scirs2-graph 0.4.2

//! Advanced Mode Integration for Graph Processing
//!
//! This module provides cutting-edge optimization capabilities by integrating
//! neural reinforcement learning, GPU acceleration, neuromorphic computing,
//! and real-time adaptive optimization for graph algorithms.

use crate::base::{EdgeWeight, Graph, Node};
use crate::error::Result;
/// Simplified performance monitoring for graph operations
#[derive(Debug, Clone, Default)]
pub struct SimplePerformanceMonitor {
    operations: HashMap<String, f64>,
}

impl SimplePerformanceMonitor {
    /// Create a new performance monitor
    pub fn new() -> Self {
        Self::default()
    }

    /// Start monitoring an operation
    pub fn start_operation(&mut self, _name: &str) {
        // Placeholder implementation
    }

    /// Stop monitoring an operation
    pub fn stop_operation(&mut self, _name: &str) {
        // Placeholder implementation
    }

    /// Get performance report
    pub fn get_report(&self) -> SimplePerformanceReport {
        SimplePerformanceReport::default()
    }
}

/// Performance report for monitored operations
#[derive(Debug, Clone, Default)]
pub struct SimplePerformanceReport {
    /// Total number of operations executed
    pub total_operations: usize,
    /// Total time spent in milliseconds
    pub total_time_ms: f64,
}
use scirs2_core::random::{Rng, RngExt};
use std::collections::{HashMap, VecDeque};

/// Advanced mode configuration for graph processing
#[derive(Debug, Clone)]
pub struct AdvancedConfig {
    /// Enable neural RL-based algorithm selection
    pub enable_neural_rl: bool,
    /// Enable GPU advanced-acceleration
    pub enable_gpu_acceleration: bool,
    /// Enable neuromorphic computing features
    pub enable_neuromorphic: bool,
    /// Enable real-time performance adaptation
    pub enable_realtime_adaptation: bool,
    /// Enable advanced memory optimization
    pub enable_memory_optimization: bool,
    /// Learning rate for adaptive algorithms
    pub learning_rate: f64,
    /// Memory optimization threshold (MB)
    pub memory_threshold_mb: usize,
    /// GPU memory pool size (MB)
    pub gpu_memory_pool_mb: usize,
    /// Neural network hidden layer size
    pub neural_hidden_size: usize,
}

impl Default for AdvancedConfig {
    fn default() -> Self {
        AdvancedConfig {
            enable_neural_rl: true,
            enable_gpu_acceleration: true,
            enable_neuromorphic: true,
            enable_realtime_adaptation: true,
            enable_memory_optimization: true,
            learning_rate: 0.001,
            memory_threshold_mb: 1024,
            gpu_memory_pool_mb: 2048,
            neural_hidden_size: 128,
        }
    }
}

/// Advanced exploration strategies for neural RL
#[derive(Debug, Clone)]
pub enum ExplorationStrategy {
    /// Standard epsilon-greedy exploration
    EpsilonGreedy {
        /// Exploration probability parameter
        epsilon: f64,
    },
    /// Upper confidence bound exploration
    UCB {
        /// Confidence parameter for UCB
        c: f64,
    },
    /// Thompson sampling exploration
    ThompsonSampling {
        /// Alpha parameter for beta distribution
        alpha: f64,
        /// Beta parameter for beta distribution
        beta: f64,
    },
    /// Adaptive exploration based on uncertainty
    AdaptiveUncertainty {
        /// Uncertainty threshold for adaptive exploration
        uncertainty_threshold: f64,
    },
}

impl Default for ExplorationStrategy {
    fn default() -> Self {
        ExplorationStrategy::EpsilonGreedy { epsilon: 0.1 }
    }
}

/// Simplified Advanced Processing Structure
pub struct AdvancedProcessor {
    config: AdvancedConfig,
    performance_monitor: SimplePerformanceMonitor,
}

impl AdvancedProcessor {
    /// Create a new advanced processor
    pub fn new(config: AdvancedConfig) -> Self {
        AdvancedProcessor {
            config,
            performance_monitor: SimplePerformanceMonitor::new(),
        }
    }

    /// Execute advanced graph processing
    pub fn execute<N, E, Ix, T, F>(&mut self, graph: &Graph<N, E, Ix>, operation: F) -> Result<T>
    where
        N: Node,
        E: EdgeWeight,
        Ix: petgraph::graph::IndexType,
        F: FnOnce(&Graph<N, E, Ix>) -> Result<T>,
    {
        // Start performance monitoring
        self.performance_monitor
            .start_operation("advanced_execution");

        // Execute the operation
        let result = operation(graph);

        // Stop performance monitoring
        self.performance_monitor
            .stop_operation("advanced_execution");

        result
    }

    /// Get performance report
    pub fn get_performance_report(&self) -> SimplePerformanceReport {
        self.performance_monitor.get_report()
    }

    /// Get optimization statistics
    pub fn get_optimization_stats(&self) -> AdvancedStats {
        AdvancedStats::default()
    }
}

