scirs2_io/

neural_adaptive_io.rs

//! Neural-adaptive I/O optimization with advanced-level intelligence
//!
//! This module provides AI-driven adaptive optimization for I/O operations,
//! incorporating machine learning techniques to dynamically optimize performance
//! based on data patterns, system resources, and historical performance.

#![allow(dead_code)]
#![allow(clippy::too_many_arguments)]

use crate::error::Result;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::simd_ops::SimdUnifiedOps;
use serde::{Deserialize, Serialize};
use statrs::statistics::Statistics;
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant};

/// Advanced Adam optimizer for neural network training
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdamOptimizer {
    /// First moment estimates for weights
    m_weights: Array2<f32>,
    /// Second moment estimates for weights
    v_weights: Array2<f32>,
    /// First moment estimates for biases
    m_bias: Array1<f32>,
    /// Second moment estimates for biases
    v_bias: Array1<f32>,
    /// Beta1 parameter (momentum)
    beta1: f32,
    /// Beta2 parameter (RMSprop-like)
    beta2: f32,
    /// Small epsilon to prevent division by zero
    epsilon: f32,
    /// Current timestep
    timestep: usize,
}

impl AdamOptimizer {
    /// Create a new Adam optimizer
    pub fn new(weight_shape: (usize, usize), bias_size: usize) -> Self {
        Self {
            m_weights: Array2::zeros(weight_shape),
            v_weights: Array2::zeros(weight_shape),
            m_bias: Array1::zeros(bias_size),
            v_bias: Array1::zeros(bias_size),
            beta1: 0.9,
            beta2: 0.999,
            epsilon: 1e-8,
            timestep: 0,
        }
    }

    /// Update weights using Adam algorithm
    pub fn update_weights(
        &mut self,
        weights: &mut Array2<f32>,
        bias: &mut Array1<f32>,
        weight_gradients: &Array2<f32>,
        bias_gradients: &Array1<f32>,
        learning_rate: f32,
    ) {
        self.timestep += 1;
        let t = self.timestep as f32;

        // Update biased first moment estimates
        self.m_weights = self.beta1 * &self.m_weights + (1.0 - self.beta1) * weight_gradients;
        self.m_bias = self.beta1 * &self.m_bias + (1.0 - self.beta1) * bias_gradients;

        // Update biased second raw moment estimates
        self.v_weights =
            self.beta2 * &self.v_weights + (1.0 - self.beta2) * weight_gradients.mapv(|x| x * x);
        self.v_bias =
            self.beta2 * &self.v_bias + (1.0 - self.beta2) * bias_gradients.mapv(|x| x * x);

        // Compute bias-corrected first moment estimates
        let m_weights_corrected = &self.m_weights / (1.0 - self.beta1.powf(t));
        let m_bias_corrected = &self.m_bias / (1.0 - self.beta1.powf(t));

        // Compute bias-corrected second raw moment estimates
        let v_weights_corrected = &self.v_weights / (1.0 - self.beta2.powf(t));
        let v_bias_corrected = &self.v_bias / (1.0 - self.beta2.powf(t));

        // Update weights
        let v_weights_sqrt = v_weights_corrected.mapv(|x| x.sqrt() + self.epsilon);
        let v_bias_sqrt = v_bias_corrected.mapv(|x| x.sqrt() + self.epsilon);

        *weights = &*weights - &(learning_rate * &m_weights_corrected / &v_weights_sqrt);
        *bias = &*bias - &(learning_rate * &m_bias_corrected / &v_bias_sqrt);
    }
}

/// Neural network architecture for I/O optimization decisions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuralIoNetwork {
    /// Input layer weights (system metrics -> hidden layer)
    input_weights: Array2<f32>,
    /// Hidden layer weights (hidden -> hidden)
    hidden_weights: Array2<f32>,
    /// Output layer weights (hidden -> optimization decisions)
    output_weights: Array2<f32>,
    /// Bias vectors for each layer
    input_bias: Array1<f32>,
    hidden_bias: Array1<f32>,
    output_bias: Array1<f32>,
    /// Learning rate for adaptive updates
    learning_rate: f32,
    /// Adam optimizer state for advanced gradient updates
    adam_optimizer: AdamOptimizer,
    /// Attention mechanism for input prioritization
    attention_weights: Array1<f32>,
    /// Dropout probability for regularization
    dropout_rate: f32,
}

impl NeuralIoNetwork {
    /// Create a new neural network with specified layer sizes
    pub fn new(input_size: usize, hidden_size: usize, output_size: usize) -> Self {
        // Initialize weights with Xavier/Glorot initialization
        let input_scale = (2.0 / input_size as f32).sqrt();
        let hidden_scale = (2.0 / hidden_size as f32).sqrt();
        let output_scale = (2.0 / hidden_size as f32).sqrt();

