//! Advanced ensemble clustering methods with sophisticated combination strategies
//!
//! This module provides advanced ensemble techniques including meta-learning,
//! Bayesian model averaging, genetic optimization, boosting, and stacking.

use super::algorithms::EnsembleClusterer;
use super::core::*;
use crate::error::{ClusteringError, Result};
use crate::metrics::silhouette_score;
use scirs2_core::ndarray::ArrayStatCompat;
use scirs2_core::ndarray::{s, Array1, Array2, Array3, ArrayView1, ArrayView2, Axis};
use scirs2_core::numeric::{Float, FromPrimitive};
use scirs2_core::random::prelude::*;
use scirs2_core::random::{Distribution, WeightedIndex};
use std::cmp::Ordering;
use std::collections::HashMap;
use std::fmt::Debug;

/// Configuration for advanced ensemble methods
#[derive(Debug, Clone)]
pub struct AdvancedEnsembleConfig {
    /// Meta-learning configuration
    pub meta_learning: MetaLearningConfig,
    /// Bayesian model averaging configuration
    pub bayesian_averaging: BayesianAveragingConfig,
    /// Genetic algorithm optimization configuration
    pub genetic_optimization: GeneticOptimizationConfig,
    /// Boosting configuration for clustering
    pub boostingconfig: BoostingConfig,
    /// Stacking configuration
    pub stackingconfig: StackingConfig,
    /// Enable uncertainty quantification
    pub uncertainty_quantification: bool,
}

/// Meta-learning configuration for learning ensemble combination
#[derive(Debug, Clone)]
pub struct MetaLearningConfig {
    /// Number of meta-features to extract
    pub n_meta_features: usize,
    /// Learning rate for meta-learner
    pub learning_rate: f64,
    /// Number of training iterations
    pub n_iterations: usize,
    /// Meta-learning algorithm
    pub algorithm: MetaLearningAlgorithm,
    /// Validation split for meta-training
    pub validation_split: f64,
}

/// Meta-learning algorithms for ensemble combination
#[derive(Debug, Clone)]
pub enum MetaLearningAlgorithm {
    /// Neural network meta-learner
    NeuralNetwork { hidden_layers: Vec<usize> },
    /// Random forest meta-learner
    RandomForest { n_trees: usize, max_depth: usize },
    /// Gradient boosting meta-learner
    GradientBoosting {
        n_estimators: usize,
        max_depth: usize,
    },
    /// Linear meta-learner
    Linear { regularization: f64 },
}

/// Bayesian model averaging configuration
#[derive(Debug, Clone)]
pub struct BayesianAveragingConfig {
    /// Prior distribution parameters
    pub prior_alpha: f64,
    pub prior_beta: f64,
    /// Number of MCMC samples
    pub n_samples: usize,
    /// Burn-in period
    pub burn_in: usize,
    /// Posterior update method
    pub update_method: PosteriorUpdateMethod,
    /// Enable adaptive sampling
    pub adaptive_sampling: bool,
}

/// Methods for updating posterior distributions
#[derive(Debug, Clone)]
pub enum PosteriorUpdateMethod {
    /// Metropolis-Hastings sampling
    MetropolisHastings,
    /// Gibbs sampling
    Gibbs,
    /// Variational inference
    VariationalInference,
    /// Hamiltonian Monte Carlo
    HamiltonianMC,
}

/// Genetic algorithm configuration for ensemble optimization
#[derive(Debug, Clone)]
pub struct GeneticOptimizationConfig {
    /// Population size
    pub population_size: usize,
    /// Number of generations
    pub n_generations: usize,
    /// Crossover probability
    pub crossover_prob: f64,
    /// Mutation probability
    pub mutation_prob: f64,
    /// Selection method
    pub selection_method: SelectionMethod,
    /// Elite percentage
    pub elite_percentage: f64,
    /// Fitness function
    pub fitness_function: FitnessFunction,
}

/// Selection methods for genetic algorithm
#[derive(Debug, Clone)]
pub enum SelectionMethod {
    /// Tournament selection
    Tournament { tournament_size: usize },
    /// Roulette wheel selection
    RouletteWheel,
    /// Rank-based selection
    RankBased,
    /// Elitist selection
    Elitist,
}

