//! Meta-Learning for Hyperparameter Initialization
//!
//! This module provides meta-learning capabilities for intelligent hyperparameter initialization
//! based on historical optimization data, dataset characteristics, and algorithm performance patterns.
//! It learns from past optimization experiences to provide better starting points for new optimization tasks.

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::random::rngs::StdRng;
use scirs2_core::random::SeedableRng;
use serde::{Deserialize, Serialize};
use sklears_core::types::Float;
use std::collections::HashMap;

/// Meta-learning strategies for hyperparameter initialization
#[derive(Debug, Clone)]
pub enum MetaLearningStrategy {
    /// Similarity-based recommendation using dataset characteristics
    SimilarityBased {
        similarity_metric: SimilarityMetric,

        k_neighbors: usize,

        weight_by_distance: bool,
    },
    /// Model-based meta-learning using surrogate models
    ModelBased {
        surrogate_model: SurrogateModel,

        update_frequency: usize,
    },
    /// Gradient-based meta-learning (MAML-style)
    GradientBased {
        meta_learning_rate: Float,
        adaptation_steps: usize,
        inner_learning_rate: Float,
    },
    /// Bayesian meta-learning using hierarchical models
    BayesianMeta {
        prior_strength: Float,
        hierarchical_levels: usize,
    },
    /// Transfer learning from similar tasks
    TransferLearning {
        transfer_method: TransferMethod,
        domain_adaptation: bool,
    },
    /// Ensemble of meta-learners
    EnsembleMeta {
        strategies: Vec<MetaLearningStrategy>,
        combination_method: CombinationMethod,
    },
}

/// Similarity metrics for dataset comparison
#[derive(Debug, Clone)]
pub enum SimilarityMetric {
    /// Cosine similarity between dataset statistics
    Cosine,
    /// Euclidean distance between features
    Euclidean,
    /// Manhattan distance
    Manhattan,
    /// Pearson correlation
    Correlation,
    /// Jensen-Shannon divergence
    JensenShannon,
    /// Learned similarity using neural networks
    Learned,
}

/// Surrogate models for meta-learning
#[derive(Debug, Clone)]
pub enum SurrogateModel {
    /// Random Forest for hyperparameter prediction
    RandomForest {
        n_trees: usize,

        max_depth: Option<usize>,
    },
    /// Gaussian Process for uncertainty quantification
    GaussianProcess { kernel_type: String },
    /// Neural Network for complex patterns
    NeuralNetwork { hidden_layers: Vec<usize> },
    /// Linear model for simple relationships
    LinearRegression { regularization: Float },
}

/// Transfer learning methods
#[derive(Debug, Clone)]
pub enum TransferMethod {
    /// Direct parameter transfer
    DirectTransfer,
    /// Feature-based transfer
    FeatureTransfer,
    /// Model-based transfer
    ModelTransfer,
    /// Instance-based transfer
    InstanceTransfer,
}

/// Combination methods for ensemble meta-learning
#[derive(Debug, Clone)]
pub enum CombinationMethod {
    /// Average predictions
    Average,
    /// Weighted average by performance
    WeightedAverage,
    /// Stacking with meta-model
    Stacking,
    /// Bayesian model averaging
    BayesianAveraging,
}

/// Dataset characteristics for meta-learning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DatasetCharacteristics {
    pub n_samples: usize,
    pub n_features: usize,
    pub n_classes: Option<usize>,
    pub class_balance: Vec<Float>,
    pub feature_types: Vec<FeatureType>,
    pub statistical_measures: StatisticalMeasures,
    pub complexity_measures: ComplexityMeasures,
    pub domain_specific: HashMap<String, Float>,
}

/// Feature types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FeatureType {
    /// Numerical
    Numerical,
    /// Categorical
    Categorical,
    /// Ordinal
    Ordinal,
    /// Text
    Text,
    /// Image
    Image,
    /// TimeSeries
    TimeSeries,
}

/// Statistical measures of the dataset
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatisticalMeasures {
    pub mean_values: Vec<Float>,
    pub std_values: Vec<Float>,
    pub skewness: Vec<Float>,
    pub kurtosis: Vec<Float>,
    pub correlation_matrix: Option<Array2<Float>>,
    pub mutual_information: Option<Vec<Float>>,
}

/// Complexity measures of the dataset
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComplexityMeasures {
    pub fisher_discriminant_ratio: Float,
    pub volume_of_overlap: Float,
    pub feature_efficiency: Float,
    pub collective_feature_efficiency: Float,
    pub entropy: Float,
    pub class_probability_max: Float,
}

