quantrs2-ml 0.1.3

//! Ensemble methods and quantum voting mechanisms for time series forecasting

use super::{config::*, models::TimeSeriesModelTrait};
use crate::error::{MLError, Result};
use scirs2_core::ndarray::{s, Array1, Array2};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::f64::consts::PI;

/// Quantum ensemble manager for time series models
#[derive(Debug, Clone)]
pub struct QuantumEnsembleManager {
    /// Ensemble configuration
    config: EnsembleConfig,

    /// Base models in the ensemble
    models: Vec<Box<dyn TimeSeriesModelTrait>>,

    /// Model weights for weighted averaging
    model_weights: Array1<f64>,

    /// Quantum voting circuit parameters
    voting_circuits: Vec<Array1<f64>>,

    /// Performance history for adaptive weighting
    performance_history: Vec<ModelPerformanceHistory>,

    /// Diversity metrics
    diversity_metrics: DiversityMetrics,
}

/// Performance history for individual models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelPerformanceHistory {
    /// Model identifier
    pub model_id: usize,

    /// Historical accuracies
    pub accuracies: Vec<f64>,

    /// Historical losses
    pub losses: Vec<f64>,

    /// Prediction confidence scores
    pub confidence_scores: Vec<f64>,

    /// Quantum fidelity measures
    pub quantum_fidelities: Vec<f64>,
}

/// Diversity metrics for ensemble models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DiversityMetrics {
    /// Pairwise correlation between model predictions
    pub prediction_correlations: Array2<f64>,

    /// Disagreement measures
    pub disagreement_scores: Array1<f64>,

    /// Quantum entanglement between models
    pub quantum_entanglement: Array2<f64>,

    /// Overall diversity score
    pub overall_diversity: f64,
}

/// Quantum voting mechanisms
#[derive(Debug, Clone)]
pub struct QuantumVotingMechanism {
    /// Voting strategy
    strategy: VotingStrategy,

    /// Quantum circuit for voting
    voting_circuit: VotingCircuit,

    /// Confidence aggregation method
    confidence_aggregation: ConfidenceAggregation,
}

/// Voting strategies for ensemble decisions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum VotingStrategy {
    /// Simple majority voting
    Majority,

    /// Weighted voting based on performance
    Weighted,

    /// Quantum superposition voting
    QuantumSuperposition,

    /// Bayesian model averaging
    BayesianAveraging,

    /// Adaptive voting based on context
    Adaptive,
}

/// Quantum voting circuit implementation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VotingCircuit {
    /// Number of qubits for voting
    num_qubits: usize,

    /// Circuit parameters
    parameters: Array1<f64>,

    /// Entanglement patterns
    entanglement_patterns: Vec<EntanglementPattern>,

    /// Measurement strategy
    measurement_strategy: MeasurementStrategy,
}

/// Entanglement patterns for quantum voting
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EntanglementPattern {
    /// Qubits involved in entanglement
    pub qubits: Vec<usize>,

    /// Entanglement strength
    pub strength: f64,

    /// Pattern type
    pub pattern_type: EntanglementType,
}

/// Types of entanglement patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum EntanglementType {
    Bell,
    GHZ,
    Cluster,
    Custom(String),
}

/// Confidence aggregation methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConfidenceAggregation {
    Average,
    WeightedAverage,
    QuantumCoherence,
    BayesianFusion,
}

/// Bootstrap aggregation for time series
#[derive(Debug, Clone)]
pub struct BootstrapAggregator {
    /// Number of bootstrap samples
    num_samples: usize,

    /// Sample size fraction
    sample_fraction: f64,

    /// Bootstrap models
    bootstrap_models: Vec<Box<dyn TimeSeriesModelTrait>>,

    /// Quantum enhancement for sampling
    quantum_sampling: bool,
}

/// Stacking ensemble implementation
#[derive(Debug, Clone)]
pub struct StackingEnsemble {
    /// Base models (level 0)
    base_models: Vec<Box<dyn TimeSeriesModelTrait>>,

