scirs2_linalg/simd_ops/neural/

compression.rs

//! Compression engines and algorithm selection for neural memory optimization.

use super::cache::{DenseLayer, TrainingMetrics};
use super::types::*;
use crate::error::{LinalgError, LinalgResult};
use scirs2_core::ndarray::{Array1, Array2, ArrayView2};
use scirs2_core::numeric::{Float, NumAssign, Zero};
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;

/// Adaptive compression engine using ML
#[derive(Debug)]
#[allow(dead_code)]
pub struct AdaptiveCompressionEngine<T> {
    /// Available compression algorithms
    compression_algorithms: Vec<CompressionAlgorithm>,
    /// Algorithm selector network
    selector_network: CompressionSelectorNetwork<T>,
    /// Performance history
    performance_history: HashMap<CompressionAlgorithm, VecDeque<CompressionMetrics>>,
    /// Real-time algorithm switcher
    real_time_switcher: RealTimeCompressionSwitcher,
    /// Quality assessor
    quality_assessor: CompressionQualityAssessor<T>,
}

/// Available compression algorithms
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum CompressionAlgorithm {
    LZ4,
    ZSTD,
    Snappy,
    Brotli,
    LZMA,
    Deflate,
    BZip2,
    Custom(String),
    NeuralCompression(String),
    AdaptiveHuffman,
    ArithmeticCoding,
}

/// Compression selector network
#[derive(Debug)]
#[allow(dead_code)]
pub struct CompressionSelectorNetwork<T> {
    /// Input feature extractors
    feature_extractors: Vec<FeatureExtractor<T>>,
    /// Decision tree ensemble
    decision_trees: Vec<CompressionDecisionTree>,
    /// Neural network classifier
    classifier_network: ClassificationNetwork<T>,
    /// Confidence estimator
    confidence_estimator: ConfidenceEstimator<T>,
}

/// Feature extractor for compression selection
#[derive(Debug)]
pub struct FeatureExtractor<T> {
    /// Feature type
    pub feature_type: FeatureType,
    /// Extraction function
    pub extractor: fn(&ArrayView2<T>) -> Vec<f64>,
    /// Feature weights
    pub weights: Array1<f64>,
}

/// Types of features for compression selection
#[derive(Debug, Clone, PartialEq)]
pub enum FeatureType {
    Entropy,
    Sparsity,
    Repetition,
    Gradient,
    Frequency,
    Correlation,
    Distribution,
    Locality,
    Compressibility,
    DataType,
}

/// Decision tree for compression algorithm selection
#[derive(Debug)]
pub struct CompressionDecisionTree {
    /// Tree nodes
    pub nodes: Vec<DecisionNode>,
    /// Leaf predictions
    pub leaves: Vec<CompressionAlgorithm>,
    /// Tree depth
    pub depth: usize,
    /// Feature importance scores
    pub feature_importance: Array1<f64>,
}

/// Decision tree node
#[derive(Debug)]
pub struct DecisionNode {
    /// Feature index to split on
    pub feature_index: usize,
    /// Split threshold
    pub threshold: f64,
    /// Left child index
    pub left_child: Option<usize>,
    /// Right child index
    pub right_child: Option<usize>,
    /// Leaf prediction
    pub prediction: Option<CompressionAlgorithm>,
}

/// Classification network for compression selection
#[derive(Debug)]
pub struct ClassificationNetwork<T> {
    /// Network layers
    pub layers: Vec<DenseLayer<T>>,
    /// Output softmax layer
    pub output_layer: SoftmaxLayer<T>,
    /// Training history
    pub training_history: VecDeque<TrainingMetrics>,
}

/// Softmax output layer
#[derive(Debug)]
pub struct SoftmaxLayer<T> {
    /// Weight matrix
    pub weights: Array2<T>,
    /// Bias vector
    pub biases: Array1<T>,
    /// Temperature parameter
    pub temperature: T,
}

/// Confidence estimator for predictions
#[derive(Debug)]
#[allow(dead_code)]
pub struct ConfidenceEstimator<T> {
    /// Bayesian neural network
    bayesian_network: BayesianNetwork<T>,
    /// Uncertainty quantification method
    uncertainty_method: UncertaintyQuantificationMethod,
    /// Confidence threshold
    confidence_threshold: f64,
}