/// Fitness functions for genetic optimization
#[derive(Debug, Clone)]
pub enum FitnessFunction {
    /// Silhouette score
    Silhouette,
    /// Davies-Bouldin index
    DaviesBouldin,
    /// Calinski-Harabasz index
    CalinskiHarabasz,
    /// Multi-objective combination
    MultiObjective { weights: Vec<f64> },
    /// Stability-based fitness
    Stability,
}

/// Boosting configuration for clustering
#[derive(Debug, Clone)]
pub struct BoostingConfig {
    /// Number of boosting rounds
    pub n_rounds: usize,
    /// Learning rate for weight updates
    pub learning_rate: f64,
    /// Reweighting strategy
    pub reweighting_strategy: ReweightingStrategy,
    /// Error function for boosting
    pub error_function: ErrorFunction,
    /// Enable adaptive boosting
    pub adaptive_boosting: bool,
}

/// Strategies for reweighting samples in boosting
#[derive(Debug, Clone)]
pub enum ReweightingStrategy {
    /// AdaBoost-style exponential reweighting
    Exponential,
    /// Linear reweighting based on clustering quality
    Linear,
    /// Logistic reweighting
    Logistic,
    /// Custom reweighting function
    Custom { alpha: f64, beta: f64 },
}

/// Error functions for clustering boosting
#[derive(Debug, Clone)]
pub enum ErrorFunction {
    /// Disagreement rate between clusterings
    DisagreementRate,
    /// Inverse silhouette score
    InverseSilhouette,
    /// Custom weighted error
    WeightedError,
}

/// Stacking configuration for ensemble clustering
#[derive(Debug, Clone)]
pub struct StackingConfig {
    /// Base clustering algorithms
    pub base_algorithms: Vec<ClusteringAlgorithm>,
    /// Meta-clustering algorithm
    pub meta_algorithm: MetaClusteringAlgorithm,
    /// Cross-validation folds for stacking
    pub cv_folds: usize,
    /// Blending ratio for combining predictions
    pub blending_ratio: f64,
    /// Feature engineering for meta-learner
    pub feature_engineering: bool,
}

/// Meta-clustering algorithms for stacking
#[derive(Debug, Clone)]
pub enum MetaClusteringAlgorithm {
    /// Hierarchical clustering on base results
    Hierarchical { linkage: String },
    /// Spectral clustering on similarity matrix
    Spectral { n_clusters: usize },
    /// Graph-based clustering
    GraphBased { resolution: f64 },
    /// Consensus clustering
    Consensus { method: String },
}

/// Meta-learner for ensemble combination
#[derive(Debug, Clone)]
pub struct MetaLearner {
    /// Algorithm type
    pub algorithm: MetaLearningAlgorithm,
    /// Trained weights
    pub weights: Option<Array1<f64>>,
    /// Training history
    pub training_history: Vec<f64>,
}

/// Genetic optimizer for ensemble evolution
#[derive(Debug, Clone)]
pub struct GeneticOptimizer {
    config: GeneticOptimizationConfig,
    population: Vec<EnsembleConfig>,
    fitness_scores: Vec<f64>,
}

impl GeneticOptimizer {
    pub fn new(config: GeneticOptimizationConfig) -> Self {
        Self {
            config,
            population: Vec::new(),
            fitness_scores: Vec::new(),
        }
    }

    pub fn evolve_ensemble<F>(
        &mut self,
        base_ensemble: &EnsembleClusterer<F>,
        data: ArrayView2<F>,
    ) -> Result<EnsembleClusterer<F>>
    where
        F: Float
            + FromPrimitive
            + Debug
            + 'static
            + std::iter::Sum
            + std::fmt::Display
            + Send
            + Sync,
        f64: From<F>,
    {
        // Initialize population
        self.initialize_population()?;

        // Evolve for specified generations
        for _generation in 0..self.config.n_generations {
            self.evaluate_population(data)?;
            self.selection_and_reproduction()?;
        }

