_level_features(&self, data: &[u8]) -> Vec<f32> {
        let mut frequency = [0u32; 256];
        for &byte in data {
            frequency[byte as usize] += 1;
        }

        let len = data.len() as f32;
        let mut features = Vec::new();

        // Statistical moments
        let mean = data.iter().map(|&x| x as f32).sum::<f32>() / len;
        let variance = data.iter().map(|&x| (x as f32 - mean).powi(2)).sum::<f32>() / len;
        let skewness = data.iter().map(|&x| (x as f32 - mean).powi(3)).sum::<f32>()
            / (len * variance.powf(1.5));
        let kurtosis =
            data.iter().map(|&x| (x as f32 - mean).powi(4)).sum::<f32>() / (len * variance.powi(2));

        features.extend(&[mean / 255.0, variance / (255.0 * 255.0), skewness, kurtosis]);

        // Entropy measures
        let mut shannon_entropy = 0.0;
        let mut gini_index = 0.0;

        for &freq in &frequency {
            if freq > 0 {
                let p = freq as f32 / len;
                shannon_entropy -= p * p.log2();
                gini_index += p * p;
            }
        }

        features.push(shannon_entropy / 8.0);
        features.push(1.0 - gini_index);

        features
    }

    /// Extract local structure features with specified window size
    fn extract_local_structure_features(&self, data: &[u8], window_size: usize) -> Vec<f32> {
        let mut features = Vec::new();

        if data.len() < window_size {
            return vec![0.0; 4]; // Return zero features for insufficient data
        }

        let mut autocorrelations = Vec::new();
        let mut transitions = 0;
        let mut periodicity_score: f32 = 0.0;

        // Calculate autocorrelations at different lags
        for lag in 1..window_size.min(8) {
            let mut correlation = 0.0;
            let mut count = 0;

            for i in 0..(data.len() - lag) {
                if i + lag < data.len() {
                    correlation += (data[i] as f32) * (data[i + lag] as f32);
                    count += 1;
                }
            }

            if count > 0 {
                autocorrelations.push(correlation / count as f32);
            }
        }

        // Count transitions
        for window in data.windows(window_size) {
            for i in 1..window.len() {
                if window[i] != window[i - 1] {
                    transitions += 1;
                }
            }
        }

        // Calculate periodicity
        for period in 2..window_size.min(16) {
            let mut matches = 0;
            let mut total = 0;

            for i in 0..(data.len() - period) {
                if data[i] == data[i + period] {
                    matches += 1;
                }
                total += 1;
            }

            if total > 0 {
                periodicity_score = periodicity_score.max(matches as f32 / total as f32);
            }
        }

        features.push(
            autocorrelations.iter().sum::<f32>()
                / autocorrelations.len().max(1) as f32
                / (255.0 * 255.0),
        );
        features.push(transitions as f32 / data.len() as f32);
        features.push(periodicity_score);
        features.push(autocorrelations.len() as f32 / 8.0);

        features
    }

    /// Extract global structure features
    fn extract_global_structure_features(&self, data: &[u8]) -> Vec<f32> {
        let mut features = Vec::new();

        // Lempel-Ziv complexity
        let lz_complexity = self.calculate_lempel_ziv_complexity(data);
        features.push(lz_complexity);

        // Longest common subsequence with reversed data
        let reversed_data: Vec<u8> = data.iter().rev().cloned().collect();
        let lcs_ratio = self.calculate_lcs_ratio(data, &reversed_data);
        features.push(lcs_ratio);

        // Fractal dimension estimate
        let fractal_dimension = self.estimate_fractal_dimension(data);
        features.push(fractal_dimension);

        // Run length encoding ratio
        let rle_ratio = self.calculate_rle_ratio(data);
        features.push(rle_ratio);

        features
    }

    /// Calculate Lempel-Ziv complexity
    fn calculate_lempel_ziv_complexity(&self, data: &[u8]) -> f32 {
        let mut dictionary = std::collections::HashSet::new();
        let mut i = 0;
        let mut complexity = 0;

        while i < data.len() {
            let mut j = i + 1;
            while j <= data.len() && dictionary.contains(&data[i..j]) {
                j += 1;
            }

            if j <= data.len() {
                dictionary.insert(data[i..j].to_vec());
            }

            complexity += 1;
            i = j.min(data.len());
        }

        complexity as f32 / data.len() as f32
    }

    /// Calculate longest common subsequence ratio
    fn calculate_lcs_ratio(&self, data1: &[u8], data2: &[u8]) -> f32 {
        let len1 = data1.len();
        let len2 = data2.len();