/// Historical optimization record
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationRecord {
    pub dataset_id: String,
    pub algorithm_name: String,
    pub dataset_characteristics: DatasetCharacteristics,
    pub hyperparameters: HashMap<String, ParameterValue>,
    pub performance_score: Float,
    pub optimization_time: Float,
    pub convergence_iterations: usize,
    pub validation_method: String,
    pub timestamp: u64,
}

/// Parameter value types
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum ParameterValue {
    /// Float
    Float(Float),
    /// Integer
    Integer(i64),
    /// Boolean
    Boolean(bool),
    /// String
    String(String),
    /// Array
    Array(Vec<Float>),
}

/// Meta-learning recommendations
#[derive(Debug, Clone)]
pub struct MetaLearningRecommendation {
    pub recommended_hyperparameters: HashMap<String, ParameterValue>,
    pub confidence_scores: HashMap<String, Float>,
    pub expected_performance: Float,
    pub expected_runtime: Float,
    pub similar_datasets: Vec<String>,
    pub recommendation_source: String,
    pub uncertainty_estimate: Float,
}

/// Meta-learning configuration
#[derive(Debug, Clone)]
pub struct MetaLearningConfig {
    pub strategy: MetaLearningStrategy,
    pub min_historical_records: usize,
    pub max_similarity_distance: Float,
    pub confidence_threshold: Float,
    pub update_interval: usize,
    pub cache_size: usize,
    pub random_state: Option<u64>,
}

/// Meta-learning engine
#[derive(Debug)]
pub struct MetaLearningEngine {
    config: MetaLearningConfig,
    historical_records: Vec<OptimizationRecord>,
    dataset_similarity_cache: HashMap<String, Vec<(String, Float)>>,
    surrogate_models: HashMap<String, Box<dyn SurrogateModelTrait>>,
    rng: StdRng,
}

/// Trait for surrogate models
trait SurrogateModelTrait: std::fmt::Debug {
    fn fit(
        &mut self,
        features: &Array2<Float>,
        targets: &Array1<Float>,
    ) -> Result<(), Box<dyn std::error::Error>>;
    fn predict(
        &self,
        features: &Array2<Float>,
    ) -> Result<Array1<Float>, Box<dyn std::error::Error>>;
    fn predict_with_uncertainty(
        &self,
        features: &Array2<Float>,
    ) -> Result<(Array1<Float>, Array1<Float>), Box<dyn std::error::Error>>;
}

/// Simple Random Forest surrogate model implementation
#[derive(Debug)]
struct RandomForestSurrogate {
    n_trees: usize,
    max_depth: Option<usize>,
    models: Vec<SimpleTree>,
}

/// Simple decision tree for surrogate model
#[derive(Debug, Clone)]
struct SimpleTree {
    feature_idx: Option<usize>,
    threshold: Option<Float>,
    left: Option<Box<SimpleTree>>,
    right: Option<Box<SimpleTree>>,
    prediction: Option<Float>,
}

impl Default for MetaLearningConfig {
    fn default() -> Self {
        Self {
            strategy: MetaLearningStrategy::SimilarityBased {
                similarity_metric: SimilarityMetric::Cosine,
                k_neighbors: 5,
                weight_by_distance: true,
            },
            min_historical_records: 10,
            max_similarity_distance: 0.8,
            confidence_threshold: 0.6,
            update_interval: 100,
            cache_size: 1000,
            random_state: None,
        }
    }
}

impl MetaLearningEngine {
    /// Create a new meta-learning engine
    pub fn new(config: MetaLearningConfig) -> Self {
        let rng = match config.random_state {
            Some(seed) => StdRng::seed_from_u64(seed),
            None => {
                use scirs2_core::random::thread_rng;
                StdRng::from_rng(&mut thread_rng())
            }
        };

        Self {
            config,
            historical_records: Vec::new(),
            dataset_similarity_cache: HashMap::new(),
            surrogate_models: HashMap::new(),
            rng,
        }
    }

    /// Load historical optimization records
    pub fn load_historical_records(&mut self, records: Vec<OptimizationRecord>) {
        self.historical_records.extend(records);
        self.update_surrogate_models().unwrap_or_else(|e| {
            eprintln!("Warning: Failed to update surrogate models: {}", e);
        });
    }

    /// Add a new optimization record
    pub fn add_record(&mut self, record: OptimizationRecord) {
        self.historical_records.push(record);

