Struct AdvancedTextCoordinator 

pub struct AdvancedTextCoordinator { /* private fields */ }

Advanced Text Processing Coordinator

The central intelligence system that coordinates all Advanced mode operations for text processing, providing adaptive optimization, intelligent resource management, and performance enhancement.

Implementations

impl AdvancedTextCoordinator

pub fn new(config: AdvancedTextConfig) -> Result<Self>

Create a new Advanced text coordinator

Examples found in repository

examples/complete_integration.rs (line 83)

    fn new() -> Result<Self> {
        println!("🔧 Initializing Complete Advanced System...");

        // Configure Advanced mode with optimized settings
        let config = AdvancedTextConfig {
            enable_gpu_acceleration: true,
            enable_simd_optimizations: true,
            enable_neural_ensemble: true,
            enable_real_time_adaptation: true,
            enable_advanced_analytics: true,
            enable_multimodal: true,
            max_memory_usage_mb: 8192,
            optimization_level: 3,
            target_throughput: 5000.0,
            enable_predictive_processing: true,
        };

        // Performance monitoring with strict thresholds
        let perf_thresholds = PerformanceThresholds {
            max_processing_time_ms: 500, // 500ms max
            min_throughput: 1000.0,      // 1000 docs/sec min
            max_memory_usage_mb: 6144,   // 6GB max
            max_cpu_utilization: 85.0,   // 85% max
            min_cache_hit_rate: 0.85,    // 85% min
        };

        let coordinator = AdvancedTextCoordinator::new(config)?;
        let performance_monitor = AdvancedPerformanceMonitor::with_thresholds(perf_thresholds);
        let simd_processor = AdvancedSIMDTextProcessor;
        let streaming_processor = AdvancedStreamingProcessor::new(WordTokenizer::default());

        println!("✅ Advanced System initialized successfully!\n");

        Ok(Self {
            coordinator,
            performance_monitor,
            simd_processor,
            streaming_processor,
        })
    }

examples/text_mode_demo.rs (line 61)

fn main() -> Result<()> {
    println!("🚀 Advanced Mode Demo - Advanced Text Processing");
    println!("================================================\n");

    // Configure Advanced mode with all advanced features enabled
    let config = AdvancedTextConfig {
        enable_gpu_acceleration: true,
        enable_simd_optimizations: true,
        enable_neural_ensemble: true,
        enable_real_time_adaptation: true,
        enable_advanced_analytics: true,
        enable_multimodal: true,
        max_memory_usage_mb: 4096, // 4GB for this demo
        optimization_level: 3,     // Maximum optimization
        target_throughput: 2000.0, // 2000 docs/sec target
        enable_predictive_processing: true,
    };

    println!("📋 Configuration:");
    println!("  • GPU Acceleration: {}", config.enable_gpu_acceleration);
    println!(
        "  • SIMD Optimizations: {}",
        config.enable_simd_optimizations
    );
    println!("  • Neural Ensemble: {}", config.enable_neural_ensemble);
    println!(
        "  • Real-time Adaptation: {}",
        config.enable_real_time_adaptation
    );
    println!(
        "  • Advanced Analytics: {}",
        config.enable_advanced_analytics
    );
    println!(
        "  • Target Throughput: {} docs/sec",
        config.target_throughput
    );
    println!("  • Memory Limit: {} MB\n", config.max_memory_usage_mb);

    // Initialize the Advanced coordinator
    println!("🔧 Initializing Advanced Text Coordinator...");
    let coordinator = AdvancedTextCoordinator::new(config)?;
    println!("✅ Coordinator initialized successfully!\n");

    // Demo 1: Advanced-optimized text processing
    demo_advancedtext_processing(&coordinator)?;

    // Demo 2: Advanced semantic similarity
    demo_semantic_similarity(&coordinator)?;

    // Demo 3: Batch classification
    demo_batch_classification(&coordinator)?;

    // Demo 4: Dynamic topic modeling
    demo_topic_modeling(&coordinator)?;

    // Demo 5: Performance reporting
    demo_performance_reporting(&coordinator)?;

    println!("🎉 Advanced Mode Demo completed successfully!");
    println!("All advanced features demonstrated with optimal performance.");

    Ok(())
}

pub fn advanced_processtext( &self, texts: &[String], ) -> Result<AdvancedTextResult>

