scirs2-ndimage 0.4.2

//! Advanced AI-Driven Streaming with Predictive Chunking
//!
//! This module implements next-generation streaming algorithms that use artificial intelligence
//! to predict optimal chunking strategies, adapt to data patterns, and optimize memory usage
//! for advanced-large image processing tasks.
//!
//! # Revolutionary Features
//!
//! - **Predictive Chunking AI**: Machine learning models that predict optimal chunk sizes
//! - **Adaptive Data Flow Control**: Dynamic adjustment of streaming parameters
//! - **Content-Aware Chunking**: Chunk boundaries based on image content analysis
//! - **Memory Pressure Prediction**: Proactive memory management with ML forecasting
//! - **Load Balancing Intelligence**: AI-driven work distribution across cores
//! - **Cache Optimization AI**: Intelligent prefetching and cache management
//! - **Bandwidth Adaptation**: Network-aware streaming for distributed processing
//! - **Error Recovery AI**: Intelligent fault tolerance and recovery strategies

use scirs2_core::ndarray::{Array1, Array2, ArrayView2, Dimension};
use scirs2_core::numeric::{Float, FromPrimitive, Zero};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant};

use crate::error::NdimageResult;
use crate::streaming::StreamConfig;
use scirs2_core::parallel_ops::*;

/// Configuration for AI-driven streaming
#[derive(Debug, Clone)]
pub struct AIStreamConfig {
    /// Base streaming configuration
    pub base_config: StreamConfig,
    /// AI model parameters
    pub ai_model_complexity: usize,
    /// Prediction window size
    pub prediction_window: usize,
    /// Learning rate for adaptation
    pub learning_rate: f64,
    /// Memory pressure threshold
    pub memory_pressure_threshold: f64,
    /// Content analysis window size
    pub content_analysis_window: usize,
    /// Load balancing sensitivity
    pub load_balance_sensitivity: f64,
    /// Cache prediction depth
    pub cache_prediction_depth: usize,
    /// Bandwidth adaptation rate
    pub bandwidth_adaptation_rate: f64,
    /// Error recovery aggressiveness
    pub error_recovery_aggressiveness: f64,
}

impl Default for AIStreamConfig {
    fn default() -> Self {
        Self {
            base_config: StreamConfig::default(),
            ai_model_complexity: 64,
            prediction_window: 10,
            learning_rate: 0.01,
            memory_pressure_threshold: 0.8,
            content_analysis_window: 5,
            load_balance_sensitivity: 0.5,
            cache_prediction_depth: 3,
            bandwidth_adaptation_rate: 0.1,
            error_recovery_aggressiveness: 0.7,
        }
    }
}

/// AI model for predictive chunking
#[derive(Debug, Clone)]
pub struct PredictiveChunkingAI {
    /// Neural network weights for chunk size prediction
    pub chunk_size_weights: Array2<f64>,
    /// Content analysis features
    pub contentfeatures: Array1<f64>,
    /// Historical performance data
    pub performancehistory: VecDeque<PerformanceMetrics>,
    /// Memory usage patterns
    pub memory_patterns: VecDeque<MemoryMetrics>,
    /// Current prediction accuracy
    pub prediction_accuracy: f64,
    /// Adaptation rate
    pub adaptation_rate: f64,
}

/// Performance metrics for AI learning
#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    pub chunk_size: usize,
    pub processing_time: Duration,
    pub memory_usage: usize,
    pub cache_hit_rate: f64,
    pub throughput: f64,
    pub error_rate: f64,
    pub timestamp: Instant,
}

/// Memory usage metrics
#[derive(Debug, Clone)]
pub struct MemoryMetrics {
    pub peak_usage: usize,
    pub average_usage: usize,
    pub fragmentation: f64,
    pub gc_frequency: f64,
    pub pressure_level: f64,
    pub timestamp: Instant,
}

/// Content analysis for intelligent chunking
#[derive(Debug, Clone)]
pub struct ContentAnalysis {
    /// Local variance in image regions
    pub variance_map: Array2<f64>,
    /// Edge density map
    pub edge_density: Array2<f64>,
    /// Frequency content analysis
    pub frequency_content: Array2<f64>,
    /// Compression ratio prediction
    pub compression_ratio: f64,
    /// Processing complexity estimate
    pub complexity_estimate: f64,
}

/// Adaptive load balancer with AI
#[derive(Debug)]
pub struct AILoadBalancer {
    /// Current work distribution
    pub work_distribution: HashMap<usize, f64>,
    /// Worker performance history
    pub worker_performance: HashMap<usize, VecDeque<f64>>,
    /// Load prediction model
    pub load_prediction_model: Array2<f64>,
    /// Adaptation strategy
    pub adaptation_strategy: LoadBalanceStrategy,
}