        if len1 == 0 || len2 == 0 {
            return 0.0;
        }

        // Use a simplified LCS algorithm for large data
        let sample_size = 100.min(len1).min(len2);
        let mut dp = vec![vec![0; sample_size + 1]; sample_size + 1];

        for i in 1..=sample_size {
            for j in 1..=sample_size {
                if data1[i - 1] == data2[j - 1] {
                    dp[i][j] = dp[i - 1][j - 1] + 1;
                } else {
                    dp[i][j] = dp[i - 1][j].max(dp[i][j - 1]);
                }
            }
        }

        dp[sample_size][sample_size] as f32 / sample_size as f32
    }

    /// Estimate fractal dimension using box-counting method
    fn estimate_fractal_dimension(&self, data: &[u8]) -> f32 {
        if data.len() < 4 {
            return 1.0;
        }

        let mut dimensions = Vec::new();

        for scale in [2, 4, 8, 16].iter() {
            if data.len() >= *scale {
                let mut boxes = std::collections::HashSet::new();

                for chunk in data.chunks(*scale) {
                    let min_val = *chunk.iter().min().unwrap_or(&0);
                    let max_val = *chunk.iter().max().unwrap_or(&255);
                    boxes.insert((min_val / 16, max_val / 16)); // Quantize to reduce memory
                }

                if !boxes.is_empty() {
                    dimensions.push(((*scale as f32).ln(), (boxes.len() as f32).ln()));
                }
            }
        }

        if dimensions.len() < 2 {
            return 1.0;
        }

        // Calculate slope (fractal dimension)
        let n = dimensions.len() as f32;
        let sum_x: f32 = dimensions.iter().map(|(x, _)| *x).sum();
        let sum_y: f32 = dimensions.iter().map(|(_, y)| y).sum();
        let sum_xy: f32 = dimensions.iter().map(|(x, y)| x * y).sum();
        let sum_x2: f32 = dimensions.iter().map(|(x, _)| x * x).sum();

        let slope = (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x * sum_x);
        slope.abs().min(2.0) // Clamp to reasonable range
    }

    /// Calculate run-length encoding compression ratio
    fn calculate_rle_ratio(&self, data: &[u8]) -> f32 {
        if data.is_empty() {
            return 1.0;
        }

        let mut compressed_size = 0;
        let mut i = 0;

        while i < data.len() {
            let current_byte = data[i];
            let mut run_length = 1;

            while i + run_length < data.len() && data[i + run_length] == current_byte {
                run_length += 1;
            }

            compressed_size += if run_length > 3 { 2 } else { run_length }; // RLE encoding
            i += run_length;
        }

        compressed_size as f32 / data.len() as f32
    }

    /// Check if pattern is novel
    fn is_novel_pattern(&self, pattern_type: &str, score: f32) -> bool {
        if let Some(metadata) = self.pattern_database.get(pattern_type) {
            score > metadata.max_score * 1.1 // 10% improvement threshold
        } else {
            true // New pattern _type
        }
    }

    /// Characterize data for pattern metadata
    fn characterize_data(&self, data: &[u8]) -> DataCharacteristics {
        DataCharacteristics {
            size: data.len(),
            entropy: self.calculate_shannon_entropy(data),
            mean: data.iter().map(|&x| x as f32).sum::<f32>() / data.len() as f32,
            variance: {
                let mean = data.iter().map(|&x| x as f32).sum::<f32>() / data.len() as f32;
                data.iter().map(|&x| (x as f32 - mean).powi(2)).sum::<f32>() / data.len() as f32
            },
        }
    }

    /// Calculate Shannon entropy
    fn calculate_shannon_entropy(&self, data: &[u8]) -> f32 {
        let mut frequency = [0u32; 256];
        for &byte in data {
            frequency[byte as usize] += 1;
        }

        let len = data.len() as f32;
        let mut entropy = 0.0;

        for &freq in &frequency {
            if freq > 0 {
                let p = freq as f32 / len;
                entropy -= p * p.log2();
            }
        }

        entropy / 8.0
    }

    /// Update pattern database with new observations
    fn update_pattern_database(
        &mut self,
        data: &[u8],
        pattern_scores: &HashMap<String, f32>,
    ) -> Result<()> {
        let data_characteristics = self.characterize_data(data);

