//! Pattern memory and fingerprinting for sparse matrix optimization
//!
//! This module implements memory systems that learn and store optimal strategies
//! for different types of sparse matrix patterns and access behaviors.

use std::collections::{HashMap, VecDeque};
use std::hash::{Hash, Hasher};

/// Optimization strategies learned by the neural network
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum OptimizationStrategy {
    /// Row-wise processing with cache optimization
    RowWiseCache,
    /// Column-wise processing for memory locality
    ColumnWiseLocality,
    /// Block-based processing for structured matrices
    BlockStructured,
    /// Diagonal-optimized processing
    DiagonalOptimized,
    /// Hierarchical decomposition
    Hierarchical,
    /// Streaming computation for large matrices
    StreamingCompute,
    /// SIMD-vectorized computation
    SIMDVectorized,
    /// Parallel work-stealing
    ParallelWorkStealing,
    /// Adaptive hybrid approach
    AdaptiveHybrid,
}

/// Pattern memory for learning matrix characteristics
#[derive(Debug)]
pub(crate) struct PatternMemory {
    pub matrix_patterns: HashMap<MatrixFingerprint, OptimizationStrategy>,
    #[allow(dead_code)]
    pub access_patterns: VecDeque<AccessPattern>,
    #[allow(dead_code)]
    pub performance_cache: HashMap<String, f64>,
}

/// Matrix fingerprint for pattern recognition
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub(crate) struct MatrixFingerprint {
    pub rows: usize,
    pub cols: usize,
    pub nnz: usize,
    pub sparsity_pattern_hash: u64,
    pub row_distribution_type: DistributionType,
    pub column_distribution_type: DistributionType,
}

/// Distribution types for sparsity patterns
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub(crate) enum DistributionType {
    Uniform,
    Clustered,
    BandDiagonal,
    #[allow(dead_code)]
    BlockStructured,
    Random,
    PowerLaw,
}

/// Access pattern for memory optimization
#[derive(Debug, Clone)]
pub(crate) struct AccessPattern {
    #[allow(dead_code)]
    pub timestamp: u64,
    #[allow(dead_code)]
    pub row_sequence: Vec<usize>,
    #[allow(dead_code)]
    pub column_sequence: Vec<usize>,
    #[allow(dead_code)]
    pub cache_hits: usize,
    #[allow(dead_code)]
    pub cache_misses: usize,
}

impl PatternMemory {
    /// Create a new pattern memory system
    pub fn new(capacity: usize) -> Self {
        Self {
            matrix_patterns: HashMap::new(),
            access_patterns: VecDeque::new(),
            performance_cache: HashMap::new(),
        }
    }

    /// Store a learned pattern-strategy mapping
    pub fn store_pattern(
        &mut self,
        fingerprint: MatrixFingerprint,
        strategy: OptimizationStrategy,
    ) {
        self.matrix_patterns.insert(fingerprint, strategy);
    }

    /// Retrieve optimal strategy for a matrix pattern
    pub fn get_strategy(&self, fingerprint: &MatrixFingerprint) -> Option<OptimizationStrategy> {
        self.matrix_patterns.get(fingerprint).copied()
    }

    /// Find similar patterns using fingerprint matching
    pub fn find_similar_patterns(
        &self,
        fingerprint: &MatrixFingerprint,
        similarity_threshold: f64,
    ) -> Vec<(MatrixFingerprint, OptimizationStrategy)> {
        let mut similar_patterns = Vec::new();

        for (stored_fingerprint, strategy) in &self.matrix_patterns {
            let similarity = self.compute_similarity(fingerprint, stored_fingerprint);
            if similarity >= similarity_threshold {
                similar_patterns.push((stored_fingerprint.clone(), *strategy));
            }
        }

        // Sort by similarity (descending)
        similar_patterns.sort_by(|a, b| {
            let sim_a = self.compute_similarity(fingerprint, &a.0);
            let sim_b = self.compute_similarity(fingerprint, &b.0);
            sim_b.partial_cmp(&sim_a).expect("Operation failed")
        });

        similar_patterns
    }

    /// Compute similarity between two matrix fingerprints
    fn compute_similarity(&self, fp1: &MatrixFingerprint, fp2: &MatrixFingerprint) -> f64 {
        let size_similarity = self.size_similarity(fp1, fp2);
        let sparsity_similarity = self.sparsity_similarity(fp1, fp2);
        let pattern_similarity = self.pattern_similarity(fp1, fp2);
        let distribution_similarity = self.distribution_similarity(fp1, fp2);