/// Load balancing strategies
#[derive(Debug, Clone)]
pub enum LoadBalanceStrategy {
    /// Gradient-based optimization
    GradientBased,
    /// Reinforcement learning
    ReinforcementLearning,
    /// Genetic algorithm
    GeneticAlgorithm,
    /// Hybrid approach
    Hybrid,
}

/// Intelligent cache manager
#[derive(Debug)]
pub struct IntelligentCacheManager {
    /// Cache usage patterns
    pub usage_patterns: HashMap<String, VecDeque<CacheAccessPattern>>,
    /// Prefetch prediction model
    pub prefetch_model: Array2<f64>,
    /// Cache replacement strategy
    pub replacement_strategy: CacheReplacementStrategy,
    /// Current cache state
    pub cachestate: Arc<RwLock<HashMap<String, CachedData>>>,
}

/// Cache access patterns
#[derive(Debug, Clone)]
pub struct CacheAccessPattern {
    pub access_time: Instant,
    pub data_id: String,
    pub access_type: CacheAccessType,
    pub hit: bool,
    pub data_size: usize,
}

/// Types of cache access
#[derive(Debug, Clone)]
pub enum CacheAccessType {
    Read,
    Write,
    Prefetch,
}

/// Cache replacement strategies
#[derive(Debug, Clone)]
pub enum CacheReplacementStrategy {
    /// AI-predicted least recently used
    AIPredictedLRU,
    /// Frequency-based with ML
    MLFrequencyBased,
    /// Content-aware replacement
    ContentAware,
    /// Adaptive hybrid
    AdaptiveHybrid,
}

/// Cached data with metadata
#[derive(Debug, Clone)]
pub struct CachedData {
    pub data: Vec<u8>,
    pub metadata: CacheMetadata,
    pub access_count: usize,
    pub last_access: Instant,
    pub predicted_next_access: Option<Instant>,
}

/// Cache metadata
#[derive(Debug, Clone)]
pub struct CacheMetadata {
    pub size: usize,
    pub creation_time: Instant,
    pub compression_level: f64,
    pub importance_score: f64,
    pub content_hash: u64,
}

/// AI-Driven Predictive Chunking
///
/// Uses machine learning to predict optimal chunk sizes based on content analysis,
/// historical performance, and system conditions.
#[allow(dead_code)]
pub fn ai_predictive_chunking<T>(
    datashape: &[usize],
    content_analysis: &ContentAnalysis,
    ai_model: &mut PredictiveChunkingAI,
    config: &AIStreamConfig,
) -> NdimageResult<Vec<ChunkSpecification>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    // Analyze current system conditions
    let system_conditions = analyze_system_conditions()?;

    // Extract content features
    let contentfeatures = extract_contentfeatures(content_analysis, config)?;

    // Prepare input for AI _model
    let model_input = prepare_model_input(&contentfeatures, &system_conditions, config)?;

    // Predict optimal chunk configuration
    let chunk_predictions = predict_chunk_configuration(&model_input, ai_model, config)?;

    // Validate and adjust predictions
    let validated_chunks = validate_chunk_predictions(&chunk_predictions, datashape, config)?;

    // Generate chunk specifications
    let chunk_specs = generate_chunk_specifications(&validated_chunks, datashape, config)?;

    // Update AI _model with feedback
    update_ai_model_with_feedback(ai_model, &chunk_specs, config)?;

    Ok(chunk_specs)
}

/// Content-Aware Adaptive Chunking
///
/// Analyzes image content to determine optimal chunk boundaries that preserve
/// important features and minimize artifacts.
#[allow(dead_code)]
pub fn content_aware_adaptive_chunking<T>(
    image: ArrayView2<T>,
    target_chunk_size: usize,
    config: &AIStreamConfig,
) -> NdimageResult<Vec<ContentAwareChunk<T>>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let _height_width = image.dim();

    // Analyze image content
    let content_analysis = analyzeimage_content(&image, config)?;

    // Detect optimal chunk boundaries
    let chunk_boundaries =
        detect_optimal_chunk_boundaries(&content_analysis, target_chunk_size, config)?;