Advanced-optimized text processing with full feature coordination

Examples found in repository

examples/complete_integration.rs (line 144)

    fn demo_integrated_pipeline(&self) -> Result<()> {
        println!("📊 Demo 1: Integrated Text Processing Pipeline");
        println!("==============================================");

        let sample_documents = vec![
            "Artificial intelligence is revolutionizing the field of natural language processing.".to_string(),
            "Machine learning algorithms can now understand context and semantic meaning in text.".to_string(),
            "Deep neural networks have enabled breakthrough performance in text classification tasks.".to_string(),
            "SIMD optimizations allow for optimized string processing in modern computing systems.".to_string(),
            "Real-time adaptation ensures optimal performance across diverse text processing workloads.".to_string(),
        ];

        println!(
            "Processing {} documents through integrated pipeline...",
            sample_documents.len()
        );

        // Start performance monitoring
        let operation_monitor = self
            .performance_monitor
            .start_operation("integrated_pipeline")?;

        let start_time = Instant::now();

        // Process through Advanced coordinator
        let result = self.coordinator.advanced_processtext(&sample_documents)?;

        let processing_time = start_time.elapsed();

        // Complete monitoring
        operation_monitor.complete(sample_documents.len())?;

        println!("\n📈 Pipeline Results:");
        println!("  • Processing Time: {processing_time:?}");
        println!(
            "  • Throughput: {:.2} docs/sec",
            result.performance_metrics.throughput
        );
        println!(
            "  • Memory Efficiency: {:.1}%",
            result.performance_metrics.memory_efficiency * 100.0
        );
        println!(
            "  • Accuracy Estimate: {:.1}%",
            result.performance_metrics.accuracy_estimate * 100.0
        );

        println!("\n🔧 Applied Optimizations:");
        for optimization in &result.optimizations_applied {
            println!("  • {optimization}");
        }

        println!("\n🎯 Confidence Scores:");
        for (metric, score) in &result.confidence_scores {
            println!("  • {}: {:.1}%", metric, score * 100.0);
        }

        println!();
        Ok(())
    }

    /// Demonstrate performance-monitored SIMD operations
    fn demo_performance_monitored_simd(&self) -> Result<()> {
        println!("⚡ Demo 2: Performance-Monitored SIMD Operations");
        println!("===============================================");

        let testtexts = [
            "The quick brown fox jumps over the lazy dog".to_string(),
            "Pack my box with five dozen liquor jugs".to_string(),
            "How vexingly quick daft zebras jump!".to_string(),
            "Bright vixens jump; dozy fowl quack".to_string(),
        ];

        println!("Running SIMD-accelerated operations with performance monitoring...");

        // Start monitoring
        let operation_monitor = self
            .performance_monitor
            .start_operation("simd_operations")?;

        let start_time = Instant::now();

        // Optimized text processing
        let testtext_refs: Vec<&str> = testtexts.iter().map(|s| s.as_str()).collect();
        let processed_results = AdvancedSIMDTextProcessor::advanced_batch_process(&testtext_refs);

        // SIMD string operations
        let char_counts: Vec<usize> = testtexts
            .iter()
            .map(|text| SimdStringOps::count_chars(text, 'o'))
            .collect();

        // Optimized similarity matrix
        let similarity_matrix =
            AdvancedSIMDTextProcessor::advanced_similarity_matrix(&testtext_refs);

        let processing_time = start_time.elapsed();

        // Complete monitoring
        operation_monitor.complete(testtexts.len())?;

        println!("\n📊 SIMD Operation Results:");
        println!("  • Processing Time: {processing_time:?}");
        println!("  • Documents Processed: {}", processed_results.len());
        println!("  • Character Counts (letter 'o'): {char_counts:?}");
        println!(
            "  • Similarity Matrix Size: {}x{}",
            similarity_matrix.len(),
            similarity_matrix[0].len()
        );

        // Display similarity matrix
        println!("\n🔗 Text Similarity Matrix:");
        for (i, row) in similarity_matrix.iter().enumerate() {
            print!("  Row {i}: [");
            for (j, &similarity) in row.iter().enumerate() {
                if j > 0 {
                    print!(", ");
                }
                print!("{similarity:.3}");
            }
            println!("]");
        }

        // Show SIMD capabilities
        println!("\n⚙️  SIMD Capabilities:");
        println!("  • SIMD Available: {}", SimdStringOps::is_available());
        println!("  • String Processing: Optimized");
        println!("  • Pattern Matching: Optimized");
        println!("  • Similarity Computation: Vectorized");

        println!();
        Ok(())
    }

    /// Demonstrate adaptive streaming processing
    fn demo_adaptive_streaming(&self) -> Result<()> {
        println!("🌊 Demo 3: Adaptive Streaming Processing");
        println!("========================================");