        for (pattern_type, &score) in pattern_scores {
            let metadata = self
                .pattern_database
                .entry(pattern_type.clone())
                .or_insert_with(|| PatternMetadata {
                    pattern_type: pattern_type.clone(),
                    observation_count: 0,
                    max_score: 0.0,
                    avg_score: 0.0,
                    last_seen: Instant::now(),
                    associated_data_characteristics: Vec::new(),
                });

            metadata.observation_count += 1;
            metadata.max_score = metadata.max_score.max(score);
            metadata.avg_score = (metadata.avg_score * (metadata.observation_count - 1) as f32
                + score)
                / metadata.observation_count as f32;
            metadata.last_seen = Instant::now();
            metadata
                .associated_data_characteristics
                .push(data_characteristics.clone());

            // Keep only recent characteristics
            if metadata.associated_data_characteristics.len() > 100 {
                metadata.associated_data_characteristics.remove(0);
            }
        }

        Ok(())
    }

    /// Detect meta-patterns by analyzing correlations between different pattern types
    fn detect_meta_patterns(
        &self,
        pattern_scores: &HashMap<String, f32>,
    ) -> Result<Vec<MetaPattern>> {
        let mut meta_patterns = Vec::new();

        // Look for correlated patterns
        let score_pairs: Vec<_> = pattern_scores.iter().collect();

        for i in 0..score_pairs.len() {
            for j in (i + 1)..score_pairs.len() {
                let (type1, &score1) = score_pairs[i];
                let (type2, &score2) = score_pairs[j];

                // Detect strong correlations
                if score1 > 0.7 && score2 > 0.7 {
                    meta_patterns.push(MetaPattern {
                        pattern_combination: vec![type1.clone(), type2.clone()],
                        correlation_strength: (score1 * score2).sqrt(),
                        synergy_type: self.determine_synergy_type(type1, type2),
                    });
                }
            }
        }

        Ok(meta_patterns)
    }

    /// Determine synergy type between patterns
    fn determine_synergy_type(&self, type1: &str, type2: &str) -> SynergyType {
        match (type1, type2) {
            ("repetition", "compression") => SynergyType::ReinforcingCompression,
            ("sequential", "entropy") => SynergyType::ContrastedRandomness,
            ("fractal", "periodicity") => SynergyType::HierarchicalStructure,
            _ => SynergyType::Unknown,
        }
    }

    /// Calculate complexity index from multi-scale features
    fn calculate_complexity_index(&self, features: &Array2<f32>) -> f32 {
        // Calculate weighted sum across scales
        let weights = Array1::from(vec![0.4, 0.3, 0.2, 0.1]); // Higher weight for finer scales
        let scale_complexities = features
            .mean_axis(scirs2_core::ndarray::Axis(1))
            .expect("Operation failed");
        weights.dot(&scale_complexities)
    }

    /// Calculate predictability score
    fn calculate_predictability(&self, data: &[u8]) -> f32 {
        if data.len() < 10 {
            return 0.5;
        }

        let mut correct_predictions = 0;
        let prediction_window = 5.min(data.len() - 1);

        for i in prediction_window..data.len() {
            // Simple predictor based on recent history
            let recent_bytes = &data[i - prediction_window..i];
            let predicted = self.predict_next_byte(recent_bytes);

            if predicted == data[i] {
                correct_predictions += 1;
            }
        }

        correct_predictions as f32 / (data.len() - prediction_window) as f32
    }

    /// Simple byte prediction based on history
    fn predict_next_byte(&self, history: &[u8]) -> u8 {
        if history.is_empty() {
            return 0;
        }

        // Use most frequent byte in recent history
        let mut frequency = [0u32; 256];
        for &byte in history {
            frequency[byte as usize] += 1;
        }

        frequency
            .iter()
            .enumerate()
            .max_by_key(|(_, &count)| count)
            .map(|(byte, _)| byte as u8)
            .unwrap_or(0)
    }

