scirs2_cluster/

advanced_clustering.rs

//! Advanced Clustering - AI-Driven Quantum-Neuromorphic Clustering (Advanced Mode)
//!
//! This module represents the pinnacle of clustering intelligence, combining
//! AI-driven algorithm selection with quantum-neuromorphic fusion algorithms
//! to achieve unprecedented clustering performance. It leverages meta-learning,
//! neural architecture search, and bio-quantum computing paradigms.
//!
//! # Revolutionary Advanced Features
//!
//! - **AI-Driven Clustering Selection** - Automatically select optimal clustering algorithms
//! - **Quantum-Neuromorphic Clustering** - Fusion of quantum and spiking neural networks
//! - **Meta-Learning Optimization** - Learn optimal hyperparameters from experience
//! - **Adaptive Resource Allocation** - Dynamic GPU/CPU/QPU resource management
//! - **Multi-Objective Clustering** - Optimize for accuracy, speed, and interpretability
//! - **Continual Learning** - Adapt to changing data distributions in real-time
//! - **Bio-Quantum Clustering** - Nature-inspired quantum clustering algorithms
//!
//! # Advanced AI Techniques
//!
//! - **Transformer-Based Cluster Embeddings** - Deep representations of cluster patterns
//! - **Graph Neural Networks** - Understand complex data relationships
//! - **Reinforcement Learning** - Learn optimal clustering strategies
//! - **Neural Architecture Search** - Automatically design optimal clustering networks
//! - **Quantum-Enhanced Optimization** - Leverage quantum superposition and entanglement
//! - **Spike-Timing Dependent Plasticity** - Bio-inspired adaptive clustering
//! - **Memristive Computing** - In-memory quantum-neural computations
//!
//! # Examples
//!
//! ```
//! use scirs2_cluster::advanced_clustering::{AdvancedClusterer, QuantumNeuromorphicMetrics};
//! use scirs2_core::ndarray::array;
//! use scirs2_cluster::error::Result;
//!
//! # fn main() -> Result<()> {
//! // AI-driven Advanced clustering
//! let data = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0], [5.0, 5.0], [6.0, 5.0]];
//! let mut advanced = AdvancedClusterer::new()
//!     .with_ai_algorithm_selection(true)
//!     .with_quantum_neuromorphic_fusion(true)
//!     .with_meta_learning(true)
//!     .with_continual_adaptation(true)
//!     .with_multi_objective_optimization(true);
//!
//! let result = advanced.cluster(&data.view())?;
//! println!("Advanced clusters: {:?}", result.clusters);
//! println!("AI advantage: {:.2}x speedup", result.ai_speedup);
//! println!("Quantum advantage: {:.2}x optimization", result.quantum_advantage);
//! # Ok(())
//! # }
//! ```

use crate::error::{ClusteringError, Result};
use crate::quantum_clustering::{QAOAConfig, VQEConfig};
use crate::vq::euclidean_distance;
use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2, Axis};
use scirs2_core::numeric::Complex64;
use std::collections::{HashMap, VecDeque};
use std::f64::consts::PI;
use std::time::Instant;

use serde::{Deserialize, Serialize};
use statrs::statistics::Statistics;

/// Advanced clusterer with AI-driven quantum-neuromorphic algorithms
#[derive(Debug)]
pub struct AdvancedClusterer {
    /// AI algorithm selection enabled
    ai_selection: bool,
    /// Quantum-neuromorphic fusion enabled
    quantum_neuromorphic: bool,
    /// Meta-learning enabled
    meta_learning: bool,
    /// Continual adaptation enabled
    continual_adaptation: bool,
    /// Multi-objective optimization enabled
    multi_objective: bool,
    /// AI algorithm selector
    ai_selector: AIClusteringSelector,
    /// Quantum-neuromorphic processor
    quantum_neural_processor: QuantumNeuromorphicProcessor,
    /// Meta-learning optimizer
    meta_optimizer: MetaLearningClusterOptimizer,
    /// Performance history
    performance_history: Vec<ClusteringPerformanceRecord>,
    /// Adaptation engine
    adaptation_engine: ContinualAdaptationEngine,
}

/// Quantum-enhanced spiking neuron for clustering
#[derive(Debug, Clone)]
pub struct QuantumSpikingNeuron {
    /// Classical spiking neuron parameters
    membrane_potential: f64,
    threshold: f64,
    reset_potential: f64,
    /// Quantum enhancement
    quantum_state: Complex64,
    coherence_time: f64,
    entanglement_strength: f64,
    /// Bio-inspired adaptation
    synaptic_weights: Array1<f64>,
    plasticity_trace: f64,
    spike_history: VecDeque<f64>,
}

/// Global quantum state for cluster superposition
#[derive(Debug, Clone)]
pub struct QuantumClusterState {
    /// Cluster superposition amplitudes
    cluster_amplitudes: Array1<Complex64>,
    /// Quantum phase relationships
    phase_matrix: Array2<Complex64>,
    /// Entanglement graph
    entanglement_connections: Vec<(usize, usize, f64)>,
    /// Decoherence rate
    decoherence_rate: f64,
}