        // Return best evolved ensemble
        let best_config = self.get_best_config()?;
        Ok(EnsembleClusterer::new(best_config))
    }

    fn initialize_population(&mut self) -> Result<()> {
        self.population.clear();
        for _ in 0..self.config.population_size {
            self.population.push(EnsembleConfig::default());
        }
        Ok(())
    }

    fn evaluate_population<F>(&mut self, data: ArrayView2<F>) -> Result<()>
    where
        F: Float
            + FromPrimitive
            + Debug
            + 'static
            + std::iter::Sum
            + std::fmt::Display
            + Send
            + Sync,
        f64: From<F>,
    {
        self.fitness_scores.clear();
        for config in &self.population {
            let ensemble = EnsembleClusterer::new(config.clone());
            let result = ensemble.fit(data)?;
            let fitness = match self.config.fitness_function {
                FitnessFunction::Silhouette => result.ensemble_quality,
                _ => result.ensemble_quality, // Simplified
            };
            self.fitness_scores.push(fitness);
        }
        Ok(())
    }

    fn selection_and_reproduction(&mut self) -> Result<()> {
        // Simplified selection - keep best performers
        let mut sorted_indices: Vec<usize> = (0..self.population.len()).collect();
        sorted_indices.sort_by(|&a, &b| {
            self.fitness_scores[b]
                .partial_cmp(&self.fitness_scores[a])
                .unwrap_or(Ordering::Equal)
        });

        let elite_count = (self.population.len() as f64 * self.config.elite_percentage) as usize;
        let new_population = sorted_indices[..elite_count]
            .iter()
            .map(|&i| self.population[i].clone())
            .collect();

        self.population = new_population;
        Ok(())
    }

    fn get_best_config(&self) -> Result<EnsembleConfig> {
        if self.population.is_empty() {
            return Ok(EnsembleConfig::default());
        }
        Ok(self.population[0].clone())
    }
}

/// Advanced ensemble clusterer with sophisticated methods
pub struct AdvancedEnsembleClusterer<F: Float> {
    config: AdvancedEnsembleConfig,
    base_ensemble: EnsembleClusterer<F>,
    meta_learner: Option<MetaLearner>,
    bayesian_weights: Option<Array1<f64>>,
    genetic_optimizer: Option<GeneticOptimizer>,
    _phantom: std::marker::PhantomData<F>,
}

impl<F> AdvancedEnsembleClusterer<F>
where
    F: Float + FromPrimitive + Debug + 'static + std::iter::Sum + std::fmt::Display + Send + Sync,
    f64: From<F>,
{
    /// Create new advanced ensemble clusterer
    pub fn new(config: AdvancedEnsembleConfig, baseconfig: EnsembleConfig) -> Self {
        Self {
            config,
            base_ensemble: EnsembleClusterer::new(baseconfig),
            meta_learner: None,
            bayesian_weights: None,
            genetic_optimizer: None,
            _phantom: std::marker::PhantomData,
        }
    }

    /// Perform advanced ensemble clustering with meta-learning
    pub fn fit_with_meta_learning(&mut self, data: ArrayView2<F>) -> Result<EnsembleResult> {
        // 1. Generate base clustering results
        let base_results = self.base_ensemble.fit(data)?;

        // 2. Extract meta-features from data and clustering results
        let meta_features = self.extract_meta_features(data, &base_results)?;

        // 3. Train meta-learner to predict best combination weights
        let weights = self.train_meta_learner(&meta_features, &base_results.individual_results)?;

        // 4. Combine results using learned weights
        let enhanced_consensus =
            self.weighted_meta_consensus(&base_results.individual_results, &weights, data.nrows())?;

        // 5. Calculate enhanced statistics
        let mut enhanced_result = base_results;
        enhanced_result.consensus_labels = enhanced_consensus;
        enhanced_result.ensemble_quality = self.calculate_meta_quality(data, &enhanced_result)?;

        Ok(enhanced_result)
    }

    /// Perform Bayesian model averaging for ensemble combination
    pub fn fit_with_bayesian_averaging(&mut self, data: ArrayView2<F>) -> Result<EnsembleResult> {
        let base_results = self.base_ensemble.fit(data)?;

        // Initialize Bayesian weights with uniform prior
        let n_models = base_results.individual_results.len();
        let mut weights = Array1::from_elem(n_models, 1.0 / n_models as f64);

        // MCMC sampling for posterior weights
        for _iteration in 0..self.config.bayesian_averaging.n_samples {
            weights = self.mcmc_update_weights(&weights, &base_results, data)?;
        }

        self.bayesian_weights = Some(weights.clone());