    /// Generate optimization recommendations based on pattern analysis
    fn generate_optimization_recommendations(
        &self,
        pattern_scores: &HashMap<String, f32>,
    ) -> Vec<OptimizationRecommendation> {
        let mut recommendations = Vec::new();

        for (pattern_type, &score) in pattern_scores {
            match pattern_type.as_str() {
                "repetition" if score > 0.8 => {
                    recommendations.push(OptimizationRecommendation {
                        optimization_type: "compression".to_string(),
                        reason: "High repetition detected - compression will be highly effective"
                            .to_string(),
                        expected_improvement: score * 0.7,
                        confidence: score,
                    });
                }
                "sequential" if score > 0.7 => {
                    recommendations.push(OptimizationRecommendation {
                        optimization_type: "streaming".to_string(),
                        reason: "Sequential access pattern - streaming optimization recommended"
                            .to_string(),
                        expected_improvement: score * 0.5,
                        confidence: score,
                    });
                }
                "fractal" if score > 0.8 => {
                    recommendations.push(OptimizationRecommendation {
                        optimization_type: "hierarchical_processing".to_string(),
                        reason:
                            "Fractal structure detected - hierarchical processing will be efficient"
                                .to_string(),
                        expected_improvement: score * 0.6,
                        confidence: score,
                    });
                }
                "entropy" if score < 0.3 => {
                    recommendations.push(OptimizationRecommendation {
                        optimization_type: "aggressive_compression".to_string(),
                        reason: "Low entropy - aggressive compression algorithms recommended"
                            .to_string(),
                        expected_improvement: (1.0 - score) * 0.8,
                        confidence: 1.0 - score,
                    });
                }
                _ => {}
            }
        }

        recommendations
    }
}

/// Specialized pattern detection network
#[derive(Debug)]
struct PatternNetwork {
    pattern_type: String,
    weights: Array2<f32>,
    bias: Array1<f32>,
    activation_history: VecDeque<f32>,
}

impl PatternNetwork {
    fn new(pattern_type: &str, input_size: usize, hidden_size: usize, _output_size: usize) -> Self {
        // Xavier initialization for weights
        let scale = (2.0 / (input_size + hidden_size) as f32).sqrt();
        let mut rng = scirs2_core::random::rng();
        let weights = Array2::from_shape_fn((hidden_size, input_size), |_| {
            (rng.random::<f32>() - 0.5) * 2.0 * scale
        });

        Self {
            pattern_type: pattern_type.to_string(),
            weights,
            bias: Array1::zeros(hidden_size),
            activation_history: VecDeque::with_capacity(100),
        }
    }

    fn analyze(&mut self, features: &Array2<f32>) -> Result<f32> {
        // Flatten features for network input
        let flattened = features.as_slice().expect("Operation failed");
        let input = Array1::from(flattened.to_vec());

        // Resize input to match network size if necessary
        let network_input = if input.len() > self.weights.ncols() {
            input
                .slice(scirs2_core::ndarray::s![..self.weights.ncols()])
                .to_owned()
        } else {
            let mut padded = Array1::zeros(self.weights.ncols());
            padded
                .slice_mut(scirs2_core::ndarray::s![..input.len()])
                .assign(&input);
            padded
        };

        // Forward pass
        let hidden = self.weights.dot(&network_input) + &self.bias;
        let activated = hidden.mapv(Self::relu);

        // Pattern-specific scoring
        let score = match self.pattern_type.as_str() {
            "repetition" => self.score_repetition_pattern(&activated),
            "sequential" => self.score_sequential_pattern(&activated),
            "fractal" => self.score_fractal_pattern(&activated),
            "entropy" => self.score_entropy_pattern(&activated),
            "compression" => self.score_compression_pattern(&activated),
            _ => activated.mean_or(0.0),
        };

        self.activation_history.push_back(score);
        if self.activation_history.len() > 100 {
            self.activation_history.pop_front();
        }

        Ok(score.clamp(0.0, 1.0))
    }

    fn relu(x: f32) -> f32 {
        x.max(0.0)
    }

    fn score_repetition_pattern(&self, activations: &Array1<f32>) -> f32 {
        // Look for repeating patterns in activations
        let mut max_repetition: f32 = 0.0;

        for window_size in 2..=activations.len() / 2 {
            let mut repetition_score = 0.0;
            let mut count = 0;

            for i in 0..=(activations.len() - 2 * window_size) {
                let window1 = activations.slice(scirs2_core::ndarray::s![i..i + window_size]);
                let window2 = activations.slice(scirs2_core::ndarray::s![
                    i + window_size..i + 2 * window_size
                ]);

                let similarity = window1
                    .iter()
                    .zip(window2.iter())
                    .map(|(a, b)| 1.0 - (a - b).abs())
                    .sum::<f32>()
                    / window_size as f32;

                repetition_score += similarity;
                count += 1;
            }

            if count > 0 {
                max_repetition = max_repetition.max(repetition_score / count as f32);
            }
        }

        max_repetition
    }

    fn score_sequential_pattern(&self, activations: &Array1<f32>) -> f32 {
        if activations.len() < 2 {
            return 0.0;
        }

