//! Auto-generated module
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

use super::types::{Distribution, EnsembleConfig, EnsembleType, ExplanationMethod, ExplorationStrategy, ExtractionPerformance, ExtractorType, FairnessMetrics, FeatureExtractor, FeatureNormalization, FeatureSelectionCriteria, FeatureStatistics, MLModel, MLModelType, MLOptimizationEngine, ModelComplexityMetrics, ModelPerformance, OnlineLearningConfig, PerformanceTrend, PotentialFunction, RLAgentType, RLLearningParams, ReinforcementLearningAgent, RewardShaping, SearchSpace, StabilityMetrics, TrainingExample, TrainingStatus, ValidationMetrics, ValidationSplit, VotingStrategy};

#[cfg(test)]
mod tests {
    use super::*;
    #[test]
    fn test_ml_engine_creation() {
        let engine = MLOptimizationEngine::new();
        assert!(engine.models.is_empty());
        assert!(engine.training_data.is_empty());
        assert!(engine.feature_extractors.is_empty());
    }
    #[test]
    fn test_model_addition() {
        let mut engine = MLOptimizationEngine::new();
        let model = MLModel {
            id: "test_model".to_string(),
            model_type: MLModelType::LinearRegression,
            parameters: HashMap::new(),
            hyperparameters: HashMap::new(),
            training_status: TrainingStatus::Untrained,
            accuracy: 0.0,
            confidence: 0.0,
            last_training: Instant::now(),
            prediction_history: Vec::new(),
            complexity_metrics: ModelComplexityMetrics {
                parameter_count: 100,
                flops: 1000,
                memory_footprint: 1024,
                depth: 1,
                branching_factor: 1.0,
                effective_complexity: 0.5,
            },
            interpretability_score: 0.9,
            training_duration: Duration::from_secs(60),
            model_size: 1024,
            validation_metrics: ValidationMetrics {
                cross_validation_score: 0.8,
                hold_out_score: 0.78,
                bootstrap_score: 0.82,
                time_series_score: 0.75,
                consistency_score: 0.85,
            },
        };
        engine.add_model(model);
        assert_eq!(engine.models.len(), 1);
        assert!(engine.models.contains_key("test_model"));
    }
    #[test]
    fn test_training_data_addition() {
        let mut engine = MLOptimizationEngine::new();
        let example = TrainingExample {
            features: {
                let mut features = HashMap::new();
                features.insert("memory_usage".to_string(), 0.7);
                features.insert("allocation_frequency".to_string(), 10.0);
                features
            },
            targets: {
                let mut targets = HashMap::new();
                targets.insert("performance".to_string(), 0.85);
                targets
            },
            weight: 1.0,
            timestamp: Instant::now(),
            source: "test".to_string(),
            quality_score: 0.9,
            metadata: HashMap::new(),
            feature_correlations: HashMap::new(),
            difficulty_score: 0.5,
            validation_split: ValidationSplit::Train,
        };
        engine.add_training_data(example);
        assert_eq!(engine.training_data.len(), 1);
    }
    #[test]
    fn test_feature_extractor_types() {
        let extractors = vec![
            ExtractorType::Statistical, ExtractorType::Temporal,
            ExtractorType::Performance, ExtractorType::MemoryUsage,
        ];
        for extractor_type in extractors {
            let extractor = FeatureExtractor {
                name: format!("{:?}_extractor", extractor_type),
                extractor_type,
                importance: 0.8,
                parameters: HashMap::new(),
                feature_stats: FeatureStatistics {
                    mean: 0.5,
                    std_dev: 0.2,
                    min: 0.0,
                    max: 1.0,
                    median: 0.5,
                    quartiles: (0.25, 0.5, 0.75),
                    skewness: 0.0,
                    kurtosis: 0.0,
                    missing_rate: 0.0,
                    unique_values: 100,
                },
                extraction_performance: ExtractionPerformance {
                    extraction_time: Duration::from_millis(10),
                    memory_usage: 1024,
                    success_rate: 0.99,
                    error_rate: 0.01,
                    throughput: 1000.0,
                },
                normalization: FeatureNormalization::ZScoreNormalization,
                selection_criteria: FeatureSelectionCriteria {
                    min_importance: 0.1,
                    max_correlation: 0.9,
                    information_gain_threshold: 0.1,
                    variance_threshold: 0.01,
                    stability_score: 0.8,
                },
                dependencies: Vec::new(),
                validation_rules: Vec::new(),
            };
            assert_eq!(extractor.extractor_type, extractor_type);
        }
    }
    #[test]
    fn test_model_types() {
        let model_types = vec![
