optirs_core/streaming/

mod.rs

// Streaming optimization for real-time learning
//
// This module provides streaming gradient descent and other online optimization
// algorithms designed for real-time data processing and low-latency inference.

#[allow(dead_code)]
use crate::error::{OptimError, Result};
use scirs2_core::ndarray::{Array1, ArrayBase, ScalarOperand};
use scirs2_core::numeric::Float;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;
use std::time::{Duration, Instant};

pub mod adaptive_streaming;
pub mod concept_drift;
pub mod enhanced_adaptive_lr;
pub mod low_latency;
pub mod streaming_metrics;

// Re-export key types for convenience
pub use concept_drift::{ConceptDriftDetector, DriftDetectorConfig, DriftEvent, DriftStatus};
pub use enhanced_adaptive_lr::{
    AdaptationStatistics, AdaptiveLRConfig, EnhancedAdaptiveLRController,
};
pub use low_latency::{LowLatencyConfig, LowLatencyMetrics, LowLatencyOptimizer};
pub use streaming_metrics::{MetricsSample, MetricsSummary, StreamingMetricsCollector};

use crate::optimizers::Optimizer;

/// Streaming optimization configuration
#[derive(Debug, Clone)]
pub struct StreamingConfig {
    /// Buffer size for mini-batches
    pub buffer_size: usize,

    /// Maximum latency budget (milliseconds)
    pub latency_budget_ms: u64,

    /// Enable adaptive learning rates
    pub adaptive_learning_rate: bool,

    /// Concept drift detection threshold
    pub drift_threshold: f64,

    /// Window size for drift detection
    pub drift_window_size: usize,

    /// Enable gradient compression
    pub gradient_compression: bool,

    /// Compression ratio (0.0 to 1.0)
    pub compression_ratio: f64,

    /// Enable asynchronous updates
    pub async_updates: bool,

    /// Maximum staleness for asynchronous updates
    pub max_staleness: usize,

    /// Enable memory-efficient processing
    pub memory_efficient: bool,

    /// Target memory usage (MB)
    pub memory_budget_mb: usize,

    /// Learning rate adaptation strategy
    pub lr_adaptation: LearningRateAdaptation,

    /// Enable adaptive batching
    pub adaptive_batching: bool,

    /// Dynamic buffer sizing
    pub dynamic_buffer_sizing: bool,

    /// Real-time priority levels
    pub enable_priority_scheduling: bool,

    /// Advanced drift detection
    pub advanced_drift_detection: bool,

    /// Predictive processing
    pub enable_prediction: bool,

    /// Quality of service guarantees
    pub qos_enabled: bool,

    /// Enable multi-stream coordination
    pub multi_stream_coordination: bool,

    /// Enable predictive streaming algorithms
    pub predictive_streaming: bool,

    /// Enable stream fusion optimization
    pub stream_fusion: bool,

    /// Advanced QoS configuration
    pub advanced_qos_config: AdvancedQoSConfig,

    /// Real-time optimization parameters
    pub real_time_config: RealTimeConfig,

    /// Pipeline parallelism degree
    pub pipeline_parallelism_degree: usize,

    /// Enable adaptive resource allocation
    pub adaptive_resource_allocation: bool,

    /// Enable distributed streaming
    pub distributed_streaming: bool,

    /// Stream processing priority
    pub processingpriority: StreamPriority,
}

impl Default for StreamingConfig {
    fn default() -> Self {
        Self {
            buffer_size: 32,
            latency_budget_ms: 10,
            adaptive_learning_rate: true,
            drift_threshold: 0.1,
            drift_window_size: 1000,
            gradient_compression: false,
            compression_ratio: 0.5,
            async_updates: false,
            max_staleness: 10,
            memory_efficient: true,
            memory_budget_mb: 100,
            lr_adaptation: LearningRateAdaptation::Adagrad,
            adaptive_batching: true,
            dynamic_buffer_sizing: true,
            enable_priority_scheduling: false,
            advanced_drift_detection: true,
            enable_prediction: false,
            qos_enabled: false,
            multi_stream_coordination: false,
            predictive_streaming: true,
            stream_fusion: true,
            advanced_qos_config: AdvancedQoSConfig::default(),
            real_time_config: RealTimeConfig::default(),
            pipeline_parallelism_degree: 2,
            adaptive_resource_allocation: true,
            distributed_streaming: false,
            processingpriority: StreamPriority::Normal,
        }
    }
}

/// Learning rate adaptation strategies for streaming
#[derive(Debug, Clone, Copy)]
pub enum LearningRateAdaptation {
    /// Fixed learning rate
    Fixed,
    /// AdaGrad-style adaptation
    Adagrad,
    /// RMSprop-style adaptation
    RMSprop,
    /// Performance-based adaptation
    PerformanceBased,
    /// Concept drift aware adaptation
    DriftAware,
    /// Adaptive momentum-based
    AdaptiveMomentum,
    /// Gradient variance-based
    GradientVariance,
    /// Predictive adaptation
    PredictiveLR,
}

/// Streaming gradient descent optimizer
pub struct StreamingOptimizer<O, A, D>
where
    A: Float + Send + Sync + ScalarOperand + Debug,
    D: scirs2_core::ndarray::Dimension,
    O: Optimizer<A, D>,
{
    /// Base optimizer
    baseoptimizer: O,

    /// Configuration
    config: StreamingConfig,

    /// Data buffer for mini-batches
    data_buffer: VecDeque<StreamingDataPoint<A>>,

    /// Gradient buffer
    gradient_buffer: Option<Array1<A>>,

    /// Learning rate adaptation state
    lr_adaptation_state: LearningRateAdaptationState<A>,

    /// Concept drift detector
    drift_detector: StreamingDriftDetector<A>,

    /// Performance metrics
    metrics: StreamingMetrics,

    /// Timing information
    timing: TimingTracker,

    /// Memory usage tracker
    memory_tracker: MemoryTracker,

    /// Asynchronous update state
    async_state: Option<AsyncUpdateState<A, D>>,

    /// Current step count
    step_count: usize,
    /// Multi-stream coordinator
    multi_stream_coordinator: Option<MultiStreamCoordinator<A>>,

    /// Predictive streaming engine
    predictive_engine: Option<PredictiveStreamingEngine<A>>,

    /// Stream fusion optimizer
    fusion_optimizer: Option<StreamFusionOptimizer<A>>,

    /// Advanced QoS manager
    qos_manager: AdvancedQoSManager,

    /// Real-time performance optimizer
    rt_optimizer: RealTimeOptimizer,

    /// Resource allocation manager
    resource_manager: Option<AdaptiveResourceManager>,

    /// Pipeline execution manager
    pipeline_manager: PipelineExecutionManager<A>,
}

/// Streaming data point
#[derive(Debug, Clone)]
pub struct StreamingDataPoint<A: Float + Send + Sync> {
    /// Feature vector
    pub features: Array1<A>,

    /// Target value (for supervised learning)
    pub target: Option<A>,

    /// Timestamp
    pub timestamp: Instant,

    /// Sample weight
    pub weight: A,

    /// Metadata
    pub metadata: HashMap<String, String>,
}

/// Learning rate adaptation state
#[derive(Debug, Clone)]
struct LearningRateAdaptationState<A: Float + Send + Sync> {
    /// Current learning rate
    current_lr: A,

    /// Accumulated squared gradients (for AdaGrad)
    accumulated_gradients: Option<Array1<A>>,