        // Create large dataset simulation
        let largetexts: Vec<String> = (0..1000)
            .map(|i| format!("This is streaming document number {i} with various content lengths and different patterns of text processing requirements."))
            .collect();

        println!(
            "Processing {} documents through adaptive streaming...",
            largetexts.len()
        );

        // Start monitoring
        let operation_monitor = self
            .performance_monitor
            .start_operation("adaptive_streaming")?;

        let start_time = Instant::now();

        // Streaming processing with parallel optimization
        let streaming_processor = AdvancedStreamingProcessor::new(WordTokenizer::default())
            .with_parallelism(4, 1024 * 1024);

        // Simple token counting for demonstration
        let mut total_tokens = 0;
        let tokenizer = WordTokenizer::default();
        for text in &largetexts {
            if let Ok(tokens) = tokenizer.tokenize(text) {
                total_tokens += tokens.len();
            }
        }

        let processing_time = start_time.elapsed();

        // Complete monitoring
        operation_monitor.complete(largetexts.len())?;

        // Get memory stats instead of performance metrics
        let (current_mem, peak_mem) = streaming_processor.memory_stats();

        println!("\n📈 Streaming Processing Results:");
        println!("  • Processing Time: {processing_time:?}");
        println!("  • Documents Processed: {}", largetexts.len());
        println!("  • Total Tokens Extracted: {total_tokens}");
        println!("  • Current Memory Usage: {current_mem} bytes");
        println!("  • Peak Memory Usage: {peak_mem} bytes");
        println!(
            "  • Throughput: {:.2} docs/sec",
            largetexts.len() as f64 / processing_time.as_secs_f64()
        );

        println!("\n🔄 Advanced Features:");
        println!("  • Parallel Processing: Enabled");
        println!("  • Memory Monitoring: Active");
        println!("  • Advanced Tokenization: Optimized");

        println!();
        Ok(())
    }

    /// Demonstrate real-time optimization and adaptation
    fn demo_realtime_optimization(&self) -> Result<()> {
        println!("🎯 Demo 4: Real-time Optimization and Adaptation");
        println!("===============================================");

        // Simulate various workload patterns
        let workload_patterns = vec![
            ("Short Documents", generate_short_documents(50)),
            ("Medium Documents", generate_medium_documents(30)),
            ("Long Documents", generate_long_documents(20)),
            ("Mixed Workload", generate_mixed_workload(40)),
        ];

        for (pattern_name, documents) in workload_patterns {
            println!("\n🔄 Processing Pattern: {pattern_name}");
            println!("  • Document Count: {}", documents.len());

            // Start monitoring for this pattern
            let operation_monitor = self
                .performance_monitor
                .start_operation(&format!("pattern_{}", pattern_name.replace(' ', "_")))?;

            let start_time = Instant::now();

            // Process with adaptive optimization
            let result = self.coordinator.advanced_processtext(&documents)?;

            let processing_time = start_time.elapsed();

            // Complete monitoring
            operation_monitor.complete(documents.len())?;

            println!("  • Processing Time: {processing_time:?}");
            println!(
                "  • Throughput: {:.2} docs/sec",
                result.performance_metrics.throughput
            );
            println!(
                "  • Optimizations Applied: {}",
                result.optimizations_applied.len()
            );

            // Show adaptive responses
            if !result.optimizations_applied.is_empty() {
                println!("  • Adaptive Responses:");
                for opt in &result.optimizations_applied {
                    println!("    - {opt}");
                }
            }
        }

        // Get optimization recommendations
        let recommendations = self.performance_monitor.get_optimization_opportunities()?;

        println!("\n💡 Current Optimization Opportunities:");
        if recommendations.is_empty() {
            println!("  • No optimization opportunities identified");
            println!("  • System is operating at optimal performance");
        } else {
            for (i, rec) in recommendations.iter().enumerate() {
                println!("  {}. [{}] {}", i + 1, rec.category, rec.recommendation);
                println!(
                    "     Impact: {:.0}% | Complexity: {}/5",
                    rec.impact_estimate * 100.0,
                    rec.complexity
                );
            }
        }

        println!();
        Ok(())
    }

examples/text_mode_demo.rs (line 105)

fn demo_advancedtext_processing(coordinator: &AdvancedTextCoordinator) -> Result<()> {
    println!("📊 Demo 1: Advanced-Optimized Text Processing");
    println!("==========================================");