    /// Meta-learner (level 1)
    meta_learner: Box<dyn TimeSeriesModelTrait>,

    /// Cross-validation folds for meta-learning
    cv_folds: usize,

    /// Quantum enhancement for meta-learning
    quantum_meta_learning: bool,
}

impl QuantumEnsembleManager {
    /// Create new quantum ensemble manager
    pub fn new(config: EnsembleConfig) -> Self {
        let num_models = config.num_models;
        let model_weights = Array1::from_elem(num_models, 1.0 / num_models as f64);

        // Initialize quantum voting circuits
        let mut voting_circuits = Vec::new();
        for model_idx in 0..num_models {
            let circuit_params = Array1::from_shape_fn(10, |i| {
                PI * (model_idx + i) as f64 / (num_models + 10) as f64
            });
            voting_circuits.push(circuit_params);
        }

        let performance_history = (0..num_models)
            .map(|i| ModelPerformanceHistory::new(i))
            .collect();
        let diversity_metrics = DiversityMetrics::new(num_models);

        Self {
            config,
            models: Vec::new(),
            model_weights,
            voting_circuits,
            performance_history,
            diversity_metrics,
        }
    }

    /// Add model to ensemble
    pub fn add_model(&mut self, model: Box<dyn TimeSeriesModelTrait>) {
        self.models.push(model);

        // Update weights if necessary
        if self.models.len() > self.model_weights.len() {
            let new_size = self.models.len();
            self.model_weights = Array1::from_elem(new_size, 1.0 / new_size as f64);
        }
    }

    /// Set models for ensemble
    pub fn set_models(&mut self, models: Vec<Box<dyn TimeSeriesModelTrait>>) {
        self.models = models;
        let num_models = self.models.len();
        self.model_weights = Array1::from_elem(num_models, 1.0 / num_models as f64);

        // Update performance history
        self.performance_history = (0..num_models)
            .map(|i| ModelPerformanceHistory::new(i))
            .collect();
        self.diversity_metrics = DiversityMetrics::new(num_models);
    }

    /// Train all models in ensemble
    pub fn fit_ensemble(&mut self, data: &Array2<f64>, targets: &Array2<f64>) -> Result<()> {
        // Extract config to avoid borrow checker issues
        let diversity_strategy = self.config.diversity_strategy.clone();
        let voting_circuits = self.voting_circuits.clone();

        for (model_idx, model) in self.models.iter_mut().enumerate() {
            // Apply diversity strategy
            let (diverse_data, diverse_targets) = Self::apply_diversity_strategy_static(
                &diversity_strategy,
                data,
                targets,
                model_idx,
                &voting_circuits,
            )?;

            // Train model
            model.fit(&diverse_data, &diverse_targets)?;

            // Record initial performance
            let predictions = model.predict(&diverse_data, diverse_targets.ncols())?;
            let performance =
                Self::calculate_model_performance_static(&predictions, &diverse_targets)?;
            self.performance_history[model_idx].update_performance(performance);
        }

        // Update diversity metrics
        self.update_diversity_metrics(data, targets)?;

        // Optimize ensemble weights
        self.optimize_ensemble_weights(data, targets)?;

        Ok(())
    }

    /// Generate ensemble predictions
    pub fn predict_ensemble(&self, data: &Array2<f64>, horizon: usize) -> Result<Array2<f64>> {
        if self.models.is_empty() {
            return Err(MLError::MLOperationError(
                "No models in ensemble".to_string(),
            ));
        }

        // Get predictions from all models
        let mut model_predictions = Vec::new();
        for model in &self.models {
            let predictions = model.predict(data, horizon)?;
            model_predictions.push(predictions);
        }

        // Combine predictions based on ensemble method
        let ensemble_prediction = match &self.config.method {
            EnsembleMethod::Average => self.average_predictions(&model_predictions)?,
            EnsembleMethod::Weighted(weights) => {
                self.weighted_average_predictions(&model_predictions, weights)?
            }
            EnsembleMethod::QuantumSuperposition => {
                self.quantum_superposition_predictions(&model_predictions)?
            }
            EnsembleMethod::Stacking => self.stacking_predictions(&model_predictions, data)?,
            EnsembleMethod::BayesianAverage => {
                self.bayesian_average_predictions(&model_predictions)?
            }
        };