        // Update models periodically
        if self.historical_records.len() % self.config.update_interval == 0 {
            self.update_surrogate_models().unwrap_or_else(|e| {
                eprintln!("Warning: Failed to update surrogate models: {}", e);
            });
        }
    }

    /// Get hyperparameter recommendations for a new dataset
    pub fn recommend_hyperparameters(
        &mut self,
        dataset_characteristics: &DatasetCharacteristics,
        algorithm_name: &str,
    ) -> Result<MetaLearningRecommendation, Box<dyn std::error::Error>> {
        if self.historical_records.len() < self.config.min_historical_records {
            return Err("Insufficient historical data for meta-learning".into());
        }

        match &self.config.strategy {
            MetaLearningStrategy::SimilarityBased { .. } => {
                self.similarity_based_recommendation(dataset_characteristics, algorithm_name)
            }
            MetaLearningStrategy::ModelBased { .. } => {
                self.model_based_recommendation(dataset_characteristics, algorithm_name)
            }
            MetaLearningStrategy::GradientBased { .. } => {
                self.gradient_based_recommendation(dataset_characteristics, algorithm_name)
            }
            MetaLearningStrategy::BayesianMeta { .. } => {
                self.bayesian_meta_recommendation(dataset_characteristics, algorithm_name)
            }
            MetaLearningStrategy::TransferLearning { .. } => {
                self.transfer_learning_recommendation(dataset_characteristics, algorithm_name)
            }
            MetaLearningStrategy::EnsembleMeta { .. } => {
                self.ensemble_meta_recommendation(dataset_characteristics, algorithm_name)
            }
        }
    }

    /// Similarity-based recommendation
    fn similarity_based_recommendation(
        &mut self,
        dataset_characteristics: &DatasetCharacteristics,
        algorithm_name: &str,
    ) -> Result<MetaLearningRecommendation, Box<dyn std::error::Error>> {
        let (similarity_metric, k_neighbors, weight_by_distance) = match &self.config.strategy {
            MetaLearningStrategy::SimilarityBased {
                similarity_metric,
                k_neighbors,
                weight_by_distance,
            } => (similarity_metric, *k_neighbors, *weight_by_distance),
            _ => unreachable!(),
        };

        // Find similar datasets
        let mut similarities = Vec::new();
        for record in &self.historical_records {
            if record.algorithm_name == algorithm_name {
                let similarity = self.calculate_similarity(
                    dataset_characteristics,
                    &record.dataset_characteristics,
                    similarity_metric,
                )?;
                similarities.push((record, similarity));
            }
        }

        // Sort by similarity and take top k
        similarities.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        similarities.truncate(k_neighbors);

        if similarities.is_empty() {
            return Err("No similar datasets found".into());
        }

        // Aggregate hyperparameters from similar datasets
        let mut aggregated_hyperparameters = HashMap::new();
        let mut confidence_scores = HashMap::new();
        let mut expected_performance = 0.0;
        let mut expected_runtime = 0.0;
        let mut total_weight = 0.0;

        for (record, similarity) in &similarities {
            let weight = if weight_by_distance { *similarity } else { 1.0 };
            total_weight += weight;

            expected_performance += record.performance_score * weight;
            expected_runtime += record.optimization_time * weight;

            for (param_name, param_value) in &record.hyperparameters {
                match param_value {
                    ParameterValue::Float(val) => {
                        let entry = aggregated_hyperparameters
                            .entry(param_name.clone())
                            .or_insert_with(|| (0.0, 0.0)); // (sum, weight_sum)
                        entry.0 += val * weight;
                        entry.1 += weight;
                    }
                    ParameterValue::Integer(val) => {
                        let entry = aggregated_hyperparameters
                            .entry(param_name.clone())
                            .or_insert_with(|| (0.0, 0.0));
                        entry.0 += *val as Float * weight;
                        entry.1 += weight;
                    }
                    _ => {
                        // For non-numeric parameters, use the most common value
                        // Simplified implementation
                    }
                }

                confidence_scores.insert(param_name.clone(), *similarity);
            }
        }

        // Convert aggregated values to recommendations
        let mut recommended_hyperparameters = HashMap::new();
        for (param_name, (sum, weight_sum)) in aggregated_hyperparameters {
            let avg_value = sum / weight_sum;
            recommended_hyperparameters.insert(param_name, ParameterValue::Float(avg_value));
        }

        expected_performance /= total_weight;
        expected_runtime /= total_weight;

        let similar_datasets = similarities
            .iter()
            .map(|(record, _)| record.dataset_id.clone())
            .collect();

        let uncertainty_estimate = 1.0
            - similarities.iter().map(|(_, sim)| sim).sum::<Float>() / similarities.len() as Float;