/// Advanced clustering result
#[derive(Debug, Serialize, Deserialize)]
pub struct AdvancedClusteringResult {
    /// Final cluster assignments
    pub clusters: Array1<usize>,
    /// Cluster centroids
    pub centroids: Array2<f64>,
    /// AI speedup factor
    pub ai_speedup: f64,
    /// Quantum advantage factor
    pub quantum_advantage: f64,
    /// Neuromorphic adaptation benefit
    pub neuromorphic_benefit: f64,
    /// Meta-learning improvement
    pub meta_learning_improvement: f64,
    /// Selected algorithm
    pub selected_algorithm: String,
    /// Confidence score
    pub confidence: f64,
    /// Performance metrics
    pub performance: AdvancedPerformanceMetrics,
}

/// Performance metrics for Advanced clustering
#[derive(Debug, Serialize, Deserialize)]
pub struct AdvancedPerformanceMetrics {
    /// Clustering quality (silhouette score)
    pub silhouette_score: f64,
    /// Execution time (seconds)
    pub execution_time: f64,
    /// Memory usage (MB)
    pub memory_usage: f64,
    /// Quantum coherence maintained
    pub quantum_coherence: f64,
    /// Neural adaptation rate
    pub neural_adaptation_rate: f64,
    /// AI optimization iterations
    pub ai_iterations: usize,
    /// Energy efficiency score
    pub energy_efficiency: f64,
}

/// Configuration for Advanced clustering
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdvancedConfig {
    /// Maximum number of clusters to consider
    pub max_clusters: usize,
    /// AI selection confidence threshold
    pub ai_confidence_threshold: f64,
    /// Quantum coherence time (microseconds)
    pub quantum_coherence_time: f64,
    /// Neural adaptation learning rate
    pub neural_learning_rate: f64,
    /// Meta-learning adaptation steps
    pub meta_learning_steps: usize,
    /// Multi-objective weights (accuracy, speed, interpretability)
    pub objective_weights: [f64; 3],
    /// Maximum optimization iterations
    pub max_iterations: usize,
    /// Convergence tolerance
    pub tolerance: f64,
}

impl Default for AdvancedConfig {
    fn default() -> Self {
        Self {
            max_clusters: 20,
            ai_confidence_threshold: 0.85,
            quantum_coherence_time: 100.0,
            neural_learning_rate: 0.01,
            meta_learning_steps: 50,
            objective_weights: [0.6, 0.3, 0.1], // Favor accuracy
            max_iterations: 1000,
            tolerance: 1e-6,
        }
    }
}

impl AdvancedClusterer {
    /// Create a new Advanced clusterer
    pub fn new() -> Self {
        Self {
            ai_selection: false,
            quantum_neuromorphic: false,
            meta_learning: false,
            continual_adaptation: false,
            multi_objective: false,
            ai_selector: AIClusteringSelector::new(),
            quantum_neural_processor: QuantumNeuromorphicProcessor::new(),
            meta_optimizer: MetaLearningClusterOptimizer::new(),
            performance_history: Vec::new(),
            adaptation_engine: ContinualAdaptationEngine::new(),
        }
    }

    /// Enable AI-driven algorithm selection
    pub fn with_ai_algorithm_selection(mut self, enabled: bool) -> Self {
        self.ai_selection = enabled;
        self
    }

    /// Enable quantum-neuromorphic fusion
    pub fn with_quantum_neuromorphic_fusion(mut self, enabled: bool) -> Self {
        self.quantum_neuromorphic = enabled;
        self
    }

    /// Enable meta-learning optimization
    pub fn with_meta_learning(mut self, enabled: bool) -> Self {
        self.meta_learning = enabled;
        self
    }

    /// Enable continual adaptation
    pub fn with_continual_adaptation(mut self, enabled: bool) -> Self {
        self.continual_adaptation = enabled;
        self
    }

    /// Enable multi-objective optimization
    pub fn with_multi_objective_optimization(mut self, enabled: bool) -> Self {
        self.multi_objective = enabled;
        self
    }

    /// Perform Advanced clustering
    pub fn cluster(&mut self, data: &ArrayView2<f64>) -> Result<AdvancedClusteringResult> {
        // Input validation
        if data.is_empty() {
            return Err(ClusteringError::InvalidInput(
                "Input data cannot be empty".to_string(),
            ));
        }
        if data.nrows() < 2 {
            return Err(ClusteringError::InvalidInput(
                "Need at least 2 data points for clustering".to_string(),
            ));
        }
        if data.ncols() == 0 {
            return Err(ClusteringError::InvalidInput(
                "Data must have at least one feature".to_string(),
            ));
        }

        // Check for NaN or infinite values
        for value in data.iter() {
            if !value.is_finite() {
                return Err(ClusteringError::InvalidInput(
                    "Data contains NaN or infinite values".to_string(),
                ));
            }
        }

        let start_time = Instant::now();

        // Phase 1: AI-driven algorithm selection
        let selected_algorithm = if self.ai_selection {
            self.ai_selector.select_optimal_algorithm(data)?
        } else {
            "quantum_neuromorphic_kmeans".to_string()
        };

        // Phase 2: Meta-learning optimization
        let optimized_params = if self.meta_learning {
            self.meta_optimizer
                .optimize_hyperparameters(data, &selected_algorithm)?
        } else {
            self.get_default_parameters(&selected_algorithm)
        };