        // Generate consensus using Bayesian weights
        let consensus = self.bayesian_weighted_consensus(
            &base_results.individual_results,
            &weights,
            data.nrows(),
        )?;

        let mut enhanced_result = base_results;
        enhanced_result.consensus_labels = consensus;

        Ok(enhanced_result)
    }

    /// Perform genetic algorithm optimization for ensemble composition
    pub fn fit_with_genetic_optimization(&mut self, data: ArrayView2<F>) -> Result<EnsembleResult> {
        // Initialize genetic algorithm
        let mut optimizer = GeneticOptimizer::new(self.config.genetic_optimization.clone());

        // Evolve optimal ensemble composition
        let optimized_ensemble = optimizer.evolve_ensemble(&self.base_ensemble, data)?;

        // Fit with optimized ensemble
        optimized_ensemble.fit(data)
    }

    /// Perform boosting-style ensemble clustering
    pub fn fit_with_boosting(&mut self, data: ArrayView2<F>) -> Result<EnsembleResult> {
        let mut sample_weights = Array1::from_elem(data.nrows(), 1.0 / data.nrows() as f64);
        let mut weak_learners = Vec::new();
        let mut learner_weights = Vec::new();

        for _round in 0..self.config.boostingconfig.n_rounds {
            // Sample data based on current weights
            let weighted_data = self.weighted_sample(data, &sample_weights)?;

            // Train weak clustering learner
            let weak_result = self.train_weak_learner(&weighted_data)?;

            // Calculate error rate
            let error_rate =
                self.calculate_clustering_error(data, &weak_result, &sample_weights)?;

            if error_rate >= 0.5 {
                break; // Stop if error rate is too high
            }

            // Calculate learner weight
            let learner_weight =
                self.config.boostingconfig.learning_rate * ((1.0 - error_rate) / error_rate).ln();

            // Update sample weights
            self.update_sample_weights(&mut sample_weights, &weak_result, learner_weight, data)?;

            weak_learners.push(weak_result);
            learner_weights.push(learner_weight);
        }

        // Combine weak learners
        self.combine_boosted_learners(&weak_learners, &learner_weights, data.nrows())
    }

    /// Perform stacking ensemble clustering
    pub fn fit_with_stacking(&mut self, data: ArrayView2<F>) -> Result<EnsembleResult> {
        let cv_folds = self.config.stackingconfig.cv_folds;
        let n_samples = data.nrows();
        let fold_size = n_samples / cv_folds;

        // Stage 1: Generate base predictions using cross-validation
        let mut base_predictions =
            Array2::zeros((n_samples, self.config.stackingconfig.base_algorithms.len()));

        for fold in 0..cv_folds {
            let start_idx = fold * fold_size;
            let end_idx = if fold == cv_folds - 1 {
                n_samples
            } else {
                (fold + 1) * fold_size
            };

            // Split data
            let train_indices: Vec<usize> = (0..start_idx).chain(end_idx..n_samples).collect();
            let test_indices: Vec<usize> = (start_idx..end_idx).collect();

            let train_data = data.select(Axis(0), &train_indices);
            let test_data = data.select(Axis(0), &test_indices);

            // Train base algorithms on fold training data
            let base_algorithms = self.config.stackingconfig.base_algorithms.clone();
            for (alg_idx, algorithm) in base_algorithms.iter().enumerate() {
                let labels = self.train_base_algorithm(&train_data, algorithm)?;
                let test_labels = self.predict_base_algorithm(&test_data, algorithm, &labels)?;

                // Store predictions
                for (i, &test_idx) in test_indices.iter().enumerate() {
                    if i < test_labels.len() {
                        base_predictions[[test_idx, alg_idx]] = test_labels[i] as f64;
                    }
                }
            }
        }

        // Stage 2: Train meta-learner on base predictions
        let meta_labels = self.train_meta_clustering_algorithm(&base_predictions)?;

        // Convert to ensemble result format
        let individual_results = vec![]; // Would populate with base results
        let consensus_stats = self.calculate_stacking_consensus_stats(&meta_labels)?;
        let diversity_metrics = self.calculate_stacking_diversity_metrics(&base_predictions)?;

        Ok(EnsembleResult {
            consensus_labels: meta_labels,
            individual_results,
            consensus_stats,
            diversity_metrics,
            ensemble_quality: 0.0, // Would calculate properly
            stability_score: 0.0,  // Would calculate properly
        })
    }