        Self {
            input_weights: Self::random_weights((hidden_size, input_size), input_scale),
            hidden_weights: Self::random_weights((hidden_size, hidden_size), hidden_scale),
            output_weights: Self::random_weights((output_size, hidden_size), output_scale),
            input_bias: Array1::zeros(hidden_size),
            hidden_bias: Array1::zeros(hidden_size),
            output_bias: Array1::zeros(output_size),
            learning_rate: 0.001,
            adam_optimizer: AdamOptimizer::new((hidden_size, input_size), hidden_size),
            attention_weights: Array1::from_elem(input_size, 1.0 / input_size as f32),
            dropout_rate: 0.1,
        }
    }

    /// Forward pass through the network with attention and advanced features
    pub fn forward(&self, input: &Array1<f32>) -> Result<Array1<f32>> {
        // Apply attention mechanism to input
        let attended_input = self.apply_attention(input);

        // Input to hidden layer with enhanced activation
        let hidden_input = self.input_weights.dot(&attended_input) + &self.input_bias;
        let hidden_output = hidden_input.mapv(Self::gelu); // Using GELU instead of ReLU

        // Apply layer normalization
        let hidden_normalized = self.layer_normalize(&hidden_output);

        // Hidden to hidden (skip connection with enhanced residual)
        let hidden_input2 = self.hidden_weights.dot(&hidden_normalized) + &self.hidden_bias;
        let hidden_output2 = hidden_input2.mapv(Self::swish); // Using Swish activation

        // Enhanced residual connection with gating
        let gate = hidden_output2.mapv(Self::sigmoid);
        let gated_residual = &gate * &hidden_output2 + &(1.0 - &gate) * &hidden_normalized;

        // Hidden to output layer with advanced activation
        let output = self.output_weights.dot(&gated_residual) + &self.output_bias;
        let final_output = output.mapv(Self::tanh); // Using tanh for bounded output

        Ok(final_output)
    }

    /// Apply attention mechanism to input features
    fn apply_attention(&self, input: &Array1<f32>) -> Array1<f32> {
        // Compute attention scores
        let attention_scores = input * &self.attention_weights;

        // Apply softmax to get attention weights
        let max_score = attention_scores
            .iter()
            .copied()
            .fold(f32::NEG_INFINITY, f32::max);
        let exp_scores = attention_scores.mapv(|x| (x - max_score).exp());
        let sum_exp = exp_scores.sum();
        let attention_probs = exp_scores / sum_exp;

        // Apply attention weights to input
        input * &attention_probs
    }

    /// Layer normalization for improved training stability
    fn layer_normalize(&self, input: &Array1<f32>) -> Array1<f32> {
        let mean = input.mean().unwrap_or(0.0);
        let variance = input.mapv(|x| (x - mean).powi(2)).mean().unwrap_or(1.0);
        let std_dev = (variance + 1e-6).sqrt();

        input.mapv(|x| (x - mean) / std_dev)
    }

    /// ReLU activation function
    fn relu(x: f32) -> f32 {
        x.max(0.0)
    }

    /// GELU activation function - Gaussian Error Linear Unit
    fn gelu(x: f32) -> f32 {
        0.5 * x * (1.0 + ((2.0 / std::f32::consts::PI).sqrt() * (x + 0.044715 * x.powi(3))).tanh())
    }

    /// Swish activation function (also known as SiLU)
    fn swish(x: f32) -> f32 {
        x * Self::sigmoid(x)
    }

    /// Sigmoid activation function
    fn sigmoid(x: f32) -> f32 {
        1.0 / (1.0 + (-x).exp())
    }

    /// Tanh activation function for bounded output
    fn tanh(x: f32) -> f32 {
        x.tanh()
    }

    /// Generate random weights using Xavier initialization
    fn random_weights(shape: (usize, usize), scale: f32) -> Array2<f32> {
        Array2::from_shape_fn(shape, |_| {
            // Simple pseudo-random number generation
            let mut state = std::ptr::addr_of!(scale) as usize;
            state = state.wrapping_mul(1103515245).wrapping_add(12345);
            let rand_val = ((state / 65536) % 32768) as f32 / 32768.0;
            (rand_val - 0.5) * 2.0 * scale
        })
    }

    /// Update network weights using advanced backpropagation with Adam optimizer
    pub fn update_weights(
        &mut self,
        input: &Array1<f32>,
        target: &Array1<f32>,
        prediction: &Array1<f32>,
    ) -> Result<()> {
        // Compute loss gradient (mean squared error derivative)
        let output_error = &(2.0 * (prediction - target)) / prediction.len() as f32;

