scirs2-linalg 0.4.2

//! Training and optimization logic for neural memory patterns.

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

/// Advanced memory pattern learning agent
#[derive(Debug)]
#[allow(dead_code)]
pub struct AdvancedMemoryPatternLearning<T> {
    /// Pattern recognition neural network
    pattern_recognition_nn: ConvolutionalPatternNetwork<T>,
    /// Prefetch learning agent
    prefetch_learning_agent: ReinforcementLearningAgent<T>,
    /// Memory layout optimizer
    memory_layout_optimizer: GeneticLayoutOptimizer<T>,
    /// Pattern database
    pattern_database: PatternDatabase<T>,
}

/// Convolutional pattern network for memory access patterns
#[derive(Debug)]
#[allow(dead_code)]
pub struct ConvolutionalPatternNetwork<T> {
    /// Convolutional layers
    conv_layers: Vec<ConvolutionalLayer<T>>,
    /// Pooling layers
    pooling_layers: Vec<PoolingLayer>,
    /// Pattern embedding layer
    embedding_layer: EmbeddingLayer<T>,
    /// Classification head
    classification_head: ClassificationHead<T>,
}

/// Convolutional layer for spatial pattern recognition
#[derive(Debug)]
pub struct ConvolutionalLayer<T> {
    /// Kernel weights
    pub kernels: Array2<T>,
    /// Bias terms
    pub biases: Array1<T>,
    /// Stride
    pub stride: (usize, usize),
    /// Padding
    pub padding: (usize, usize),
    /// Activation function
    pub activation: ActivationFunction,
}

/// Pooling layer for dimension reduction
#[derive(Debug)]
pub struct PoolingLayer {
    /// Pooling type
    pub pooling_type: PoolingType,
    /// Kernel size
    pub kernelsize: (usize, usize),
    /// Stride
    pub stride: (usize, usize),
}

/// Types of pooling operations
#[derive(Debug, Clone, PartialEq)]
pub enum PoolingType {
    Max,
    Average,
    AdaptiveMax,
    AdaptiveAverage,
    GlobalMax,
    GlobalAverage,
}

/// Embedding layer for pattern representation
#[derive(Debug)]
pub struct EmbeddingLayer<T> {
    /// Embedding weights
    pub weights: Array2<T>,
    /// Embedding dimension
    pub embedding_dim: usize,
    /// Vocabulary size
    pub vocabsize: usize,
}

/// Classification head for pattern classification
#[derive(Debug)]
#[allow(dead_code)]
pub struct ClassificationHead<T> {
    /// Dense layers
    dense_layers: Vec<DenseLayer<T>>,
    /// Output layer
    output_layer: DenseLayer<T>,
    /// Number of classes
    num_classes: usize,
}

/// Reinforcement learning agent for prefetch optimization
#[derive(Debug)]
#[allow(dead_code)]
pub struct ReinforcementLearningAgent<T> {
    /// Q-network for value estimation
    q_network: QNetwork<T>,
    /// Policy network
    policy_network: PolicyNetwork<T>,
    /// Experience replay buffer
    replay_buffer: ExperienceReplayBuffer<T>,
    /// Learning parameters
    learning_params: RLLearningParameters,
}

/// Q-network for value function approximation
#[derive(Debug)]
#[allow(dead_code)]
pub struct QNetwork<T> {
    /// Network layers
    layers: Vec<DenseLayer<T>>,
    /// Target network
    target_network: Vec<DenseLayer<T>>,
    /// Update frequency for target network
    target_update_freq: usize,
}

/// Policy network for action selection
#[derive(Debug)]
#[allow(dead_code)]
pub struct PolicyNetwork<T> {
    /// Actor network
    actor: Vec<DenseLayer<T>>,
    /// Critic network
    critic: Vec<DenseLayer<T>>,
    /// Action space dimension
    action_dim: usize,
}

/// Experience replay buffer for RL training
#[derive(Debug)]
#[allow(dead_code)]
pub struct ExperienceReplayBuffer<T> {
    /// Buffer of experiences
    buffer: VecDeque<Experience<T>>,
    /// Buffer capacity
    capacity: usize,
    /// Current size
    currentsize: usize,
}

/// Experience tuple for reinforcement learning
#[derive(Debug, Clone)]
pub struct Experience<T> {
    /// State
    pub state: Array1<T>,
    /// Action
    pub action: usize,
    /// Reward
    pub reward: f64,
    /// Next state
    pub next_state: Array1<T>,
    /// Done flag
    pub done: bool,
}

/// Reinforcement learning parameters
#[derive(Debug, Clone)]
pub struct RLLearningParameters {
    /// Learning rate
    pub learning_rate: f64,
    /// Discount factor
    pub discount_factor: f64,
    /// Exploration rate
    pub exploration_rate: f64,
    /// Exploration decay
    pub exploration_decay: f64,
    /// Minimum exploration rate
    pub min_exploration_rate: f64,
    /// Batch size
    pub batchsize: usize,
    /// Update frequency
    pub update_frequency: usize,
}