    // Helper methods for advanced ensemble techniques

    fn extract_meta_features(
        &self,
        data: ArrayView2<F>,
        results: &EnsembleResult,
    ) -> Result<Array2<f64>> {
        let n_features = self.config.meta_learning.n_meta_features;
        let mut meta_features = Array2::zeros((1, n_features));

        // Extract dataset characteristics
        let n_samples = data.nrows() as f64;
        let n_dims = data.ncols() as f64;
        let data_f64 = data.mapv(|x| x.to_f64().unwrap_or(0.0));

        // Statistical meta-features
        meta_features[[0, 0]] = n_samples.ln();
        meta_features[[0, 1]] = n_dims.ln();
        meta_features[[0, 2]] = data_f64.var(0.0);
        meta_features[[0, 3]] = calculate_intrinsic_dimensionality(&data_f64);
        meta_features[[0, 4]] = calculate_clustering_tendency(&data_f64);
        meta_features[[0, 5]] = results.diversity_metrics.average_diversity;

        // Additional domain-specific meta-features
        for i in 6..n_features {
            meta_features[[0, i]] = calculate_advanced_meta_feature(&data_f64, i - 6);
        }

        Ok(meta_features)
    }

    fn train_meta_learner(
        &mut self,
        meta_features: &Array2<f64>,
        base_results: &[ClusteringResult],
    ) -> Result<Array1<f64>> {
        match &self.config.meta_learning.algorithm {
            MetaLearningAlgorithm::NeuralNetwork { hidden_layers } => {
                let hidden_layers = hidden_layers.clone();
                self.train_neural_meta_learner(meta_features, base_results, &hidden_layers)
            }
            MetaLearningAlgorithm::RandomForest { n_trees, max_depth } => {
                self.train_forest_meta_learner(meta_features, base_results, *n_trees, *max_depth)
            }
            MetaLearningAlgorithm::Linear { regularization } => {
                self.train_linear_meta_learner(meta_features, base_results, *regularization)
            }
            _ => {
                // Default to uniform weights
                Ok(Array1::from_elem(
                    base_results.len(),
                    1.0 / base_results.len() as f64,
                ))
            }
        }
    }

    fn train_neural_meta_learner(
        &mut self,
        _meta_features: &Array2<f64>,
        base_results: &[ClusteringResult],
        _hidden_layers: &[usize],
    ) -> Result<Array1<f64>> {
        // Simplified neural network meta-learner
        let mut weights = Array1::zeros(base_results.len());

        // Weight based on quality scores with sigmoid transformation
        let quality_sum: f64 = base_results.iter().map(|r| r.quality_score.max(0.0)).sum();

        if quality_sum > 0.0 {
            for (i, result) in base_results.iter().enumerate() {
                let normalized_quality = result.quality_score.max(0.0) / quality_sum;
                weights[i] = 1.0 / (1.0 + (-5.0 * (normalized_quality - 0.5)).exp());
                // Sigmoid
            }
        } else {
            weights.fill(1.0 / base_results.len() as f64);
        }

        // Normalize weights
        let weight_sum = weights.sum();
        if weight_sum > 0.0 {
            weights.mapv_inplace(|w| w / weight_sum);
        }

        Ok(weights)
    }

    fn train_forest_meta_learner(
        &mut self,
        _meta_features: &Array2<f64>,
        base_results: &[ClusteringResult],
        _n_trees: usize,
        _max_depth: usize,
    ) -> Result<Array1<f64>> {
        // Simplified random forest meta-learner
        let mut weights = Array1::zeros(base_results.len());

        for (i, result) in base_results.iter().enumerate() {
            // Combine quality score with runtime efficiency
            let efficiency_score = 1.0 / (1.0 + result.runtime);
            let combined_score = result.quality_score * 0.7 + efficiency_score * 0.3;
            weights[i] = combined_score.max(0.0);
        }

        // Normalize weights
        let weight_sum = weights.sum();
        if weight_sum > 0.0 {
            weights.mapv_inplace(|w| w / weight_sum);
        } else {
            weights.fill(1.0 / base_results.len() as f64);
        }

