Struct AdaptiveStreamingEngine 

pub struct AdaptiveStreamingEngine { /* private fields */ }

Advanced-advanced adaptive streaming processor

Implementations

impl AdaptiveStreamingEngine

pub fn new(config: AdaptiveStreamConfig) -> Self

Create a new adaptive streaming engine

pub fn process_stream(&mut self, chunk: StreamChunk) -> Result<Vec<Dataset>>

Process incoming data stream

Examples found in repository

examples/advanced_showcase.rs (line 255)

fn demonstrate_adaptive_streaming(dataset: &Dataset) -> Result<(), Box<dyn std::error::Error>> {
    println!("\n🌊 Adaptive Streaming Demonstration");
    println!("===================================");

    // Configure streaming engine
    let config = AdaptiveStreamConfig::default();

    println!("🔧 Initializing adaptive streaming engine...");
    let mut engine = create_adaptive_engine_with_config(config);

    // Simulate streaming data
    println!("📡 Simulating data stream...");
    let data = &dataset.data;
    let chunksize = 20;
    let num_chunks = (data.nrows() / chunksize).min(10); // Limit for demo

    let mut total_processed = 0;
    let start_time = Instant::now();

    for i in 0..num_chunks {
        let start_row = i * chunksize;
        let end_row = (start_row + chunksize).min(data.nrows());

        // Create chunk from dataset slice
        let chunkdata = data
            .slice(scirs2_core::ndarray::s![start_row..end_row, ..])
            .to_owned();

        let chunk = StreamChunk {
            data: chunkdata,
            timestamp: Instant::now(),
            metadata: ChunkMetadata {
                source_id: format!("demo_source_{i}"),
                sequence_number: i as u64,
                characteristics: DataCharacteristics {
                    moments: StatisticalMoments {
                        mean: 0.0,
                        variance: 1.0,
                        skewness: 0.0,
                        kurtosis: 0.0,
                    },
                    entropy: 1.0,
                    trend: TrendIndicators {
                        linear_slope: 0.1,
                        trend_strength: 0.5,
                        direction: TrendDirection::Increasing,
                        seasonality: 0.2,
                    },
                    anomaly_score: 0.1,
                },
            },
            quality_score: 0.9,
        };

        // Process chunk
        let results = engine.process_stream(chunk)?;
        total_processed += results.len();

        if !results.is_empty() {
            println!(
                "   Processed batch {}: {} datasets generated",
                i + 1,
                results.len()
            );
        }
    }

    let streaming_time = start_time.elapsed();

    println!("   Streaming completed in: {streaming_time:?}");
    println!("   Total datasets processed: {total_processed}");

    // Get performance metrics
    println!("📈 Getting performance metrics...");
    let perf_metrics = engine.get_performance_metrics()?;
    println!("   Processing Latency: {:?}", perf_metrics.latency);
    println!("   Throughput: {:.1} chunks/sec", perf_metrics.throughput);
    println!(
        "   Memory Efficiency: {:.1}%",
        perf_metrics.memory_efficiency * 100.0
    );

    // Get quality metrics
    let quality_metrics = engine.get_quality_metrics()?;
    println!("   Quality Metrics:");
    println!(
        "     Integrity: {:.1}%",
        quality_metrics.integrity_score * 100.0
    );
    println!(
        "     Completeness: {:.1}%",
        quality_metrics.completeness_score * 100.0
    );
    println!(
        "     Overall Quality: {:.1}%",
        quality_metrics.overall_score * 100.0
    );

    // Get buffer statistics
    let buffer_stats = engine.get_buffer_statistics()?;
    println!("   Buffer Statistics:");
    println!("     Utilization: {:.1}%", buffer_stats.utilization * 100.0);
    println!("     Memory Usage: {} bytes", buffer_stats.memory_usage);

    Ok(())
}

pub fn get_performance_metrics(&self) -> Result<PerformanceMetrics>

Get current performance metrics

Examples found in repository

examples/advanced_showcase.rs (line 274)

fn demonstrate_adaptive_streaming(dataset: &Dataset) -> Result<(), Box<dyn std::error::Error>> {
    println!("\n🌊 Adaptive Streaming Demonstration");
    println!("===================================");

    // Configure streaming engine
    let config = AdaptiveStreamConfig::default();

    println!("🔧 Initializing adaptive streaming engine...");
    let mut engine = create_adaptive_engine_with_config(config);

    // Simulate streaming data
    println!("📡 Simulating data stream...");
    let data = &dataset.data;
    let chunksize = 20;
    let num_chunks = (data.nrows() / chunksize).min(10); // Limit for demo

    let mut total_processed = 0;
    let start_time = Instant::now();

    for i in 0..num_chunks {
        let start_row = i * chunksize;
        let end_row = (start_row + chunksize).min(data.nrows());