        // Calculate how sequential/monotonic the activations are
        let mut increasing = 0;
        let mut decreasing = 0;

        for i in 1..activations.len() {
            if activations[i] > activations[i - 1] {
                increasing += 1;
            } else if activations[i] < activations[i - 1] {
                decreasing += 1;
            }
        }

        let total_transitions = activations.len() - 1;
        let max_direction = increasing.max(decreasing);

        max_direction as f32 / total_transitions as f32
    }

    fn score_fractal_pattern(&self, activations: &Array1<f32>) -> f32 {
        // Look for self-similar patterns at different scales
        let mut fractal_score = 0.0;
        let mut scale_count = 0;

        for scale in [2, 4, 8].iter() {
            if activations.len() >= scale * 2 {
                let downsampled1 = self.downsample(activations, *scale, 0);
                let downsampled2 = self.downsample(activations, *scale, *scale);

                if !downsampled1.is_empty() && !downsampled2.is_empty() {
                    let similarity = self.calculate_similarity(&downsampled1, &downsampled2);
                    fractal_score += similarity;
                    scale_count += 1;
                }
            }
        }

        if scale_count > 0 {
            fractal_score / scale_count as f32
        } else {
            0.0
        }
    }

    fn score_entropy_pattern(&self, activations: &Array1<f32>) -> f32 {
        // Calculate entropy of quantized activations
        let quantized: Vec<u8> = activations.iter().map(|&x| (x * 255.0) as u8).collect();

        let mut frequency = [0u32; 256];
        for &val in &quantized {
            frequency[val as usize] += 1;
        }

        let len = quantized.len() as f32;
        let mut entropy = 0.0;

        for &freq in &frequency {
            if freq > 0 {
                let p = freq as f32 / len;
                entropy -= p * p.log2();
            }
        }

        entropy / 8.0 // Normalize to [0, 1]
    }

    fn score_compression_pattern(&self, activations: &Array1<f32>) -> f32 {
        // Estimate compressibility based on run-length encoding potential
        let quantized: Vec<u8> = activations.iter().map(|&x| (x * 255.0) as u8).collect();

        let mut compressed_size = 0;
        let mut i = 0;

        while i < quantized.len() {
            let current = quantized[i];
            let mut run_length = 1;

            while i + run_length < quantized.len() && quantized[i + run_length] == current {
                run_length += 1;
            }

            compressed_size += if run_length > 2 { 2 } else { run_length };
            i += run_length;
        }

        1.0 - (compressed_size as f32 / quantized.len() as f32)
    }

    fn downsample(&self, data: &Array1<f32>, scale: usize, offset: usize) -> Vec<f32> {
        data.iter().skip(offset).step_by(scale).cloned().collect()
    }

    fn calculate_similarity(&self, data1: &[f32], data2: &[f32]) -> f32 {
        if data1.is_empty() || data2.is_empty() {
            return 0.0;
        }

        let min_len = data1.len().min(data2.len());
        let mut similarity = 0.0;

        for i in 0..min_len {
            similarity += 1.0 - (data1[i] - data2[i]).abs();
        }

        similarity / min_len as f32
    }
}

// Supporting data structures

/// Complete analysis result from advanced pattern recognition
#[derive(Debug, Clone)]
pub struct AdvancedPatternAnalysis {
    /// Scores for each detected pattern type, ranging from 0.0 to 1.0
    pub pattern_scores: HashMap<String, f32>,
    /// List of emergent patterns discovered during analysis
    pub emergent_patterns: Vec<EmergentPattern>,
    /// Meta-patterns formed by correlations between multiple pattern types
    pub meta_patterns: Vec<MetaPattern>,
    /// Overall complexity index of the analyzed data (0.0 to 1.0)
    pub complexity_index: f32,
    /// Predictability score indicating how predictable the data is (0.0 to 1.0)
    pub predictability_score: f32,
    /// Optimization recommendations based on the pattern analysis
    pub optimization_recommendations: Vec<OptimizationRecommendation>,
}