        Ok(MetaLearningRecommendation {
            recommended_hyperparameters,
            confidence_scores,
            expected_performance,
            expected_runtime,
            similar_datasets,
            recommendation_source: "SimilarityBased".to_string(),
            uncertainty_estimate,
        })
    }

    /// Model-based recommendation using surrogate models
    fn model_based_recommendation(
        &mut self,
        dataset_characteristics: &DatasetCharacteristics,
        algorithm_name: &str,
    ) -> Result<MetaLearningRecommendation, Box<dyn std::error::Error>> {
        let model_key = format!("{}_{}", algorithm_name, "hyperparams");

        if let Some(model) = self.surrogate_models.get(&model_key) {
            let features = self.extract_features(dataset_characteristics)?;
            let features_2d = Array2::from_shape_vec((1, features.len()), features.to_vec())?;

            let (predictions, uncertainties) = model.predict_with_uncertainty(&features_2d)?;

            // Convert predictions to hyperparameter recommendations
            let mut recommended_hyperparameters = HashMap::new();
            let mut confidence_scores = HashMap::new();

            // Simplified: assume first prediction is for a specific hyperparameter
            recommended_hyperparameters.insert(
                "learning_rate".to_string(),
                ParameterValue::Float(predictions[0]),
            );
            confidence_scores.insert("learning_rate".to_string(), 1.0 - uncertainties[0]);

            Ok(MetaLearningRecommendation {
                recommended_hyperparameters,
                confidence_scores,
                expected_performance: predictions[0],
                expected_runtime: 100.0, // Placeholder
                similar_datasets: vec![],
                recommendation_source: "ModelBased".to_string(),
                uncertainty_estimate: uncertainties[0],
            })
        } else {
            Err("Surrogate model not available".into())
        }
    }

    /// Gradient-based meta-learning recommendation
    fn gradient_based_recommendation(
        &mut self,
        _dataset_characteristics: &DatasetCharacteristics,
        algorithm_name: &str,
    ) -> Result<MetaLearningRecommendation, Box<dyn std::error::Error>> {
        // Simplified gradient-based meta-learning
        // In practice, this would implement MAML or similar algorithms

        let similar_records: Vec<&OptimizationRecord> = self
            .historical_records
            .iter()
            .filter(|r| r.algorithm_name == algorithm_name)
            .collect();

        if similar_records.is_empty() {
            return Err("No historical records for algorithm".into());
        }

        // Simulate gradient-based adaptation
        let mut adapted_hyperparameters = HashMap::new();
        let mut confidence_scores = HashMap::new();

        // Use the best performing record as starting point
        let best_record = similar_records
            .iter()
            .max_by(|a, b| {
                a.performance_score
                    .partial_cmp(&b.performance_score)
                    .unwrap()
            })
            .unwrap();

        for (param_name, param_value) in &best_record.hyperparameters {
            adapted_hyperparameters.insert(param_name.clone(), param_value.clone());
            confidence_scores.insert(param_name.clone(), 0.8); // High confidence from gradient adaptation
        }

        Ok(MetaLearningRecommendation {
            recommended_hyperparameters: adapted_hyperparameters,
            confidence_scores,
            expected_performance: best_record.performance_score,
            expected_runtime: best_record.optimization_time,
            similar_datasets: vec![best_record.dataset_id.clone()],
            recommendation_source: "GradientBased".to_string(),
            uncertainty_estimate: 0.2,
        })
    }

    /// Bayesian meta-learning recommendation
    fn bayesian_meta_recommendation(
        &mut self,
        _dataset_characteristics: &DatasetCharacteristics,
        algorithm_name: &str,
    ) -> Result<MetaLearningRecommendation, Box<dyn std::error::Error>> {
        // Simplified Bayesian meta-learning
        let relevant_records: Vec<&OptimizationRecord> = self
            .historical_records
            .iter()
            .filter(|r| r.algorithm_name == algorithm_name)
            .collect();

        if relevant_records.is_empty() {
            return Err("No historical records for algorithm".into());
        }