    /// Exponential moving average of squared gradients (for RMSprop)
    ema_squared_gradients: Option<Array1<A>>,

    /// Performance history
    performance_history: VecDeque<A>,

    /// Last adaptation time
    last_adaptation: Instant,

    /// Adaptation frequency
    adaptation_frequency: Duration,
}

/// Streaming concept drift detection
#[derive(Debug, Clone)]
struct StreamingDriftDetector<A: Float + Send + Sync> {
    /// Window of recent losses
    loss_window: VecDeque<A>,

    /// Historical loss statistics
    historical_mean: A,
    historical_std: A,

    /// Drift detection threshold
    threshold: A,

    /// Last drift detection time
    last_drift: Option<Instant>,

    /// Drift count
    drift_count: usize,
}

/// Streaming performance metrics
#[derive(Debug, Clone)]
pub struct StreamingMetrics {
    /// Total samples processed
    pub samples_processed: usize,

    /// Current processing rate (samples/second)
    pub processing_rate: f64,

    /// Average latency per sample (milliseconds)
    pub avg_latency_ms: f64,

    /// 95th percentile latency (milliseconds)
    pub p95_latency_ms: f64,

    /// Memory usage (MB)
    pub memory_usage_mb: f64,

    /// Concept drifts detected
    pub drift_count: usize,

    /// Current loss
    pub current_loss: f64,

    /// Learning rate
    pub current_learning_rate: f64,

    /// Throughput violations (exceeded latency budget)
    pub throughput_violations: usize,
}

/// Timing tracker for performance monitoring
#[derive(Debug)]
struct TimingTracker {
    /// Latency samples
    latency_samples: VecDeque<Duration>,

    /// Last processing start time
    last_start: Option<Instant>,

    /// Processing start time for current batch
    batch_start: Option<Instant>,

    /// Maximum samples to keep
    max_samples: usize,
}

/// Memory usage tracker
#[derive(Debug)]
struct MemoryTracker {
    /// Current estimated usage (bytes)
    current_usage: usize,

    /// Peak usage
    peak_usage: usize,

    /// Memory budget (bytes)
    budget: usize,

    /// Usage history
    usage_history: VecDeque<usize>,
}

/// Asynchronous update state
#[derive(Debug)]
struct AsyncUpdateState<A: Float, D: scirs2_core::ndarray::Dimension> {
    /// Pending gradients
    pending_gradients: Vec<ArrayBase<scirs2_core::ndarray::OwnedRepr<A>, D>>,

    /// Update queue
    update_queue: VecDeque<AsyncUpdate<A, D>>,

    /// Staleness counter
    staleness_counter: HashMap<usize, usize>,

    /// Update thread handle
    update_thread: Option<std::thread::JoinHandle<()>>,
}

/// Asynchronous update entry
#[derive(Debug, Clone)]
struct AsyncUpdate<A: Float, D: scirs2_core::ndarray::Dimension> {
    /// Parameter update
    update: ArrayBase<scirs2_core::ndarray::OwnedRepr<A>, D>,

    /// Timestamp
    timestamp: Instant,

    /// Priority
    priority: UpdatePriority,

    /// Staleness
    staleness: usize,
}

/// Update priority levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
enum UpdatePriority {
    Low,
    Normal,
    High,
    Critical,
}

impl<O, A, D> StreamingOptimizer<O, A, D>
where
    A: Float
        + Default
        + Clone
        + Send
        + Sync
        + std::fmt::Debug
        + ScalarOperand
        + std::iter::Sum
        + std::ops::DivAssign,
    D: scirs2_core::ndarray::Dimension,
    O: Optimizer<A, D> + Send + Sync,
{
    /// Create a new streaming optimizer
    pub fn new(baseoptimizer: O, config: StreamingConfig) -> Result<Self> {
        let lr_adaptation_state = LearningRateAdaptationState {
            current_lr: A::from(0.01).unwrap(), // Default learning rate
            accumulated_gradients: None,
            ema_squared_gradients: None,
            performance_history: VecDeque::with_capacity(100),
            last_adaptation: Instant::now(),
            adaptation_frequency: Duration::from_millis(1000),
        };

        let drift_detector = StreamingDriftDetector {
            loss_window: VecDeque::with_capacity(config.drift_window_size),
            historical_mean: A::zero(),
            historical_std: A::one(),
            threshold: A::from(config.drift_threshold).unwrap(),
            last_drift: None,
            drift_count: 0,
        };

        let timing = TimingTracker {
            latency_samples: VecDeque::with_capacity(1000),
            last_start: None,
            batch_start: None,
            max_samples: 1000,
        };

        let memory_tracker = MemoryTracker {
            current_usage: 0,
            peak_usage: 0,
            budget: config.memory_budget_mb * 1024 * 1024,
            usage_history: VecDeque::with_capacity(100),
        };

        let async_state = if config.async_updates {
            Some(AsyncUpdateState {
                pending_gradients: Vec::new(),
                update_queue: VecDeque::new(),
                staleness_counter: HashMap::new(),
                update_thread: None,
            })
        } else {
            None
        };

        // Initialize advanced components
        let multi_stream_coordinator = if config.multi_stream_coordination {
            Some(MultiStreamCoordinator::new(&config)?)
        } else {
            None
        };

        let predictive_engine = if config.predictive_streaming {
            Some(PredictiveStreamingEngine::new(&config)?)
        } else {
            None
        };

        let fusion_optimizer = if config.stream_fusion {
            Some(StreamFusionOptimizer::new(&config)?)
        } else {
            None
        };

        let qos_manager = AdvancedQoSManager::new(config.advanced_qos_config.clone());
        let rt_optimizer = RealTimeOptimizer::new(config.real_time_config.clone())?;

        let resource_manager = if config.adaptive_resource_allocation {
            Some(AdaptiveResourceManager::new(&config)?)
        } else {
            None
        };

        let pipeline_manager = PipelineExecutionManager::new(
            config.pipeline_parallelism_degree,
            config.processingpriority,
        );

        // Save buffer_size before moving config
        let buffer_size = config.buffer_size;

        Ok(Self {
            baseoptimizer,
            config,
            data_buffer: VecDeque::with_capacity(buffer_size),
            gradient_buffer: None,
            lr_adaptation_state,
            drift_detector,
            metrics: StreamingMetrics::default(),
            timing,
            memory_tracker,
            async_state,
            step_count: 0,
            multi_stream_coordinator,
            predictive_engine,
            fusion_optimizer,
            qos_manager,
            rt_optimizer,
            resource_manager,
            pipeline_manager,
        })
    }

    /// Process a single streaming data point
    #[allow(clippy::too_many_arguments)]
    pub fn process_sample(
        &mut self,
        data_point: StreamingDataPoint<A>,
    ) -> Result<Option<Array1<A>>> {
        let starttime = Instant::now();
        self.timing.batch_start = Some(starttime);

        // Add to buffer
        self.data_buffer.push_back(data_point);
        self.update_memory_usage();

        // Check if buffer is full or latency budget is approaching
        let should_update = self.data_buffer.len() >= self.config.buffer_size
            || self.should_force_update(starttime);

        if should_update {
            let result = self.process_buffer()?;

            // Update timing metrics
            let latency = starttime.elapsed();
            self.update_timing_metrics(latency);

            // Check for concept drift
            if let Some(ref update) = result {
                self.check_concept_drift(update)?;
            }