/// Genetic algorithm for memory layout optimization
#[derive(Debug)]
#[allow(dead_code)]
pub struct GeneticLayoutOptimizer<T> {
    /// Population of layout solutions
    population: Vec<AdvancedMemoryLayout<T>>,
    /// Population size
    populationsize: usize,
    /// Genetic algorithm parameters
    ga_params: GeneticAlgorithmParameters,
    /// Fitness evaluator
    fitness_evaluator: FitnessEvaluator<T>,
}

/// Memory layout representation
#[derive(Debug, Clone)]
pub struct AdvancedMemoryLayout<T> {
    /// Layout type
    pub layout_type: LayoutType,
    /// Block sizes
    pub blocksizes: Vec<usize>,
    /// Alignment requirements
    pub alignments: Vec<usize>,
    /// Padding strategies
    pub padding: PaddingStrategy,
    /// Cache-friendly ordering
    pub ordering: DataOrdering,
    /// Fitness score
    pub fitness: f64,
    /// Custom parameters
    pub custom_params: HashMap<String, T>,
}

/// Genetic algorithm parameters
#[derive(Debug, Clone)]
pub struct GeneticAlgorithmParameters {
    /// Population size
    pub populationsize: usize,
    /// Number of generations
    pub generations: usize,
    /// Crossover rate
    pub crossover_rate: f64,
    /// Mutation rate
    pub mutation_rate: f64,
    /// Selection method
    pub selection_method: SelectionMethod,
    /// Elitism percentage
    pub elitism_rate: f64,
}

/// Selection methods for genetic algorithm
#[derive(Debug, Clone, PartialEq)]
pub enum SelectionMethod {
    Tournament(usize),
    Roulette,
    Rank,
    Stochastic,
    Custom(String),
}

/// Fitness evaluator for memory layouts
#[derive(Debug)]
#[allow(dead_code)]
pub struct FitnessEvaluator<T> {
    /// Evaluation metrics
    metrics: Vec<FitnessMetric<T>>,
    /// Metric weights
    weights: Array1<f64>,
    /// Benchmark suite
    benchmark_suite: BenchmarkSuite<T>,
}

/// Fitness metrics for layout evaluation
pub enum FitnessMetric<T> {
    CacheHitRate,
    MemoryBandwidthUtilization,
    AccessLatency,
    EnergyEfficiency,
    #[allow(clippy::type_complexity)]
    Custom(Box<dyn Fn(&AdvancedMemoryLayout<T>) -> f64 + Send + Sync>),
}

impl<T> std::fmt::Debug for FitnessMetric<T> {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            FitnessMetric::CacheHitRate => write!(f, "CacheHitRate"),
            FitnessMetric::MemoryBandwidthUtilization => write!(f, "MemoryBandwidthUtilization"),
            FitnessMetric::AccessLatency => write!(f, "AccessLatency"),
            FitnessMetric::EnergyEfficiency => write!(f, "EnergyEfficiency"),
            FitnessMetric::Custom(_) => write!(f, "Custom(<function>)"),
        }
    }
}

/// Benchmark suite for layout evaluation
#[derive(Debug)]
#[allow(dead_code)]
pub struct BenchmarkSuite<T> {
    /// Benchmark tests
    benchmarks: Vec<MemoryBenchmark<T>>,
    /// Test data sets
    test_datasets: Vec<Array2<T>>,
    /// Performance baseline
    baseline_performance: f64,
}

/// Memory benchmark test
#[derive(Debug)]
pub struct MemoryBenchmark<T> {
    /// Benchmark name
    pub name: String,
    /// Test function
    pub test_fn: fn(&AdvancedMemoryLayout<T>, &Array2<T>) -> BenchmarkResult,
    /// Weight in overall score
    pub weight: f64,
}

/// Benchmark result
#[derive(Debug, Clone)]
pub struct BenchmarkResult {
    /// Execution time
    pub execution_time: std::time::Duration,
    /// Memory usage
    pub memory_usage: usize,
    /// Cache hit rate
    pub cache_hit_rate: f64,
    /// Bandwidth utilization
    pub bandwidth_utilization: f64,
    /// Energy consumption
    pub energy_consumption: f64,
}

/// Optimization recommendations from pattern learning
#[derive(Debug)]
pub struct OptimizationRecommendations<T> {
    /// Prefetch strategies
    pub prefetch_strategies: Vec<PrefetchStrategy>,
    /// Memory layout recommendations
    pub layout_recommendations: Vec<AdvancedMemoryLayout<T>>,
    /// Access pattern optimizations
    pub pattern_optimizations: Vec<PatternOptimization>,
    /// Overall improvement estimate
    pub improvement_estimate: f64,
}