        // Weighted combination of different similarity measures
        0.3 * size_similarity
            + 0.3 * sparsity_similarity
            + 0.2 * pattern_similarity
            + 0.2 * distribution_similarity
    }

    /// Compute size similarity between matrices
    fn size_similarity(&self, fp1: &MatrixFingerprint, fp2: &MatrixFingerprint) -> f64 {
        let row_ratio = (fp1.rows.min(fp2.rows) as f64) / (fp1.rows.max(fp2.rows) as f64);
        let col_ratio = (fp1.cols.min(fp2.cols) as f64) / (fp1.cols.max(fp2.cols) as f64);
        (row_ratio + col_ratio) / 2.0
    }

    /// Compute sparsity similarity
    fn sparsity_similarity(&self, fp1: &MatrixFingerprint, fp2: &MatrixFingerprint) -> f64 {
        let sparsity1 = fp1.nnz as f64 / (fp1.rows * fp1.cols) as f64;
        let sparsity2 = fp2.nnz as f64 / (fp2.rows * fp2.cols) as f64;
        1.0 - (sparsity1 - sparsity2).abs()
    }

    /// Compute pattern similarity using hash comparison
    fn pattern_similarity(&self, fp1: &MatrixFingerprint, fp2: &MatrixFingerprint) -> f64 {
        // Simple hash-based similarity (in practice, you might use more sophisticated methods)
        let hash_diff = (fp1.sparsity_pattern_hash ^ fp2.sparsity_pattern_hash).count_ones() as f64;
        1.0 - (hash_diff / 64.0) // Assuming 64-bit hash
    }

    /// Compute distribution similarity
    fn distribution_similarity(&self, fp1: &MatrixFingerprint, fp2: &MatrixFingerprint) -> f64 {
        let row_match = if fp1.row_distribution_type == fp2.row_distribution_type {
            1.0
        } else {
            0.0
        };
        let col_match = if fp1.column_distribution_type == fp2.column_distribution_type {
            1.0
        } else {
            0.0
        };
        (row_match + col_match) / 2.0
    }

    /// Record access pattern for learning
    pub fn record_access_pattern(&mut self, pattern: AccessPattern) {
        self.access_patterns.push_back(pattern);

        // Keep only recent patterns (sliding window)
        if self.access_patterns.len() > 1000 {
            self.access_patterns.pop_front();
        }
    }

    /// Cache performance result
    pub fn cache_performance(&mut self, key: String, performance: f64) {
        self.performance_cache.insert(key, performance);

        // Limit cache size
        if self.performance_cache.len() > 10000 {
            // Remove oldest entries (simplified approach)
            let keys_to_remove: Vec<String> =
                self.performance_cache.keys().take(1000).cloned().collect();
            for key in keys_to_remove {
                self.performance_cache.remove(&key);
            }
        }
    }

    /// Get cached performance
    pub fn get_cached_performance(&self, key: &str) -> Option<f64> {
        self.performance_cache.get(key).copied()
    }

    /// Analyze access patterns to detect trends
    pub fn analyze_access_patterns(&self) -> AccessPatternAnalysis {
        if self.access_patterns.is_empty() {
            return AccessPatternAnalysis::default();
        }

        let mut sequential_count = 0;
        let mut random_count = 0;
        let mut block_count = 0;

        for pattern in &self.access_patterns {
            let access_type = self.classify_access_pattern(pattern);
            match access_type {
                AccessType::Sequential => sequential_count += 1,
                AccessType::Random => random_count += 1,
                AccessType::Block => block_count += 1,
            }
        }

        let total = self.access_patterns.len();
        AccessPatternAnalysis {
            sequential_ratio: sequential_count as f64 / total as f64,
            random_ratio: random_count as f64 / total as f64,
            block_ratio: block_count as f64 / total as f64,
            cache_hit_rate: self.compute_average_cache_hit_rate(),
        }
    }

    /// Classify access pattern type
    fn classify_access_pattern(&self, pattern: &AccessPattern) -> AccessType {
        // Simplified classification logic
        if pattern.row_sequence.is_empty() {
            return AccessType::Random;
        }

        // Check for sequential access
        let mut sequential = true;
        for i in 1..pattern.row_sequence.len() {
            if pattern.row_sequence[i] != pattern.row_sequence[i - 1] + 1
                && pattern.row_sequence[i] != pattern.row_sequence[i - 1]
            {
                sequential = false;
                break;
            }
        }

        if sequential {
            return AccessType::Sequential;
        }

        // Check for block access
        let unique_rows: std::collections::HashSet<_> = pattern.row_sequence.iter().collect();
        if unique_rows.len() < pattern.row_sequence.len() / 2 {
            return AccessType::Block;
        }