        Ok(ensemble_prediction)
    }

    /// Apply diversity strategy to training data
    fn apply_diversity_strategy(
        &self,
        data: &Array2<f64>,
        targets: &Array2<f64>,
        model_idx: usize,
    ) -> Result<(Array2<f64>, Array2<f64>)> {
        Self::apply_diversity_strategy_static(
            &self.config.diversity_strategy,
            data,
            targets,
            model_idx,
            &self.voting_circuits,
        )
    }

    /// Static version of apply diversity strategy
    fn apply_diversity_strategy_static(
        strategy: &DiversityStrategy,
        data: &Array2<f64>,
        targets: &Array2<f64>,
        model_idx: usize,
        voting_circuits: &[Array1<f64>],
    ) -> Result<(Array2<f64>, Array2<f64>)> {
        match strategy {
            DiversityStrategy::RandomInit => {
                // Same data, different initialization
                Ok((data.clone(), targets.clone()))
            }
            DiversityStrategy::Bootstrap => Self::bootstrap_sample_static(data, targets),
            DiversityStrategy::FeatureBagging => {
                Self::feature_bagging_static(data, targets, model_idx)
            }
            DiversityStrategy::QuantumDiversity => {
                Self::quantum_diversity_transform_static(data, targets, model_idx, voting_circuits)
            }
        }
    }

    /// Bootstrap sampling for diversity
    fn bootstrap_sample(
        &self,
        data: &Array2<f64>,
        targets: &Array2<f64>,
    ) -> Result<(Array2<f64>, Array2<f64>)> {
        Self::bootstrap_sample_static(data, targets)
    }

    /// Static bootstrap sampling
    fn bootstrap_sample_static(
        data: &Array2<f64>,
        targets: &Array2<f64>,
    ) -> Result<(Array2<f64>, Array2<f64>)> {
        let n_samples = data.nrows();
        let mut sampled_data = Array2::zeros(data.dim());
        let mut sampled_targets = Array2::zeros(targets.dim());

        for i in 0..n_samples {
            let sample_idx = fastrand::usize(0..n_samples);
            sampled_data.row_mut(i).assign(&data.row(sample_idx));
            sampled_targets.row_mut(i).assign(&targets.row(sample_idx));
        }

        Ok((sampled_data, sampled_targets))
    }

    /// Feature bagging for diversity
    fn feature_bagging(
        &self,
        data: &Array2<f64>,
        targets: &Array2<f64>,
        model_idx: usize,
    ) -> Result<(Array2<f64>, Array2<f64>)> {
        Self::feature_bagging_static(data, targets, model_idx)
    }

    /// Static feature bagging
    fn feature_bagging_static(
        data: &Array2<f64>,
        targets: &Array2<f64>,
        _model_idx: usize,
    ) -> Result<(Array2<f64>, Array2<f64>)> {
        let n_features = data.ncols();
        let feature_fraction = 0.7; // Use 70% of features
        let n_selected = ((n_features as f64) * feature_fraction) as usize;

        // Select random features
        let mut selected_features = Vec::new();
        for _ in 0..n_selected {
            let feature_idx = fastrand::usize(0..n_features);
            if !selected_features.contains(&feature_idx) {
                selected_features.push(feature_idx);
            }
        }

        // Ensure we have at least some features
        if selected_features.is_empty() {
            selected_features.push(0);
        }

        // Create subset of data
        let mut subset_data = Array2::zeros((data.nrows(), selected_features.len()));
        for (new_idx, &old_idx) in selected_features.iter().enumerate() {
            subset_data
                .column_mut(new_idx)
                .assign(&data.column(old_idx));
        }

        Ok((subset_data, targets.clone()))
    }

    /// Quantum diversity transformation
    fn quantum_diversity_transform(
        &self,
        data: &Array2<f64>,
        targets: &Array2<f64>,
        model_idx: usize,
    ) -> Result<(Array2<f64>, Array2<f64>)> {
        Self::quantum_diversity_transform_static(data, targets, model_idx, &self.voting_circuits)
    }

    /// Static quantum diversity transformation
    fn quantum_diversity_transform_static(
        data: &Array2<f64>,
        targets: &Array2<f64>,
        model_idx: usize,
        voting_circuits: &[Array1<f64>],
    ) -> Result<(Array2<f64>, Array2<f64>)> {
        let mut transformed = data.clone();

        if model_idx < voting_circuits.len() {
            let circuit_params = &voting_circuits[model_idx];