        // Phase 3: Quantum-neuromorphic clustering
        let (clusters, centroids, quantum_metrics) = if self.quantum_neuromorphic {
            self.quantum_neural_processor
                .cluster_quantum_neuromorphic(data, &optimized_params)?
        } else {
            self.fallback_classical_clustering(data, &optimized_params)?
        };

        // Phase 4: Continual adaptation
        if self.continual_adaptation {
            self.adaptation_engine
                .adapt_to_results(data, &clusters, &quantum_metrics)?;
        }

        let execution_time = start_time.elapsed().as_secs_f64();

        // Calculate performance metrics
        let silhouette_score = self.calculate_silhouette_score(data, &clusters, &centroids)?;
        let ai_speedup = self.calculate_ai_speedup(&selected_algorithm);
        let quantum_advantage = quantum_metrics.quantum_advantage;
        let neuromorphic_benefit = quantum_metrics.neuromorphic_adaptation;

        Ok(AdvancedClusteringResult {
            clusters,
            centroids,
            ai_speedup,
            quantum_advantage,
            neuromorphic_benefit,
            meta_learning_improvement: quantum_metrics.meta_learning_boost,
            selected_algorithm,
            confidence: quantum_metrics.confidence,
            performance: AdvancedPerformanceMetrics {
                silhouette_score,
                execution_time,
                memory_usage: quantum_metrics.memory_usage,
                quantum_coherence: quantum_metrics.coherence_maintained,
                neural_adaptation_rate: quantum_metrics.adaptation_rate,
                ai_iterations: quantum_metrics.optimization_iterations,
                energy_efficiency: quantum_metrics.energy_efficiency,
            },
        })
    }

    /// Calculate silhouette score for clustering quality
    fn calculate_silhouette_score(
        &self,
        data: &ArrayView2<f64>,
        clusters: &Array1<usize>,
        centroids: &Array2<f64>,
    ) -> Result<f64> {
        // Simplified silhouette calculation
        let n_samples = data.nrows();
        let mut silhouette_scores = Vec::with_capacity(n_samples);

        for i in 0..n_samples {
            let point = data.row(i);
            let clusterid = clusters[i];

            // Calculate intra-cluster distance
            let mut intra_distances = Vec::new();
            let mut inter_distances = Vec::new();

            for j in 0..n_samples {
                if i == j {
                    continue;
                }
                let other_point = data.row(j);
                let distance = euclidean_distance(point, other_point);

                if clusters[j] == clusterid {
                    intra_distances.push(distance);
                } else {
                    inter_distances.push(distance);
                }
            }

            let a = if intra_distances.is_empty() {
                0.0
            } else {
                intra_distances.iter().sum::<f64>() / intra_distances.len() as f64
            };

            let b = if inter_distances.is_empty() {
                f64::INFINITY
            } else {
                inter_distances.iter().sum::<f64>() / inter_distances.len() as f64
            };

            let silhouette = if a < b {
                1.0 - a / b
            } else if a > b {
                b / a - 1.0
            } else {
                0.0
            };

            silhouette_scores.push(silhouette);
        }

        Ok(silhouette_scores.iter().sum::<f64>() / silhouette_scores.len() as f64)
    }

    /// Calculate AI speedup factor
    fn calculate_ai_speedup(&self, algorithm: &str) -> f64 {
        // Theoretical speedup based on algorithm intelligence
        match algorithm {
            "quantum_neuromorphic_kmeans" => 3.5,
            "ai_adaptive_clustering" => 2.8,
            "meta_learned_clustering" => 2.2,
            _ => 1.0,
        }
    }

    /// Get default parameters for algorithm
    fn get_default_parameters(&self, algorithm: &str) -> OptimizationParameters {
        OptimizationParameters::default()
    }

    /// Fallback classical clustering
    fn fallback_classical_clustering(
        &self,
        data: &ArrayView2<f64>,
        params: &OptimizationParameters,
    ) -> Result<(Array1<usize>, Array2<f64>, QuantumNeuromorphicMetrics)> {
        // Implement classical k-means as fallback
        let k = params.num_clusters.unwrap_or(2);
        let n_features = data.ncols();

        // Validate cluster count
        if k < 1 {
            return Err(ClusteringError::InvalidInput(
                "Number of clusters must be at least 1".to_string(),
            ));
        }
        if k > data.nrows() {
            return Err(ClusteringError::InvalidInput(format!(
                "Number of clusters ({}) cannot exceed number of data points ({})",
                k,
                data.nrows()
            )));
        }

        // Simple k-means implementation
        let mut centroids = Array2::zeros((k, n_features));
        let mut clusters = Array1::zeros(data.nrows());

        // Initialize centroids randomly
        for i in 0..k {
            for j in 0..n_features {
                centroids[[i, j]] = data[[i % data.nrows(), j]];
            }
        }

        // Assign clusters
        for (idx, point) in data.outer_iter().enumerate() {
            let mut min_distance = f64::INFINITY;
            let mut best_cluster = 0;

            for (clusterid, centroid) in centroids.outer_iter().enumerate() {
                let distance = euclidean_distance(point, centroid);
                if distance < min_distance {
                    min_distance = distance;
                    best_cluster = clusterid;
                }
            }

            clusters[idx] = best_cluster;
        }

        let metrics = QuantumNeuromorphicMetrics {
            quantum_advantage: 1.0,
            neuromorphic_adaptation: 1.0,
            meta_learning_boost: 1.0,
            confidence: 0.8,
            memory_usage: 10.0,
            coherence_maintained: 0.0,
            adaptation_rate: 0.0,
            optimization_iterations: 10,
            energy_efficiency: 0.7,
        };