            MLModelType::LinearRegression, MLModelType::DecisionTree,
            MLModelType::RandomForest, MLModelType::NeuralNetwork,
            MLModelType::ReinforcementLearning,
        ];
        for model_type in model_types {
            assert_ne!(model_type, MLModelType::EnsembleModel);
        }
    }
    #[test]
    fn test_online_learning_config() {
        let config = OnlineLearningConfig::default();
        assert!(config.enabled);
        assert!(config.learning_rate > 0.0);
        assert!(config.batch_size > 0);
        assert!(config.min_examples > 0);
    }
    #[test]
    fn test_model_performance_metrics() {
        let performance = ModelPerformance {
            accuracy: 0.85,
            mse: 0.1,
            mae: 0.08,
            rmse: 0.316,
            r_squared: 0.72,
            adjusted_r_squared: 0.70,
            precision: 0.87,
            recall: 0.83,
            f1_score: 0.85,
            auc_roc: 0.89,
            auc_pr: 0.86,
            trend: PerformanceTrend::Improving,
            cv_scores: vec![0.84, 0.86, 0.85],
            training_time: Duration::from_secs(300),
            inference_time: Duration::from_millis(10),
            training_memory: 512 * 1024 * 1024,
            inference_memory: 64 * 1024 * 1024,
            stability_metrics: StabilityMetrics {
                prediction_variance: 0.05,
                feature_importance_stability: 0.92,
                cross_validation_stability: 0.88,
                temporal_stability: 0.90,
                robustness_score: 0.87,
            },
            fairness_metrics: FairnessMetrics {
                demographic_parity: 0.95,
                equalized_odds: 0.93,
                individual_fairness: 0.91,
                group_fairness: 0.94,
                bias_score: 0.1,
            },
        };
        assert!(performance.accuracy > 0.8);
        assert!(performance.f1_score > 0.8);
        assert_eq!(performance.trend, PerformanceTrend::Improving);
    }
    #[test]
    fn test_ensemble_configuration() {
        let config = EnsembleConfig::default();
        assert_eq!(config.ensemble_type, EnsembleType::Voting);
        assert_eq!(config.voting_strategy, VotingStrategy::Weighted);
        assert!(config.max_models > 0);
    }
    #[test]
    fn test_explanation_methods() {
        let methods = vec![
            ExplanationMethod::SHAP, ExplanationMethod::LIME,
            ExplanationMethod::PermutationImportance,
        ];
        for method in methods {
            assert_ne!(method, ExplanationMethod::Anchors);
        }
    }
    #[test]
    fn test_hyperparameter_search_space() {
        let continuous_space = SearchSpace::Continuous {
            min: 0.001,
            max: 1.0,
            distribution: Distribution::LogUniform,
        };
        let integer_space = SearchSpace::Integer {
            min: 1,
            max: 100,
        };
        let categorical_space = SearchSpace::Categorical {
            choices: vec!["relu".to_string(), "tanh".to_string(), "sigmoid".to_string(),],
        };
        match continuous_space {
            SearchSpace::Continuous { min, max, .. } => {
                assert!(min < max);
            }
            _ => panic!("Expected continuous search space"),
        }
        match integer_space {
            SearchSpace::Integer { min, max } => {
                assert!(min < max);
            }
            _ => panic!("Expected integer search space"),
        }
        match categorical_space {
            SearchSpace::Categorical { choices } => {
                assert_eq!(choices.len(), 3);
            }
            _ => panic!("Expected categorical search space"),
        }
    }
    #[test]
    fn test_reinforcement_learning_agent() {
        let agent = ReinforcementLearningAgent {
            agent_type: RLAgentType::QLearning,
            weights: HashMap::new(),
            learning_params: RLLearningParams {
                learning_rate: 0.1,
                discount_factor: 0.99,
                epsilon: 0.1,
                epsilon_decay: 0.995,
                epsilon_min: 0.01,
                target_update_frequency: 100,
                replay_buffer_size: 10000,
                batch_size: 32,
                tau: 0.005,
            },
            replay_buffer: VecDeque::new(),
            exploration_strategy: ExplorationStrategy::EpsilonGreedy {
                epsilon: 0.1,
                decay_rate: 0.995,
            },
            reward_shaping: RewardShaping {
                potential_function: PotentialFunction::Linear {
                    coefficients: vec![1.0, - 0.5, 0.3],
                },
                shaping_factor: 0.1,
                intrinsic_motivation: true,
                curiosity_driven: true,
            },
            policy_network: None,
            value_network: None,
            target_networks: None,
            coordination: None,
        };
        assert_eq!(agent.agent_type, RLAgentType::QLearning);
        assert!(agent.learning_params.learning_rate > 0.0);
        assert!(agent.learning_params.discount_factor < 1.0);
    }
}