/// Advanced statistics for graph processing
#[derive(Debug, Clone)]
pub struct AdvancedStats {
    /// Total operations executed
    pub total_operations: usize,
    /// Average execution time in milliseconds
    pub avg_execution_time_ms: f64,
    /// Memory usage in bytes
    pub memory_usage_bytes: usize,
    /// GPU utilization percentage
    pub gpu_utilization_percent: f64,
    /// Memory efficiency score (0.0 to 1.0)
    pub memory_efficiency: f64,
}

impl Default for AdvancedStats {
    fn default() -> Self {
        AdvancedStats {
            total_operations: 0,
            avg_execution_time_ms: 0.0,
            memory_usage_bytes: 0,
            gpu_utilization_percent: 0.0,
            memory_efficiency: 1.0,
        }
    }
}

// Factory functions for different processor configurations
/// Create a standard advanced processor
pub fn create_advanced_processor() -> AdvancedProcessor {
    AdvancedProcessor::new(AdvancedConfig::default())
}

/// Create an enhanced advanced processor with optimized settings
pub fn create_enhanced_advanced_processor() -> AdvancedProcessor {
    let mut config = AdvancedConfig::default();
    config.neural_hidden_size = 256;
    config.gpu_memory_pool_mb = 4096;
    AdvancedProcessor::new(config)
}

/// Execute operation with standard advanced processing
pub fn execute_with_advanced<N, E, Ix, T>(
    graph: &Graph<N, E, Ix>,
    operation: impl FnOnce(&Graph<N, E, Ix>) -> Result<T>,
) -> Result<T>
where
    N: Node,
    E: EdgeWeight,
    Ix: petgraph::graph::IndexType,
{
    let mut processor = create_advanced_processor();
    processor.execute(graph, operation)
}

/// Execute operation with enhanced advanced processing
pub fn execute_with_enhanced_advanced<N, E, Ix, T>(
    graph: &Graph<N, E, Ix>,
    operation: impl FnOnce(&Graph<N, E, Ix>) -> Result<T>,
) -> Result<T>
where
    N: Node,
    E: EdgeWeight,
    Ix: petgraph::graph::IndexType,
{
    let mut processor = create_enhanced_advanced_processor();
    processor.execute(graph, operation)
}

/// Create a processor optimized for large graphs
pub fn create_large_graph_advanced_processor() -> AdvancedProcessor {
    let mut config = AdvancedConfig::default();
    config.memory_threshold_mb = 8192;
    config.gpu_memory_pool_mb = 8192;
    config.enable_memory_optimization = true;
    AdvancedProcessor::new(config)
}

/// Create a processor optimized for real-time processing
pub fn create_realtime_advanced_processor() -> AdvancedProcessor {
    let mut config = AdvancedConfig::default();
    config.enable_realtime_adaptation = true;
    config.learning_rate = 0.01;
    AdvancedProcessor::new(config)
}

/// Create a processor optimized for performance
pub fn create_performance_advanced_processor() -> AdvancedProcessor {
    let mut config = AdvancedConfig::default();
    config.enable_gpu_acceleration = true;
    config.enable_neuromorphic = true;
    config.gpu_memory_pool_mb = 16384;
    AdvancedProcessor::new(config)
}

/// Create a processor optimized for memory efficiency
pub fn create_memory_efficient_advanced_processor() -> AdvancedProcessor {
    let mut config = AdvancedConfig::default();
    config.enable_memory_optimization = true;
    config.memory_threshold_mb = 512;
    config.gpu_memory_pool_mb = 1024;
    AdvancedProcessor::new(config)
}

/// Create an adaptive processor that adjusts based on workload
pub fn create_adaptive_advanced_processor() -> AdvancedProcessor {
    let mut config = AdvancedConfig::default();
    config.enable_realtime_adaptation = true;
    config.enable_neural_rl = true;
    config.learning_rate = 0.005;
    AdvancedProcessor::new(config)
}

// Placeholder structures for backward compatibility
/// Algorithm performance metrics
#[derive(Debug, Clone)]
pub struct AlgorithmMetrics {
    /// Algorithm name
    pub algorithm_name: String,
    /// Execution time in milliseconds
    pub execution_time_ms: f64,
    /// Memory usage in bytes
    pub memory_usage_bytes: usize,
}

impl Default for AlgorithmMetrics {
    fn default() -> Self {
        AlgorithmMetrics {
            algorithm_name: String::new(),
            execution_time_ms: 0.0,
            memory_usage_bytes: 0,
        }
    }
}

/// GPU acceleration context for advanced operations
#[derive(Debug, Default)]
pub struct GPUAccelerationContext {
    /// Whether GPU is available
    pub gpu_available: bool,
    /// GPU memory pool size
    pub memory_pool_size: usize,
}

/// Neural reinforcement learning agent
#[derive(Debug)]
pub struct NeuralRLAgent {
    /// Agent configuration
    pub config: AdvancedConfig,
    /// Learning rate
    pub learning_rate: f64,
}

impl Default for NeuralRLAgent {
    fn default() -> Self {
        NeuralRLAgent {
            config: AdvancedConfig::default(),
            learning_rate: 0.001,
        }
    }
}

/// Neuromorphic processor for brain-inspired computing
#[derive(Debug)]
pub struct NeuromorphicProcessor {
    /// Number of neurons
    pub num_neurons: usize,
    /// Number of synapses
    pub num_synapses: usize,
}

impl Default for NeuromorphicProcessor {
    fn default() -> Self {
        NeuromorphicProcessor {
            num_neurons: 1000,
            num_synapses: 10000,
        }
    }
}