    // Create content-aware chunks
    let mut chunks = Vec::new();

    for boundary in chunk_boundaries {
        let chunk_data = extract_chunk_with_overlap(&image, &boundary, config)?;
        let chunk_metadata = compute_chunk_metadata(&chunk_data, &boundary, &content_analysis)?;

        let content_chunk = ContentAwareChunk {
            data: chunk_data,
            boundary,
            metadata: chunk_metadata.clone(),
            processing_priority: compute_processing_priority(&chunk_metadata)?,
            overlap_strategy: determine_overlap_strategy(&chunk_metadata, config)?,
        };

        chunks.push(content_chunk);
    }

    // Sort chunks by processing priority
    chunks.sort_by(|a, b| {
        b.processing_priority
            .partial_cmp(&a.processing_priority)
            .expect("Operation failed")
    });

    Ok(chunks)
}

/// Intelligent Memory Management with Prediction
///
/// Uses AI to predict memory pressure and proactively manage memory allocation
/// to prevent out-of-memory conditions during streaming.
#[allow(dead_code)]
pub fn intelligent_memory_management(
    current_usage: &MemoryMetrics,
    prediction_model: &mut Array2<f64>,
    config: &AIStreamConfig,
) -> NdimageResult<MemoryManagementStrategy> {
    // Predict future memory _usage
    let memory_forecast = predict_memory_usage(current_usage, prediction_model, config)?;

    // Assess memory pressure risk
    let pressure_risk = assess_memory_pressure_risk(&memory_forecast, config)?;

    // Determine optimal strategy
    let strategy = if pressure_risk > config.memory_pressure_threshold {
        // High pressure: aggressive memory management
        MemoryManagementStrategy::Aggressive {
            chunk_size_reduction: 0.5,
            cache_size_reduction: 0.3,
            garbage_collection_frequency: 2.0,
            swap_strategy: SwapStrategy::Predictive,
        }
    } else if pressure_risk > config.memory_pressure_threshold * 0.7 {
        // Medium pressure: moderate management
        MemoryManagementStrategy::Moderate {
            chunk_size_reduction: 0.2,
            cache_optimization: true,
            prefetch_reduction: 0.5,
        }
    } else {
        // Low pressure: optimistic management
        MemoryManagementStrategy::Optimistic {
            chunk_size_increase: 0.1,
            cache_expansion: true,
            prefetch_increase: 0.3,
        }
    };

    // Update prediction _model
    update_memory_prediction_model(prediction_model, current_usage, &memory_forecast, config)?;

    Ok(strategy)
}

/// AI-Enhanced Load Balancing
///
/// Uses machine learning to optimally distribute work across available cores
/// based on worker performance patterns and task characteristics.
#[allow(dead_code)]
pub fn ai_enhanced_load_balancing<T>(
    tasks: &[ProcessingTask<T>],
    load_balancer: &mut AILoadBalancer,
    config: &AIStreamConfig,
) -> NdimageResult<HashMap<usize, Vec<ProcessingTask<T>>>>
where
    T: Float + FromPrimitive + Copy + Send + Sync + Clone,
{
    let num_workers = num_cpus::get();
    let mut worker_assignments: HashMap<usize, Vec<ProcessingTask<T>>> = HashMap::new();

    // Initialize worker assignments
    for i in 0..num_workers {
        worker_assignments.insert(i, Vec::new());
    }

    // Analyze task characteristics
    let task_analysis = analyze_task_characteristics(tasks, config)?;

    // Predict worker performance for each task type
    let performance_predictions = predict_worker_performance(
        &task_analysis,
        &load_balancer.worker_performance,
        &load_balancer.load_prediction_model,
        config,
    )?;

    // Optimize task assignment using AI strategy
    match load_balancer.adaptation_strategy {
        LoadBalanceStrategy::GradientBased => {
            assign_tasks_gradient_based(
                tasks,
                &performance_predictions,
                &mut worker_assignments,
                config,
            )?;
        }
        LoadBalanceStrategy::ReinforcementLearning => {
            assign_tasks_reinforcement_learning(
                tasks,
                &performance_predictions,
                &mut worker_assignments,
                load_balancer,
                config,
            )?;
        }
        LoadBalanceStrategy::GeneticAlgorithm => {
            assign_tasks_genetic_algorithm(
                tasks,
                &performance_predictions,
                &mut worker_assignments,
                config,
            )?;
        }
        LoadBalanceStrategy::Hybrid => {
            assign_tasks_hybrid_approach(
                tasks,
                &performance_predictions,
                &mut worker_assignments,
                load_balancer,
                config,
            )?;
        }
    }

    // Update load _balancer with new assignments
    update_load_balancerstate(load_balancer, &worker_assignments, config)?;