    let sampletexts = vec![
        "Artificial intelligence is transforming the way we process and understand text data.".to_string(),
        "Machine learning algorithms can extract meaningful patterns from large corpora of documents.".to_string(),
        "Natural language processing combines computational linguistics with statistical analysis.".to_string(),
        "Deep learning models like transformers have revolutionized text understanding tasks.".to_string(),
        "The future of AI lies in developing more efficient and accurate language models.".to_string(),
    ];

    println!(
        "Processing {} documents with Advanced optimization...",
        sampletexts.len()
    );

    let start_time = std::time::Instant::now();
    let result = coordinator.advanced_processtext(&sampletexts)?;
    let processing_time = start_time.elapsed();

    println!("\n📈 Results:");
    println!("  • Processing Time: {processing_time:?}");
    println!(
        "  • Throughput: {:.2} docs/sec",
        result.performance_metrics.throughput
    );
    println!(
        "  • Memory Efficiency: {:.1}%",
        result.performance_metrics.memory_efficiency * 100.0
    );
    println!(
        "  • Accuracy Estimate: {:.1}%",
        result.performance_metrics.accuracy_estimate * 100.0
    );

    println!("\n🔧 Optimizations Applied:");
    for optimization in &result.optimizations_applied {
        println!("  • {optimization}");
    }

    println!("\n⏱️  Timing Breakdown:");
    println!(
        "  • Preprocessing: {:?}",
        result.timing_breakdown.preprocessing_time
    );
    println!(
        "  • Neural Processing: {:?}",
        result.timing_breakdown.neural_processing_time
    );
    println!(
        "  • Analytics: {:?}",
        result.timing_breakdown.analytics_time
    );
    println!(
        "  • Optimization: {:?}",
        result.timing_breakdown.optimization_time
    );

    println!("\n🎯 Confidence Scores:");
    for (metric, score) in &result.confidence_scores {
        println!("  • {}: {:.1}%", metric, score * 100.0);
    }

    println!(
        "\n📐 Vector Embeddings Shape: {:?}",
        result.primary_result.vectors.shape()
    );
    println!();

    Ok(())
}

pub fn advanced_semantic_similarity( &self, text1: &str, text2: &str, ) -> Result<AdvancedSemanticSimilarityResult>

Optimized semantic similarity with advanced optimizations

Examples found in repository

examples/text_mode_demo.rs (line 180)

fn demo_semantic_similarity(coordinator: &AdvancedTextCoordinator) -> Result<()> {
    println!("🔍 Demo 2: Advanced Semantic Similarity");
    println!("=======================================");

    let text_pairs = [
        ("The cat sat on the mat", "A feline rested on the rug"),
        (
            "Machine learning is a subset of artificial intelligence",
            "AI includes machine learning as one of its components",
        ),
        ("The weather is sunny today", "It's raining heavily outside"),
    ];

    for (i, (text1, text2)) in text_pairs.iter().enumerate() {
        println!("\n📝 Text Pair {}:", i + 1);
        println!("  Text 1: \"{text1}\"");
        println!("  Text 2: \"{text2}\"");

        let result = coordinator.advanced_semantic_similarity(text1, text2)?;

        println!("\n📊 Similarity Metrics:");
        println!("  • Cosine Similarity: {:.3}", result.cosine_similarity);
        println!("  • Semantic Similarity: {:.3}", result.semantic_similarity);
        println!(
            "  • Contextual Similarity: {:.3}",
            result.contextual_similarity
        );
        println!("  • Confidence Score: {:.3}", result.confidence_score);
        println!("  • Processing Time: {:?}", result.processing_time);
    }

    println!();
    Ok(())
}

pub fn advanced_classify_batch( &self, texts: &[String], categories: &[String], ) -> Result<AdvancedBatchClassificationResult>

Advanced-optimized batch text classification

Examples found in repository

examples/text_mode_demo.rs (line 225)

fn demo_batch_classification(coordinator: &AdvancedTextCoordinator) -> Result<()> {
    println!("🏷️  Demo 3: Batch Text Classification");
    println!("===================================");

    let texts = vec![
        "This movie was absolutely fantastic! Great acting and storyline.".to_string(),
        "The service at this restaurant was terrible and the food was cold.".to_string(),
        "The new software update includes several bug fixes and performance improvements."
            .to_string(),
        "Breaking news: Major earthquake hits the coastal region.".to_string(),
    ];

    let categories = vec![
        "positive_review".to_string(),
        "negative_review".to_string(),
        "technology".to_string(),
        "news".to_string(),
    ];

    println!(
        "Classifying {} texts into {} categories...",
        texts.len(),
        categories.len()
    );
    println!("\n📊 Categories: {categories:?}");

    let result = coordinator.advanced_classify_batch(&texts, &categories)?;