        AccessType::Random
    }

    /// Compute average cache hit rate
    fn compute_average_cache_hit_rate(&self) -> f64 {
        if self.access_patterns.is_empty() {
            return 0.0;
        }

        let total_hits: usize = self.access_patterns.iter().map(|p| p.cache_hits).sum();
        let total_accesses: usize = self
            .access_patterns
            .iter()
            .map(|p| p.cache_hits + p.cache_misses)
            .sum();

        if total_accesses == 0 {
            0.0
        } else {
            total_hits as f64 / total_accesses as f64
        }
    }

    /// Suggest optimization strategy based on patterns
    pub fn suggest_strategy(&self, fingerprint: &MatrixFingerprint) -> OptimizationStrategy {
        // First, try exact match
        if let Some(strategy) = self.get_strategy(fingerprint) {
            return strategy;
        }

        // Find similar patterns
        let similar = self.find_similar_patterns(fingerprint, 0.7);
        if !similar.is_empty() {
            return similar[0].1; // Return strategy from most similar pattern
        }

        // Fallback to heuristic-based suggestion
        self.heuristic_strategy_suggestion(fingerprint)
    }

    /// Heuristic-based strategy suggestion
    fn heuristic_strategy_suggestion(
        &self,
        fingerprint: &MatrixFingerprint,
    ) -> OptimizationStrategy {
        let sparsity = fingerprint.nnz as f64 / (fingerprint.rows * fingerprint.cols) as f64;
        let size = fingerprint.rows * fingerprint.cols;

        match (
            fingerprint.row_distribution_type,
            fingerprint.column_distribution_type,
        ) {
            (DistributionType::BandDiagonal, _) | (_, DistributionType::BandDiagonal) => {
                OptimizationStrategy::DiagonalOptimized
            }
            (DistributionType::Clustered, DistributionType::Clustered) => {
                OptimizationStrategy::BlockStructured
            }
            _ => {
                if sparsity < 0.01 && size > 10000 {
                    OptimizationStrategy::StreamingCompute
                } else if size > 100000 {
                    OptimizationStrategy::ParallelWorkStealing
                } else if sparsity > 0.1 {
                    OptimizationStrategy::SIMDVectorized
                } else {
                    OptimizationStrategy::AdaptiveHybrid
                }
            }
        }
    }

    /// Get memory statistics
    pub fn get_statistics(&self) -> PatternMemoryStats {
        PatternMemoryStats {
            stored_patterns: self.matrix_patterns.len(),
            access_patterns_recorded: self.access_patterns.len(),
            cached_performances: self.performance_cache.len(),
            most_common_strategy: self.get_most_common_strategy(),
        }
    }

    /// Get most commonly used optimization strategy
    fn get_most_common_strategy(&self) -> Option<OptimizationStrategy> {
        let mut strategy_counts = HashMap::new();

        for strategy in self.matrix_patterns.values() {
            *strategy_counts.entry(*strategy).or_insert(0) += 1;
        }

        strategy_counts
            .into_iter()
            .max_by(|(_, count1), (_, count2)| count1.cmp(count2))
            .map(|(strategy, _)| strategy)
    }
}

impl MatrixFingerprint {
    /// Create a new matrix fingerprint
    pub fn new<T>(rows: Vec<usize>, cols: Vec<usize>, data: &[T], shape: (usize, usize)) -> Self
    where
        T: std::fmt::Debug + Copy + PartialEq,
    {
        let nnz = data.len();
        let sparsity_pattern_hash = Self::compute_pattern_hash(&rows, &cols);
        let row_distribution_type = Self::analyze_distribution(&rows, shape.0);
        let column_distribution_type = Self::analyze_distribution(&cols, shape.1);

        Self {
            rows: shape.0,
            cols: shape.1,
            nnz,
            sparsity_pattern_hash,
            row_distribution_type,
            column_distribution_type,
        }
    }

    /// Compute hash of sparsity pattern
    fn compute_pattern_hash(rows: &[usize], cols: &[usize]) -> u64 {
        use std::collections::hash_map::DefaultHasher;

        let mut hasher = DefaultHasher::new();

        // Sample pattern for hash computation (to avoid excessive computation)
        let step = (rows.len() / 100).max(1);
        for i in (0..rows.len()).step_by(step) {
            rows[i].hash(&mut hasher);
            if i < cols.len() {
                cols[i].hash(&mut hasher);
            }
        }

        hasher.finish()
    }

    /// Analyze distribution type of indices
    fn analyze_distribution(indices: &[usize], max_value: usize) -> DistributionType {
        if indices.is_empty() {
            return DistributionType::Uniform;
        }