    Ok(worker_assignments)
}

/// Intelligent Cache Management with Prefetching
///
/// Uses AI to predict future data access patterns and optimize cache management
/// with intelligent prefetching strategies.
#[allow(dead_code)]
pub fn intelligent_cache_management<T>(
    access_pattern: &CacheAccessPattern,
    cache_manager: &mut IntelligentCacheManager,
    config: &AIStreamConfig,
) -> NdimageResult<CacheManagementDecision>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    // Update access patterns
    update_access_patterns(cache_manager, access_pattern)?;

    // Predict future access patterns
    let access_predictions = predict_future_accesses(
        &cache_manager.usage_patterns,
        &cache_manager.prefetch_model,
        config,
    )?;

    // Determine prefetch candidates
    let prefetch_candidates = determine_prefetch_candidates(&access_predictions, config)?;

    // Evaluate cache replacement needs
    let replacement_decisions = evaluate_cache_replacement(
        &cache_manager.cachestate,
        &cache_manager.replacement_strategy,
        &access_predictions,
        config,
    )?;

    // Generate cache management decision
    let decision = CacheManagementDecision {
        prefetch_items: prefetch_candidates,
        evict_items: replacement_decisions.evict_items,
        cache_size_adjustment: replacement_decisions.size_adjustment,
        priority_adjustments: replacement_decisions.priority_adjustments,
    };

    // Update prediction model
    update_cache_prediction_model(cache_manager, &decision, config)?;

    Ok(decision)
}

/// Adaptive Bandwidth Management
///
/// Adjusts streaming parameters based on network conditions and bandwidth availability
/// for distributed image processing scenarios.
#[allow(dead_code)]
pub fn adaptive_bandwidth_management(
    current_bandwidth: f64,
    network_conditions: &NetworkConditions,
    bandwidthhistory: &VecDeque<BandwidthMeasurement>,
    config: &AIStreamConfig,
) -> NdimageResult<BandwidthAdaptationStrategy> {
    // Predict future _bandwidth availability
    let bandwidth_forecast = predict_bandwidth_availability(
        current_bandwidth,
        network_conditions,
        bandwidthhistory,
        config,
    )?;

    // Analyze network stability
    let stability_analysis = analyze_network_stability(bandwidthhistory, config)?;

    // Determine optimal streaming parameters
    let streaming_params =
        optimize_streaming_parameters(&bandwidth_forecast, &stability_analysis, config)?;

    // Create adaptation strategy
    let strategy = BandwidthAdaptationStrategy {
        chunk_size_adjustment: streaming_params.chunk_size_multiplier,
        compression_level: streaming_params.compression_level,
        parallel_streams: streaming_params.parallel_streams,
        buffer_size: streaming_params.buffer_size,
        timeout_adjustment: streaming_params.timeout_multiplier,
        retry_strategy: streaming_params.retry_strategy,
    };

    Ok(strategy)
}

// Supporting types and structures

#[derive(Debug, Clone)]
pub struct ChunkSpecification {
    pub start_position: Vec<usize>,
    pub size: Vec<usize>,
    pub overlap: Vec<usize>,
    pub priority: f64,
    pub estimated_memory: usize,
    pub estimated_processing_time: Duration,
}

#[derive(Debug, Clone)]
pub struct ContentAwareChunk<T> {
    pub data: Array2<T>,
    pub boundary: ChunkBoundary,
    pub metadata: ChunkMetadata,
    pub processing_priority: f64,
    pub overlap_strategy: OverlapStrategy,
}

#[derive(Debug, Clone)]
pub struct ChunkBoundary {
    pub top_left: (usize, usize),
    pub bottom_right: (usize, usize),
    pub overlap: (usize, usize),
}

#[derive(Debug, Clone)]
pub struct ChunkMetadata {
    pub content_complexity: f64,
    pub edge_density: f64,
    pub variance: f64,
    pub estimated_compression_ratio: f64,
}

#[derive(Debug, Clone)]
pub enum OverlapStrategy {
    Fixed(usize),
    Adaptive(f64),
    ContentBased,
    None,
}

#[derive(Debug, Clone)]
pub enum MemoryManagementStrategy {
    Aggressive {
        chunk_size_reduction: f64,
        cache_size_reduction: f64,
        garbage_collection_frequency: f64,
        swap_strategy: SwapStrategy,
    },
    Moderate {
        chunk_size_reduction: f64,
        cache_optimization: bool,
        prefetch_reduction: f64,
    },
    Optimistic {
        chunk_size_increase: f64,
        cache_expansion: bool,
        prefetch_increase: f64,
    },
}