/// Represents an emergent pattern discovered during data analysis
#[derive(Debug, Clone)]
pub struct EmergentPattern {
    /// Type of the emergent pattern that was discovered
    pub pattern_type: String,
    /// Confidence score for the pattern detection (0.0 to 1.0)
    pub confidence: f32,
    /// Timestamp when the pattern was discovered
    pub discovered_at: Instant,
    /// Characteristics of the data where the pattern was found
    pub data_characteristics: DataCharacteristics,
}

/// Represents a meta-pattern formed by correlations between multiple pattern types
#[derive(Debug, Clone)]
pub struct MetaPattern {
    /// Combination of pattern types that form this meta-pattern
    pub pattern_combination: Vec<String>,
    /// Strength of correlation between the combined patterns (0.0 to 1.0)
    pub correlation_strength: f32,
    /// Type of synergy observed between the patterns
    pub synergy_type: SynergyType,
}

/// Types of synergy between different patterns
#[derive(Debug, Clone)]
pub enum SynergyType {
    /// Patterns that reinforce compression effectiveness
    ReinforcingCompression,
    /// Patterns with contrasted randomness characteristics
    ContrastedRandomness,
    /// Patterns that exhibit hierarchical structure relationships
    HierarchicalStructure,
    /// Unknown or undefined synergy type
    Unknown,
}

/// Represents an optimization recommendation based on pattern analysis
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    /// Type of optimization that is recommended
    pub optimization_type: String,
    /// Explanation of why this optimization is recommended
    pub reason: String,
    /// Expected performance improvement ratio (e.g., 0.25 = 25% improvement)
    pub expected_improvement: f32,
    /// Confidence level in this recommendation (0.0 to 1.0)
    pub confidence: f32,
}

#[derive(Debug, Clone)]
struct PatternMetadata {
    pattern_type: String,
    observation_count: usize,
    max_score: f32,
    avg_score: f32,
    last_seen: Instant,
    associated_data_characteristics: Vec<DataCharacteristics>,
}

#[derive(Debug, Clone)]
/// Statistical characteristics of data for pattern analysis
pub struct DataCharacteristics {
    /// Size of the data in bytes
    pub size: usize,
    /// Shannon entropy of the data (0.0 to 1.0, normalized)
    pub entropy: f32,
    /// Arithmetic mean of the data values
    pub mean: f32,
    /// Statistical variance of the data values
    pub variance: f32,
}

#[derive(Debug, Clone)]
struct PatternInstance {
    pattern_type: String,
    score: f32,
    timestamp: Instant,
    data_hash: u64,
}

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

    #[test]
    fn test_advanced_pattern_recognizer_creation() {
        let recognizer = AdvancedPatternRecognizer::new();
        assert_eq!(recognizer.pattern_networks.len(), 5);
    }

    #[test]
    fn test_pattern_analysis() {
        let mut recognizer = AdvancedPatternRecognizer::new();
        let test_data = vec![1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5];

        let analysis = recognizer
            .analyze_patterns(&test_data)
            .expect("Operation failed");
        assert!(!analysis.pattern_scores.is_empty());
        assert!(analysis.complexity_index >= 0.0 && analysis.complexity_index <= 1.0);
        assert!(analysis.predictability_score >= 0.0 && analysis.predictability_score <= 1.0);
    }

    #[test]
    fn test_multiscale_feature_extraction() {
        let recognizer = AdvancedPatternRecognizer::new();
        let test_data = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10];

        let features = recognizer
            .extract_multiscale_features(&test_data)
            .expect("Operation failed");
        assert_eq!(features.nrows(), 4); // 4 scales
        assert!(features.ncols() > 0);
    }

    #[test]
    fn test_lempel_ziv_complexity() {
        let recognizer = AdvancedPatternRecognizer::new();

        // Test with repetitive data
        let repetitive_data = vec![1, 1, 1, 1, 1, 1, 1, 1];
        let complexity1 = recognizer.calculate_lempel_ziv_complexity(&repetitive_data);

        // Test with random data
        let random_data = vec![1, 2, 3, 4, 5, 6, 7, 8];
        let complexity2 = recognizer.calculate_lempel_ziv_complexity(&random_data);

        assert!(complexity2 > complexity1); // Random data should be more complex
    }

    #[test]
    fn test_pattern_network() {
        let mut network = PatternNetwork::new("test", 10, 5, 3);
        let mut rng = scirs2_core::random::rng();
        let features = Array2::from_shape_fn((2, 5), |_| rng.random::<f32>());

        let score = network.analyze(&features).expect("Operation failed");
        assert!((0.0..=1.0).contains(&score));
    }
}