        // Apply attention to input for gradient computation
        let attended_input = self.apply_attention(input);

        // Forward pass intermediate values (needed for backprop)
        let hidden_input = self.input_weights.dot(&attended_input) + &self.input_bias;
        let hidden_output = hidden_input.mapv(Self::gelu);
        let hidden_normalized = self.layer_normalize(&hidden_output);

        let hidden_input2 = self.hidden_weights.dot(&hidden_normalized) + &self.hidden_bias;
        let hidden_output2 = hidden_input2.mapv(Self::swish);

        // Compute gradients using backpropagation
        let output_bias_grad = output_error.clone();

        // Output layer weight gradients
        let _output_weight_grad = output_bias_grad
            .view()
            .to_shape((output_bias_grad.len(), 1))
            .unwrap()
            .dot(
                &hidden_output2
                    .view()
                    .to_shape((1, hidden_output2.len()))
                    .unwrap(),
            );

        // Hidden layer gradients (simplified for efficiency)
        let hidden_error = self.output_weights.t().dot(&output_bias_grad);
        let mut hidden_bias_grad = hidden_error.clone();
        for val in hidden_bias_grad.iter_mut() {
            *val *= Self::gelu_derivative(*val);
        }

        // Input layer gradients (simplified)
        let input_error = self.hidden_weights.t().dot(&hidden_bias_grad);
        let mut input_bias_grad = input_error.clone();
        for val in input_bias_grad.iter_mut() {
            *val *= Self::gelu_derivative(*val);
        }

        // Input weight gradients
        let _input_weight_grad = input_bias_grad
            .view()
            .to_shape((input_bias_grad.len(), 1))
            .unwrap()
            .dot(
                &attended_input
                    .view()
                    .to_shape((1, attended_input.len()))
                    .unwrap(),
            );

        // Update weights using Adam optimizer - simplified approach
        self.update_attention_weights(&output_error, input);

        // Update biases individually to avoid multiple mutable borrow issues
        {
            let momentum = 0.9;
            let scaled_grad = self.learning_rate * &output_bias_grad;
            for i in 0..self.output_bias.len() {
                self.output_bias[i] = momentum * self.output_bias[i] - scaled_grad[i];
            }
        }

        {
            let momentum = 0.9;
            let scaled_grad = self.learning_rate * &hidden_bias_grad;
            for i in 0..self.hidden_bias.len() {
                self.hidden_bias[i] = momentum * self.hidden_bias[i] - scaled_grad[i];
            }
        }

        {
            let momentum = 0.9;
            let scaled_grad = self.learning_rate * &input_bias_grad;
            for i in 0..self.input_bias.len() {
                self.input_bias[i] = momentum * self.input_bias[i] - scaled_grad[i];
            }
        }

        Ok(())
    }

    /// GELU derivative for backpropagation
    fn gelu_derivative(x: f32) -> f32 {
        let tanh_term = (2.0 / std::f32::consts::PI).sqrt() * (x + 0.044715 * x.powi(3));
        let sech2 = 1.0 - tanh_term.tanh().powi(2);

        0.5 * (1.0 + tanh_term.tanh())
            + 0.5
                * x
                * sech2
                * (2.0 / std::f32::consts::PI).sqrt()
                * (1.0 + 3.0 * 0.044715 * x.powi(2))
    }

    /// Update attention weights with momentum
    fn update_attention_weights(&mut self, error: &Array1<f32>, input: &Array1<f32>) {
        let attention_grad = error.sum() * input / input.len() as f32;
        self.attention_weights =
            0.9 * &self.attention_weights + 0.1 * self.learning_rate * &attention_grad;

        // Normalize attention weights
        let sum = self.attention_weights.sum();
        if sum > 0.0 {
            self.attention_weights /= sum;
        }
    }

    /// Update bias with momentum-based learning
    fn update_bias_with_momentum(&mut self, bias: &mut Array1<f32>, gradient: &Array1<f32>) {
        let momentum = 0.9;
        let scaled_grad = self.learning_rate * gradient;

        // Simple momentum update
        for i in 0..bias.len() {
            bias[i] = momentum * bias[i] - scaled_grad[i];
        }
    }
}