            Ok(result)
        } else {
            Ok(None)
        }
    }

    fn should_force_update(&self, starttime: Instant) -> bool {
        if let Some(batch_start) = self.timing.batch_start {
            let elapsed = starttime.duration_since(batch_start);
            elapsed.as_millis() as u64 >= self.config.latency_budget_ms / 2
        } else {
            false
        }
    }

    fn process_buffer(&mut self) -> Result<Option<Array1<A>>> {
        if self.data_buffer.is_empty() {
            return Ok(None);
        }

        // Compute mini-batch gradient
        let gradient = self.compute_mini_batch_gradient()?;

        // Apply gradient compression if enabled
        let compressed_gradient = if self.config.gradient_compression {
            self.compress_gradient(&gradient)?
        } else {
            gradient
        };

        // Adapt learning rate
        self.adapt_learning_rate(&compressed_gradient)?;

        // Apply optimizer step
        let current_params = self.get_current_parameters()?;
        let updated_params = if self.config.async_updates {
            self.async_update(&current_params, &compressed_gradient)?
        } else {
            self.sync_update(&current_params, &compressed_gradient)?
        };

        // Clear buffer
        self.data_buffer.clear();
        self.step_count += 1;

        // Update metrics
        self.update_metrics();

        Ok(Some(updated_params))
    }

    fn compute_mini_batch_gradient(&self) -> Result<Array1<A>> {
        if self.data_buffer.is_empty() {
            return Err(OptimError::InvalidConfig("Empty data buffer".to_string()));
        }

        let batch_size = self.data_buffer.len();
        let featuredim = self.data_buffer[0].features.len();
        let mut gradient = Array1::zeros(featuredim);

        // Simplified gradient computation (would depend on loss function)
        for data_point in &self.data_buffer {
            // For demonstration: compute a simple linear regression gradient
            if let Some(target) = data_point.target {
                let prediction = A::zero(); // Would compute actual prediction
                let error = prediction - target;

                for (i, &feature) in data_point.features.iter().enumerate() {
                    gradient[i] = gradient[i] + error * feature * data_point.weight;
                }
            }
        }

        // Average over batch
        let batch_size_a = A::from(batch_size).unwrap();
        gradient.mapv_inplace(|g| g / batch_size_a);

        Ok(gradient)
    }

    fn compress_gradient(&self, gradient: &Array1<A>) -> Result<Array1<A>> {
        let k = (gradient.len() as f64 * self.config.compression_ratio) as usize;
        let mut compressed = gradient.clone();

        // Top-k sparsification
        let mut abs_values: Vec<(usize, A)> = gradient
            .iter()
            .enumerate()
            .map(|(i, &g)| (i, g.abs()))
            .collect();

        abs_values.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());

        // Zero out all but top-k elements
        for (i, _) in abs_values.iter().skip(k) {
            compressed[*i] = A::zero();
        }

        Ok(compressed)
    }

    fn adapt_learning_rate(&mut self, gradient: &Array1<A>) -> Result<()> {
        if !self.config.adaptive_learning_rate {
            return Ok(());
        }

        match self.config.lr_adaptation {
            LearningRateAdaptation::Fixed => {
                // Do nothing
            }
            LearningRateAdaptation::Adagrad => {
                self.adapt_adagrad(gradient)?;
            }
            LearningRateAdaptation::RMSprop => {
                self.adapt_rmsprop(gradient)?;
            }
            LearningRateAdaptation::PerformanceBased => {
                self.adapt_performance_based()?;
            }
            LearningRateAdaptation::DriftAware => {
                self.adapt_drift_aware()?;
            }
            LearningRateAdaptation::AdaptiveMomentum => {
                self.adapt_momentum_based(gradient)?;
            }
            LearningRateAdaptation::GradientVariance => {
                self.adapt_gradient_variance(gradient)?;
            }
            LearningRateAdaptation::PredictiveLR => {
                self.adapt_predictive()?;
            }
        }

        Ok(())
    }

    fn adapt_adagrad(&mut self, gradient: &Array1<A>) -> Result<()> {
        if self.lr_adaptation_state.accumulated_gradients.is_none() {
            self.lr_adaptation_state.accumulated_gradients = Some(Array1::zeros(gradient.len()));
        }

        let acc_grads = self
            .lr_adaptation_state
            .accumulated_gradients
            .as_mut()
            .unwrap();

        // Update accumulated gradients
        for i in 0..gradient.len() {
            acc_grads[i] = acc_grads[i] + gradient[i] * gradient[i];
        }

        // Compute adaptive learning rate (simplified)
        let base_lr = A::from(0.01).unwrap();
        let eps = A::from(1e-8).unwrap();
        let norm_sum = acc_grads.iter().copied().sum::<A>();
        let adaptive_factor = (norm_sum + eps).sqrt();

        self.lr_adaptation_state.current_lr = base_lr / adaptive_factor;

        Ok(())
    }

    fn adapt_rmsprop(&mut self, gradient: &Array1<A>) -> Result<()> {
        if self.lr_adaptation_state.ema_squared_gradients.is_none() {
            self.lr_adaptation_state.ema_squared_gradients = Some(Array1::zeros(gradient.len()));
        }

        let ema_grads = self
            .lr_adaptation_state
            .ema_squared_gradients
            .as_mut()
            .unwrap();
        let decay = A::from(0.9).unwrap();
        let one_minus_decay = A::one() - decay;

        // Update exponential moving average
        for i in 0..gradient.len() {
            ema_grads[i] = decay * ema_grads[i] + one_minus_decay * gradient[i] * gradient[i];
        }

        // Compute adaptive learning rate
        let base_lr = A::from(0.01).unwrap();
        let eps = A::from(1e-8).unwrap();
        let rms = ema_grads.iter().copied().sum::<A>().sqrt();

        self.lr_adaptation_state.current_lr = base_lr / (rms + eps);

        Ok(())
    }

    fn adapt_performance_based(&mut self) -> Result<()> {
        // Adapt based on recent performance
        if self.lr_adaptation_state.performance_history.len() < 2 {
            return Ok(());
        }

        let recent_perf = self.lr_adaptation_state.performance_history.back().unwrap();
        let prev_perf = self
            .lr_adaptation_state
            .performance_history
            .get(self.lr_adaptation_state.performance_history.len() - 2)
            .unwrap();

        let improvement = *prev_perf - *recent_perf; // Assuming lower is better

        if improvement > A::zero() {
            // Performance improved, slightly increase LR
            self.lr_adaptation_state.current_lr =
                self.lr_adaptation_state.current_lr * A::from(1.01).unwrap();
        } else {
            // Performance degraded, decrease LR
            self.lr_adaptation_state.current_lr =
                self.lr_adaptation_state.current_lr * A::from(0.99).unwrap();
        }

        Ok(())
    }

    fn adapt_drift_aware(&mut self) -> Result<()> {
        // Increase learning rate if drift was recently detected
        if let Some(last_drift) = self.drift_detector.last_drift {
            let time_since_drift = last_drift.elapsed();
            if time_since_drift < Duration::from_secs(60) {
                // Recent drift detected, increase learning rate
                self.lr_adaptation_state.current_lr =
                    self.lr_adaptation_state.current_lr * A::from(1.5).unwrap();
            }
        }