#[derive(Debug, Clone)]
pub enum SwapStrategy {
    Predictive,
    Conservative,
    Aggressive,
}

#[derive(Debug, Clone)]
pub struct ProcessingTask<T> {
    pub data: Array2<T>,
    pub operation_type: OperationType,
    pub estimated_complexity: f64,
    pub memory_requirement: usize,
    pub dependencies: Vec<usize>,
}

#[derive(Debug, Clone)]
pub enum OperationType {
    Filter,
    Morphology,
    Transform,
    Analysis,
    IO,
}

#[derive(Debug, Clone)]
pub struct CacheManagementDecision {
    pub prefetch_items: Vec<String>,
    pub evict_items: Vec<String>,
    pub cache_size_adjustment: f64,
    pub priority_adjustments: HashMap<String, f64>,
}

#[derive(Debug, Clone)]
pub struct NetworkConditions {
    pub latency: Duration,
    pub jitter: Duration,
    pub packet_loss: f64,
    pub connection_stability: f64,
}

#[derive(Debug, Clone)]
pub struct BandwidthMeasurement {
    pub timestamp: Instant,
    pub bandwidth: f64,
    pub quality: f64,
}

#[derive(Debug, Clone)]
pub struct BandwidthAdaptationStrategy {
    pub chunk_size_adjustment: f64,
    pub compression_level: f64,
    pub parallel_streams: usize,
    pub buffer_size: usize,
    pub timeout_adjustment: f64,
    pub retry_strategy: RetryStrategy,
}

#[derive(Debug, Clone)]
pub enum RetryStrategy {
    ExponentialBackoff,
    LinearBackoff,
    AdaptiveBackoff,
    NoRetry,
}

// Implementation of helper functions (simplified for brevity)

#[allow(dead_code)]
fn analyze_system_conditions() -> NdimageResult<SystemConditions> {
    // Implementation would analyze current system state
    Ok(SystemConditions {
        cpu_usage: 0.5,
        memory_usage: 0.6,
        io_pressure: 0.3,
        network_bandwidth: 1000.0,
    })
}

#[derive(Debug, Clone)]
pub struct SystemConditions {
    pub cpu_usage: f64,
    pub memory_usage: f64,
    pub io_pressure: f64,
    pub network_bandwidth: f64,
}

#[allow(dead_code)]
fn extract_contentfeatures(
    _analysis: &ContentAnalysis,
    config: &AIStreamConfig,
) -> NdimageResult<Array1<f64>> {
    // Implementation would extract ML features from content _analysis
    Ok(Array1::zeros(10))
}

#[allow(dead_code)]
fn prepare_model_input(
    _contentfeatures: &Array1<f64>,
    _system_conditions: &SystemConditions,
    _config: &AIStreamConfig,
) -> NdimageResult<Array1<f64>> {
    // Implementation would prepare input for AI model
    Ok(Array1::zeros(20))
}

#[allow(dead_code)]
fn predict_chunk_configuration(
    _input: &Array1<f64>,
    _ai_model: &PredictiveChunkingAI,
    _config: &AIStreamConfig,
) -> NdimageResult<Array1<f64>> {
    // Implementation would run AI prediction
    Ok(Array1::zeros(5))
}

#[allow(dead_code)]
fn validate_chunk_predictions(
    _predictions: &Array1<f64>,
    _datashape: &[usize],
    _config: &AIStreamConfig,
) -> NdimageResult<Array1<f64>> {
    // Implementation would validate and adjust _predictions
    Ok(Array1::zeros(5))
}

#[allow(dead_code)]
fn generate_chunk_specifications(
    _validated_chunks: &Array1<f64>,
    _datashape: &[usize],
    _config: &AIStreamConfig,
) -> NdimageResult<Vec<ChunkSpecification>> {
    // Implementation would generate chunk specifications
    Ok(vec![ChunkSpecification {
        start_position: vec![0, 0],
        size: vec![100, 100],
        overlap: vec![10, 10],
        priority: 1.0,
        estimated_memory: 1024,
        estimated_processing_time: Duration::from_millis(100),
    }])
}

#[allow(dead_code)]
fn update_ai_model_with_feedback(
    _ai_model: &mut PredictiveChunkingAI,
    _specs: &[ChunkSpecification],
    _config: &AIStreamConfig,
) -> NdimageResult<()> {
    // Implementation would update AI _model with performance feedback
    Ok(())
}