/// Bayesian neural network for uncertainty quantification
#[derive(Debug)]
pub struct BayesianNetwork<T> {
    /// Weight distributions
    pub weight_distributions: Vec<WeightDistribution<T>>,
    /// Variational parameters
    pub variational_params: VariationalParameters<T>,
    /// Monte Carlo samples
    pub mc_samples: usize,
}

/// Weight distribution for Bayesian networks
#[derive(Debug)]
pub struct WeightDistribution<T> {
    /// Mean weights
    pub mean: Array2<T>,
    /// Log variance of weights
    pub log_variance: Array2<T>,
    /// Prior distribution
    pub prior: PriorDistribution<T>,
}

/// Prior distribution types
pub enum PriorDistribution<T> {
    Normal { mean: T, variance: T },
    Uniform { min: T, max: T },
    Laplace { location: T, scale: T },
    Custom(Box<dyn Fn(T) -> f64 + Send + Sync>),
}

impl<T: std::fmt::Debug> std::fmt::Debug for PriorDistribution<T> {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            PriorDistribution::Normal { mean, variance } => f
                .debug_struct("Normal")
                .field("mean", mean)
                .field("variance", variance)
                .finish(),
            PriorDistribution::Uniform { min, max } => f
                .debug_struct("Uniform")
                .field("min", min)
                .field("max", max)
                .finish(),
            PriorDistribution::Laplace { location, scale } => f
                .debug_struct("Laplace")
                .field("location", location)
                .field("scale", scale)
                .finish(),
            PriorDistribution::Custom(_) => f.debug_tuple("Custom").field(&"<function>").finish(),
        }
    }
}

/// Variational parameters
#[derive(Debug)]
pub struct VariationalParameters<T> {
    /// KL divergence weight
    pub kl_weight: T,
    /// Number of samples
    pub num_samples: usize,
    /// Reparameterization noise
    pub epsilon: T,
}

/// Uncertainty quantification methods
#[derive(Debug, Clone, PartialEq)]
pub enum UncertaintyQuantificationMethod {
    MonteCarlo,
    Variational,
    Ensemble,
    DeepGaussianProcess,
    ConformalPrediction,
}

/// Compression performance metrics
#[derive(Debug, Clone)]
pub struct CompressionMetrics {
    /// Compression ratio
    pub compression_ratio: f64,
    /// Compression speed (MB/s)
    pub compression_speed: f64,
    /// Decompression speed (MB/s)
    pub decompression_speed: f64,
    /// Memory usage during compression
    pub memory_usage: usize,
    /// Quality loss (if applicable)
    pub quality_loss: f64,
    /// Energy consumption
    pub energy_consumption: f64,
    /// Timestamp
    pub timestamp: std::time::Instant,
}

/// Real-time compression algorithm switcher
#[derive(Debug)]
#[allow(dead_code)]
pub struct RealTimeCompressionSwitcher {
    /// Current algorithm
    current_algorithm: CompressionAlgorithm,
    /// Switch threshold
    switch_threshold: f64,
    /// Switching overhead
    switching_overhead: HashMap<(CompressionAlgorithm, CompressionAlgorithm), f64>,
    /// Performance predictor
    performance_predictor: CompressionPerformancePredictor,
}

/// Compression performance predictor
#[derive(Debug)]
#[allow(dead_code)]
pub struct CompressionPerformancePredictor {
    /// Prediction models for each algorithm
    models: HashMap<CompressionAlgorithm, PredictionModel>,
    /// Model ensemble
    ensemble: ModelEnsemble,
    /// Prediction accuracy
    accuracy: f64,
}

/// Prediction model for compression performance
#[derive(Debug)]
#[allow(dead_code)]
pub struct PredictionModel {
    /// Model type
    model_type: ModelType,
    /// Model parameters
    parameters: Vec<f64>,
    /// Feature scaling parameters
    feature_scaling: FeatureScaling,
}

/// Types of prediction models
#[derive(Debug, Clone, PartialEq)]
pub enum ModelType {
    LinearRegression,
    RandomForest,
    GradientBoosting,
    NeuralNetwork,
    SupportVectorMachine,
    GaussianProcess,
}

/// Feature scaling parameters
#[derive(Debug, Clone)]
pub struct FeatureScaling {
    /// Feature means
    pub means: Array1<f64>,
    /// Feature standard deviations
    pub stds: Array1<f64>,
    /// Scaling method
    pub method: ScalingMethod,
}