            // Apply quantum transformation
            for mut row in transformed.rows_mut() {
                for (i, val) in row.iter_mut().enumerate() {
                    let param_idx = i % circuit_params.len();
                    let phase = circuit_params[param_idx];
                    *val = *val * phase.cos() + 0.1 * (phase * *val).sin();
                }
            }
        }

        Ok((transformed, targets.clone()))
    }

    /// Calculate model performance metrics
    fn calculate_model_performance(
        &self,
        predictions: &Array2<f64>,
        targets: &Array2<f64>,
    ) -> Result<f64> {
        Self::calculate_model_performance_static(predictions, targets)
    }

    /// Static calculate model performance metrics
    fn calculate_model_performance_static(
        predictions: &Array2<f64>,
        targets: &Array2<f64>,
    ) -> Result<f64> {
        if predictions.shape() != targets.shape() {
            return Err(MLError::DimensionMismatch(
                "Predictions and targets must have same shape".to_string(),
            ));
        }

        // Calculate MAE as performance metric
        let mae: f64 = predictions
            .iter()
            .zip(targets.iter())
            .map(|(p, t)| (p - t).abs())
            .sum::<f64>()
            / predictions.len() as f64;

        // Convert to accuracy-like metric (higher is better)
        Ok(1.0 / (1.0 + mae))
    }

    /// Update diversity metrics for ensemble
    fn update_diversity_metrics(
        &mut self,
        data: &Array2<f64>,
        targets: &Array2<f64>,
    ) -> Result<()> {
        let num_models = self.models.len();

        // Calculate prediction correlations
        let mut predictions = Vec::new();
        for model in &self.models {
            let pred = model.predict(data, targets.ncols())?;
            predictions.push(pred);
        }

        // Update correlation matrix
        for i in 0..num_models {
            for j in 0..num_models {
                let correlation =
                    self.calculate_prediction_correlation(&predictions[i], &predictions[j])?;
                self.diversity_metrics.prediction_correlations[[i, j]] = correlation;
            }
        }

        // Calculate disagreement scores
        for i in 0..num_models {
            let mut disagreement = 0.0;
            for j in 0..num_models {
                if i != j {
                    disagreement +=
                        1.0 - self.diversity_metrics.prediction_correlations[[i, j]].abs();
                }
            }
            self.diversity_metrics.disagreement_scores[i] = disagreement / (num_models - 1) as f64;
        }

        // Calculate overall diversity
        let avg_correlation = self
            .diversity_metrics
            .prediction_correlations
            .mean()
            .unwrap_or(0.0);
        self.diversity_metrics.overall_diversity = 1.0 - avg_correlation.abs();

        Ok(())
    }

    /// Calculate correlation between two prediction arrays
    fn calculate_prediction_correlation(
        &self,
        pred1: &Array2<f64>,
        pred2: &Array2<f64>,
    ) -> Result<f64> {
        if pred1.shape() != pred2.shape() {
            return Err(MLError::DimensionMismatch(
                "Prediction arrays must have same shape".to_string(),
            ));
        }

        let flat1: Vec<f64> = pred1.iter().cloned().collect();
        let flat2: Vec<f64> = pred2.iter().cloned().collect();

        let mean1 = flat1.iter().sum::<f64>() / flat1.len() as f64;
        let mean2 = flat2.iter().sum::<f64>() / flat2.len() as f64;

        let mut numerator = 0.0;
        let mut sum_sq1 = 0.0;
        let mut sum_sq2 = 0.0;

        for (v1, v2) in flat1.iter().zip(flat2.iter()) {
            let dev1 = v1 - mean1;
            let dev2 = v2 - mean2;

            numerator += dev1 * dev2;
            sum_sq1 += dev1 * dev1;
            sum_sq2 += dev2 * dev2;
        }

        let denominator = (sum_sq1 * sum_sq2).sqrt();
        if denominator < 1e-10 {
            Ok(0.0)
        } else {
            Ok(numerator / denominator)
        }
    }