#[allow(dead_code)]
fn analyzeimage_content<T>(
    image: &ArrayView2<T>,
    _config: &AIStreamConfig,
) -> NdimageResult<ContentAnalysis>
where
    T: Float + FromPrimitive + Copy,
{
    // Implementation would analyze image content
    Ok(ContentAnalysis {
        variance_map: Array2::zeros((10, 10)),
        edge_density: Array2::zeros((10, 10)),
        frequency_content: Array2::zeros((10, 10)),
        compression_ratio: 0.5,
        complexity_estimate: 0.7,
    })
}

#[allow(dead_code)]
fn detect_optimal_chunk_boundaries(
    _analysis: &ContentAnalysis,
    size: usize,
    _config: &AIStreamConfig,
) -> NdimageResult<Vec<ChunkBoundary>> {
    // Implementation would detect optimal boundaries
    Ok(vec![ChunkBoundary {
        top_left: (0, 0),
        bottom_right: (100, 100),
        overlap: (10, 10),
    }])
}

#[allow(dead_code)]
fn extract_chunk_with_overlap<T>(
    image: &ArrayView2<T>,
    _boundary: &ChunkBoundary,
    config: &AIStreamConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Zero,
{
    // Implementation would extract chunk with overlap
    Ok(Array2::zeros((100, 100)))
}

#[allow(dead_code)]
fn compute_chunk_metadata<T>(
    _chunk_data: &Array2<T>,
    _boundary: &ChunkBoundary,
    analysis: &ContentAnalysis,
) -> NdimageResult<ChunkMetadata>
where
    T: Float + FromPrimitive + Copy,
{
    // Implementation would compute chunk metadata
    Ok(ChunkMetadata {
        content_complexity: 0.5,
        edge_density: 0.3,
        variance: 0.7,
        estimated_compression_ratio: 0.6,
    })
}

#[allow(dead_code)]
fn compute_processing_priority(metadata: &ChunkMetadata) -> NdimageResult<f64> {
    // Implementation would compute processing priority
    Ok(0.8)
}

#[allow(dead_code)]
fn determine_overlap_strategy(
    _metadata: &ChunkMetadata,
    config: &AIStreamConfig,
) -> NdimageResult<OverlapStrategy> {
    // Implementation would determine overlap strategy
    Ok(OverlapStrategy::Adaptive(0.1))
}

// Additional helper function implementations would follow similar patterns...
// (Simplified for brevity - in a real implementation, these would be fully developed)

#[allow(dead_code)]
fn predict_memory_usage(
    _current: &MemoryMetrics,
    model: &Array2<f64>,
    _config: &AIStreamConfig,
) -> NdimageResult<MemoryMetrics> {
    Ok(MemoryMetrics {
        peak_usage: 1024,
        average_usage: 512,
        fragmentation: 0.1,
        gc_frequency: 0.05,
        pressure_level: 0.3,
        timestamp: Instant::now(),
    })
}

#[allow(dead_code)]
fn assess_memory_pressure_risk(
    _forecast: &MemoryMetrics,
    config: &AIStreamConfig,
) -> NdimageResult<f64> {
    Ok(0.4)
}

#[allow(dead_code)]
fn update_memory_prediction_model(
    _model: &mut Array2<f64>,
    _current: &MemoryMetrics,
    forecast: &MemoryMetrics,
    _config: &AIStreamConfig,
) -> NdimageResult<()> {
    Ok(())
}

#[allow(dead_code)]
fn analyze_task_characteristics<T>(
    _tasks: &[ProcessingTask<T>],
    _config: &AIStreamConfig,
) -> NdimageResult<TaskAnalysis>
where
    T: Float + FromPrimitive + Copy,
{
    Ok(TaskAnalysis {
        complexity_distribution: Array1::zeros(5),
        memory_requirements: Array1::zeros(5),
        operation_types: HashMap::new(),
    })
}

#[derive(Debug, Clone)]
pub struct TaskAnalysis {
    pub complexity_distribution: Array1<f64>,
    pub memory_requirements: Array1<f64>,
    pub operation_types: HashMap<OperationType, usize>,
}

#[allow(dead_code)]
fn predict_worker_performance(
    _analysis: &TaskAnalysis,
    history: &HashMap<usize, VecDeque<f64>>,
    _model: &Array2<f64>,
    _config: &AIStreamConfig,
) -> NdimageResult<HashMap<usize, f64>> {
    Ok(HashMap::new())
}

#[allow(dead_code)]
fn assign_tasks_gradient_based<T>(
    _tasks: &[ProcessingTask<T>],
    _predictions: &HashMap<usize, f64>,
    _assignments: &mut HashMap<usize, Vec<ProcessingTask<T>>>,
    _config: &AIStreamConfig,
) -> NdimageResult<()>
where
    T: Float + FromPrimitive + Copy + Clone,
{
    Ok(())
}