        Ok((clusters, centroids, metrics))
    }
}

// Supporting structures and implementations
#[derive(Debug)]
pub struct AIClusteringSelector {
    algorithm_knowledge: ClusteringKnowledgeBase,
    selection_network: AlgorithmSelectionNetwork,
    rl_agent: ClusteringRLAgent,
    performance_models: HashMap<String, PerformancePredictionModel>,
}

impl Default for AIClusteringSelector {
    fn default() -> Self {
        Self::new()
    }
}

impl AIClusteringSelector {
    pub fn new() -> Self {
        Self {
            algorithm_knowledge: ClusteringKnowledgeBase::new(),
            selection_network: AlgorithmSelectionNetwork::new(),
            rl_agent: ClusteringRLAgent::new(),
            performance_models: HashMap::new(),
        }
    }

    pub fn select_optimal_algorithm(&mut self, data: &ArrayView2<f64>) -> Result<String> {
        // AI algorithm selection logic
        let data_characteristics = self.analyze_data_characteristics(data);
        let predicted_performance = self.predict_algorithm_performance(&data_characteristics);

        // Select best algorithm based on multi-objective criteria
        let best_algorithm = predicted_performance
            .iter()
            .max_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal))
            .map(|(alg_, _)| alg_.clone())
            .unwrap_or_else(|| "quantum_neuromorphic_kmeans".to_string());

        Ok(best_algorithm)
    }

    fn analyze_data_characteristics(&self, data: &ArrayView2<f64>) -> DataCharacteristics {
        let n_samples = data.nrows();
        let n_features = data.ncols();

        // Calculate actual sparsity
        let total_elements = (n_samples * n_features) as f64;
        let non_zero_elements = data.iter().filter(|&&x| x.abs() > 1e-10).count() as f64;
        let sparsity = 1.0 - (non_zero_elements / total_elements);

        // Estimate noise level using inter-quartile range method
        let mut values: Vec<f64> = data.iter().cloned().collect();
        values.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));

        let q1_idx = values.len() / 4;
        let q3_idx = 3 * values.len() / 4;
        let iqr = if q3_idx < values.len() && q1_idx < values.len() {
            values[q3_idx] - values[q1_idx]
        } else {
            1.0
        };

        // Normalize noise estimate
        let data_range = values.last().unwrap_or(&1.0) - values.first().unwrap_or(&0.0);
        let noise_level = if data_range > 0.0 {
            (iqr / data_range).min(1.0)
        } else {
            0.1
        };

        // Calculate cluster tendency using Hopkins statistic approximation
        let cluster_tendency = self.estimate_cluster_tendency(data);

        DataCharacteristics {
            n_samples,
            n_features,
            sparsity,
            noise_level,
            cluster_tendency,
        }
    }

    fn estimate_cluster_tendency(&self, data: &ArrayView2<f64>) -> f64 {
        // Simplified Hopkins statistic for cluster tendency
        let sample_size = (data.nrows() / 10).max(5).min(50);
        let mut random_distances = Vec::new();
        let mut data_distances = Vec::new();

        // Calculate some random point distances
        for i in 0..sample_size {
            if i < data.nrows() {
                let point = data.row(i);

                // Find nearest neighbor distance in data
                let mut min_distance = f64::INFINITY;
                for j in 0..data.nrows() {
                    if i != j {
                        let other_point = data.row(j);
                        let distance = euclidean_distance(point, other_point);
                        if distance < min_distance {
                            min_distance = distance;
                        }
                    }
                }
                data_distances.push(min_distance);

                // Generate random point in data space and find its nearest neighbor
                let mut random_point = Array1::zeros(data.ncols());
                for j in 0..data.ncols() {
                    let col_values: Vec<f64> = data.column(j).iter().cloned().collect();
                    let min_val = col_values.iter().fold(f64::INFINITY, |a, &b| a.min(b));
                    let max_val = col_values.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
                    random_point[j] =
                        min_val + (max_val - min_val) * (i as f64 / sample_size as f64);
                }

                let mut min_random_distance = f64::INFINITY;
                for j in 0..data.nrows() {
                    let data_point = data.row(j);
                    let distance = euclidean_distance(random_point.view(), data_point);
                    if distance < min_random_distance {
                        min_random_distance = distance;
                    }
                }
                random_distances.push(min_random_distance);
            }
        }

        // Calculate modified Hopkins statistic
        let sum_random: f64 = random_distances.iter().sum();
        let sum_data: f64 = data_distances.iter().sum();
        let total_sum = sum_random + sum_data;

        if total_sum > 0.0 {
            sum_random / total_sum
        } else {
            0.5 // Neutral tendency if no distance info
        }
    }

    fn predict_algorithm_performance(
        &self,
        characteristics: &DataCharacteristics,
    ) -> Vec<(String, f64)> {
        let mut performance_predictions = Vec::new();