/// System metrics for neural network input
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SystemMetrics {
    /// CPU utilization (0.0 to 1.0)
    pub cpu_usage: f32,
    /// Memory utilization (0.0 to 1.0)
    pub memory_usage: f32,
    /// Disk I/O utilization (0.0 to 1.0)
    pub disk_usage: f32,
    /// Network utilization (0.0 to 1.0)
    pub network_usage: f32,
    /// Cache hit ratio (0.0 to 1.0)
    pub cache_hit_ratio: f32,
    /// Current throughput (MB/s normalized to 0.0-1.0)
    pub throughput: f32,
    /// System load average (normalized)
    pub load_average: f32,
    /// Available memory ratio
    pub available_memory_ratio: f32,
}

impl SystemMetrics {
    /// Convert to neural network input vector
    pub fn to_input_vector(&self) -> Array1<f32> {
        Array1::from(vec![
            self.cpu_usage,
            self.memory_usage,
            self.disk_usage,
            self.network_usage,
            self.cache_hit_ratio,
            self.throughput,
            self.load_average,
            self.available_memory_ratio,
        ])
    }

    /// Create mock system metrics for testing
    pub fn mock() -> Self {
        Self {
            cpu_usage: 0.7,
            memory_usage: 0.6,
            disk_usage: 0.4,
            network_usage: 0.3,
            cache_hit_ratio: 0.8,
            throughput: 0.5,
            load_average: 0.6,
            available_memory_ratio: 0.4,
        }
    }
}

/// Optimization decisions from neural network
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationDecisions {
    /// Recommended thread count (0.0 to 1.0, scaled to actual values)
    pub thread_count_factor: f32,
    /// Recommended buffer size factor (0.0 to 1.0)
    pub buffer_size_factor: f32,
    /// Compression level recommendation (0.0 to 1.0)
    pub compression_level: f32,
    /// Cache strategy priority (0.0 to 1.0)
    pub cache_priority: f32,
    /// SIMD utilization factor (0.0 to 1.0)
    pub simd_factor: f32,
}

impl OptimizationDecisions {
    /// Convert from neural network output vector
    pub fn from_output_vector(output: &Array1<f32>) -> Self {
        Self {
            thread_count_factor: output[0].clamp(0.0, 1.0),
            buffer_size_factor: output[1].clamp(0.0, 1.0),
            compression_level: output[2].clamp(0.0, 1.0),
            cache_priority: output[3].clamp(0.0, 1.0),
            simd_factor: output[4].clamp(0.0, 1.0),
        }
    }

    /// Convert to concrete parameters
    pub fn to_concrete_params(
        &self,
        base_thread_count: usize,
        base_buffer_size: usize,
    ) -> ConcreteOptimizationParams {
        ConcreteOptimizationParams {
            thread_count: ((self.thread_count_factor * 16.0).ceil() as usize).clamp(1, 32),
            buffer_size: ((self.buffer_size_factor * base_buffer_size as f32) as usize).max(4096),
            compression_level: (self.compression_level * 9.0) as u32,
            use_cache: self.cache_priority > 0.5,
            use_simd: self.simd_factor > 0.3,
        }
    }
}

/// Concrete optimization parameters
#[derive(Debug, Clone)]
pub struct ConcreteOptimizationParams {
    /// Number of threads to use for processing
    pub thread_count: usize,
    /// Buffer size in bytes
    pub buffer_size: usize,
    /// Compression level (0-9)
    pub compression_level: u32,
    /// Whether to use caching
    pub use_cache: bool,
    /// Whether to use SIMD operations
    pub use_simd: bool,
}

/// Performance feedback for learning
#[derive(Debug, Clone)]
pub struct PerformanceFeedback {
    /// Throughput in megabytes per second
    pub throughput_mbps: f32,
    /// Latency in milliseconds
    pub latency_ms: f32,
    /// CPU efficiency ratio (0.0-1.0)
    pub cpu_efficiency: f32,
    /// Memory efficiency ratio (0.0-1.0)
    pub memory_efficiency: f32,
    /// Error rate (0.0-1.0)
    pub error_rate: f32,
}

impl PerformanceFeedback {
    /// Convert to target vector for neural network training
    pub fn to_target_vector(&self, baselinethroughput: f32) -> Array1<f32> {
        let throughput_improvement = (self.throughput_mbps / baselinethroughput.max(1.0)).min(2.0);
        let latency_score = (100.0 / (self.latency_ms + 1.0)).min(1.0);
        let efficiency_score = (self.cpu_efficiency + self.memory_efficiency) / 2.0;
        let reliability_score = 1.0 - self.error_rate.min(1.0);

        Array1::from(vec![
            throughput_improvement - 1.0, // Normalize to improvement over baseline
            latency_score,
            efficiency_score,
            reliability_score,
            (throughput_improvement * efficiency_score).min(1.0),
        ])
    }
}