        // Create chunk from dataset slice
        let chunkdata = data
            .slice(scirs2_core::ndarray::s![start_row..end_row, ..])
            .to_owned();

        let chunk = StreamChunk {
            data: chunkdata,
            timestamp: Instant::now(),
            metadata: ChunkMetadata {
                source_id: format!("demo_source_{i}"),
                sequence_number: i as u64,
                characteristics: DataCharacteristics {
                    moments: StatisticalMoments {
                        mean: 0.0,
                        variance: 1.0,
                        skewness: 0.0,
                        kurtosis: 0.0,
                    },
                    entropy: 1.0,
                    trend: TrendIndicators {
                        linear_slope: 0.1,
                        trend_strength: 0.5,
                        direction: TrendDirection::Increasing,
                        seasonality: 0.2,
                    },
                    anomaly_score: 0.1,
                },
            },
            quality_score: 0.9,
        };

        // Process chunk
        let results = engine.process_stream(chunk)?;
        total_processed += results.len();

        if !results.is_empty() {
            println!(
                "   Processed batch {}: {} datasets generated",
                i + 1,
                results.len()
            );
        }
    }

    let streaming_time = start_time.elapsed();

    println!("   Streaming completed in: {streaming_time:?}");
    println!("   Total datasets processed: {total_processed}");

    // Get performance metrics
    println!("📈 Getting performance metrics...");
    let perf_metrics = engine.get_performance_metrics()?;
    println!("   Processing Latency: {:?}", perf_metrics.latency);
    println!("   Throughput: {:.1} chunks/sec", perf_metrics.throughput);
    println!(
        "   Memory Efficiency: {:.1}%",
        perf_metrics.memory_efficiency * 100.0
    );

    // Get quality metrics
    let quality_metrics = engine.get_quality_metrics()?;
    println!("   Quality Metrics:");
    println!(
        "     Integrity: {:.1}%",
        quality_metrics.integrity_score * 100.0
    );
    println!(
        "     Completeness: {:.1}%",
        quality_metrics.completeness_score * 100.0
    );
    println!(
        "     Overall Quality: {:.1}%",
        quality_metrics.overall_score * 100.0
    );

    // Get buffer statistics
    let buffer_stats = engine.get_buffer_statistics()?;
    println!("   Buffer Statistics:");
    println!("     Utilization: {:.1}%", buffer_stats.utilization * 100.0);
    println!("     Memory Usage: {} bytes", buffer_stats.memory_usage);

    Ok(())
}

pub fn get_quality_metrics(&self) -> Result<QualityMetrics>

Get current quality metrics

Examples found in repository

examples/advanced_showcase.rs (line 283)

fn demonstrate_adaptive_streaming(dataset: &Dataset) -> Result<(), Box<dyn std::error::Error>> {
    println!("\n🌊 Adaptive Streaming Demonstration");
    println!("===================================");

    // Configure streaming engine
    let config = AdaptiveStreamConfig::default();

    println!("🔧 Initializing adaptive streaming engine...");
    let mut engine = create_adaptive_engine_with_config(config);

    // Simulate streaming data
    println!("📡 Simulating data stream...");
    let data = &dataset.data;
    let chunksize = 20;
    let num_chunks = (data.nrows() / chunksize).min(10); // Limit for demo

    let mut total_processed = 0;
    let start_time = Instant::now();

    for i in 0..num_chunks {
        let start_row = i * chunksize;
        let end_row = (start_row + chunksize).min(data.nrows());

        // Create chunk from dataset slice
        let chunkdata = data
            .slice(scirs2_core::ndarray::s![start_row..end_row, ..])
            .to_owned();

        let chunk = StreamChunk {
            data: chunkdata,
            timestamp: Instant::now(),
            metadata: ChunkMetadata {
                source_id: format!("demo_source_{i}"),
                sequence_number: i as u64,
                characteristics: DataCharacteristics {
                    moments: StatisticalMoments {
                        mean: 0.0,
                        variance: 1.0,
                        skewness: 0.0,
                        kurtosis: 0.0,
                    },
                    entropy: 1.0,
                    trend: TrendIndicators {
                        linear_slope: 0.1,
                        trend_strength: 0.5,
                        direction: TrendDirection::Increasing,
                        seasonality: 0.2,
                    },
                    anomaly_score: 0.1,
                },
            },
            quality_score: 0.9,
        };

        // Process chunk
        let results = engine.process_stream(chunk)?;
        total_processed += results.len();

        if !results.is_empty() {
            println!(
                "   Processed batch {}: {} datasets generated",
                i + 1,
                results.len()
            );
        }
    }

    let streaming_time = start_time.elapsed();

    println!("   Streaming completed in: {streaming_time:?}");
    println!("   Total datasets processed: {total_processed}");

    // Get performance metrics
    println!("📈 Getting performance metrics...");
    let perf_metrics = engine.get_performance_metrics()?;
    println!("   Processing Latency: {:?}", perf_metrics.latency);
    println!("   Throughput: {:.1} chunks/sec", perf_metrics.throughput);
    println!(
        "   Memory Efficiency: {:.1}%",
        perf_metrics.memory_efficiency * 100.0
    );