    /// Optimize ensemble weights based on performance
    fn optimize_ensemble_weights(
        &mut self,
        data: &Array2<f64>,
        targets: &Array2<f64>,
    ) -> Result<()> {
        let num_models = self.models.len();

        // Get model predictions
        let mut model_performances = Vec::new();
        for model in &self.models {
            let predictions = model.predict(data, targets.ncols())?;
            let performance = self.calculate_model_performance(&predictions, targets)?;
            model_performances.push(performance);
        }

        // Normalize performances to get weights
        let total_performance: f64 = model_performances.iter().sum();
        if total_performance > 1e-10 {
            for (i, &performance) in model_performances.iter().enumerate() {
                self.model_weights[i] = performance / total_performance;
            }
        } else {
            // Equal weights if all models perform poorly
            self.model_weights.fill(1.0 / num_models as f64);
        }

        Ok(())
    }

    /// Average predictions from multiple models
    fn average_predictions(&self, predictions: &[Array2<f64>]) -> Result<Array2<f64>> {
        if predictions.is_empty() {
            return Err(MLError::DataError("No predictions to average".to_string()));
        }

        let mut avg_pred = Array2::zeros(predictions[0].dim());
        for pred in predictions {
            avg_pred = avg_pred + pred;
        }

        Ok(avg_pred / predictions.len() as f64)
    }

    /// Weighted average of predictions
    fn weighted_average_predictions(
        &self,
        predictions: &[Array2<f64>],
        weights: &[f64],
    ) -> Result<Array2<f64>> {
        if predictions.is_empty() {
            return Err(MLError::DataError("No predictions to average".to_string()));
        }

        if predictions.len() != weights.len() {
            return Err(MLError::DimensionMismatch(
                "Number of predictions must match number of weights".to_string(),
            ));
        }

        let mut weighted_pred = Array2::zeros(predictions[0].dim());
        for (pred, &weight) in predictions.iter().zip(weights.iter()) {
            weighted_pred = weighted_pred + pred * weight;
        }

        Ok(weighted_pred)
    }

    /// Quantum superposition ensemble prediction
    fn quantum_superposition_predictions(
        &self,
        predictions: &[Array2<f64>],
    ) -> Result<Array2<f64>> {
        if predictions.is_empty() {
            return Err(MLError::DataError(
                "No predictions for quantum superposition".to_string(),
            ));
        }

        let (n_samples, n_features) = predictions[0].dim();
        let mut ensemble_pred = Array2::zeros((n_samples, n_features));

        // Create quantum superposition of predictions
        for i in 0..n_samples {
            for j in 0..n_features {
                let mut superposition = 0.0;
                let mut normalization = 0.0;

                for (k, pred) in predictions.iter().enumerate() {
                    // Quantum amplitude based on model index
                    let amplitude = ((k as f64 + 1.0) * PI / predictions.len() as f64).cos();
                    superposition += pred[[i, j]] * amplitude;
                    normalization += amplitude * amplitude;
                }

                if normalization > 1e-10 {
                    ensemble_pred[[i, j]] = superposition / normalization.sqrt();
                } else {
                    ensemble_pred[[i, j]] = superposition;
                }
            }
        }

        Ok(ensemble_pred)
    }

    /// Stacking ensemble prediction (placeholder)
    fn stacking_predictions(
        &self,
        predictions: &[Array2<f64>],
        data: &Array2<f64>,
    ) -> Result<Array2<f64>> {
        // For now, use weighted average as placeholder
        self.weighted_average_predictions(predictions, &self.model_weights.to_vec())
    }

    /// Bayesian model averaging (placeholder)
    fn bayesian_average_predictions(&self, predictions: &[Array2<f64>]) -> Result<Array2<f64>> {
        // For now, use performance-weighted average
        let weights: Vec<f64> = self
            .performance_history
            .iter()
            .map(|h| h.get_average_accuracy())
            .collect();

        self.weighted_average_predictions(predictions, &weights)
    }

    /// Get ensemble diversity metrics
    pub fn get_diversity_metrics(&self) -> &DiversityMetrics {
        &self.diversity_metrics
    }

    /// Get model weights
    pub fn get_model_weights(&self) -> &Array1<f64> {
        &self.model_weights
    }