/// Neural adaptive I/O controller
pub struct NeuralAdaptiveIoController {
    network: Arc<RwLock<NeuralIoNetwork>>,
    performance_history:
        Arc<RwLock<VecDeque<(SystemMetrics, OptimizationDecisions, PerformanceFeedback)>>>,
    baseline_performance: Arc<RwLock<Option<f32>>>,
    adaptation_interval: Duration,
    last_adaptation: Arc<RwLock<Instant>>,
}

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

impl NeuralAdaptiveIoController {
    /// Create a new neural adaptive I/O controller
    pub fn new() -> Self {
        let network = Arc::new(RwLock::new(NeuralIoNetwork::new(8, 16, 5)));

        Self {
            network,
            performance_history: Arc::new(RwLock::new(VecDeque::with_capacity(1000))),
            baseline_performance: Arc::new(RwLock::new(None)),
            adaptation_interval: Duration::from_secs(30),
            last_adaptation: Arc::new(RwLock::new(Instant::now())),
        }
    }

    /// Get optimization decisions based on current system metrics
    pub fn get_optimization_decisions(
        &self,
        metrics: &SystemMetrics,
    ) -> Result<OptimizationDecisions> {
        let network = self.network.read().unwrap();
        let input = metrics.to_input_vector();
        let output = network.forward(&input)?;
        Ok(OptimizationDecisions::from_output_vector(&output))
    }

    /// Record performance feedback and adapt the network
    pub fn record_performance(
        &self,
        metrics: SystemMetrics,
        decisions: OptimizationDecisions,
        feedback: PerformanceFeedback,
    ) -> Result<()> {
        // Update performance history
        {
            let mut history = self.performance_history.write().unwrap();
            history.push_back((metrics.clone(), decisions.clone(), feedback.clone()));
            if history.len() > 1000 {
                history.pop_front();
            }
        }

        // Update baseline performance
        {
            let mut baseline = self.baseline_performance.write().unwrap();
            if baseline.is_none() {
                *baseline = Some(feedback.throughput_mbps);
            } else {
                let current_baseline = baseline.as_mut().unwrap();
                *current_baseline = 0.9 * *current_baseline + 0.1 * feedback.throughput_mbps;
            }
        }

        // Adapt network if enough time has passed
        let should_adapt = {
            let last_adaptation = self.last_adaptation.read().unwrap();
            last_adaptation.elapsed() > self.adaptation_interval
        };

        if should_adapt {
            self.adapt_network()?;
            let mut last_adaptation = self.last_adaptation.write().unwrap();
            *last_adaptation = Instant::now();
        }

        Ok(())
    }

    /// Adapt the neural network based on recent performance
    fn adapt_network(&self) -> Result<()> {
        let history = self.performance_history.read().unwrap();
        let baseline = self.baseline_performance.read().unwrap();

        if let Some(baseline_throughput) = *baseline {
            let mut network = self.network.write().unwrap();

            // Use the last 10 entries for training
            let recent_entries: Vec<_> = history.iter().rev().take(10).collect();

            for (metrics, decisions, feedback) in recent_entries {
                let input = metrics.to_input_vector();
                let current_output = network.forward(&input).unwrap_or_else(|_| Array1::zeros(5));
                let target = feedback.to_target_vector(baseline_throughput);

                network.update_weights(&input, &target, &current_output)?;
            }
        }

        Ok(())
    }

    /// Get adaptation statistics
    pub fn get_adaptation_stats(&self) -> AdaptationStats {
        let history = self.performance_history.read().unwrap();
        let baseline = self.baseline_performance.read().unwrap();

        let recent_performance: Vec<f32> = history
            .iter()
            .rev()
            .take(50)
            .map(|(_, _, feedback)| feedback.throughput_mbps)
            .collect();

        let avg_recent_performance = if !recent_performance.is_empty() {
            recent_performance.iter().sum::<f32>() / recent_performance.len() as f32
        } else {
            0.0
        };

        let improvement_ratio = baseline
            .map(|b| avg_recent_performance / b.max(1.0))
            .unwrap_or(1.0);

        AdaptationStats {
            total_adaptations: history.len(),
            recent_avg_throughput: avg_recent_performance,
            baseline_throughput: baseline.unwrap_or(0.0),
            improvement_ratio,
            adaptation_effectiveness: (improvement_ratio - 1.0).max(0.0),
        }
    }
}

/// Statistics about neural adaptation performance
#[derive(Debug, Clone)]
pub struct AdaptationStats {
    /// Total number of adaptations performed
    pub total_adaptations: usize,
    /// Recent average throughput in MB/s
    pub recent_avg_throughput: f32,
    /// Baseline throughput for comparison
    pub baseline_throughput: f32,
    /// Improvement ratio over baseline
    pub improvement_ratio: f32,
    /// Effectiveness of adaptation (0.0-1.0)
    pub adaptation_effectiveness: f32,
}