        Ok(())
    }

    fn check_concept_drift(&mut self, update: &Array1<A>) -> Result<()> {
        // Simplified concept drift detection based on loss
        let current_loss = A::from(self.metrics.current_loss).unwrap();

        self.drift_detector.loss_window.push_back(current_loss);
        if self.drift_detector.loss_window.len() > self.config.drift_window_size {
            self.drift_detector.loss_window.pop_front();
        }

        if self.drift_detector.loss_window.len() >= 10 {
            // Compute statistics
            let mean = self.drift_detector.loss_window.iter().cloned().sum::<A>()
                / A::from(self.drift_detector.loss_window.len()).unwrap();

            let variance = self
                .drift_detector
                .loss_window
                .iter()
                .map(|&loss| {
                    let diff = loss - mean;
                    diff * diff
                })
                .sum::<A>()
                / A::from(self.drift_detector.loss_window.len()).unwrap();

            let std = variance.sqrt();

            // Check for drift (simplified statistical test)
            let z_score = (current_loss - self.drift_detector.historical_mean).abs()
                / (self.drift_detector.historical_std + A::from(1e-8).unwrap());

            if z_score > self.drift_detector.threshold {
                // Drift detected
                self.drift_detector.last_drift = Some(Instant::now());
                self.drift_detector.drift_count += 1;
                self.metrics.drift_count = self.drift_detector.drift_count;

                // Update historical statistics
                self.drift_detector.historical_mean = mean;
                self.drift_detector.historical_std = std;

                // Trigger learning rate adaptation
                if matches!(
                    self.config.lr_adaptation,
                    LearningRateAdaptation::DriftAware
                ) {
                    self.adapt_drift_aware()?;
                }
            }
        }

        Ok(())
    }

    fn get_current_parameters(&self) -> Result<Array1<A>> {
        // Placeholder - would get actual parameters from model
        // For now, return an empty Array1 as a placeholder
        Ok(Array1::zeros(0))
    }

    fn sync_update(&mut self, params: &Array1<A>, gradient: &Array1<A>) -> Result<Array1<A>> {
        // Apply gradient update synchronously
        // We need to ensure proper type conversion from Array1<A> to Array<A, D>
        // This will only work if D is compatible with Ix1 (1D)
        let params_owned = params.clone();
        let gradient_owned = gradient.clone();

        // Convert Array1<A> to Array<A, D> - this requires D to be Ix1
        let params_generic = params_owned.into_dimensionality::<D>()?;
        let gradient_generic = gradient_owned.into_dimensionality::<D>()?;

        let result = self
            .baseoptimizer
            .step(&params_generic, &gradient_generic)?;

        // Convert back to Array1
        Ok(result.into_dimensionality::<scirs2_core::ndarray::Ix1>()?)
    }

    fn async_update(&mut self, params: &Array1<A>, gradient: &Array1<A>) -> Result<Array1<A>> {
        if let Some(ref mut async_state) = self.async_state {
            // Add to update queue
            let gradient_generic = gradient.clone().into_dimensionality::<D>()?;
            let update = AsyncUpdate {
                update: gradient_generic,
                timestamp: Instant::now(),
                priority: UpdatePriority::Normal,
                staleness: 0,
            };

            async_state.update_queue.push_back(update);

            // Process updates if queue is full or max staleness reached
            if async_state.update_queue.len() >= self.config.buffer_size
                || self.max_staleness_reached()
            {
                return self.process_async_updates();
            }
        }

        // Return current parameters for now
        self.get_current_parameters()
    }

    fn max_staleness_reached(&self) -> bool {
        if let Some(ref async_state) = self.async_state {
            async_state
                .update_queue
                .iter()
                .any(|update| update.staleness >= self.config.max_staleness)
        } else {
            false
        }
    }

    fn process_async_updates(&mut self) -> Result<Array1<A>> {
        // Simplified async update processing
        if let Some(ref mut async_state) = self.async_state {
            if let Some(update) = async_state.update_queue.pop_front() {
                let current_params = self.get_current_parameters()?;
                // Only works for 1D arrays, need to handle differently for other dimensions
                if let (Ok(params_1d), Ok(_update_1d)) = (
                    current_params.into_dimensionality::<scirs2_core::ndarray::Ix1>(),
                    update
                        .update
                        .into_dimensionality::<scirs2_core::ndarray::Ix1>(),
                ) {
                    // This only works if D = Ix1, need a better approach
                    // For now, just return the current parameters
                    return Ok(params_1d);
                }
            }
        }

        self.get_current_parameters()
    }

    fn update_timing_metrics(&mut self, latency: Duration) {
        self.timing.latency_samples.push_back(latency);
        if self.timing.latency_samples.len() > self.timing.max_samples {
            self.timing.latency_samples.pop_front();
        }

        // Check for throughput violations
        if latency.as_millis() as u64 > self.config.latency_budget_ms {
            self.metrics.throughput_violations += 1;
        }
    }

    fn update_memory_usage(&mut self) {
        // Estimate memory usage
        let buffer_size = self.data_buffer.len() * std::mem::size_of::<StreamingDataPoint<A>>();
        let gradient_size = self
            .gradient_buffer
            .as_ref()
            .map(|g| g.len() * std::mem::size_of::<A>())
            .unwrap_or(0);

        self.memory_tracker.current_usage = buffer_size + gradient_size;
        self.memory_tracker.peak_usage = self
            .memory_tracker
            .peak_usage
            .max(self.memory_tracker.current_usage);

        self.memory_tracker
            .usage_history
            .push_back(self.memory_tracker.current_usage);
        if self.memory_tracker.usage_history.len() > 100 {
            self.memory_tracker.usage_history.pop_front();
        }
    }

    fn update_metrics(&mut self) {
        self.metrics.samples_processed += self.data_buffer.len();

        // Update processing rate
        if let Some(batch_start) = self.timing.batch_start {
            let elapsed = batch_start.elapsed().as_secs_f64();
            if elapsed > 0.0 {
                self.metrics.processing_rate = self.data_buffer.len() as f64 / elapsed;
            }
        }

        // Update latency metrics
        if !self.timing.latency_samples.is_empty() {
            let sum: Duration = self.timing.latency_samples.iter().sum();
            self.metrics.avg_latency_ms =
                sum.as_millis() as f64 / self.timing.latency_samples.len() as f64;

            // Compute 95th percentile
            let mut sorted_latencies: Vec<_> = self.timing.latency_samples.iter().collect();
            sorted_latencies.sort();
            let p95_index = (0.95 * sorted_latencies.len() as f64) as usize;
            if p95_index < sorted_latencies.len() {
                self.metrics.p95_latency_ms = sorted_latencies[p95_index].as_millis() as f64;
            }
        }

        // Update memory metrics
        self.metrics.memory_usage_mb = self.memory_tracker.current_usage as f64 / (1024.0 * 1024.0);

        // Update learning rate
        self.metrics.current_learning_rate =
            self.lr_adaptation_state.current_lr.to_f64().unwrap_or(0.0);
    }

    /// Get current streaming metrics
    pub fn get_metrics(&self) -> &StreamingMetrics {
        &self.metrics
    }

    /// Check if streaming optimizer is healthy (within budgets)
    pub fn is_healthy(&self) -> StreamingHealthStatus {
        let mut warnings = Vec::new();
        let mut is_healthy = true;

        // Check latency budget
        if self.metrics.avg_latency_ms > self.config.latency_budget_ms as f64 {
            warnings.push("Average latency exceeds budget".to_string());
            is_healthy = false;
        }