    // Get quality metrics
    let quality_metrics = engine.get_quality_metrics()?;
    println!("   Quality Metrics:");
    println!(
        "     Integrity: {:.1}%",
        quality_metrics.integrity_score * 100.0
    );
    println!(
        "     Completeness: {:.1}%",
        quality_metrics.completeness_score * 100.0
    );
    println!(
        "     Overall Quality: {:.1}%",
        quality_metrics.overall_score * 100.0
    );

    // Get buffer statistics
    let buffer_stats = engine.get_buffer_statistics()?;
    println!("   Buffer Statistics:");
    println!("     Utilization: {:.1}%", buffer_stats.utilization * 100.0);
    println!("     Memory Usage: {} bytes", buffer_stats.memory_usage);

    Ok(())
}

pub fn get_buffer_statistics(&self) -> Result<BufferStatistics>

Get buffer statistics

Examples found in repository

examples/advanced_showcase.rs (line 299)

fn demonstrate_adaptive_streaming(dataset: &Dataset) -> Result<(), Box<dyn std::error::Error>> {
    println!("\n🌊 Adaptive Streaming Demonstration");
    println!("===================================");

    // Configure streaming engine
    let config = AdaptiveStreamConfig::default();

    println!("🔧 Initializing adaptive streaming engine...");
    let mut engine = create_adaptive_engine_with_config(config);

    // Simulate streaming data
    println!("📡 Simulating data stream...");
    let data = &dataset.data;
    let chunksize = 20;
    let num_chunks = (data.nrows() / chunksize).min(10); // Limit for demo

    let mut total_processed = 0;
    let start_time = Instant::now();

    for i in 0..num_chunks {
        let start_row = i * chunksize;
        let end_row = (start_row + chunksize).min(data.nrows());

        // Create chunk from dataset slice
        let chunkdata = data
            .slice(scirs2_core::ndarray::s![start_row..end_row, ..])
            .to_owned();

        let chunk = StreamChunk {
            data: chunkdata,
            timestamp: Instant::now(),
            metadata: ChunkMetadata {
                source_id: format!("demo_source_{i}"),
                sequence_number: i as u64,
                characteristics: DataCharacteristics {
                    moments: StatisticalMoments {
                        mean: 0.0,
                        variance: 1.0,
                        skewness: 0.0,
                        kurtosis: 0.0,
                    },
                    entropy: 1.0,
                    trend: TrendIndicators {
                        linear_slope: 0.1,
                        trend_strength: 0.5,
                        direction: TrendDirection::Increasing,
                        seasonality: 0.2,
                    },
                    anomaly_score: 0.1,
                },
            },
            quality_score: 0.9,
        };

        // Process chunk
        let results = engine.process_stream(chunk)?;
        total_processed += results.len();

        if !results.is_empty() {
            println!(
                "   Processed batch {}: {} datasets generated",
                i + 1,
                results.len()
            );
        }
    }

    let streaming_time = start_time.elapsed();

    println!("   Streaming completed in: {streaming_time:?}");
    println!("   Total datasets processed: {total_processed}");

    // Get performance metrics
    println!("📈 Getting performance metrics...");
    let perf_metrics = engine.get_performance_metrics()?;
    println!("   Processing Latency: {:?}", perf_metrics.latency);
    println!("   Throughput: {:.1} chunks/sec", perf_metrics.throughput);
    println!(
        "   Memory Efficiency: {:.1}%",
        perf_metrics.memory_efficiency * 100.0
    );

    // Get quality metrics
    let quality_metrics = engine.get_quality_metrics()?;
    println!("   Quality Metrics:");
    println!(
        "     Integrity: {:.1}%",
        quality_metrics.integrity_score * 100.0
    );
    println!(
        "     Completeness: {:.1}%",
        quality_metrics.completeness_score * 100.0
    );
    println!(
        "     Overall Quality: {:.1}%",
        quality_metrics.overall_score * 100.0
    );

    // Get buffer statistics
    let buffer_stats = engine.get_buffer_statistics()?;
    println!("   Buffer Statistics:");
    println!("     Utilization: {:.1}%", buffer_stats.utilization * 100.0);
    println!("     Memory Usage: {} bytes", buffer_stats.memory_usage);

    Ok(())
}

impl AdaptiveStreamingEngine

Enhanced Adaptive Streaming Engine with Quantum and Neural Optimization

pub fn with_quantum_neural_optimization(config: AdaptiveStreamConfig) -> Self

Create advanced streaming engine with quantum and neural optimization

pub fn quantum_optimize( &mut self, performance_metrics: &PerformanceMetrics, ) -> Result<OptimizationConfig>

Optimize using quantum-inspired algorithms

pub fn neural_adapt( &mut self, features: &Array1<f64>, targets: &Array1<f64>, ) -> Result<LearningStatistics>

Learn and adapt using neural system

pub fn predict_future_performance( &self, horizon: Duration, ) -> Result<PerformancePredictionPoint>

Predict future performance using advanced models