        // Bayesian inference for hyperparameter distributions
        let mut hyperparameter_distributions = HashMap::new();
        let mut confidence_scores = HashMap::new();

        for record in &relevant_records {
            for (param_name, param_value) in &record.hyperparameters {
                if let ParameterValue::Float(val) = param_value {
                    let entry = hyperparameter_distributions
                        .entry(param_name.clone())
                        .or_insert_with(Vec::new);
                    entry.push(*val);
                }
            }
        }

        let mut recommended_hyperparameters = HashMap::new();
        for (param_name, values) in hyperparameter_distributions {
            let mean = values.iter().sum::<Float>() / values.len() as Float;
            let variance =
                values.iter().map(|v| (v - mean).powi(2)).sum::<Float>() / values.len() as Float;

            recommended_hyperparameters.insert(param_name.clone(), ParameterValue::Float(mean));
            confidence_scores.insert(param_name, 1.0 / (1.0 + variance)); // Higher confidence for lower variance
        }

        let avg_performance = relevant_records
            .iter()
            .map(|r| r.performance_score)
            .sum::<Float>()
            / relevant_records.len() as Float;

        Ok(MetaLearningRecommendation {
            recommended_hyperparameters,
            confidence_scores,
            expected_performance: avg_performance,
            expected_runtime: 100.0, // Placeholder
            similar_datasets: relevant_records
                .iter()
                .map(|r| r.dataset_id.clone())
                .collect(),
            recommendation_source: "BayesianMeta".to_string(),
            uncertainty_estimate: 0.3,
        })
    }

    /// Transfer learning recommendation
    fn transfer_learning_recommendation(
        &mut self,
        dataset_characteristics: &DatasetCharacteristics,
        algorithm_name: &str,
    ) -> Result<MetaLearningRecommendation, Box<dyn std::error::Error>> {
        // Find the most similar domain
        let mut best_similarity = 0.0;
        let mut best_record = None;

        for record in &self.historical_records {
            if record.algorithm_name == algorithm_name {
                let similarity = self.calculate_similarity(
                    dataset_characteristics,
                    &record.dataset_characteristics,
                    &SimilarityMetric::Cosine,
                )?;

                if similarity > best_similarity {
                    best_similarity = similarity;
                    best_record = Some(record);
                }
            }
        }

        if let Some(record) = best_record {
            let mut confidence_scores = HashMap::new();
            for param_name in record.hyperparameters.keys() {
                confidence_scores.insert(param_name.clone(), best_similarity);
            }

            Ok(MetaLearningRecommendation {
                recommended_hyperparameters: record.hyperparameters.clone(),
                confidence_scores,
                expected_performance: record.performance_score * best_similarity,
                expected_runtime: record.optimization_time,
                similar_datasets: vec![record.dataset_id.clone()],
                recommendation_source: "TransferLearning".to_string(),
                uncertainty_estimate: 1.0 - best_similarity,
            })
        } else {
            Err("No suitable source domain found for transfer learning".into())
        }
    }

    /// Ensemble meta-learning recommendation
    fn ensemble_meta_recommendation(
        &mut self,
        dataset_characteristics: &DatasetCharacteristics,
        algorithm_name: &str,
    ) -> Result<MetaLearningRecommendation, Box<dyn std::error::Error>> {
        let (strategies, combination_method) = match &self.config.strategy {
            MetaLearningStrategy::EnsembleMeta {
                strategies,
                combination_method,
            } => (strategies, combination_method),
            _ => unreachable!(),
        };

        let mut recommendations = Vec::new();

        // Get recommendations from each strategy
        for strategy in strategies {
            let mut temp_config = self.config.clone();
            temp_config.strategy = strategy.clone();
            let mut temp_engine = MetaLearningEngine::new(temp_config);
            temp_engine.historical_records = self.historical_records.clone();

            if let Ok(rec) =
                temp_engine.recommend_hyperparameters(dataset_characteristics, algorithm_name)
            {
                recommendations.push(rec);
            }
        }

        if recommendations.is_empty() {
            return Err("No recommendations from ensemble strategies".into());
        }

        // Combine recommendations
        match combination_method {
            CombinationMethod::Average => self.average_recommendations(recommendations),
            CombinationMethod::WeightedAverage => {
                self.weighted_average_recommendations(recommendations)
            }
            _ => {
                // Default to average for other methods
                self.average_recommendations(recommendations)
            }
        }
    }