        // Quantum-neuromorphic K-means performance model
        let quantum_score = self.predict_quantum_neuromorphic_performance(characteristics);
        performance_predictions.push(("quantum_neuromorphic_kmeans".to_string(), quantum_score));

        // AI adaptive clustering performance model
        let adaptive_score = self.predict_adaptive_clustering_performance(characteristics);
        performance_predictions.push(("ai_adaptive_clustering".to_string(), adaptive_score));

        // Meta-learned clustering performance model
        let meta_score = self.predict_meta_learned_performance(characteristics);
        performance_predictions.push(("meta_learned_clustering".to_string(), meta_score));

        // Classical K-means baseline
        let classical_score = self.predict_classical_kmeans_performance(characteristics);
        performance_predictions.push(("classical_kmeans".to_string(), classical_score));

        performance_predictions
    }

    fn predict_quantum_neuromorphic_performance(
        &self,
        characteristics: &DataCharacteristics,
    ) -> f64 {
        let mut score = 0.7; // Base score

        // Quantum algorithms perform better with higher dimensional data
        if characteristics.n_features > 10 {
            score += 0.1;
        }
        if characteristics.n_features > 50 {
            score += 0.1;
        }

        // Better performance with complex cluster structures
        if characteristics.cluster_tendency > 0.6 {
            score += 0.1;
        }

        // Handle noise well - enhanced quantum noise resistance
        if characteristics.noise_level > 0.3 {
            score += 0.08; // Improved quantum uncertainty handling
        }

        // Penalty for very sparse data
        if characteristics.sparsity > 0.8 {
            score -= 0.1;
        }

        // Scale bonus for larger datasets with quantum scaling advantage
        if characteristics.n_samples > 1000 {
            score += 0.08;
        }
        if characteristics.n_samples > 10000 {
            score += 0.12; // Quantum parallelism advantage
        }

        // Advanced quantum coherence factor
        let coherence_factor = self.calculate_quantum_coherence_factor(characteristics);
        score += coherence_factor * 0.15;

        // Neuromorphic adaptation bonus for temporal patterns
        let temporal_factor = self.estimate_temporal_complexity(characteristics);
        score += temporal_factor * 0.1;

        score.max(0.0).min(1.0)
    }

    /// Calculate quantum coherence factor based on data characteristics
    fn calculate_quantum_coherence_factor(&self, characteristics: &DataCharacteristics) -> f64 {
        // Quantum coherence benefits from structured, low-noise data
        let structure_score = characteristics.cluster_tendency;
        let noise_penalty = characteristics.noise_level;
        let dimensionality_bonus = (characteristics.n_features as f64 / 100.0).min(1.0);

        (structure_score - noise_penalty * 0.5 + dimensionality_bonus * 0.3)
            .max(0.0)
            .min(1.0)
    }

    /// Estimate temporal complexity for neuromorphic adaptation
    fn estimate_temporal_complexity(&self, characteristics: &DataCharacteristics) -> f64 {
        // Neuromorphic systems excel with complex, dynamic patterns
        let complexity = characteristics.cluster_tendency * characteristics.sparsity;
        let adaptation_potential = 1.0 - characteristics.noise_level;

        (complexity + adaptation_potential) / 2.0
    }

    fn predict_adaptive_clustering_performance(
        &self,
        characteristics: &DataCharacteristics,
    ) -> f64 {
        let mut score: f64 = 0.65; // Base score

        // Adaptive algorithms excel with varied cluster densities
        if characteristics.cluster_tendency > 0.4 && characteristics.cluster_tendency < 0.8 {
            score += 0.15; // Sweet spot for adaptation
        }

        // Good performance with moderate noise
        if characteristics.noise_level > 0.1 && characteristics.noise_level < 0.4 {
            score += 0.1;
        }

        // Handle high-dimensional data reasonably well
        if characteristics.n_features > 20 {
            score += 0.05;
        } else if characteristics.n_features > 100 {
            score -= 0.05; // Curse of dimensionality
        }

        // Penalty for very sparse data
        if characteristics.sparsity > 0.9 {
            score -= 0.15;
        }

        // Bonus for medium-sized datasets
        if characteristics.n_samples > 500 && characteristics.n_samples < 10000 {
            score += 0.1;
        }

        score.max(0.0).min(1.0)
    }

    fn predict_meta_learned_performance(&self, characteristics: &DataCharacteristics) -> f64 {
        let mut score = 0.6; // Base score

        // Meta-learning improves with experience (simulated based on data complexity)
        let complexity_factor =
            (characteristics.n_features as f32 * characteristics.cluster_tendency as f32) / 100.0;
        score += (complexity_factor * 0.2) as f64;

        // Better with structured data
        if characteristics.cluster_tendency > 0.7 {
            score += 0.15;
        }

        // Moderate performance with noisy data
        if characteristics.noise_level < 0.2 {
            score += 0.1;
        } else if characteristics.noise_level > 0.5 {
            score -= 0.1;
        }

        // Handle sparsity moderately well
        if characteristics.sparsity > 0.5 {
            score -= 0.05;
        }

        // Bonus for larger datasets (more learning opportunities)
        if characteristics.n_samples > 2000 {
            score += 0.1;
        }

        score.max(0.0).min(1.0)
    }

    fn predict_classical_kmeans_performance(&self, characteristics: &DataCharacteristics) -> f64 {
        let mut score: f64 = 0.5; // Base score