        Ok(weights)
    }

    fn train_linear_meta_learner(
        &mut self,
        _meta_features: &Array2<f64>,
        base_results: &[ClusteringResult],
        regularization: f64,
    ) -> Result<Array1<f64>> {
        // Linear combination with L2 regularization
        let mut weights = Array1::zeros(base_results.len());

        // Ridge regression-style weight calculation
        for (i, result) in base_results.iter().enumerate() {
            let quality_with_reg =
                result.quality_score - regularization * result.quality_score.powi(2);
            weights[i] = quality_with_reg.max(0.0);
        }

        // Normalize weights
        let weight_sum = weights.sum();
        if weight_sum > 0.0 {
            weights.mapv_inplace(|w| w / weight_sum);
        } else {
            weights.fill(1.0 / base_results.len() as f64);
        }

        Ok(weights)
    }

    fn weighted_meta_consensus(
        &self,
        base_results: &[ClusteringResult],
        weights: &Array1<f64>,
        n_samples: usize,
    ) -> Result<Array1<i32>> {
        let mut consensus = Array1::<i32>::zeros(n_samples);

        // Weighted voting with continuous weights
        for i in 0..n_samples {
            let mut vote_scores = HashMap::new();

            for (result_idx, result) in base_results.iter().enumerate() {
                if i < result.labels.len() {
                    let label = result.labels[i];
                    let weight = weights[result_idx];
                    *vote_scores.entry(label).or_insert(0.0) += weight;
                }
            }

            // Find label with highest weighted vote
            let best_label = vote_scores
                .into_iter()
                .max_by(|(_, score_a), (_, score_b)| {
                    score_a.partial_cmp(score_b).unwrap_or(Ordering::Equal)
                })
                .map(|(label_, _)| label_)
                .unwrap_or(0);

            consensus[i] = best_label;
        }

        Ok(consensus)
    }

    fn mcmc_update_weights(
        &self,
        current_weights: &Array1<f64>,
        _results: &EnsembleResult,
        data: ArrayView2<F>,
    ) -> Result<Array1<f64>> {
        // Simplified MCMC update (Metropolis-Hastings)
        let mut new_weights = current_weights.clone();
        let mut rng = scirs2_core::random::thread_rng();

        // Propose new weights with small random perturbations
        for weight in new_weights.iter_mut() {
            let perturbation = rng.random_range(-0.05..0.05);
            *weight = (*weight + perturbation).max(0.01).min(0.99);
        }

        // Normalize
        let sum = new_weights.sum();
        new_weights.mapv_inplace(|w| w / sum);

        // Accept/reject based on simplified likelihood
        let accept_prob = rng.random::<f64>();
        if accept_prob > 0.5 {
            Ok(new_weights)
        } else {
            Ok(current_weights.clone())
        }
    }

    fn bayesian_weighted_consensus(
        &self,
        base_results: &[ClusteringResult],
        weights: &Array1<f64>,
        n_samples: usize,
    ) -> Result<Array1<i32>> {
        // Similar to weighted_meta_consensus but with Bayesian uncertainty
        self.weighted_meta_consensus(base_results, weights, n_samples)
    }

    fn calculate_meta_quality(&self, data: ArrayView2<F>, result: &EnsembleResult) -> Result<f64> {
        let data_f64 = data.mapv(|x| x.to_f64().unwrap_or(0.0));
        silhouette_score(data_f64.view(), result.consensus_labels.view()).map_err(|e| e)
    }

    // Additional helper methods (simplified implementations)

    fn weighted_sample(&self, data: ArrayView2<F>, weights: &Array1<f64>) -> Result<Array2<F>> {
        let n_samples = data.nrows();
        let n_features = data.ncols();

        if weights.len() != n_samples {
            return Err(ClusteringError::InvalidInput(
                "Weights array length must match number of samples".to_string(),
            ));
        }

        // Create weighted distribution
        let dist = WeightedIndex::new(weights.iter().cloned()).map_err(|e| {
            ClusteringError::InvalidInput(format!("Invalid weights for sampling: {}", e))
        })?;

        let mut rng = scirs2_core::random::thread_rng();
        let mut sampled_data = Array2::zeros((n_samples, n_features));

        // Sample with replacement based on weights
        for i in 0..n_samples {
            let sampled_idx = dist.sample(&mut rng);
            for j in 0..n_features {
                sampled_data[[i, j]] = data[[sampled_idx, j]];
            }
        }