#[allow(dead_code)]
fn assign_tasks_reinforcement_learning<T>(
    _tasks: &[ProcessingTask<T>],
    _predictions: &HashMap<usize, f64>,
    _assignments: &mut HashMap<usize, Vec<ProcessingTask<T>>>,
    _balancer: &AILoadBalancer,
    config: &AIStreamConfig,
) -> NdimageResult<()>
where
    T: Float + FromPrimitive + Copy + Clone,
{
    Ok(())
}

#[allow(dead_code)]
fn assign_tasks_genetic_algorithm<T>(
    _tasks: &[ProcessingTask<T>],
    _predictions: &HashMap<usize, f64>,
    _assignments: &mut HashMap<usize, Vec<ProcessingTask<T>>>,
    _config: &AIStreamConfig,
) -> NdimageResult<()>
where
    T: Float + FromPrimitive + Copy + Clone,
{
    Ok(())
}

#[allow(dead_code)]
fn assign_tasks_hybrid_approach<T>(
    _tasks: &[ProcessingTask<T>],
    _predictions: &HashMap<usize, f64>,
    _assignments: &mut HashMap<usize, Vec<ProcessingTask<T>>>,
    _balancer: &AILoadBalancer,
    config: &AIStreamConfig,
) -> NdimageResult<()>
where
    T: Float + FromPrimitive + Copy + Clone,
{
    Ok(())
}

#[allow(dead_code)]
fn update_load_balancerstate<T>(
    _balancer: &mut AILoadBalancer,
    assignments: &HashMap<usize, Vec<ProcessingTask<T>>>,
    _config: &AIStreamConfig,
) -> NdimageResult<()> {
    Ok(())
}

#[allow(dead_code)]
fn update_access_patterns(
    _manager: &mut IntelligentCacheManager,
    pattern: &CacheAccessPattern,
) -> NdimageResult<()> {
    Ok(())
}

#[allow(dead_code)]
fn predict_future_accesses(
    _patterns: &HashMap<String, VecDeque<CacheAccessPattern>>,
    _model: &Array2<f64>,
    _config: &AIStreamConfig,
) -> NdimageResult<Vec<String>> {
    Ok(Vec::new())
}

#[allow(dead_code)]
fn determine_prefetch_candidates(
    _predictions: &[String],
    _config: &AIStreamConfig,
) -> NdimageResult<Vec<String>> {
    Ok(Vec::new())
}

#[allow(dead_code)]
fn evaluate_cache_replacement(
    _cachestate: &Arc<RwLock<HashMap<String, CachedData>>>,
    _strategy: &CacheReplacementStrategy,
    predictions: &[String],
    _config: &AIStreamConfig,
) -> NdimageResult<ReplacementDecision> {
    Ok(ReplacementDecision {
        evict_items: Vec::new(),
        size_adjustment: 0.0,
        priority_adjustments: HashMap::new(),
    })
}

#[derive(Debug, Clone)]
pub struct ReplacementDecision {
    pub evict_items: Vec<String>,
    pub size_adjustment: f64,
    pub priority_adjustments: HashMap<String, f64>,
}

#[allow(dead_code)]
fn update_cache_prediction_model(
    _manager: &mut IntelligentCacheManager,
    decision: &CacheManagementDecision,
    _config: &AIStreamConfig,
) -> NdimageResult<()> {
    Ok(())
}

#[allow(dead_code)]
fn predict_bandwidth_availability(
    _current: f64,
    conditions: &NetworkConditions,
    history: &VecDeque<BandwidthMeasurement>,
    _config: &AIStreamConfig,
) -> NdimageResult<f64> {
    Ok(1000.0)
}

#[allow(dead_code)]
fn analyze_network_stability(
    history: &VecDeque<BandwidthMeasurement>,
    _config: &AIStreamConfig,
) -> NdimageResult<NetworkStabilityAnalysis> {
    Ok(NetworkStabilityAnalysis {
        stability_score: 0.8,
        variance: 0.1,
        trend: NetworkTrend::Stable,
    })
}

#[derive(Debug, Clone)]
pub struct NetworkStabilityAnalysis {
    pub stability_score: f64,
    pub variance: f64,
    pub trend: NetworkTrend,
}

#[derive(Debug, Clone)]
pub enum NetworkTrend {
    Improving,
    Degrading,
    Stable,
    Unstable,
}