/// Advanced-high performance I/O processor with neural adaptation
pub struct AdvancedIoProcessor {
    controller: NeuralAdaptiveIoController,
    current_params: Arc<RwLock<ConcreteOptimizationParams>>,
    performance_monitor: Arc<RwLock<PerformanceMonitor>>,
}

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

impl AdvancedIoProcessor {
    /// Create a new advanced I/O processor
    pub fn new() -> Self {
        Self {
            controller: NeuralAdaptiveIoController::new(),
            current_params: Arc::new(RwLock::new(ConcreteOptimizationParams {
                thread_count: 4,
                buffer_size: 64 * 1024,
                compression_level: 6,
                use_cache: true,
                use_simd: true,
            })),
            performance_monitor: Arc::new(RwLock::new(PerformanceMonitor::new())),
        }
    }

    /// Process data with neural-adaptive optimization
    pub fn process_data_adaptive(&mut self, data: &[u8]) -> Result<Vec<u8>> {
        let start_time = Instant::now();

        // Get current system metrics
        let metrics = self.get_system_metrics();

        // Get optimization decisions from neural network
        let decisions = self.controller.get_optimization_decisions(&metrics)?;
        let concrete_params = decisions.to_concrete_params(4, 64 * 1024);

        // Update current parameters
        {
            let mut params = self.current_params.write().unwrap();
            *params = concrete_params.clone();
        }

        // Process data with optimized parameters
        let result = self.process_with_params(data, &concrete_params)?;

        // Record performance feedback
        let processing_time = start_time.elapsed();
        let throughput =
            (data.len() as f32) / (processing_time.as_secs_f64() as f32 * 1024.0 * 1024.0);

        let feedback = PerformanceFeedback {
            throughput_mbps: throughput,
            latency_ms: processing_time.as_millis() as f32,
            cpu_efficiency: 0.8, // Simplified - would measure actual CPU efficiency
            memory_efficiency: 0.7, // Simplified - would measure actual memory efficiency
            error_rate: 0.0,     // No errors in this example
        };

        self.controller
            .record_performance(metrics, decisions, feedback)?;

        Ok(result)
    }

    /// Process data with specific parameters
    fn process_with_params(
        &self,
        data: &[u8],
        params: &ConcreteOptimizationParams,
    ) -> Result<Vec<u8>> {
        let mut result = Vec::with_capacity(data.len());

        if params.use_simd && data.len() >= 32 {
            // SIMD-accelerated processing
            let simd_result = self.process_simd_optimized(data)?;
            result.extend_from_slice(&simd_result);
        } else {
            // Standard processing
            result.extend_from_slice(data);
        }

        // Apply compression if requested
        if params.compression_level > 0 {
            result = self.compress_data(&result, params.compression_level)?;
        }

        Ok(result)
    }

    /// SIMD-optimized data processing
    fn process_simd_optimized(&self, data: &[u8]) -> Result<Vec<u8>> {
        // Convert to f32 for SIMD operations
        let float_data: Vec<f32> = data.iter().map(|&x| x as f32).collect();
        let array = Array1::from(float_data);

        // Apply SIMD operations using the trait
        let ones_array = Array1::ones(array.len());
        let array_view = array.view();
        let ones_view = ones_array.view();
        let processed = f32::simd_add(&array_view, &ones_view);

        // Convert back to u8
        let result: Vec<u8> = processed.iter().map(|&x| x as u8).collect();
        Ok(result)
    }

    /// Compress data using specified level
    fn compress_data(&self, data: &[u8], level: u32) -> Result<Vec<u8>> {
        // Simplified compression - in reality would use actual compression algorithms
        Ok(data.to_vec())
    }

    /// Get current system metrics (simplified)
    fn get_system_metrics(&self) -> SystemMetrics {
        SystemMetrics::mock()
    }

    /// Get current performance statistics
    pub fn get_performance_stats(&self) -> AdaptationStats {
        self.controller.get_adaptation_stats()
    }
}

/// Performance monitoring helper
#[derive(Debug)]
struct PerformanceMonitor {
    operation_count: usize,
    total_processing_time: Duration,
    total_bytes_processed: usize,
}

impl PerformanceMonitor {
    fn new() -> Self {
        Self {
            operation_count: 0,
            total_processing_time: Duration::default(),
            total_bytes_processed: 0,
        }
    }
}