        // Check memory budget
        if self.memory_tracker.current_usage > self.memory_tracker.budget {
            warnings.push("Memory usage exceeds budget".to_string());
            is_healthy = false;
        }

        // Check for frequent concept drift
        if self.metrics.drift_count > 10 && self.step_count > 0 {
            let drift_rate = self.metrics.drift_count as f64 / self.step_count as f64;
            if drift_rate > 0.1 {
                warnings.push("High concept drift rate detected".to_string());
            }
        }

        StreamingHealthStatus {
            is_healthy,
            warnings,
            metrics: self.metrics.clone(),
        }
    }

    /// Force processing of current buffer
    pub fn flush(&mut self) -> Result<Option<Array1<A>>> {
        if !self.data_buffer.is_empty() {
            self.process_buffer()
        } else {
            Ok(None)
        }
    }

    /// Adaptive momentum-based learning rate adaptation
    fn adapt_momentum_based(&mut self, gradient: &Array1<A>) -> Result<()> {
        // Initialize momentum if needed
        if self.lr_adaptation_state.ema_squared_gradients.is_none() {
            self.lr_adaptation_state.ema_squared_gradients = Some(Array1::zeros(gradient.len()));
        }

        let momentum = self
            .lr_adaptation_state
            .ema_squared_gradients
            .as_mut()
            .unwrap();
        let beta = A::from(0.9).unwrap();
        let one_minus_beta = A::one() - beta;

        // Update momentum
        for i in 0..gradient.len() {
            momentum[i] = beta * momentum[i] + one_minus_beta * gradient[i];
        }

        // Adapt learning rate based on momentum magnitude
        let momentum_norm = momentum.iter().map(|&m| m * m).sum::<A>().sqrt();
        let base_lr = A::from(0.01).unwrap();
        let adaptation_factor = A::one() + momentum_norm * A::from(0.1).unwrap();

        self.lr_adaptation_state.current_lr = base_lr / adaptation_factor;

        Ok(())
    }

    /// Gradient variance-based learning rate adaptation
    fn adapt_gradient_variance(&mut self, gradient: &Array1<A>) -> Result<()> {
        // Track gradient variance
        if self.lr_adaptation_state.accumulated_gradients.is_none() {
            self.lr_adaptation_state.accumulated_gradients = Some(Array1::zeros(gradient.len()));
            self.lr_adaptation_state.ema_squared_gradients = Some(Array1::zeros(gradient.len()));
        }

        let mean_grad = self
            .lr_adaptation_state
            .accumulated_gradients
            .as_mut()
            .unwrap();
        let mean_squared_grad = self
            .lr_adaptation_state
            .ema_squared_gradients
            .as_mut()
            .unwrap();

        let alpha = A::from(0.99).unwrap(); // Decay for moving average
        let one_minus_alpha = A::one() - alpha;

        // Update running means
        for i in 0..gradient.len() {
            mean_grad[i] = alpha * mean_grad[i] + one_minus_alpha * gradient[i];
            mean_squared_grad[i] =
                alpha * mean_squared_grad[i] + one_minus_alpha * gradient[i] * gradient[i];
        }

        // Compute variance
        let variance = mean_squared_grad
            .iter()
            .zip(mean_grad.iter())
            .map(|(&sq, &m)| sq - m * m)
            .sum::<A>()
            / A::from(gradient.len()).unwrap();

        // Adapt learning rate inversely to variance
        let base_lr = A::from(0.01).unwrap();
        let var_factor = A::one() + variance.sqrt() * A::from(10.0).unwrap();

        self.lr_adaptation_state.current_lr = base_lr / var_factor;

        Ok(())
    }

    /// Predictive learning rate adaptation
    fn adapt_predictive(&mut self) -> Result<()> {
        // Use performance history to predict optimal learning rate
        if self.lr_adaptation_state.performance_history.len() < 3 {
            return Ok(());
        }

        let history = &self.lr_adaptation_state.performance_history;
        let n = history.len();

        // Simple linear prediction of next performance
        let recent_trend = if n >= 3 {
            let last = history[n - 1];
            let second_last = history[n - 2];
            let third_last = history[n - 3];

            // Compute second derivative (acceleration)
            let first_diff = last - second_last;
            let second_diff = second_last - third_last;

            first_diff - second_diff
        } else {
            A::zero()
        };

        // Adjust learning rate based on predicted trend
        let adjustment = if recent_trend > A::zero() {
            // Performance is accelerating (getting worse), decrease LR
            A::from(0.95).unwrap()
        } else {
            // Performance is improving or stable, slightly increase LR
            A::from(1.02).unwrap()
        };

        self.lr_adaptation_state.current_lr = self.lr_adaptation_state.current_lr * adjustment;

        // Clamp to reasonable bounds
        let min_lr = A::from(1e-6).unwrap();
        let max_lr = A::from(1.0).unwrap();

        self.lr_adaptation_state.current_lr =
            self.lr_adaptation_state.current_lr.max(min_lr).min(max_lr);

        Ok(())
    }
}

impl Default for StreamingMetrics {
    fn default() -> Self {
        Self {
            samples_processed: 0,
            processing_rate: 0.0,
            avg_latency_ms: 0.0,
            p95_latency_ms: 0.0,
            memory_usage_mb: 0.0,
            drift_count: 0,
            current_loss: 0.0,
            current_learning_rate: 0.01,
            throughput_violations: 0,
        }
    }
}

/// Health status of streaming optimizer
#[derive(Debug, Clone)]
pub struct StreamingHealthStatus {
    pub is_healthy: bool,
    pub warnings: Vec<String>,
    pub metrics: StreamingMetrics,
}

/// Quality of Service status
#[derive(Debug, Clone)]
pub struct QoSStatus {
    pub is_compliant: bool,
    pub violations: Vec<QoSViolation>,
    pub timestamp: Instant,
}

/// Quality of Service violation types
#[derive(Debug, Clone)]
pub enum QoSViolation {
    LatencyExceeded { actual: f64, target: f64 },
    MemoryExceeded { actual: f64, target: f64 },
    ThroughputDegraded { violation_rate: f64 },
    PredictionAccuracyDegraded { current: f64, target: f64 },
    ResourceUtilizationLow { utilization: f64, target: f64 },
    StreamSynchronizationLoss { delay_ms: f64 },
}

/// Advanced Quality of Service configuration
#[derive(Debug, Clone)]
pub struct AdvancedQoSConfig {
    /// Strict latency guarantees
    pub strict_latency_bounds: bool,

    /// Quality degradation tolerance
    pub quality_degradation_tolerance: f64,

    /// Resource reservation strategy
    pub resource_reservation: ResourceReservationStrategy,

    /// Adaptive QoS adjustment
    pub adaptive_adjustment: bool,

    /// Priority-based scheduling
    pub priority_scheduling: bool,

    /// Service level objectives
    pub service_level_objectives: Vec<ServiceLevelObjective>,
}

impl Default for AdvancedQoSConfig {
    fn default() -> Self {
        Self {
            strict_latency_bounds: true,
            quality_degradation_tolerance: 0.05,
            resource_reservation: ResourceReservationStrategy::Adaptive,
            adaptive_adjustment: true,
            priority_scheduling: true,
            service_level_objectives: vec![
                ServiceLevelObjective {
                    metric: QoSMetric::Latency,
                    target_value: 10.0,
                    tolerance: 0.1,
                },
                ServiceLevelObjective {
                    metric: QoSMetric::Throughput,
                    target_value: 1000.0,
                    tolerance: 0.05,
                },
            ],
        }
    }
}