    println!("\n📈 Classification Results:");
    println!(
        "  • Total Classifications: {}",
        result.classifications.len()
    );
    println!(
        "  • Average Confidence: {:.1}%",
        result.confidence_estimates.iter().sum::<f64>() / result.confidence_estimates.len() as f64
            * 100.0
    );
    println!("  • Processing Time: {:?}", result.processing_time);
    println!(
        "  • Throughput: {:.2} docs/sec",
        result.performance_metrics.throughput
    );

    for (i, confidence) in result.confidence_estimates.iter().enumerate() {
        println!("  • Text {}: {:.1}% confidence", i + 1, confidence * 100.0);
    }

    println!();
    Ok(())
}

pub fn advanced_topic_modeling( &self, documents: &[String], num_topics: usize, ) -> Result<AdvancedTopicModelingResult>

Advanced-advanced topic modeling with dynamic optimization

Examples found in repository

examples/text_mode_demo.rs (line 274)

fn demo_topic_modeling(coordinator: &AdvancedTextCoordinator) -> Result<()> {
    println!("📚 Demo 4: Dynamic Topic Modeling");
    println!("================================");

    let documents = vec![
        "Machine learning algorithms are used to build predictive models from data.".to_string(),
        "Deep neural networks can learn complex patterns in high-dimensional data.".to_string(),
        "Natural language processing helps computers understand human language.".to_string(),
        "Computer vision enables machines to interpret and analyze visual information.".to_string(),
        "Reinforcement learning trains agents to make decisions through trial and error."
            .to_string(),
        "Data science combines statistics, programming, and domain expertise.".to_string(),
        "Artificial intelligence aims to create systems that can perform human-like tasks."
            .to_string(),
    ];

    println!(
        "Analyzing {} documents for topic extraction...",
        documents.len()
    );

    let result = coordinator.advanced_topic_modeling(&documents, 3)?; // Extract 3 topics

    println!("\n📊 Topic Modeling Results:");
    println!("  • Processing Time: {:?}", result.processing_time);

    println!("\n📈 Quality Metrics:");
    println!(
        "  • Coherence Score: {:.3}",
        result.quality_metrics.coherence_score
    );
    println!(
        "  • Diversity Score: {:.3}",
        result.quality_metrics.diversity_score
    );
    println!(
        "  • Stability Score: {:.3}",
        result.quality_metrics.stability_score
    );
    println!(
        "  • Interpretability Score: {:.3}",
        result.quality_metrics.interpretability_score
    );

    println!();
    Ok(())
}

pub fn get_performance_report(&self) -> Result<AdvancedTextPerformanceReport>

Get comprehensive performance report

Examples found in repository

examples/text_mode_demo.rs (line 307)

fn demo_performance_reporting(coordinator: &AdvancedTextCoordinator) -> Result<()> {
    println!("📊 Demo 5: Performance Reporting");
    println!("===============================");

    let report = coordinator.get_performance_report()?;

    println!("\n🔧 Current Performance Metrics:");
    println!(
        "  • Processing Time: {:?}",
        report.current_metrics.processing_time
    );
    println!(
        "  • Throughput: {:.2} docs/sec",
        report.current_metrics.throughput
    );
    println!(
        "  • Memory Efficiency: {:.1}%",
        report.current_metrics.memory_efficiency * 100.0
    );
    println!(
        "  • Accuracy Estimate: {:.1}%",
        report.current_metrics.accuracy_estimate * 100.0
    );

    println!("\n🖥️  System Utilization:");
    println!("  • CPU: {:.1}%", report.system_utilization.cpu_utilization);
    println!(
        "  • Memory: {:.1}%",
        report.system_utilization.memory_utilization
    );
    println!("  • GPU: {:.1}%", report.system_utilization.gpu_utilization);
    println!(
        "  • Cache Hit Rate: {:.1}%",
        report.system_utilization.cache_hit_rate * 100.0
    );

    println!("\n💡 Optimization Recommendations:");
    for recommendation in &report.optimization_recommendations {
        println!(
            "  • [{}] {} (Impact: {:.0}%)",
            recommendation.category,
            recommendation.recommendation,
            recommendation.impact_estimate * 100.0
        );
    }

    println!("\n⚠️  Performance Bottlenecks:");
    for bottleneck in &report.bottleneck_analysis {
        println!(
            "  • {} ({:.0}% impact): {}",
            bottleneck.component,
            bottleneck.impact * 100.0,
            bottleneck.description
        );
        println!("    Suggestion: {}", bottleneck.suggested_fix);
    }

    println!();
    Ok(())
}