/// Reinforcement Learning Agent for I/O optimization
#[derive(Debug, Clone)]
pub struct ReinforcementLearningAgent {
    /// Q-table for state-action values
    q_table: HashMap<String, HashMap<String, f32>>,
    /// Exploration rate (epsilon for epsilon-greedy)
    exploration_rate: f32,
    /// Learning rate for Q-learning
    learning_rate: f32,
    /// Discount factor for future rewards
    discount_factor: f32,
    /// Current state
    current_state: Option<String>,
    /// Action history for learning
    action_history: VecDeque<(String, String, f32)>, // (state, action, reward)
}

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

impl ReinforcementLearningAgent {
    /// Create a new RL agent
    pub fn new() -> Self {
        Self {
            q_table: HashMap::new(),
            exploration_rate: 0.1,
            learning_rate: 0.1,
            discount_factor: 0.95,
            current_state: None,
            action_history: VecDeque::with_capacity(1000),
        }
    }

    /// Choose action using epsilon-greedy policy
    pub fn choose_action(&mut self, state: &str) -> String {
        let actions = vec![
            "increase_threads".to_string(),
            "decrease_threads".to_string(),
            "increase_buffer".to_string(),
            "decrease_buffer".to_string(),
            "enable_compression".to_string(),
            "disable_compression".to_string(),
            "enable_simd".to_string(),
            "disable_simd".to_string(),
        ];

        // Exploration vs exploitation - simplified for now to avoid rand ICE
        if self.exploration_rate > 0.5 {
            // Explore: choose first action (simplified)
            actions[0].clone()
        } else {
            // Exploit: choose best known action
            self.get_best_action(state, &actions)
        }
    }

    /// Get best action for given state
    fn get_best_action(&self, state: &str, actions: &[String]) -> String {
        if let Some(state_actions) = self.q_table.get(state) {
            actions
                .iter()
                .max_by(|a, b| {
                    let value_a = state_actions.get(*a).unwrap_or(&0.0);
                    let value_b = state_actions.get(*b).unwrap_or(&0.0);
                    value_a.partial_cmp(value_b).unwrap()
                })
                .cloned()
                .unwrap_or_else(|| actions[0].clone())
        } else {
            actions[0].clone()
        }
    }

    /// Update Q-values based on reward
    pub fn update_q_value(&mut self, state: &str, action: &str, reward: f32, nextstate: &str) {
        // First, get the max next Q value
        let max_next_q = self
            .q_table
            .get(nextstate)
            .map(|actions| actions.values().copied().fold(f32::NEG_INFINITY, f32::max))
            .unwrap_or(0.0);

        // Then get mutable reference to current Q value
        let current_q = self
            .q_table
            .entry(state.to_string())
            .or_default()
            .entry(action.to_string())
            .or_insert(0.0);

        let td_target = reward + self.discount_factor * max_next_q;
        let td_error = td_target - *current_q;
        *current_q += self.learning_rate * td_error;

        // Record in history
        self.action_history
            .push_back((state.to_string(), action.to_string(), reward));
        if self.action_history.len() > 1000 {
            self.action_history.pop_front();
        }

        // Decay exploration rate
        self.exploration_rate = (self.exploration_rate * 0.995).max(0.01);
    }

    /// Get current learning statistics
    pub fn get_learning_stats(&self) -> ReinforcementLearningStats {
        let avg_reward = if !self.action_history.is_empty() {
            self.action_history.iter().map(|(_, _, r)| r).sum::<f32>()
                / self.action_history.len() as f32
        } else {
            0.0
        };

        ReinforcementLearningStats {
            total_states: self.q_table.len(),
            total_actions: self.action_history.len(),
            average_reward: avg_reward,
            exploration_rate: self.exploration_rate,
            q_table_size: self.q_table.values().map(|actions| actions.len()).sum(),
        }
    }
}

/// Reinforcement learning statistics
#[derive(Debug, Clone)]
pub struct ReinforcementLearningStats {
    /// Total number of states visited
    pub total_states: usize,
    /// Total number of actions taken
    pub total_actions: usize,
    /// Average reward received
    pub average_reward: f32,
    /// Current exploration rate (epsilon)
    pub exploration_rate: f32,
    /// Total size of Q-table
    pub q_table_size: usize,
}

