scirs2-linalg 0.4.2

//! Cache management and prediction module for neural memory optimization.

use super::types::*;
use crate::error::{LinalgError, LinalgResult};
use scirs2_core::ndarray::{Array1, Array2, Array3};
use scirs2_core::numeric::{Float, NumAssign, Zero};
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;

/// Neural cache prediction model using deep learning
#[derive(Debug)]
#[allow(dead_code)]
pub struct NeuralCachePredictionModel<T> {
    /// Convolutional layers for pattern recognition
    conv_layers: Vec<ConvolutionalLayer<T>>,
    /// LSTM layers for temporal modeling
    lstm_layers: Vec<LstmLayer<T>>,
    /// Dense layers for prediction
    dense_layers: Vec<DenseLayer<T>>,
    /// Prediction accuracy history
    accuracy_history: VecDeque<f64>,
    /// Training data buffer
    training_buffer: VecDeque<CacheAccessPattern<T>>,
    /// Model parameters
    model_params: NeuralModelParameters,
}

/// Convolutional layer for spatial pattern recognition
#[derive(Debug)]
pub struct ConvolutionalLayer<T> {
    /// Kernel weights
    pub kernels: Array3<T>,
    /// Bias terms
    pub biases: Array1<T>,
    /// Stride
    pub stride: (usize, usize),
    /// Padding
    pub padding: (usize, usize),
    /// Activation function
    pub activation: ActivationFunction,
}

/// LSTM layer for temporal sequence modeling
#[derive(Debug)]
pub struct LstmLayer<T> {
    /// Input gate weights
    pub input_weights: Array2<T>,
    /// Forget gate weights
    pub forget_weights: Array2<T>,
    /// Output gate weights
    pub output_weights: Array2<T>,
    /// Cell state weights
    pub cell_weights: Array2<T>,
    /// Hidden state
    pub hidden_state: Array1<T>,
    /// Cell state
    pub cell_state: Array1<T>,
}

/// Dense (fully connected) layer
#[derive(Debug)]
pub struct DenseLayer<T> {
    /// Weight matrix
    pub weights: Array2<T>,
    /// Bias vector
    pub biases: Array1<T>,
    /// Activation function
    pub activation: ActivationFunction,
    /// Dropout rate
    pub dropout_rate: f64,
}

/// Cache access pattern for training
#[derive(Debug, Clone)]
pub struct CacheAccessPattern<T> {
    /// Memory addresses accessed
    pub addresses: Vec<usize>,
    /// Access order
    pub access_order: Vec<usize>,
    /// Data types
    pub data_types: Vec<DataType>,
    /// Access sizes
    pub accesssizes: Vec<usize>,
    /// Temporal spacing
    pub temporal_spacing: Vec<f64>,
    /// Spatial locality score
    pub spatial_locality: f64,
    /// Temporal locality score
    pub temporal_locality: f64,
    /// Cache hit/miss pattern
    pub hit_miss_pattern: Vec<bool>,
    /// Context information
    pub context: AccessContext<T>,
}

/// Neural model parameters
#[derive(Debug, Clone)]
pub struct NeuralModelParameters {
    /// Learning rate
    pub learning_rate: f64,
    /// Batch size
    pub batchsize: usize,
    /// Number of epochs
    pub epochs: usize,
    /// Regularization strength
    pub regularization: f64,
    /// Dropout rate
    pub dropout_rate: f64,
    /// Early stopping patience
    pub early_stopping_patience: usize,
    /// Validation split
    pub validation_split: f64,
    /// Optimizer type
    pub optimizer: OptimizerType,
}

/// Optimizer types for neural network training
#[derive(Debug, Clone, PartialEq)]
pub enum OptimizerType {
    SGD,
    Adam,
    RMSprop,
    AdaGrad,
    AdaDelta,
    Nadam,
    Custom(String),
}

/// Cache performance prediction result
#[derive(Debug, Clone)]
pub struct CachePerformancePrediction {
    /// Predicted cache hit rate
    pub hit_rate: f64,
    /// Predicted average latency
    pub average_latency: f64,
    /// Confidence in prediction
    pub confidence: f64,
    /// Bottleneck analysis
    pub bottlenecks: Vec<PerformanceBottleneck>,
}

/// Performance bottleneck identification
#[derive(Debug, Clone)]
pub struct PerformanceBottleneck {
    /// Bottleneck type
    pub bottleneck_type: BottleneckType,
    /// Severity score
    pub severity: f64,
    /// Mitigation suggestions
    pub mitigation: String,
}

/// Types of performance bottlenecks
#[derive(Debug, Clone, PartialEq)]
pub enum BottleneckType {
    CacheMiss,
    MemoryBandwidth,
    TLBMiss,
    NumaTraffic,
    FalseSharing,
    Contention,
}

/// Training metrics
#[derive(Debug, Clone)]
pub struct TrainingMetrics {
    /// Loss value
    pub loss: f64,
    /// Accuracy
    pub accuracy: f64,
    /// Validation loss
    pub validation_loss: f64,
    /// Validation accuracy
    pub validation_accuracy: f64,
    /// Epoch number
    pub epoch: usize,
    /// Training time
    pub training_time: std::time::Duration,
}