/// Feature scaling methods
#[derive(Debug, Clone, PartialEq)]
pub enum ScalingMethod {
    StandardScaling,
    MinMaxScaling,
    RobustScaling,
    Normalization,
    PowerTransformation,
}

/// Model ensemble for improved predictions
#[derive(Debug)]
#[allow(dead_code)]
pub struct ModelEnsemble {
    /// Individual models
    models: Vec<PredictionModel>,
    /// Model weights
    weights: Array1<f64>,
    /// Ensemble method
    ensemble_method: EnsembleMethod,
}

/// Ensemble methods
#[derive(Debug, Clone, PartialEq)]
pub enum EnsembleMethod {
    Voting,
    Averaging,
    Stacking,
    Boosting,
    Bagging,
}

/// Compression quality assessor
#[derive(Debug)]
#[allow(dead_code)]
pub struct CompressionQualityAssessor<T> {
    /// Quality metrics
    quality_metrics: Vec<QualityMetric<T>>,
    /// Perceptual quality model
    perceptual_model: PerceptualQualityModel<T>,
    /// Acceptable quality threshold
    quality_threshold: f64,
}

/// Quality metrics for compression
pub enum QualityMetric<T> {
    MeanSquaredError,
    PeakSignalToNoiseRatio,
    StructuralSimilarity,
    FrobeniusNorm,
    SpectralNorm,
    RelativeError,
    #[allow(clippy::type_complexity)]
    Custom(Box<dyn Fn(&ArrayView2<T>, &ArrayView2<T>) -> f64 + Send + Sync>),
}

impl<T> std::fmt::Debug for QualityMetric<T> {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            QualityMetric::MeanSquaredError => write!(f, "MeanSquaredError"),
            QualityMetric::PeakSignalToNoiseRatio => write!(f, "PeakSignalToNoiseRatio"),
            QualityMetric::StructuralSimilarity => write!(f, "StructuralSimilarity"),
            QualityMetric::FrobeniusNorm => write!(f, "FrobeniusNorm"),
            QualityMetric::SpectralNorm => write!(f, "SpectralNorm"),
            QualityMetric::RelativeError => write!(f, "RelativeError"),
            QualityMetric::Custom(_) => write!(f, "Custom(<function>)"),
        }
    }
}

/// Perceptual quality model
#[derive(Debug)]
#[allow(dead_code)]
pub struct PerceptualQualityModel<T> {
    /// Feature extractors for perceptual features
    feature_extractors: Vec<PerceptualFeatureExtractor<T>>,
    /// Quality prediction network
    quality_network: QualityPredictionNetwork<T>,
    /// Human perception weights
    perception_weights: Array1<f64>,
}

/// Perceptual feature extractor
#[derive(Debug)]
pub struct PerceptualFeatureExtractor<T> {
    /// Feature type
    pub feature_type: PerceptualFeatureType,
    /// Extraction function
    pub extractor: fn(&ArrayView2<T>) -> Array1<f64>,
    /// Feature importance
    pub importance: f64,
}

/// Types of perceptual features
#[derive(Debug, Clone, PartialEq)]
pub enum PerceptualFeatureType {
    EdgeDensity,
    TextureComplexity,
    Contrast,
    Brightness,
    ColorDistribution,
    SpatialFrequency,
    Gradients,
    LocalPatterns,
}

/// Quality prediction network
#[derive(Debug)]
pub struct QualityPredictionNetwork<T> {
    /// Network layers
    pub layers: Vec<DenseLayer<T>>,
    /// Attention mechanism
    pub attention: AttentionMechanism<T>,
    /// Output layer
    pub output: DenseLayer<T>,
}

/// Attention mechanism for quality prediction
#[derive(Debug)]
pub struct AttentionMechanism<T> {
    /// Query weights
    pub query_weights: Array2<T>,
    /// Key weights
    pub key_weights: Array2<T>,
    /// Value weights
    pub value_weights: Array2<T>,
    /// Attention scores
    pub attention_scores: Array2<T>,
}

/// Compression constraints
#[derive(Debug, Clone)]
pub struct CompressionConstraints {
    /// Maximum compression time
    pub max_compression_time: std::time::Duration,
    /// Minimum compression ratio
    pub min_compression_ratio: f64,
    /// Maximum quality loss
    pub max_quality_loss: f64,
    /// Memory budget
    pub memory_budget: usize,
}