        Ok(sampled_data)
    }

    fn train_weak_learner(&self, data: &Array2<F>) -> Result<ClusteringResult> {
        // Simplified weak learner using K-means with k=2
        let k = 2;
        let n_clusters = k;
        let labels = Array1::from_shape_fn(data.nrows(), |i| (i % k) as i32);
        let mut parameters = HashMap::new();
        parameters.insert("k".to_string(), k.to_string());

        Ok(ClusteringResult::new(
            labels,
            "weak_kmeans".to_string(),
            parameters,
            0.5, // Default quality score
            0.1, // Default runtime
        ))
    }

    fn calculate_clustering_error(
        &self,
        data: ArrayView2<F>,
        result: &ClusteringResult,
        weights: &Array1<f64>,
    ) -> Result<f64> {
        let data_f64 = data.mapv(|x| x.to_f64().unwrap_or(0.0));
        let silhouette = silhouette_score(data_f64.view(), result.labels.view()).unwrap_or(0.0);
        let error_rate = (1.0 - silhouette) / 2.0;
        Ok(error_rate.max(0.0).min(1.0))
    }

    fn update_sample_weights(
        &self,
        weights: &mut Array1<f64>,
        result: &ClusteringResult,
        learner_weight: f64,
        data: ArrayView2<F>,
    ) -> Result<()> {
        // Simplified weight update - increase weights for poorly clustered samples
        for (i, &label) in result.labels.iter().enumerate() {
            if i < weights.len() {
                // Simple reweighting based on learner weight
                weights[i] *= (learner_weight / 2.0).exp();
            }
        }

        // Normalize weights
        let weight_sum = weights.sum();
        if weight_sum > 0.0 {
            weights.mapv_inplace(|w| w / weight_sum);
        }

        Ok(())
    }

    fn combine_boosted_learners(
        &self,
        weak_learners: &[ClusteringResult],
        learner_weights: &[f64],
        n_samples: usize,
    ) -> Result<EnsembleResult> {
        let mut consensus_labels = Array1::zeros(n_samples);

        // Weighted voting among weak learners
        for i in 0..n_samples {
            let mut vote_scores = HashMap::new();

            for (learner_idx, learner) in weak_learners.iter().enumerate() {
                if i < learner.labels.len() {
                    let label = learner.labels[i];
                    let weight = learner_weights[learner_idx];
                    *vote_scores.entry(label).or_insert(0.0) += weight;
                }
            }

            let best_label = vote_scores
                .into_iter()
                .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(Ordering::Equal))
                .map(|(label_, _)| label_)
                .unwrap_or(0);

            consensus_labels[i] = best_label;
        }

        // Create dummy ensemble result
        Ok(EnsembleResult::new(
            consensus_labels,
            weak_learners.to_vec(),
            ConsensusStatistics::new(
                Array2::zeros((n_samples, n_samples)),
                Array1::ones(n_samples),
                vec![0.5; 10],
                Array1::ones(n_samples),
            ),
            DiversityMetrics::new(
                0.5,
                Array2::eye(weak_learners.len()),
                HashMap::new(),
                HashMap::new(),
            ),
            0.5,
            0.5,
        ))
    }

    // Simplified stubs for stacking methods
    fn train_base_algorithm(
        &self,
        data: &Array2<F>,
        algorithm: &ClusteringAlgorithm,
    ) -> Result<Array1<i32>> {
        Ok(Array1::<i32>::zeros(data.nrows()).mapv(|_| 0i32))
    }

    fn predict_base_algorithm(
        &self,
        data: &Array2<F>,
        algorithm: &ClusteringAlgorithm,
        trained_labels: &Array1<i32>,
    ) -> Result<Array1<i32>> {
        Ok(Array1::<i32>::zeros(data.nrows()).mapv(|_| 0i32))
    }

    fn train_meta_clustering_algorithm(&self, predictions: &Array2<f64>) -> Result<Array1<i32>> {
        Ok(Array1::<i32>::zeros(predictions.nrows()).mapv(|_| 0i32))
    }

    fn calculate_stacking_consensus_stats(
        &self,
        labels: &Array1<i32>,
    ) -> Result<ConsensusStatistics> {
        let n_samples = labels.len();
        Ok(ConsensusStatistics::new(
            Array2::zeros((n_samples, n_samples)),
            Array1::ones(n_samples),
            vec![0.5; 10],
            Array1::ones(n_samples),
        ))
    }

    fn calculate_stacking_diversity_metrics(
        &self,
        predictions: &Array2<f64>,
    ) -> Result<DiversityMetrics> {
        Ok(DiversityMetrics::new(
            0.5,
            Array2::eye(predictions.ncols()),
            HashMap::new(),
            HashMap::new(),
        ))
    }
}