    /// Calculate similarity between datasets
    fn calculate_similarity(
        &self,
        dataset1: &DatasetCharacteristics,
        dataset2: &DatasetCharacteristics,
        metric: &SimilarityMetric,
    ) -> Result<Float, Box<dyn std::error::Error>> {
        let features1 = self.extract_features(dataset1)?;
        let features2 = self.extract_features(dataset2)?;

        match metric {
            SimilarityMetric::Cosine => {
                let dot_product = features1
                    .iter()
                    .zip(features2.iter())
                    .map(|(a, b)| a * b)
                    .sum::<Float>();
                let norm1 = (features1.iter().map(|x| x * x).sum::<Float>()).sqrt();
                let norm2 = (features2.iter().map(|x| x * x).sum::<Float>()).sqrt();
                Ok(dot_product / (norm1 * norm2))
            }
            SimilarityMetric::Euclidean => {
                let distance = features1
                    .iter()
                    .zip(features2.iter())
                    .map(|(a, b)| (a - b).powi(2))
                    .sum::<Float>()
                    .sqrt();
                Ok(1.0 / (1.0 + distance))
            }
            SimilarityMetric::Manhattan => {
                let distance = features1
                    .iter()
                    .zip(features2.iter())
                    .map(|(a, b)| (a - b).abs())
                    .sum::<Float>();
                Ok(1.0 / (1.0 + distance))
            }
            _ => {
                // Default to cosine similarity
                let dot_product = features1
                    .iter()
                    .zip(features2.iter())
                    .map(|(a, b)| a * b)
                    .sum::<Float>();
                let norm1 = (features1.iter().map(|x| x * x).sum::<Float>()).sqrt();
                let norm2 = (features2.iter().map(|x| x * x).sum::<Float>()).sqrt();
                Ok(dot_product / (norm1 * norm2))
            }
        }
    }

    /// Extract feature vector from dataset characteristics
    fn extract_features(
        &self,
        characteristics: &DatasetCharacteristics,
    ) -> Result<Array1<Float>, Box<dyn std::error::Error>> {
        let mut features = Vec::new();

        // Basic dataset statistics
        features.push(characteristics.n_samples as Float);
        features.push(characteristics.n_features as Float);
        features.push(characteristics.n_classes.unwrap_or(0) as Float);

        // Statistical measures
        if !characteristics.statistical_measures.mean_values.is_empty() {
            features.extend(&characteristics.statistical_measures.mean_values);
        }

        // Complexity measures
        features.push(
            characteristics
                .complexity_measures
                .fisher_discriminant_ratio,
        );
        features.push(characteristics.complexity_measures.volume_of_overlap);
        features.push(characteristics.complexity_measures.feature_efficiency);
        features.push(characteristics.complexity_measures.entropy);

        Ok(Array1::from_vec(features))
    }

    /// Update surrogate models with new data
    fn update_surrogate_models(&mut self) -> Result<(), Box<dyn std::error::Error>> {
        // Group records by algorithm
        let mut algorithm_groups: HashMap<String, Vec<&OptimizationRecord>> = HashMap::new();

        for record in &self.historical_records {
            algorithm_groups
                .entry(record.algorithm_name.clone())
                .or_default()
                .push(record);
        }

        // Train surrogate models for each algorithm
        for (algorithm_name, records) in algorithm_groups {
            if records.len() >= 5 {
                // Minimum records for training
                let model_key = format!("{}_{}", algorithm_name, "hyperparams");

                // Extract features and targets
                let mut features_vec = Vec::new();
                let mut targets = Vec::new();

                for record in &records {
                    let features = self.extract_features(&record.dataset_characteristics)?;
                    features_vec.extend(features.to_vec());
                    targets.push(record.performance_score);
                }

                let n_features = self
                    .extract_features(&records[0].dataset_characteristics)?
                    .len();
                let features_2d =
                    Array2::from_shape_vec((records.len(), n_features), features_vec)?;
                let targets_1d = Array1::from_vec(targets);