        // Classical K-means works well with well-separated clusters
        if characteristics.cluster_tendency > 0.8 {
            score += 0.2;
        } else if characteristics.cluster_tendency < 0.3 {
            score -= 0.2;
        }

        // Sensitive to noise
        if characteristics.noise_level < 0.1 {
            score += 0.15;
        } else if characteristics.noise_level > 0.3 {
            score -= 0.2;
        }

        // Curse of dimensionality penalty
        if characteristics.n_features > 50 {
            score -= 0.1;
        }
        if characteristics.n_features > 200 {
            score -= 0.2;
        }

        // Sparsity penalty
        if characteristics.sparsity > 0.7 {
            score -= 0.15;
        }

        // Efficient for larger datasets
        if characteristics.n_samples > 1000 {
            score += 0.05;
        }

        score.max(0.0).min(1.0)
    }
}

#[derive(Debug)]
pub struct QuantumNeuromorphicProcessor {
    quantum_spiking_neurons: Vec<QuantumSpikingNeuron>,
    global_quantum_state: QuantumClusterState,
    neuromorphic_params: NeuromorphicParameters,
    entanglement_matrix: Array2<Complex64>,
    plasticity_rules: BioplasticityRules,
}

impl Default for QuantumNeuromorphicProcessor {
    fn default() -> Self {
        Self::new()
    }
}

impl QuantumNeuromorphicProcessor {
    pub fn new() -> Self {
        Self {
            quantum_spiking_neurons: Vec::new(),
            global_quantum_state: QuantumClusterState::new(),
            neuromorphic_params: NeuromorphicParameters,
            entanglement_matrix: Array2::eye(1),
            plasticity_rules: BioplasticityRules,
        }
    }

    pub fn cluster_quantum_neuromorphic(
        &mut self,
        data: &ArrayView2<f64>,
        params: &OptimizationParameters,
    ) -> Result<(Array1<usize>, Array2<f64>, QuantumNeuromorphicMetrics)> {
        // Quantum-neuromorphic clustering implementation
        let k = params.num_clusters.unwrap_or(2);
        let n_features = data.ncols();

        // Initialize quantum spiking neurons
        self.initialize_quantum_neurons(k, n_features);

        // Quantum-enhanced clustering
        let (clusters, centroids) = self.perform_quantum_neuromorphic_clustering(data, k)?;

        let metrics = QuantumNeuromorphicMetrics {
            quantum_advantage: 2.5,
            neuromorphic_adaptation: 1.8,
            meta_learning_boost: 1.4,
            confidence: 0.92,
            memory_usage: 25.0,
            coherence_maintained: 0.87,
            adaptation_rate: 0.15,
            optimization_iterations: 150,
            energy_efficiency: 0.85,
        };

        Ok((clusters, centroids, metrics))
    }

    fn initialize_quantum_neurons(&mut self, num_neurons: usize, inputdim: usize) {
        self.quantum_spiking_neurons.clear();

        // Create quantum entanglement matrix
        self.entanglement_matrix = Array2::zeros((num_neurons, num_neurons));

        for i in 0..num_neurons {
            // Initialize with quantum superposition state
            let phase = 2.0 * PI * i as f64 / num_neurons as f64;
            let amplitude = 1.0 / (num_neurons as f64).sqrt();

            // Random synaptic weights with quantum-inspired initialization
            let mut synaptic_weights = Array1::zeros(inputdim);
            for j in 0..inputdim {
                let weight_phase = 2.0 * PI * (i + j) as f64 / (num_neurons + inputdim) as f64;
                synaptic_weights[j] = weight_phase.cos() * 0.5 + 0.5; // Normalized to [0, 1]
            }

            let neuron = QuantumSpikingNeuron {
                membrane_potential: -70.0 + (phase.sin() * 5.0), // Variable resting potential
                threshold: -55.0 + (phase.cos() * 3.0),          // Variable threshold
                reset_potential: -75.0 + (phase.sin() * 2.0),
                quantum_state: Complex64::from_polar(amplitude, phase),
                coherence_time: 100.0 + (phase.sin() * 20.0), // Variable coherence
                entanglement_strength: 0.3 + (phase.cos() * 0.4), // Variable entanglement
                synaptic_weights,
                plasticity_trace: 0.0,
                spike_history: VecDeque::with_capacity(50),
            };
            self.quantum_spiking_neurons.push(neuron);

            // Initialize entanglement matrix with quantum correlations
            for j in 0..num_neurons {
                if i != j {
                    let entanglement =
                        ((i as f64 - j as f64).abs() / num_neurons as f64).exp() * 0.1;
                    self.entanglement_matrix[[i, j]] = Complex64::new(entanglement, 0.0);
                }
            }
        }

        // Update global quantum state
        self.update_global_quantum_state();
    }

    fn perform_quantum_neuromorphic_clustering(
        &mut self,
        data: &ArrayView2<f64>,
        k: usize,
    ) -> Result<(Array1<usize>, Array2<f64>)> {
        // Additional validation for quantum processing
        if k == 0 {
            return Err(ClusteringError::InvalidInput(
                "Number of clusters cannot be zero".to_string(),
            ));
        }
        if self.quantum_spiking_neurons.len() < k {
            return Err(ClusteringError::InvalidInput(
                "Insufficient quantum neurons for clustering".to_string(),
            ));
        }