/// Bandwidth measurement
#[derive(Debug, Clone)]
pub struct BandwidthMeasurement {
    /// Timestamp
    pub timestamp: std::time::Instant,
    /// Read bandwidth (GB/s)
    pub read_bandwidth: f64,
    /// Write bandwidth (GB/s)
    pub write_bandwidth: f64,
    /// Total bandwidth utilization
    pub total_utilization: f64,
    /// Memory pressure
    pub memory_pressure: f64,
    /// Queue depth
    pub queue_depth: usize,
}

/// Bandwidth monitor for memory subsystem
#[derive(Debug)]
#[allow(dead_code)]
pub struct BandwidthMonitor {
    /// Current bandwidth utilization
    current_utilization: f64,
    /// Bandwidth history
    bandwidth_history: VecDeque<BandwidthMeasurement>,
    /// Saturation detector
    saturation_detector: SaturationDetector,
    /// Prediction model
    bandwidth_predictor: BandwidthPredictor,
}

/// Saturation detector for memory bandwidth
#[derive(Debug)]
#[allow(dead_code)]
pub struct SaturationDetector {
    /// Saturation threshold
    saturation_threshold: f64,
    /// Detection algorithm
    detection_algorithm: SaturationDetectionAlgorithm,
    /// Current saturation level
    current_saturation: f64,
    /// Saturation history
    saturation_history: VecDeque<f64>,
}

/// Algorithms for detecting bandwidth saturation
#[derive(Debug, Clone, PartialEq)]
pub enum SaturationDetectionAlgorithm {
    ThresholdBased,
    TrendAnalysis,
    StatisticalAnomalyDetection,
    MachineLearning,
    HybridApproach,
}

/// Bandwidth predictor
#[derive(Debug)]
#[allow(dead_code)]
pub struct BandwidthPredictor {
    /// Prediction model
    model: BandwidthPredictionModel,
    /// Historical accuracy
    accuracy: f64,
    /// Prediction horizon
    prediction_horizon: std::time::Duration,
}

/// Bandwidth prediction models
#[derive(Debug)]
pub enum BandwidthPredictionModel {
    ARIMA,
    LSTM,
    Prophet,
    LinearRegression,
    Ensemble(Vec<Box<BandwidthPredictionModel>>),
}

/// Bandwidth saturation prediction
#[derive(Debug, Clone)]
pub struct BandwidthSaturationPrediction {
    /// Predicted saturation level
    pub saturation_level: f64,
    /// Time to saturation
    pub time_to_saturation: Option<std::time::Duration>,
    /// Confidence in prediction
    pub confidence: f64,
    /// Recommended actions
    pub recommendations: Vec<String>,
}

// Implementations
impl<T> NeuralCachePredictionModel<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            conv_layers: Vec::new(),
            lstm_layers: Vec::new(),
            dense_layers: Vec::new(),
            accuracy_history: VecDeque::new(),
            training_buffer: VecDeque::new(),
            model_params: NeuralModelParameters::default(),
        })
    }

    pub fn predict_performance(
        &self,
        _pattern: &CacheAccessPattern<T>,
    ) -> LinalgResult<CachePerformancePrediction> {
        // Simplified prediction
        Ok(CachePerformancePrediction {
            hit_rate: 0.85,
            average_latency: 2.5,
            confidence: 0.9,
            bottlenecks: Vec::new(),
        })
    }
}

impl Default for NeuralModelParameters {
    fn default() -> Self {
        Self {
            learning_rate: 0.001,
            batchsize: 32,
            epochs: 100,
            regularization: 0.01,
            dropout_rate: 0.1,
            early_stopping_patience: 10,
            validation_split: 0.2,
            optimizer: OptimizerType::Adam,
        }
    }
}

impl<T> DenseLayer<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),
            activation: ActivationFunction::ReLU,
            dropout_rate: 0.0,
        })
    }
}

impl BandwidthMonitor {
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            current_utilization: 0.0,
            bandwidth_history: VecDeque::new(),
            saturation_detector: SaturationDetector::new(),
            bandwidth_predictor: BandwidthPredictor::new(),
        })
    }

    pub fn predict_saturation(&self) -> LinalgResult<BandwidthSaturationPrediction> {
        Ok(BandwidthSaturationPrediction {
            saturation_level: 0.3,
            time_to_saturation: Some(std::time::Duration::from_secs(60)),
            confidence: 0.85,
            recommendations: vec!["Reduce memory traffic".to_string()],
        })
    }
}

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

impl SaturationDetector {
    pub fn new() -> Self {
        Self {
            saturation_threshold: 0.8,
            detection_algorithm: SaturationDetectionAlgorithm::ThresholdBased,
            current_saturation: 0.0,
            saturation_history: VecDeque::new(),
        }
    }
}

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

impl BandwidthPredictor {
    pub fn new() -> Self {
        Self {
            model: BandwidthPredictionModel::LinearRegression,
            accuracy: 0.8,
            prediction_horizon: std::time::Duration::from_secs(30),
        }
    }
}

impl<T> CacheAccessPattern<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn from_workload(workload: &WorkloadCharacteristics) -> Self {
        Self {
            addresses: Vec::new(),
            access_order: Vec::new(),
            data_types: Vec::new(),
            accesssizes: Vec::new(),
            temporal_spacing: Vec::new(),
            spatial_locality: 0.5,
            temporal_locality: 0.5,
            hit_miss_pattern: Vec::new(),
            context: AccessContext::default(),
        }
    }
}