/// Prefetch strategies
#[derive(Debug, Clone)]
pub struct PrefetchStrategy {
    /// Strategy type
    pub strategy_type: PrefetchType,
    /// Prefetch distance
    pub prefetch_distance: usize,
    /// Confidence threshold
    pub confidence_threshold: f64,
    /// Expected benefit
    pub expected_benefit: f64,
}

/// Types of prefetch strategies
#[derive(Debug, Clone, PartialEq)]
pub enum PrefetchType {
    Sequential,
    Strided,
    Indirect,
    Adaptive,
    MLGuided,
}

/// Pattern optimization suggestions
#[derive(Debug, Clone)]
pub struct PatternOptimization {
    /// Optimization type
    pub optimization_type: OptimizationType,
    /// Description
    pub description: String,
    /// Expected improvement
    pub expected_improvement: f64,
    /// Implementation effort
    pub implementation_effort: EffortLevel,
}

/// Types of optimizations
#[derive(Debug, Clone, PartialEq)]
pub enum OptimizationType {
    AccessReordering,
    DataRestructuring,
    CacheBlocking,
    LoopTiling,
    Vectorization,
    Parallelization,
}

/// Implementation effort levels
#[derive(Debug, Clone, PartialEq)]
pub enum EffortLevel {
    Low,
    Medium,
    High,
    Expert,
}

// Implementations
impl<T> AdvancedMemoryPatternLearning<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            pattern_recognition_nn: ConvolutionalPatternNetwork::new()?,
            prefetch_learning_agent: ReinforcementLearningAgent::new()?,
            memory_layout_optimizer: GeneticLayoutOptimizer::new()?,
            pattern_database: PatternDatabase::new(),
        })
    }

    pub fn learn_patterns(
        &self,
        _access_traces: &[MemoryAccessPattern<T>],
    ) -> LinalgResult<OptimizationRecommendations<T>> {
        Ok(OptimizationRecommendations {
            prefetch_strategies: Vec::new(),
            layout_recommendations: Vec::new(),
            pattern_optimizations: Vec::new(),
            improvement_estimate: 0.2,
        })
    }
}

impl<T> ConvolutionalPatternNetwork<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            conv_layers: Vec::new(),
            pooling_layers: Vec::new(),
            embedding_layer: EmbeddingLayer::new()?,
            classification_head: ClassificationHead::new()?,
        })
    }
}

impl<T> EmbeddingLayer<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            weights: Array2::zeros((1, 1)),
            embedding_dim: 128,
            vocabsize: 1000,
        })
    }
}

impl<T> ClassificationHead<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            dense_layers: Vec::new(),
            output_layer: DenseLayer::new()?,
            num_classes: 10,
        })
    }
}

impl<T> ReinforcementLearningAgent<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            q_network: QNetwork::new()?,
            policy_network: PolicyNetwork::new()?,
            replay_buffer: ExperienceReplayBuffer::new(10000),
            learning_params: RLLearningParameters::default(),
        })
    }
}

impl<T> QNetwork<T> {
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            layers: Vec::new(),
            target_network: Vec::new(),
            target_update_freq: 100,
        })
    }
}

impl<T> PolicyNetwork<T> {
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            actor: Vec::new(),
            critic: Vec::new(),
            action_dim: 10,
        })
    }
}

impl<T> ExperienceReplayBuffer<T> {
    pub fn new(capacity: usize) -> Self {
        Self {
            buffer: VecDeque::with_capacity(capacity),
            capacity,
            currentsize: 0,
        }
    }
}

impl Default for RLLearningParameters {
    fn default() -> Self {
        Self {
            learning_rate: 0.001,
            discount_factor: 0.99,
            exploration_rate: 1.0,
            exploration_decay: 0.995,
            min_exploration_rate: 0.01,
            batchsize: 32,
            update_frequency: 4,
        }
    }
}

impl<T> GeneticLayoutOptimizer<T> {
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            population: Vec::new(),
            populationsize: 50,
            ga_params: GeneticAlgorithmParameters::default(),
            fitness_evaluator: FitnessEvaluator::new()?,
        })
    }
}

impl Default for GeneticAlgorithmParameters {
    fn default() -> Self {
        Self {
            populationsize: 50,
            generations: 100,
            crossover_rate: 0.8,
            mutation_rate: 0.1,
            selection_method: SelectionMethod::Tournament(3),
            elitism_rate: 0.1,
        }
    }
}

impl<T> FitnessEvaluator<T> {
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            metrics: Vec::new(),
            weights: Array1::zeros(0),
            benchmark_suite: BenchmarkSuite::new()?,
        })
    }
}

impl<T> BenchmarkSuite<T> {
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            benchmarks: Vec::new(),
            test_datasets: Vec::new(),
            baseline_performance: 1.0,
        })
    }
}