    /// Get performance history
    pub fn get_performance_history(&self) -> &[ModelPerformanceHistory] {
        &self.performance_history
    }
}

impl ModelPerformanceHistory {
    /// Create new performance history
    pub fn new(model_id: usize) -> Self {
        Self {
            model_id,
            accuracies: Vec::new(),
            losses: Vec::new(),
            confidence_scores: Vec::new(),
            quantum_fidelities: Vec::new(),
        }
    }

    /// Update performance with new metrics
    pub fn update_performance(&mut self, accuracy: f64) {
        self.accuracies.push(accuracy);
        self.losses.push(1.0 - accuracy); // Simple loss calculation
        self.confidence_scores.push(accuracy);
        self.quantum_fidelities.push(accuracy * 0.9); // Simplified quantum fidelity
    }

    /// Get average accuracy
    pub fn get_average_accuracy(&self) -> f64 {
        if self.accuracies.is_empty() {
            0.5 // Default value
        } else {
            self.accuracies.iter().sum::<f64>() / self.accuracies.len() as f64
        }
    }

    /// Get latest accuracy
    pub fn get_latest_accuracy(&self) -> f64 {
        self.accuracies.last().copied().unwrap_or(0.5)
    }
}

impl DiversityMetrics {
    /// Create new diversity metrics
    pub fn new(num_models: usize) -> Self {
        Self {
            prediction_correlations: Array2::zeros((num_models, num_models)),
            disagreement_scores: Array1::zeros(num_models),
            quantum_entanglement: Array2::zeros((num_models, num_models)),
            overall_diversity: 0.0,
        }
    }
}

impl QuantumVotingMechanism {
    /// Create new quantum voting mechanism
    pub fn new(strategy: VotingStrategy, num_qubits: usize) -> Self {
        let voting_circuit = VotingCircuit::new(num_qubits);

        Self {
            strategy,
            voting_circuit,
            confidence_aggregation: ConfidenceAggregation::QuantumCoherence,
        }
    }

    /// Apply quantum voting to ensemble decisions
    pub fn quantum_vote(
        &self,
        predictions: &[Array2<f64>],
        confidences: &[f64],
    ) -> Result<Array2<f64>> {
        match &self.strategy {
            VotingStrategy::QuantumSuperposition => {
                self.quantum_superposition_vote(predictions, confidences)
            }
            VotingStrategy::Adaptive => self.adaptive_quantum_vote(predictions, confidences),
            _ => {
                // Default to weighted average
                self.weighted_vote(predictions, confidences)
            }
        }
    }

    /// Quantum superposition voting
    fn quantum_superposition_vote(
        &self,
        predictions: &[Array2<f64>],
        confidences: &[f64],
    ) -> Result<Array2<f64>> {
        if predictions.is_empty() {
            return Err(MLError::DataError("No predictions for voting".to_string()));
        }

        let (n_samples, n_features) = predictions[0].dim();
        let mut voted_pred = Array2::zeros((n_samples, n_features));

        // Apply quantum voting
        for i in 0..n_samples {
            for j in 0..n_features {
                let mut superposition = 0.0;
                let mut normalization = 0.0;

                for (k, pred) in predictions.iter().enumerate() {
                    let confidence = confidences.get(k).copied().unwrap_or(1.0);
                    let quantum_amplitude = confidence.sqrt()
                        * ((k as f64 + 1.0) * PI / predictions.len() as f64).cos();

                    superposition += pred[[i, j]] * quantum_amplitude;
                    normalization += quantum_amplitude * quantum_amplitude;
                }

                if normalization > 1e-10 {
                    voted_pred[[i, j]] = superposition / normalization.sqrt();
                } else {
                    voted_pred[[i, j]] = superposition;
                }
            }
        }

        Ok(voted_pred)
    }

    /// Adaptive quantum voting
    fn adaptive_quantum_vote(
        &self,
        predictions: &[Array2<f64>],
        confidences: &[f64],
    ) -> Result<Array2<f64>> {
        // For now, use quantum superposition
        self.quantum_superposition_vote(predictions, confidences)
    }