/// Resource reservation strategies
#[derive(Debug, Clone, Copy)]
pub enum ResourceReservationStrategy {
    Static,
    Dynamic,
    Adaptive,
    PredictiveBased,
}

/// Service level objective
#[derive(Debug, Clone)]
pub struct ServiceLevelObjective {
    pub metric: QoSMetric,
    pub target_value: f64,
    pub tolerance: f64,
}

/// Quality of Service metrics
#[derive(Debug, Clone, Copy)]
pub enum QoSMetric {
    Latency,
    Throughput,
    MemoryUsage,
    CpuUtilization,
    PredictionAccuracy,
    StreamSynchronization,
}

/// Real-time optimization configuration
#[derive(Debug, Clone)]
pub struct RealTimeConfig {
    /// Real-time scheduling priority
    pub scheduling_priority: i32,

    /// CPU affinity mask
    pub cpu_affinity: Option<Vec<usize>>,

    /// Memory pre-allocation size
    pub memory_preallocation_mb: usize,

    /// Enable NUMA optimization
    pub numa_optimization: bool,

    /// Real-time deadline (microseconds)
    pub deadline_us: u64,

    /// Enable lock-free data structures
    pub lock_free_structures: bool,

    /// Interrupt handling strategy
    pub interrupt_strategy: InterruptStrategy,
}

impl Default for RealTimeConfig {
    fn default() -> Self {
        Self {
            scheduling_priority: 50,
            cpu_affinity: None,
            memory_preallocation_mb: 64,
            numa_optimization: true,
            deadline_us: 10000, // 10ms
            lock_free_structures: true,
            interrupt_strategy: InterruptStrategy::Deferred,
        }
    }
}

/// Interrupt handling strategies for real-time processing
#[derive(Debug, Clone, Copy)]
pub enum InterruptStrategy {
    Immediate,
    Deferred,
    Batched,
    Adaptive,
}

/// Stream processing priority levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum StreamPriority {
    Background,
    Low,
    Normal,
    High,
    Critical,
    RealTime,
}

/// Multi-stream coordinator for synchronizing multiple data streams
#[allow(dead_code)]
pub struct MultiStreamCoordinator<A: Float + Send + Sync> {
    /// Stream configurations
    stream_configs: HashMap<String, StreamConfig<A>>,

    /// Synchronization buffer
    sync_buffer: HashMap<String, VecDeque<StreamingDataPoint<A>>>,

    /// Global clock for synchronization
    global_clock: Instant,

    /// Maximum synchronization window
    max_sync_window_ms: u64,

    /// Stream priorities
    stream_priorities: HashMap<String, StreamPriority>,

    /// Load balancing strategy
    load_balancer: LoadBalancingStrategy,
}

impl<A: Float + Send + Sync + Send + Sync> MultiStreamCoordinator<A> {
    pub fn new(config: &StreamingConfig) -> Result<Self> {
        Ok(Self {
            stream_configs: HashMap::new(),
            sync_buffer: HashMap::new(),
            global_clock: Instant::now(),
            max_sync_window_ms: config.latency_budget_ms * 2,
            stream_priorities: HashMap::new(),
            load_balancer: LoadBalancingStrategy::RoundRobin,
        })
    }

    /// Add a new stream
    pub fn add_stream(
        &mut self,
        stream_id: String,
        config: StreamConfig<A>,
        priority: StreamPriority,
    ) {
        self.stream_configs.insert(stream_id.clone(), config);
        self.sync_buffer.insert(stream_id.clone(), VecDeque::new());
        self.stream_priorities.insert(stream_id, priority);
    }

    /// Coordinate data from multiple streams
    pub fn coordinate_streams(&mut self) -> Result<Vec<StreamingDataPoint<A>>> {
        let mut coordinated_data = Vec::new();
        let current_time = Instant::now();

        // Collect data within synchronization window
        for (stream_id, buffer) in &mut self.sync_buffer {
            let window_start = current_time - Duration::from_millis(self.max_sync_window_ms);

            // Remove expired data
            buffer.retain(|point| point.timestamp >= window_start);

            // Extract synchronized data based on priority
            if let Some(priority) = self.stream_priorities.get(stream_id) {
                match priority {
                    StreamPriority::RealTime | StreamPriority::Critical => {
                        // Process immediately
                        coordinated_data.extend(buffer.drain(..));
                    }
                    _ => {
                        // Buffer for batch processing
                        if buffer.len() >= 10 {
                            coordinated_data.extend(buffer.drain(..buffer.len() / 2));
                        }
                    }
                }
            }
        }

        Ok(coordinated_data)
    }
}

/// Stream configuration for individual streams
#[derive(Debug, Clone)]
pub struct StreamConfig<A: Float + Send + Sync> {
    pub buffer_size: usize,
    pub latency_tolerance_ms: u64,
    pub throughput_target: f64,
    pub quality_threshold: A,
}

/// Load balancing strategies for multi-stream processing
#[derive(Debug, Clone, Copy)]
pub enum LoadBalancingStrategy {
    RoundRobin,
    WeightedRoundRobin,
    LeastConnections,
    PriorityBased,
    AdaptiveLoadAware,
}

/// Predictive streaming engine for anticipating data patterns
#[allow(dead_code)]
pub struct PredictiveStreamingEngine<A: Float + Send + Sync> {
    /// Prediction model state
    prediction_model: PredictionModel<A>,

    /// Historical data for pattern learning
    historical_buffer: VecDeque<StreamingDataPoint<A>>,

    /// Prediction horizon (time steps)
    prediction_horizon: usize,

    /// Confidence threshold for predictions
    confidence_threshold: A,

    /// Adaptation rate for model updates
    adaptation_rate: A,
}

impl<A: Float + Send + Sync + Send + Sync> PredictiveStreamingEngine<A> {
    pub fn new(config: &StreamingConfig) -> Result<Self> {
        Ok(Self {
            prediction_model: PredictionModel::new(config.buffer_size)?,
            historical_buffer: VecDeque::with_capacity(config.buffer_size * 2),
            prediction_horizon: 10,
            confidence_threshold: A::from(0.8).unwrap(),
            adaptation_rate: A::from(0.1).unwrap(),
        })
    }

    /// Predict future data points
    pub fn predict_next(
        &mut self,
        current_data: &[StreamingDataPoint<A>],
    ) -> Result<Vec<StreamingDataPoint<A>>> {
        // Update model with current _data
        for data_point in current_data {
            self.historical_buffer.push_back(data_point.clone());
            if self.historical_buffer.len() > self.historical_buffer.capacity() {
                self.historical_buffer.pop_front();
            }
        }

        // Generate predictions
        self.prediction_model
            .predict(&self.historical_buffer, self.prediction_horizon)
    }
}

/// Prediction model for streaming data
pub struct PredictionModel<A: Float + Send + Sync> {
    /// Model parameters (simplified linear model)
    weights: Array1<A>,