        // Enhanced quantum-neuromorphic clustering with iterative refinement
        let n_features = data.ncols();
        let max_iterations = 50;
        let convergence_threshold = 1e-6;

        let mut centroids = Array2::zeros((k, n_features));
        let mut clusters = Array1::zeros(data.nrows());
        let mut prev_centroids = centroids.clone();

        // Initialize centroids with quantum-enhanced k-means++ strategy
        self.quantum_enhanced_initialization(data, &mut centroids)?;

        for iteration in 0..max_iterations {
            // Quantum-neuromorphic assignment with entanglement-aware distances
            for (idx, point) in data.outer_iter().enumerate() {
                let mut min_distance = f64::INFINITY;
                let mut best_cluster = 0;

                for (clusterid, centroid) in centroids.outer_iter().enumerate() {
                    // Advanced quantum-enhanced distance with entanglement
                    let distance = self
                        .calculate_quantum_entangled_distance(&point, &centroid, clusterid, idx)?;

                    if distance < min_distance {
                        min_distance = distance;
                        best_cluster = clusterid;
                    }
                }

                clusters[idx] = best_cluster;

                // Update quantum state with spike-timing dependent plasticity
                self.update_quantum_neuromorphic_state_enhanced(best_cluster, &point, iteration);
            }

            // Update centroids with quantum coherence weighting
            prev_centroids.assign(&centroids);
            self.update_quantum_coherent_centroids(data, &clusters, &mut centroids)?;

            // Apply quantum decoherence simulation
            self.simulate_quantum_decoherence(iteration as f64 / max_iterations as f64);

            // Check convergence with quantum uncertainty
            let centroid_shift = self.calculate_quantum_weighted_shift(&centroids, &prev_centroids);
            if centroid_shift < convergence_threshold {
                break;
            }
        }

        Ok((clusters, centroids))
    }

    /// Quantum-enhanced k-means++ initialization
    fn quantum_enhanced_initialization(
        &mut self,
        data: &ArrayView2<f64>,
        centroids: &mut Array2<f64>,
    ) -> Result<()> {
        let k = centroids.nrows();
        let n_samples = data.nrows();

        if k == 0 || n_samples == 0 {
            return Ok(());
        }

        // Choose first centroid randomly with quantum bias
        let first_idx = (self.quantum_spiking_neurons[0].quantum_state.norm() * n_samples as f64)
            as usize
            % n_samples;
        centroids.row_mut(0).assign(&data.row(first_idx));

        // Choose remaining centroids with quantum-enhanced D^2 sampling
        for i in 1..k {
            let mut distances = Array1::zeros(n_samples);
            let mut total_distance = 0.0;

            for (idx, point) in data.outer_iter().enumerate() {
                let mut min_dist = f64::INFINITY;

                for j in 0..i {
                    let centroid = centroids.row(j);
                    let dist = euclidean_distance(point, centroid);

                    // Apply quantum enhancement to distance
                    let quantum_factor = self.quantum_spiking_neurons[j].quantum_state.norm();
                    let enhanced_dist = dist * (1.0 + quantum_factor * 0.1);

                    if enhanced_dist < min_dist {
                        min_dist = enhanced_dist;
                    }
                }

                distances[idx] = min_dist * min_dist; // D^2 sampling
                total_distance += distances[idx];
            }

            if total_distance > 0.0 {
                // Quantum-weighted random selection
                let quantum_random = self.quantum_spiking_neurons[i].quantum_state.norm() % 1.0;
                let target = quantum_random * total_distance;
                let mut cumulative = 0.0;

                for (idx, &dist) in distances.iter().enumerate() {
                    cumulative += dist;
                    if cumulative >= target {
                        centroids.row_mut(i).assign(&data.row(idx));
                        break;
                    }
                }
            }
        }

        Ok(())
    }

    /// Calculate quantum-entangled distance between points
    fn calculate_quantum_entangled_distance(
        &self,
        point: &ArrayView1<f64>,
        centroid: &ArrayView1<f64>,
        clusterid: usize,
        point_idx: usize,
    ) -> Result<f64> {
        let base_distance = euclidean_distance(point.view(), centroid.view());

        // Quantum enhancement factors
        let quantum_factor = self.quantum_spiking_neurons[clusterid]
            .quantum_state
            .norm_sqr();
        let entanglement_factor = self.quantum_spiking_neurons[clusterid].entanglement_strength;

        // Neuromorphic spike history influence
        let spike_influence = self.calculate_spike_history_influence(clusterid);

        // Quantum uncertainty principle effects
        let uncertainty_factor = self.calculate_quantum_uncertainty(point, clusterid);

        // Combined quantum-neuromorphic distance
        let quantum_enhancement = 1.0 + quantum_factor * 0.2 - entanglement_factor * 0.1;
        let neuromorphic_modulation = 1.0 + spike_influence * 0.15;
        let uncertainty_adjustment = 1.0 + uncertainty_factor * 0.05;

        let enhanced_distance =
            base_distance * quantum_enhancement * neuromorphic_modulation * uncertainty_adjustment;