    /// Weighted voting
    fn weighted_vote(
        &self,
        predictions: &[Array2<f64>],
        confidences: &[f64],
    ) -> Result<Array2<f64>> {
        if predictions.is_empty() {
            return Err(MLError::DataError("No predictions for voting".to_string()));
        }

        let mut weighted_pred = Array2::zeros(predictions[0].dim());
        let total_confidence: f64 = confidences.iter().sum();

        if total_confidence > 1e-10 {
            for (pred, &confidence) in predictions.iter().zip(confidences.iter()) {
                weighted_pred = weighted_pred + pred * (confidence / total_confidence);
            }
        } else {
            // Equal weights if no confidence information
            for pred in predictions {
                weighted_pred = weighted_pred + pred;
            }
            weighted_pred = weighted_pred / predictions.len() as f64;
        }

        Ok(weighted_pred)
    }
}

impl VotingCircuit {
    /// Create new voting circuit
    pub fn new(num_qubits: usize) -> Self {
        let parameters =
            Array1::from_shape_fn(num_qubits * 2, |i| PI * i as f64 / (num_qubits * 2) as f64);

        let entanglement_patterns = vec![EntanglementPattern {
            qubits: (0..num_qubits).collect(),
            strength: 1.0,
            pattern_type: EntanglementType::GHZ,
        }];

        Self {
            num_qubits,
            parameters,
            entanglement_patterns,
            measurement_strategy: MeasurementStrategy::Computational,
        }
    }

    /// Execute quantum voting circuit
    pub fn execute_voting(&self, inputs: &[f64]) -> Result<Array1<f64>> {
        // Simplified quantum circuit execution
        let mut outputs = Array1::zeros(inputs.len());

        for (i, &input) in inputs.iter().enumerate() {
            let param_idx = i % self.parameters.len();
            let phase = self.parameters[param_idx] * input;
            outputs[i] = phase.cos(); // Simplified measurement
        }

        Ok(outputs)
    }
}

/// Ensemble performance analyzer
pub struct EnsemblePerformanceAnalyzer {
    metrics: Vec<String>,
}

impl EnsemblePerformanceAnalyzer {
    /// Create new ensemble performance analyzer
    pub fn new() -> Self {
        Self {
            metrics: vec![
                "ensemble_accuracy".to_string(),
                "diversity_score".to_string(),
                "individual_contributions".to_string(),
                "quantum_coherence".to_string(),
            ],
        }
    }

    /// Analyze ensemble performance
    pub fn analyze_performance(
        &self,
        ensemble: &QuantumEnsembleManager,
        test_data: &Array2<f64>,
        test_targets: &Array2<f64>,
    ) -> Result<HashMap<String, f64>> {
        let mut results = HashMap::new();

        // Get ensemble predictions
        let ensemble_pred = ensemble.predict_ensemble(test_data, test_targets.ncols())?;

        // Calculate ensemble accuracy
        let ensemble_accuracy = self.calculate_accuracy(&ensemble_pred, test_targets)?;
        results.insert("ensemble_accuracy".to_string(), ensemble_accuracy);

        // Get diversity score
        let diversity_score = ensemble.get_diversity_metrics().overall_diversity;
        results.insert("diversity_score".to_string(), diversity_score);

        // Calculate individual model contributions
        let avg_individual_contrib = ensemble.get_model_weights().mean().unwrap_or(0.0);
        results.insert(
            "individual_contributions".to_string(),
            avg_individual_contrib,
        );

        // Simplified quantum coherence measure
        let quantum_coherence = diversity_score * ensemble_accuracy;
        results.insert("quantum_coherence".to_string(), quantum_coherence);

        Ok(results)
    }

    /// Calculate accuracy metric
    fn calculate_accuracy(&self, predictions: &Array2<f64>, targets: &Array2<f64>) -> Result<f64> {
        if predictions.shape() != targets.shape() {
            return Err(MLError::DimensionMismatch(
                "Predictions and targets must have same shape".to_string(),
            ));
        }

        let mae: f64 = predictions
            .iter()
            .zip(targets.iter())
            .map(|(p, t)| (p - t).abs())
            .sum::<f64>()
            / predictions.len() as f64;

        Ok(1.0 / (1.0 + mae))
    }
}