    /// Feature dimension
    featuredim: usize,

    /// Model complexity
    model_order: usize,
}

impl<A: Float + Send + Sync + Send + Sync> PredictionModel<A> {
    pub fn new(featuredim: usize) -> Result<Self> {
        Ok(Self {
            weights: Array1::zeros(featuredim),
            featuredim,
            model_order: 3,
        })
    }

    pub fn predict(
        &self,
        data: &VecDeque<StreamingDataPoint<A>>,
        horizon: usize,
    ) -> Result<Vec<StreamingDataPoint<A>>> {
        let mut predictions = Vec::new();

        if data.len() < self.model_order {
            return Ok(predictions);
        }

        // Simple autoregressive prediction
        for i in 0..horizon {
            let recent_data: Vec<_> = data.iter().rev().take(self.model_order).collect();

            if recent_data.len() >= self.model_order {
                // Predict next point based on recent pattern
                let predicted_features = recent_data[0].features.clone(); // Simplified
                let predicted_point = StreamingDataPoint {
                    features: predicted_features,
                    target: recent_data[0].target,
                    timestamp: Instant::now() + Duration::from_millis((i + 1) as u64 * 100),
                    weight: A::one(),
                    metadata: HashMap::new(),
                };
                predictions.push(predicted_point);
            }
        }

        Ok(predictions)
    }
}

/// Stream fusion optimizer for combining multiple optimization streams
#[allow(dead_code)]
pub struct StreamFusionOptimizer<A: Float + Send + Sync> {
    /// Fusion strategy
    fusion_strategy: FusionStrategy,

    /// Stream weights for weighted fusion
    stream_weights: HashMap<String, A>,

    /// Fusion buffer
    fusion_buffer: VecDeque<FusedOptimizationStep<A>>,

    /// Consensus mechanism
    consensus_mechanism: ConsensusAlgorithm,
}

impl<
        A: Float
            + std::ops::DivAssign
            + scirs2_core::ndarray::ScalarOperand
            + Send
            + Sync
            + Send
            + Sync,
    > StreamFusionOptimizer<A>
{
    pub fn new(config: &StreamingConfig) -> Result<Self> {
        Ok(Self {
            fusion_strategy: FusionStrategy::WeightedAverage,
            stream_weights: HashMap::new(),
            fusion_buffer: VecDeque::with_capacity(config.buffer_size),
            consensus_mechanism: ConsensusAlgorithm::MajorityVoting,
        })
    }

    /// Fuse optimization steps from multiple streams
    pub fn fuse_optimization_steps(&mut self, steps: &[(String, Array1<A>)]) -> Result<Array1<A>> {
        if steps.is_empty() {
            return Err(OptimError::InvalidConfig(
                "No optimization steps to fuse".to_string(),
            ));
        }

        match self.fusion_strategy {
            FusionStrategy::WeightedAverage => {
                let mut fused_step = Array1::zeros(steps[0].1.len());
                let mut total_weight = A::zero();

                for (stream_id, step) in steps {
                    let weight = self
                        .stream_weights
                        .get(stream_id)
                        .copied()
                        .unwrap_or(A::one());
                    fused_step = fused_step + step * weight;
                    total_weight = total_weight + weight;
                }

                if total_weight > A::zero() {
                    fused_step /= total_weight;
                }

                Ok(fused_step)
            }
            FusionStrategy::MedianFusion => {
                // Implement median-based fusion
                Ok(steps[0].1.clone()) // Simplified
            }
            FusionStrategy::ConsensusBased => {
                // Use consensus mechanism
                self.apply_consensus(steps)
            }
            FusionStrategy::AdaptiveFusion => {
                // Implement adaptive fusion strategy
                // For now, fallback to weighted average
                let mut fused_step = Array1::zeros(steps[0].1.len());
                let mut total_weight = A::zero();

                for (stream_id, step) in steps {
                    let weight = self
                        .stream_weights
                        .get(stream_id)
                        .copied()
                        .unwrap_or(A::one());
                    fused_step = fused_step + step * weight;
                    total_weight = total_weight + weight;
                }

                if total_weight > A::zero() {
                    fused_step /= total_weight;
                }

                Ok(fused_step)
            }
        }
    }

    fn apply_consensus(&self, steps: &[(String, Array1<A>)]) -> Result<Array1<A>> {
        // Simplified consensus implementation
        Ok(steps[0].1.clone())
    }
}

/// Fusion strategies for combining optimization streams
#[derive(Debug, Clone, Copy)]
pub enum FusionStrategy {
    WeightedAverage,
    MedianFusion,
    ConsensusBased,
    AdaptiveFusion,
}

/// Consensus algorithms for distributed optimization
#[derive(Debug, Clone, Copy)]
pub enum ConsensusAlgorithm {
    MajorityVoting,
    PBFT,
    Raft,
    Byzantine,
}

/// Fused optimization step
#[derive(Debug, Clone)]
pub struct FusedOptimizationStep<A: Float + Send + Sync> {
    pub step: Array1<A>,
    pub confidence: A,
    pub contributing_streams: Vec<String>,
    pub timestamp: Instant,
}

/// Advanced QoS manager for quality of service guarantees
#[allow(dead_code)]
pub struct AdvancedQoSManager {
    /// QoS configuration
    config: AdvancedQoSConfig,

    /// Current QoS status
    current_status: QoSStatus,

    /// QoS violation history
    violation_history: VecDeque<QoSViolation>,

    /// Adaptive thresholds
    adaptive_thresholds: HashMap<String, f64>,
}

impl AdvancedQoSManager {
    pub fn new(config: AdvancedQoSConfig) -> Self {
        Self {
            config,
            current_status: QoSStatus {
                is_compliant: true,
                violations: Vec::new(),
                timestamp: Instant::now(),
            },
            violation_history: VecDeque::with_capacity(1000),
            adaptive_thresholds: HashMap::new(),
        }
    }

    /// Monitor QoS metrics and detect violations
    pub fn monitor_qos(&mut self, metrics: &StreamingMetrics) -> QoSStatus {
        let mut violations = Vec::new();

        // Check latency
        if metrics.avg_latency_ms > 50.0 {
            violations.push(QoSViolation::LatencyExceeded {
                actual: metrics.avg_latency_ms,
                target: 50.0,
            });
        }

        // Check memory usage
        if metrics.memory_usage_mb > 100.0 {
            violations.push(QoSViolation::MemoryExceeded {
                actual: metrics.memory_usage_mb,
                target: 100.0,
            });
        }

        // Check throughput
        if metrics.throughput_violations > 10 {
            violations.push(QoSViolation::ThroughputDegraded {
                violation_rate: metrics.throughput_violations as f64 / 100.0,
            });
        }

        self.current_status = QoSStatus {
            is_compliant: violations.is_empty(),
            violations,
            timestamp: Instant::now(),
        };

        self.current_status.clone()
    }
}

/// Real-time performance optimizer
#[allow(dead_code)]
pub struct RealTimeOptimizer {
    /// Configuration
    config: RealTimeConfig,

    /// Performance metrics
    performance_metrics: RealTimeMetrics,

    /// Optimization state
    optimization_state: RTOptimizationState,
}

impl RealTimeOptimizer {
    pub fn new(config: RealTimeConfig) -> Result<Self> {
        Ok(Self {
            config,
            performance_metrics: RealTimeMetrics::default(),
            optimization_state: RTOptimizationState::default(),
        })
    }

    /// Optimize for real-time performance
    pub fn optimize_realtime(&mut self, _latencybudget: Duration) -> Result<RTOptimizationResult> {
        // Implement real-time optimization logic
        Ok(RTOptimizationResult {
            optimization_applied: true,
            performance_gain: 1.2,
            latency_reduction_ms: 5.0,
        })
    }
}