// Implementations
impl<T> AdaptiveCompressionEngine<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            compression_algorithms: vec![
                CompressionAlgorithm::LZ4,
                CompressionAlgorithm::ZSTD,
                CompressionAlgorithm::Snappy,
            ],
            selector_network: CompressionSelectorNetwork::new()?,
            performance_history: HashMap::new(),
            real_time_switcher: RealTimeCompressionSwitcher::new(),
            quality_assessor: CompressionQualityAssessor::new()?,
        })
    }

    pub fn select_algorithm(
        &self,
        _data: &ArrayView2<T>,
        _constraints: &CompressionConstraints,
    ) -> LinalgResult<CompressionAlgorithm> {
        // Simplified selection
        Ok(CompressionAlgorithm::LZ4)
    }
}

impl<T> CompressionSelectorNetwork<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            feature_extractors: Vec::new(),
            decision_trees: Vec::new(),
            classifier_network: ClassificationNetwork::new()?,
            confidence_estimator: ConfidenceEstimator::new()?,
        })
    }
}

impl<T> ClassificationNetwork<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            layers: Vec::new(),
            output_layer: SoftmaxLayer::new()?,
            training_history: VecDeque::new(),
        })
    }
}

impl<T> SoftmaxLayer<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            weights: Array2::zeros((1, 1)),
            biases: Array1::zeros(1),
            temperature: T::one(),
        })
    }
}

impl<T> ConfidenceEstimator<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            bayesian_network: BayesianNetwork::new()?,
            uncertainty_method: UncertaintyQuantificationMethod::MonteCarlo,
            confidence_threshold: 0.8,
        })
    }
}

impl<T> BayesianNetwork<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            weight_distributions: Vec::new(),
            variational_params: VariationalParameters::new(),
            mc_samples: 100,
        })
    }
}

impl<T> Default for VariationalParameters<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    fn default() -> Self {
        Self::new()
    }
}

impl<T> VariationalParameters<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> Self {
        Self {
            kl_weight: T::one(),
            num_samples: 10,
            epsilon: T::from(0.001).expect("Operation failed"),
        }
    }
}

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

impl RealTimeCompressionSwitcher {
    pub fn new() -> Self {
        Self {
            current_algorithm: CompressionAlgorithm::LZ4,
            switch_threshold: 0.1,
            switching_overhead: HashMap::new(),
            performance_predictor: CompressionPerformancePredictor::new(),
        }
    }
}

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

impl CompressionPerformancePredictor {
    pub fn new() -> Self {
        Self {
            models: HashMap::new(),
            ensemble: ModelEnsemble::new(),
            accuracy: 0.85,
        }
    }
}

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

impl ModelEnsemble {
    pub fn new() -> Self {
        Self {
            models: Vec::new(),
            weights: Array1::zeros(0),
            ensemble_method: EnsembleMethod::Averaging,
        }
    }
}

impl<T> CompressionQualityAssessor<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            quality_metrics: Vec::new(),
            perceptual_model: PerceptualQualityModel::new()?,
            quality_threshold: 0.95,
        })
    }
}

impl<T> PerceptualQualityModel<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            feature_extractors: Vec::new(),
            quality_network: QualityPredictionNetwork::new()?,
            perception_weights: Array1::zeros(0),
        })
    }
}

impl<T> QualityPredictionNetwork<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            layers: Vec::new(),
            attention: AttentionMechanism::new()?,
            output: DenseLayer::new()?,
        })
    }
}

impl<T> AttentionMechanism<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            query_weights: Array2::zeros((1, 1)),
            key_weights: Array2::zeros((1, 1)),
            value_weights: Array2::zeros((1, 1)),
            attention_scores: Array2::zeros((1, 1)),
        })
    }
}

impl Default for CompressionConstraints {
    fn default() -> Self {
        #[cfg(target_pointer_width = "32")]
        let memory_budget = 256 * 1024 * 1024; // 256MB for 32-bit
        #[cfg(target_pointer_width = "64")]
        let memory_budget = 1024 * 1024 * 1024; // 1GB for 64-bit

        Self {
            max_compression_time: std::time::Duration::from_millis(100),
            min_compression_ratio: 1.5,
            max_quality_loss: 0.01,
            memory_budget,
        }
    }
}