/// Advanced Ensemble Neural Network for robustness
#[derive(Debug, Clone)]
pub struct EnsembleNeuralNetwork {
    /// Multiple neural networks for ensemble prediction
    networks: Vec<NeuralIoNetwork>,
    /// Weights for each network in the ensemble
    ensemble_weights: Array1<f32>,
    /// Performance history for each network
    network_performance: Vec<f32>,
}

impl Default for EnsembleNeuralNetwork {
    fn default() -> Self {
        Self::new(3, 8, 16, 5)
    }
}

impl EnsembleNeuralNetwork {
    /// Create a new ensemble network
    pub fn new(
        num_networks: usize,
        input_size: usize,
        hidden_size: usize,
        output_size: usize,
    ) -> Self {
        let networks = (0..num_networks)
            .map(|_| NeuralIoNetwork::new(input_size, hidden_size, output_size))
            .collect();

        let ensemble_weights = Array1::from_elem(num_networks, 1.0 / num_networks as f32);
        let network_performance = vec![1.0; num_networks];

        Self {
            networks,
            ensemble_weights,
            network_performance,
        }
    }

    /// Ensemble forward pass with weighted average
    pub fn forward_ensemble(&self, input: &Array1<f32>) -> Result<Array1<f32>> {
        let mut predictions = Vec::new();

        for network in &self.networks {
            let prediction = network.forward(input)?;
            predictions.push(prediction);
        }

        // Weighted average of predictions
        let mut ensemble_output = Array1::zeros(predictions[0].len());
        for (i, prediction) in predictions.iter().enumerate() {
            ensemble_output = ensemble_output + self.ensemble_weights[i] * prediction;
        }

        Ok(ensemble_output)
    }

    /// Update ensemble weights based on individual network performance
    pub fn update_ensemble_weights(&mut self, individual_errors: &[f32]) {
        // Update performance tracking
        for (i, &error) in individual_errors.iter().enumerate() {
            self.network_performance[i] =
                0.9 * self.network_performance[i] + 0.1 * (1.0 / (error + 0.001));
        }

        // Update ensemble weights (higher weight for better performing networks)
        let total_performance: f32 = self.network_performance.iter().sum();
        for (i, &performance) in self.network_performance.iter().enumerate() {
            self.ensemble_weights[i] = performance / total_performance;
        }
    }

    /// Train all networks in the ensemble
    pub fn train_ensemble(&mut self, input: &Array1<f32>, target: &Array1<f32>) -> Result<()> {
        let mut individual_errors = Vec::new();

        for network in &mut self.networks {
            let prediction = network.forward(input)?;
            let error = (target - &prediction).mapv(|x| x * x).mean().unwrap_or(1.0);
            individual_errors.push(error);

            network.update_weights(input, target, &prediction)?;
        }

        self.update_ensemble_weights(&individual_errors);
        Ok(())
    }

    /// Get ensemble statistics
    pub fn get_ensemble_stats(&self) -> EnsembleStats {
        EnsembleStats {
            num_networks: self.networks.len(),
            ensemble_weights: self.ensemble_weights.clone(),
            network_performance: self.network_performance.clone(),
            weight_entropy: -self
                .ensemble_weights
                .iter()
                .map(|&w| if w > 0.0 { w * w.ln() } else { 0.0 })
                .sum::<f32>(),
        }
    }
}

/// Ensemble learning statistics
#[derive(Debug, Clone)]
pub struct EnsembleStats {
    /// Number of networks in the ensemble
    pub num_networks: usize,
    /// Weights assigned to each network
    pub ensemble_weights: Array1<f32>,
    /// Performance metrics for each network
    pub network_performance: Vec<f32>,
    /// Entropy of the ensemble weights
    pub weight_entropy: f32,
}

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

    #[test]
    fn test_neural_network_forward() {
        let network = NeuralIoNetwork::new(8, 16, 5);
        let input = Array1::from(vec![0.5; 8]);
        let output = network.forward(&input).unwrap();
        assert_eq!(output.len(), 5);
        assert!(output.iter().all(|&x| (0.0..=1.0).contains(&x)));
    }

    #[test]
    fn test_system_metrics_conversion() {
        let metrics = SystemMetrics::mock();
        let input_vector = metrics.to_input_vector();
        assert_eq!(input_vector.len(), 8);
    }

    #[test]
    fn test_optimization_decisions() {
        let output = Array1::from(vec![0.8, 0.6, 0.4, 0.9, 0.7]);
        let decisions = OptimizationDecisions::from_output_vector(&output);
        let params = decisions.to_concrete_params(4, 64 * 1024);

        assert!(params.thread_count >= 1 && params.thread_count <= 32);
        assert!(params.buffer_size >= 4096);
        assert!(params.compression_level <= 9);
    }

    #[test]
    fn test_advanced_think_processor() {
        let mut processor = AdvancedIoProcessor::new();
        let test_data = vec![1, 2, 3, 4, 5];
        let result = processor.process_data_adaptive(&test_data).unwrap();
        assert!(!result.is_empty());
    }
}