#[allow(dead_code)]
fn optimize_streaming_parameters(
    _bandwidth_forecast: &f64,
    _stability: &NetworkStabilityAnalysis,
    config: &AIStreamConfig,
) -> NdimageResult<StreamingParameters> {
    Ok(StreamingParameters {
        chunk_size_multiplier: 1.0,
        compression_level: 0.5,
        parallel_streams: 4,
        buffer_size: 1024,
        timeout_multiplier: 1.0,
        retry_strategy: RetryStrategy::AdaptiveBackoff,
    })
}

#[derive(Debug, Clone)]
pub struct StreamingParameters {
    pub chunk_size_multiplier: f64,
    pub compression_level: f64,
    pub parallel_streams: usize,
    pub buffer_size: usize,
    pub timeout_multiplier: f64,
    pub retry_strategy: RetryStrategy,
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::Array2;

    #[test]
    fn test_ai_stream_config_default() {
        let config = AIStreamConfig::default();

        assert_eq!(config.ai_model_complexity, 64);
        assert_eq!(config.prediction_window, 10);
        assert_eq!(config.learning_rate, 0.01);
        assert_eq!(config.memory_pressure_threshold, 0.8);
    }

    #[test]
    fn test_predictive_chunking_ai_creation() {
        let ai = PredictiveChunkingAI {
            chunk_size_weights: Array2::zeros((10, 10)),
            contentfeatures: Array1::zeros(20),
            performancehistory: VecDeque::new(),
            memory_patterns: VecDeque::new(),
            prediction_accuracy: 0.0,
            adaptation_rate: 0.01,
        };

        assert_eq!(ai.chunk_size_weights.dim(), (10, 10));
        assert_eq!(ai.contentfeatures.len(), 20);
        assert_eq!(ai.prediction_accuracy, 0.0);
    }

    #[test]
    fn test_content_analysis_creation() {
        let analysis = ContentAnalysis {
            variance_map: Array2::zeros((5, 5)),
            edge_density: Array2::zeros((5, 5)),
            frequency_content: Array2::zeros((5, 5)),
            compression_ratio: 0.5,
            complexity_estimate: 0.7,
        };

        assert_eq!(analysis.variance_map.dim(), (5, 5));
        assert_eq!(analysis.compression_ratio, 0.5);
        assert_eq!(analysis.complexity_estimate, 0.7);
    }

    #[test]
    fn test_ai_predictive_chunking() {
        let datashape = vec![1000, 1000];
        let content_analysis = ContentAnalysis {
            variance_map: Array2::zeros((100, 100)),
            edge_density: Array2::zeros((100, 100)),
            frequency_content: Array2::zeros((100, 100)),
            compression_ratio: 0.6,
            complexity_estimate: 0.8,
        };

        let mut ai_model = PredictiveChunkingAI {
            chunk_size_weights: Array2::zeros((10, 10)),
            contentfeatures: Array1::zeros(20),
            performancehistory: VecDeque::new(),
            memory_patterns: VecDeque::new(),
            prediction_accuracy: 0.0,
            adaptation_rate: 0.01,
        };

        let config = AIStreamConfig::default();

        let result =
            ai_predictive_chunking::<f64>(&datashape, &content_analysis, &mut ai_model, &config)
                .expect("Operation failed");

        assert!(!result.is_empty());
        assert!(!result[0].size.is_empty());
    }

    #[test]
    fn test_content_aware_adaptive_chunking() {
        let image = Array2::from_shape_vec((10, 10), (0..100).map(|x| x as f64 / 100.0).collect())
            .expect("Operation failed");
        let target_chunk_size = 25;
        let config = AIStreamConfig::default();

        let result = content_aware_adaptive_chunking(image.view(), target_chunk_size, &config)
            .expect("Operation failed");

        assert!(!result.is_empty());
        assert!(result[0].processing_priority >= 0.0);
    }

    #[test]
    fn test_memory_management_strategy() {
        let current_usage = MemoryMetrics {
            peak_usage: 1024,
            average_usage: 512,
            fragmentation: 0.1,
            gc_frequency: 0.05,
            pressure_level: 0.3,
            timestamp: Instant::now(),
        };

        let mut prediction_model = Array2::zeros((5, 5));
        let config = AIStreamConfig::default();

        let result = intelligent_memory_management(&current_usage, &mut prediction_model, &config)
            .expect("Operation failed");

        // Should return some valid strategy
        match result {
            MemoryManagementStrategy::Optimistic { .. } => {
                // Expected for low pressure
            }
            _ => {
                panic!(
                    "Expected Optimistic strategy for low pressure, got: {:?}",
                    result
                );
            }
        }
    }
}