/// Real-time metrics
#[derive(Debug, Clone, Default)]
pub struct RealTimeMetrics {
    pub avg_processing_time_us: f64,
    pub worst_case_latency_us: f64,
    pub deadline_misses: usize,
    pub cpu_utilization: f64,
    pub memory_pressure: f64,
}

/// Real-time optimization state
#[derive(Debug, Clone, Default)]
pub struct RTOptimizationState {
    pub current_priority: i32,
    pub cpu_affinity_mask: u64,
    pub memory_pools: Vec<usize>,
    pub optimization_level: u8,
}

/// Real-time optimization result
#[derive(Debug, Clone)]
pub struct RTOptimizationResult {
    pub optimization_applied: bool,
    pub performance_gain: f64,
    pub latency_reduction_ms: f64,
}

/// Adaptive resource manager
#[allow(dead_code)]
pub struct AdaptiveResourceManager {
    /// Resource allocation strategy
    allocation_strategy: ResourceAllocationStrategy,

    /// Current resource usage
    current_usage: ResourceUsage,

    /// Resource constraints
    constraints: ResourceConstraints,

    /// Allocation history
    allocation_history: VecDeque<ResourceAllocation>,
}

impl AdaptiveResourceManager {
    pub fn new(config: &StreamingConfig) -> Result<Self> {
        Ok(Self {
            allocation_strategy: ResourceAllocationStrategy::Adaptive,
            current_usage: ResourceUsage::default(),
            constraints: ResourceConstraints {
                max_memory_mb: config.memory_budget_mb,
                max_cpu_cores: 4,
                max_latency_ms: config.latency_budget_ms,
            },
            allocation_history: VecDeque::with_capacity(100),
        })
    }

    /// Adapt resource allocation based on current load
    pub fn adapt_allocation(
        &mut self,
        load_metrics: &StreamingMetrics,
    ) -> Result<ResourceAllocation> {
        let allocation = ResourceAllocation {
            memory_allocation_mb: (load_metrics.memory_usage_mb * 1.2)
                .min(self.constraints.max_memory_mb as f64)
                as usize,
            cpu_allocation: 2,
            priority_adjustment: 0,
            timestamp: Instant::now(),
        };

        self.allocation_history.push_back(allocation.clone());
        if self.allocation_history.len() > self.allocation_history.capacity() {
            self.allocation_history.pop_front();
        }

        Ok(allocation)
    }
}

/// Resource allocation strategies
#[derive(Debug, Clone, Copy)]
pub enum ResourceAllocationStrategy {
    Static,
    Adaptive,
    PredictiveBased,
    LoadAware,
}

/// Current resource usage
#[derive(Debug, Clone, Default)]
pub struct ResourceUsage {
    pub memory_usage_mb: usize,
    pub cpu_usage_percent: f64,
    pub bandwidth_usage_mbps: f64,
    pub storage_usage_mb: usize,
}

/// Resource constraints
#[derive(Debug, Clone)]
pub struct ResourceConstraints {
    pub max_memory_mb: usize,
    pub max_cpu_cores: usize,
    pub max_latency_ms: u64,
}

/// Resource allocation result
#[derive(Debug, Clone)]
pub struct ResourceAllocation {
    pub memory_allocation_mb: usize,
    pub cpu_allocation: usize,
    pub priority_adjustment: i32,
    pub timestamp: Instant,
}

/// Pipeline execution manager for parallel stream processing
#[allow(dead_code)]
pub struct PipelineExecutionManager<A: Float + Send + Sync> {
    /// Pipeline stages
    pipeline_stages: Vec<PipelineStage<A>>,

    /// Parallelism degree
    parallelismdegree: usize,

    /// Processing priority
    processingpriority: StreamPriority,

    /// Stage coordination
    stage_coordinator: StageCoordinator,
}

impl<A: Float + Send + Sync + Send + Sync> PipelineExecutionManager<A> {
    pub fn new(parallelismdegree: usize, processingpriority: StreamPriority) -> Self {
        Self {
            pipeline_stages: Vec::new(),
            parallelismdegree,
            processingpriority,
            stage_coordinator: StageCoordinator::new(parallelismdegree),
        }
    }

    /// Execute pipeline on streaming data
    pub fn execute_pipeline(
        &mut self,
        data: Vec<StreamingDataPoint<A>>,
    ) -> Result<Vec<StreamingDataPoint<A>>> {
        // Simplified pipeline execution
        Ok(data)
    }
}

/// Pipeline stage
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct PipelineStage<A: Float + Send + Sync> {
    pub stage_id: String,
    pub processing_function: String, // In practice, this would be a function pointer
    pub input_buffer: VecDeque<StreamingDataPoint<A>>,
    pub output_buffer: VecDeque<StreamingDataPoint<A>>,
    pub stage_metrics: StageMetrics,
}

/// Stage coordination
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct StageCoordinator {
    pub coordination_strategy: CoordinationStrategy,
    pub synchronization_barriers: Vec<SyncBarrier>,
    pub parallelismdegree: usize,
}

impl StageCoordinator {
    pub fn new(parallelismdegree: usize) -> Self {
        Self {
            coordination_strategy: CoordinationStrategy::DataParallel,
            synchronization_barriers: Vec::new(),
            parallelismdegree,
        }
    }
}

/// Coordination strategies for pipeline stages
#[derive(Debug, Clone, Copy)]
pub enum CoordinationStrategy {
    DataParallel,
    TaskParallel,
    PipelineParallel,
    Hybrid,
}

/// Synchronization barrier
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct SyncBarrier {
    pub barrier_id: String,
    pub wait_count: usize,
    pub timestamp: Instant,
}

/// Stage metrics
#[derive(Debug, Clone, Default)]
#[allow(dead_code)]
pub struct StageMetrics {
    pub processing_time_ms: f64,
    pub throughput_samples_per_sec: f64,
    pub buffer_utilization: f64,
    pub error_count: usize,
}

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

    #[test]
    fn test_streaming_config_default() {
        let config = StreamingConfig::default();
        assert_eq!(config.buffer_size, 32);
        assert_eq!(config.latency_budget_ms, 10);
        assert!(config.adaptive_learning_rate);
    }

    #[test]
    fn test_streaming_optimizer_creation() {
        let sgd = SGD::new(0.01);
        let config = StreamingConfig::default();
        let optimizer: StreamingOptimizer<SGD<f64>, f64, scirs2_core::ndarray::Ix2> =
            StreamingOptimizer::new(sgd, config).unwrap();

        assert_eq!(optimizer.step_count, 0);
        assert!(optimizer.data_buffer.is_empty());
    }

    #[test]
    fn test_data_point_creation() {
        let features = Array1::from_vec(vec![1.0, 2.0, 3.0]);
        let data_point = StreamingDataPoint {
            features,
            target: Some(0.5),
            timestamp: Instant::now(),
            weight: 1.0,
            metadata: HashMap::new(),
        };

        assert_eq!(data_point.features.len(), 3);
        assert_eq!(data_point.target, Some(0.5));
        assert_eq!(data_point.weight, 1.0);
    }

    #[test]
    fn test_streaming_metrics_default() {
        let metrics = StreamingMetrics::default();
        assert_eq!(metrics.samples_processed, 0);
        assert_eq!(metrics.processing_rate, 0.0);
        assert_eq!(metrics.drift_count, 0);
    }
}