                // Create and train surrogate model
                let mut surrogate = Box::new(RandomForestSurrogate::new(10, Some(5)));
                surrogate.fit(&features_2d, &targets_1d)?;

                self.surrogate_models.insert(model_key, surrogate);
            }
        }

        Ok(())
    }

    /// Average multiple recommendations
    fn average_recommendations(
        &self,
        recommendations: Vec<MetaLearningRecommendation>,
    ) -> Result<MetaLearningRecommendation, Box<dyn std::error::Error>> {
        if recommendations.is_empty() {
            return Err("No recommendations to average".into());
        }

        let mut aggregated_hyperparameters = HashMap::new();
        let mut confidence_scores = HashMap::new();
        let mut expected_performance = 0.0;
        let mut expected_runtime = 0.0;
        let mut uncertainty_estimate = 0.0;

        let n_recommendations = recommendations.len() as Float;

        for rec in &recommendations {
            expected_performance += rec.expected_performance;
            expected_runtime += rec.expected_runtime;
            uncertainty_estimate += rec.uncertainty_estimate;

            for (param_name, param_value) in &rec.recommended_hyperparameters {
                if let ParameterValue::Float(val) = param_value {
                    *aggregated_hyperparameters
                        .entry(param_name.clone())
                        .or_insert(0.0) += val;
                }
            }

            for (param_name, confidence) in &rec.confidence_scores {
                *confidence_scores.entry(param_name.clone()).or_insert(0.0) += confidence;
            }
        }

        // Average the values
        let mut recommended_hyperparameters = HashMap::new();
        for (param_name, sum) in aggregated_hyperparameters {
            recommended_hyperparameters.insert(
                param_name.clone(),
                ParameterValue::Float(sum / n_recommendations),
            );
            if let Some(conf_sum) = confidence_scores.get_mut(&param_name) {
                *conf_sum /= n_recommendations;
            }
        }

        Ok(MetaLearningRecommendation {
            recommended_hyperparameters,
            confidence_scores,
            expected_performance: expected_performance / n_recommendations,
            expected_runtime: expected_runtime / n_recommendations,
            similar_datasets: vec![], // Combine all similar datasets if needed
            recommendation_source: "EnsembleAverage".to_string(),
            uncertainty_estimate: uncertainty_estimate / n_recommendations,
        })
    }

    /// Weighted average of recommendations
    fn weighted_average_recommendations(
        &self,
        recommendations: Vec<MetaLearningRecommendation>,
    ) -> Result<MetaLearningRecommendation, Box<dyn std::error::Error>> {
        if recommendations.is_empty() {
            return Err("No recommendations to average".into());
        }

        // Use confidence as weights (inverse of uncertainty)
        let weights: Vec<Float> = recommendations
            .iter()
            .map(|r| 1.0 - r.uncertainty_estimate)
            .collect();

        let total_weight: Float = weights.iter().sum();

        let mut aggregated_hyperparameters = HashMap::new();
        let mut confidence_scores = HashMap::new();
        let mut expected_performance = 0.0;
        let mut expected_runtime = 0.0;
        let mut uncertainty_estimate = 0.0;

        for (i, rec) in recommendations.iter().enumerate() {
            let weight = weights[i] / total_weight;

            expected_performance += rec.expected_performance * weight;
            expected_runtime += rec.expected_runtime * weight;
            uncertainty_estimate += rec.uncertainty_estimate * weight;

            for (param_name, param_value) in &rec.recommended_hyperparameters {
                if let ParameterValue::Float(val) = param_value {
                    *aggregated_hyperparameters
                        .entry(param_name.clone())
                        .or_insert(0.0) += val * weight;
                }
            }

            for (param_name, confidence) in &rec.confidence_scores {
                *confidence_scores.entry(param_name.clone()).or_insert(0.0) += confidence * weight;
            }
        }

        let mut recommended_hyperparameters = HashMap::new();
        for (param_name, weighted_sum) in aggregated_hyperparameters {
            recommended_hyperparameters.insert(param_name, ParameterValue::Float(weighted_sum));
        }

        Ok(MetaLearningRecommendation {
            recommended_hyperparameters,
            confidence_scores,
            expected_performance,
            expected_runtime,
            similar_datasets: vec![],
            recommendation_source: "EnsembleWeightedAverage".to_string(),
            uncertainty_estimate,
        })
    }
}

impl RandomForestSurrogate {
    fn new(n_trees: usize, max_depth: Option<usize>) -> Self {
        Self {
            n_trees,
            max_depth,
            models: Vec::new(),
        }
    }
}

impl SurrogateModelTrait for RandomForestSurrogate {
    fn fit(
        &mut self,
        _features: &Array2<Float>,
        targets: &Array1<Float>,
    ) -> Result<(), Box<dyn std::error::Error>> {
        self.models.clear();

        for _ in 0..self.n_trees {
            let tree = SimpleTree {
                feature_idx: None,
                threshold: None,
                left: None,
                right: None,
                prediction: Some(targets.mean().unwrap_or(0.0)),
            };
            self.models.push(tree);
        }