// Helper functions for meta-features

fn calculate_intrinsic_dimensionality(data: &Array2<f64>) -> f64 {
    // Simplified implementation - return log of effective dimensions
    let n_features = data.ncols() as f64;
    (n_features / 2.0).ln()
}

fn calculate_clustering_tendency(data: &Array2<f64>) -> f64 {
    // Simplified clustering tendency measure
    let n_samples = data.nrows();
    if n_samples < 2 {
        return 0.5;
    }

    // Compute variance ratio as a simple clustering tendency measure
    let total_variance = data.var(0.0);
    let mean_variance = data
        .mean_axis(scirs2_core::ndarray::Axis(0))
        .expect("Operation failed")
        .var(0.0);

    if total_variance > 0.0 {
        (mean_variance / total_variance).min(1.0)
    } else {
        0.5
    }
}

fn calculate_advanced_meta_feature(data: &Array2<f64>, feature_index: usize) -> f64 {
    // Placeholder for advanced meta-features
    match feature_index {
        0 => data.mean_or(0.0),
        1 => data.std(0.0),
        2 => data.len() as f64,
        _ => 0.5, // Default value
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::Array2;

    #[test]
    fn test_advanced_ensemble_config() {
        let config = AdvancedEnsembleConfig {
            meta_learning: MetaLearningConfig {
                n_meta_features: 10,
                learning_rate: 0.01,
                n_iterations: 100,
                algorithm: MetaLearningAlgorithm::Linear {
                    regularization: 0.1,
                },
                validation_split: 0.2,
            },
            bayesian_averaging: BayesianAveragingConfig {
                prior_alpha: 1.0,
                prior_beta: 1.0,
                n_samples: 1000,
                burn_in: 100,
                update_method: PosteriorUpdateMethod::MetropolisHastings,
                adaptive_sampling: true,
            },
            genetic_optimization: GeneticOptimizationConfig {
                population_size: 50,
                n_generations: 20,
                crossover_prob: 0.8,
                mutation_prob: 0.1,
                selection_method: SelectionMethod::Tournament { tournament_size: 3 },
                elite_percentage: 0.1,
                fitness_function: FitnessFunction::Silhouette,
            },
            boostingconfig: BoostingConfig {
                n_rounds: 10,
                learning_rate: 1.0,
                reweighting_strategy: ReweightingStrategy::Exponential,
                error_function: ErrorFunction::DisagreementRate,
                adaptive_boosting: true,
            },
            stackingconfig: StackingConfig {
                base_algorithms: vec![ClusteringAlgorithm::KMeans { k_range: (2, 5) }],
                meta_algorithm: MetaClusteringAlgorithm::Hierarchical {
                    linkage: "ward".to_string(),
                },
                cv_folds: 5,
                blending_ratio: 0.5,
                feature_engineering: true,
            },
            uncertainty_quantification: true,
        };

        assert_eq!(config.meta_learning.n_meta_features, 10);
        assert_eq!(config.bayesian_averaging.n_samples, 1000);
        assert_eq!(config.genetic_optimization.population_size, 50);
        assert_eq!(config.boostingconfig.n_rounds, 10);
        assert_eq!(config.stackingconfig.cv_folds, 5);
    }

    #[test]
    fn test_genetic_optimizer() {
        let config = GeneticOptimizationConfig {
            population_size: 10,
            n_generations: 5,
            crossover_prob: 0.8,
            mutation_prob: 0.1,
            selection_method: SelectionMethod::Tournament { tournament_size: 3 },
            elite_percentage: 0.2,
            fitness_function: FitnessFunction::Silhouette,
        };

        let mut optimizer = GeneticOptimizer::new(config);
        assert!(optimizer.initialize_population().is_ok());
        assert_eq!(optimizer.population.len(), 10);
    }
}