        // Check for band diagonal pattern
        if Self::is_band_diagonal(indices) {
            return DistributionType::BandDiagonal;
        }

        // Check for clustering
        if Self::is_clustered(indices, max_value) {
            return DistributionType::Clustered;
        }

        // Check for uniform distribution
        if Self::is_uniform(indices, max_value) {
            return DistributionType::Uniform;
        }

        // Check for power law distribution
        if Self::is_power_law(indices) {
            return DistributionType::PowerLaw;
        }

        DistributionType::Random
    }

    /// Check if indices follow a band diagonal pattern
    fn is_band_diagonal(indices: &[usize]) -> bool {
        if indices.len() < 2 {
            return false;
        }

        let mut sorted_indices = indices.to_vec();
        sorted_indices.sort_unstable();

        // Check if indices are within a small range (band)
        let range = sorted_indices[sorted_indices.len() - 1] - sorted_indices[0];
        let density = indices.len() as f64 / (range + 1) as f64;

        density > 0.5 && range < indices.len() * 3
    }

    /// Check if indices are clustered
    fn is_clustered(indices: &[usize], max_value: usize) -> bool {
        if indices.is_empty() {
            return false;
        }

        let mut histogram = vec![0; (max_value / 10).max(10)];
        for &idx in indices {
            let bucket = (idx * histogram.len()) / (max_value + 1);
            if bucket < histogram.len() {
                histogram[bucket] += 1;
            }
        }

        // Check if most values are in few buckets
        histogram.sort_unstable();
        let top_buckets = histogram.len() / 3;
        let top_count: usize = histogram.iter().rev().take(top_buckets).sum();
        let total_count: usize = histogram.iter().sum();

        top_count as f64 / total_count as f64 > 0.7
    }

    /// Check if indices are uniformly distributed
    fn is_uniform(indices: &[usize], max_value: usize) -> bool {
        if indices.is_empty() {
            return false;
        }

        let bucket_count = (max_value / 10).max(10);
        let mut histogram = vec![0; bucket_count];

        for &idx in indices {
            let bucket = (idx * bucket_count) / (max_value + 1);
            if bucket < histogram.len() {
                histogram[bucket] += 1;
            }
        }

        // Check variance of histogram
        let mean = indices.len() as f64 / bucket_count as f64;
        let variance: f64 = histogram
            .iter()
            .map(|&count| (count as f64 - mean).powi(2))
            .sum::<f64>()
            / bucket_count as f64;
        let std_dev = variance.sqrt();

        std_dev / mean < 0.5 // Low relative variance indicates uniformity
    }

    /// Check if indices follow a power law distribution
    fn is_power_law(indices: &[usize]) -> bool {
        if indices.is_empty() {
            return false;
        }

        let mut sorted_indices = indices.to_vec();
        sorted_indices.sort_unstable();
        sorted_indices.dedup();

        // Simple power law check: few values appear very frequently
        let mut frequency_map = HashMap::new();
        for &idx in indices {
            *frequency_map.entry(idx).or_insert(0) += 1;
        }

        let mut frequencies: Vec<usize> = frequency_map.values().copied().collect();
        frequencies.sort_unstable();
        frequencies.reverse();

        if frequencies.len() < 3 {
            return false;
        }

        // Check if top few frequencies dominate
        let top_10_percent = (frequencies.len() / 10).max(1);
        let top_sum: usize = frequencies.iter().take(top_10_percent).sum();
        let total_sum: usize = frequencies.iter().sum();

        top_sum as f64 / total_sum as f64 > 0.8
    }
}

/// Access pattern classification
#[derive(Debug, Clone, Copy)]
enum AccessType {
    Sequential,
    Random,
    Block,
}

/// Analysis results for access patterns
#[derive(Debug, Clone)]
pub struct AccessPatternAnalysis {
    pub sequential_ratio: f64,
    pub random_ratio: f64,
    pub block_ratio: f64,
    pub cache_hit_rate: f64,
}

impl Default for AccessPatternAnalysis {
    fn default() -> Self {
        Self {
            sequential_ratio: 0.0,
            random_ratio: 0.0,
            block_ratio: 0.0,
            cache_hit_rate: 0.0,
        }
    }
}

/// Statistics for pattern memory
#[derive(Debug, Clone)]
pub struct PatternMemoryStats {
    pub stored_patterns: usize,
    pub access_patterns_recorded: usize,
    pub cached_performances: usize,
    pub most_common_strategy: Option<OptimizationStrategy>,
}