        Ok(())
    }

    fn predict(
        &self,
        features: &Array2<Float>,
    ) -> Result<Array1<Float>, Box<dyn std::error::Error>> {
        let n_samples = features.nrows();
        let mut predictions = Array1::zeros(n_samples);

        for i in 0..n_samples {
            let mut sum = 0.0;
            for tree in &self.models {
                sum += tree.prediction.unwrap_or(0.0);
            }
            predictions[i] = sum / self.models.len() as Float;
        }

        Ok(predictions)
    }

    fn predict_with_uncertainty(
        &self,
        features: &Array2<Float>,
    ) -> Result<(Array1<Float>, Array1<Float>), Box<dyn std::error::Error>> {
        let predictions = self.predict(features)?;
        let uncertainties = Array1::from_elem(predictions.len(), 0.1); // Placeholder uncertainty
        Ok((predictions, uncertainties))
    }
}

/// Convenience function for meta-learning based hyperparameter initialization
pub fn meta_learning_recommend(
    dataset_characteristics: &DatasetCharacteristics,
    algorithm_name: &str,
    historical_records: Vec<OptimizationRecord>,
    config: Option<MetaLearningConfig>,
) -> Result<MetaLearningRecommendation, Box<dyn std::error::Error>> {
    let config = config.unwrap_or_default();
    let mut engine = MetaLearningEngine::new(config);
    engine.load_historical_records(historical_records);
    engine.recommend_hyperparameters(dataset_characteristics, algorithm_name)
}

#[allow(non_snake_case)]
#[cfg(test)]
mod tests {
    use super::*;

    fn create_sample_dataset_characteristics() -> DatasetCharacteristics {
        DatasetCharacteristics {
            n_samples: 1000,
            n_features: 10,
            n_classes: Some(2),
            class_balance: vec![0.6, 0.4],
            feature_types: vec![FeatureType::Numerical; 10],
            statistical_measures: StatisticalMeasures {
                mean_values: vec![0.0; 10],
                std_values: vec![1.0; 10],
                skewness: vec![0.0; 10],
                kurtosis: vec![3.0; 10],
                correlation_matrix: None,
                mutual_information: None,
            },
            complexity_measures: ComplexityMeasures {
                fisher_discriminant_ratio: 1.5,
                volume_of_overlap: 0.3,
                feature_efficiency: 0.8,
                collective_feature_efficiency: 0.7,
                entropy: 0.9,
                class_probability_max: 0.6,
            },
            domain_specific: HashMap::new(),
        }
    }

    #[test]
    fn test_meta_learning_engine_creation() {
        let config = MetaLearningConfig::default();
        let engine = MetaLearningEngine::new(config);
        assert_eq!(engine.historical_records.len(), 0);
    }

    #[test]
    fn test_similarity_calculation() {
        let config = MetaLearningConfig::default();
        let engine = MetaLearningEngine::new(config);

        let dataset1 = create_sample_dataset_characteristics();
        let dataset2 = create_sample_dataset_characteristics();

        let similarity = engine
            .calculate_similarity(&dataset1, &dataset2, &SimilarityMetric::Cosine)
            .unwrap();
        assert!(similarity >= 0.0 && similarity <= 1.0);
    }

    #[test]
    fn test_feature_extraction() {
        let config = MetaLearningConfig::default();
        let engine = MetaLearningEngine::new(config);

        let dataset = create_sample_dataset_characteristics();
        let features = engine.extract_features(&dataset).unwrap();

        assert!(features.len() > 0);
    }

    #[test]
    fn test_meta_learning_recommendation() {
        let dataset_characteristics = create_sample_dataset_characteristics();

        let mut hyperparameters = HashMap::new();
        hyperparameters.insert("learning_rate".to_string(), ParameterValue::Float(0.01));
        hyperparameters.insert("n_estimators".to_string(), ParameterValue::Integer(100));

        let record = OptimizationRecord {
            dataset_id: "test_dataset".to_string(),
            algorithm_name: "RandomForest".to_string(),
            dataset_characteristics: dataset_characteristics.clone(),
            hyperparameters,
            performance_score: 0.85,
            optimization_time: 120.0,
            convergence_iterations: 50,
            validation_method: "5-fold-cv".to_string(),
            timestamp: 1234567890,
        };

        let result =
            meta_learning_recommend(&dataset_characteristics, "RandomForest", vec![record], None);

        // Should fail due to insufficient historical data
        assert!(result.is_err());
    }
}
