scirs2-io 0.6.2

Input/Output utilities module for SciRS2 (scirs2-io)
Documentation 
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//! Auto-generated test module (consolidated from inline `#[cfg(test)] mod` blocks)

use std::time::Duration;

use super::*;

#[cfg(test)]
mod tests_2 {
    use super::*;
    #[test]
    fn test_advanced_think_coordinator_creation() {
        let coordinator = AdvancedCoordinator::new();
        assert!(coordinator.is_ok());
    }
    #[test]
    fn test_entropy_calculation() {
        let coordinator = AdvancedCoordinator::new().expect("Operation failed");
        let uniform_data = vec![1, 2, 3, 4, 5, 6, 7, 8];
        let repeated_data = vec![1, 1, 1, 1, 1, 1, 1, 1];
        let uniform_entropy = coordinator.calculate_advanced_entropy(&uniform_data);
        let repeated_entropy = coordinator.calculate_advanced_entropy(&repeated_data);
        assert!(uniform_entropy > repeated_entropy);
    }
    #[test]
    fn test_data_pattern_detection() {
        let coordinator = AdvancedCoordinator::new().expect("Operation failed");
        let test_data = vec![1, 2, 3, 4, 5, 6, 7, 8];
        let patterns = coordinator
            .detect_data_patterns(&test_data)
            .expect("Operation failed");
        assert!(patterns.sequential_factor > 0.5);
    }
    #[test]
    fn test_processing_strategy_execution() {
        let coordinator = AdvancedCoordinator::new().expect("Operation failed");
        let test_data = vec![1, 2, 3, 4, 5];
        let result = coordinator
            .execute_simd_optimized_strategy(&test_data)
            .expect("Operation failed");
        assert!(!result.processed_data.is_empty());
        assert_eq!(result.strategy_type, StrategyType::SimdOptimized);
    }
    #[test]
    fn test_comprehensive_intelligence_gathering() {
        let coordinator = AdvancedCoordinator::new().expect("Operation failed");
        let test_data = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
        let intelligence = coordinator
            .gather_comprehensive_intelligence(&test_data)
            .expect("Operation failed");
        assert!(intelligence.data_entropy >= 0.0 && intelligence.data_entropy <= 1.0);
        assert!(
            intelligence.compression_potential >= 0.0 && intelligence.compression_potential <= 1.0
        );
    }
    #[test]
    fn test_meta_learning_reports_real_state() {
        let mut meta = MetaLearningSystem::new();
        assert_eq!(meta.get_total_adaptations(), 0);
        assert_eq!(meta.get_autonomous_capabilities(), 0);
        assert_eq!(meta.apply_transferred_knowledge(b"abc").expect("ok"), 0.0);
        assert_eq!(meta.get_transfer_confidence(b"abc").expect("ok"), 0.0);
        let structured = vec![0u8; 256];
        let patterns = meta
            .extract_domain_patterns(&structured, "test")
            .expect("patterns");
        assert_eq!(patterns.len(), 1);
        assert!(patterns[0].confidence > 0.5);
        let opts = meta
            .learn_transferable_optimizations(&patterns)
            .expect("opts");
        assert!(!opts.is_empty());
        assert!(meta.get_autonomous_capabilities() >= 1);
        assert!(meta.apply_transferred_knowledge(&structured).expect("ok") > 0.0);
        assert!(meta.get_transfer_confidence(&structured).expect("ok") > 0.0);
        assert!(meta
            .extract_domain_patterns(&[], "test")
            .expect("ok")
            .is_empty());
    }
    #[test]
    fn test_performance_intelligence_reports_real_state() {
        let mut perf = PerformanceIntelligence::new();
        assert_eq!(perf.get_current_efficiency(), 0.0);
        assert_eq!(perf.get_overall_improvement(), 0.0);
        assert_eq!(perf.get_intelligence_level(), 0.0);
        let stats = perf.get_statistics();
        assert_eq!(stats.total_analyses, 0);
        assert_eq!(stats.prediction_accuracy, 0.0);
        let result = ProcessingResult {
            data: vec![1, 2, 3],
            strategy_used: StrategyType::Advanced,
            processing_time: Duration::from_millis(1),
            efficiency_score: 0.9,
            quality_metrics: QualityMetrics {
                data_integrity: 1.0,
                compression_efficiency: 0.9,
                processing_accuracy: 0.95,
                memory_efficiency: 0.9,
                overall_quality: 0.92,
            },
            intelligence_level: IntelligenceLevel::Advanced,
            adaptive_improvements: AdaptiveImprovements {
                efficiency_gain: 1.2,
                strategy_optimization: 0.9,
                resource_utilization: 0.85,
                learning_acceleration: 1.5,
            },
        };
        perf.update_efficiency_metrics(&result).expect("ok");
        assert_eq!(perf.get_current_efficiency(), 0.9);
        assert_eq!(perf.get_statistics().total_analyses, 1);
        assert!((perf.get_overall_improvement() - 20.0).abs() < 1e-3);
        assert_eq!(perf.get_statistics().optimization_success_rate, 1.0);
    }
}