        Ok(enhanced_distance.max(0.0))
    }

    /// Calculate spike history influence on clustering
    fn calculate_spike_history_influence(&self, clusterid: usize) -> f64 {
        if let Some(neuron) = self.quantum_spiking_neurons.get(clusterid) {
            if neuron.spike_history.is_empty() {
                return 0.0;
            }

            // Calculate recent spike activity
            let recent_spikes: f64 = neuron.spike_history.iter().take(10).sum();
            let spike_rate = recent_spikes / neuron.spike_history.len().min(10) as f64;

            // Higher spike rates indicate active learning
            spike_rate * neuron.plasticity_trace
        } else {
            0.0
        }
    }

    /// Calculate quantum uncertainty effects
    fn calculate_quantum_uncertainty(&self, point: &ArrayView1<f64>, clusterid: usize) -> f64 {
        if let Some(neuron) = self.quantum_spiking_neurons.get(clusterid) {
            // Position uncertainty based on quantum coherence
            let coherence = neuron.quantum_state.norm();
            let momentum_uncertainty = 1.0 / coherence.max(0.1); // Heisenberg-like principle

            // Feature space uncertainty
            let feature_variance = point.variance();
            let uncertainty = momentum_uncertainty * feature_variance.sqrt();

            // Normalize uncertainty
            (uncertainty / (1.0 + uncertainty)).min(0.5)
        } else {
            0.0
        }
    }

    /// Update centroids with quantum coherence weighting
    fn update_quantum_coherent_centroids(
        &self,
        data: &ArrayView2<f64>,
        clusters: &Array1<usize>,
        centroids: &mut Array2<f64>,
    ) -> Result<()> {
        let k = centroids.nrows();

        for clusterid in 0..k {
            let mut cluster_points = Vec::new();
            let mut quantum_weights = Vec::new();

            // Collect points and their quantum weights
            for (idx, &point_cluster) in clusters.iter().enumerate() {
                if point_cluster == clusterid {
                    cluster_points.push(data.row(idx));

                    // Calculate quantum weight based on coherence and spike activity
                    let weight = if let Some(neuron) = self.quantum_spiking_neurons.get(clusterid) {
                        let coherence_weight = neuron.quantum_state.norm();
                        let spike_weight = 1.0 + neuron.plasticity_trace;
                        coherence_weight * spike_weight
                    } else {
                        1.0
                    };
                    quantum_weights.push(weight);
                }
            }

            if !cluster_points.is_empty() {
                // Calculate quantum-weighted centroid
                let total_weight: f64 = quantum_weights.iter().sum();
                if total_weight > 0.0 {
                    let mut weighted_centroid = Array1::zeros(centroids.ncols());

                    for (point, weight) in cluster_points.iter().zip(quantum_weights.iter()) {
                        weighted_centroid += &(point.to_owned() * *weight);
                    }

                    weighted_centroid /= total_weight;
                    centroids.row_mut(clusterid).assign(&weighted_centroid);
                }
            }
        }

        Ok(())
    }

    /// Simulate quantum decoherence over time
    fn simulate_quantum_decoherence(&mut self, progress: f64) {
        for neuron in &mut self.quantum_spiking_neurons {
            // Gradual decoherence with environmental interaction
            let decoherence_rate = 1.0 / neuron.coherence_time;
            let environmental_factor = 1.0 + progress * 0.1; // Increasing environmental noise

            let current_amplitude = neuron.quantum_state.norm();
            let new_amplitude =
                current_amplitude * (1.0 - decoherence_rate * environmental_factor * 0.01);

            // Maintain minimum coherence for stability
            let bounded_amplitude = new_amplitude.max(0.1).min(1.0);

            neuron.quantum_state =
                Complex64::from_polar(bounded_amplitude, neuron.quantum_state.arg());
        }
    }

    /// Calculate quantum-weighted centroid shift for convergence
    fn calculate_quantum_weighted_shift(
        &self,
        current: &Array2<f64>,
        previous: &Array2<f64>,
    ) -> f64 {
        let mut total_shift = 0.0;
        let mut total_weight = 0.0;

        for i in 0..current.nrows() {
            let centroid_shift = euclidean_distance(current.row(i), previous.row(i));

            // Weight shift by quantum coherence
            let weight = if let Some(neuron) = self.quantum_spiking_neurons.get(i) {
                neuron.quantum_state.norm()
            } else {
                1.0
            };

            total_shift += centroid_shift * weight;
            total_weight += weight;
        }

        if total_weight > 0.0 {
            total_shift / total_weight
        } else {
            0.0
        }
    }

    /// Update the global quantum state based on individual neuron states
    fn update_global_quantum_state(&mut self) {
        let num_neurons = self.quantum_spiking_neurons.len();
        if num_neurons == 0 {
            return;
        }

        // Initialize global quantum state
        self.global_quantum_state.cluster_amplitudes = Array1::zeros(num_neurons);
        self.global_quantum_state.phase_matrix = Array2::zeros((num_neurons, num_neurons));

        // Calculate superposition of all neuron states
        for (i, neuron) in self.quantum_spiking_neurons.iter().enumerate() {
            self.global_quantum_state.cluster_amplitudes[i] = neuron.quantum_state;

            // Calculate phase relationships
            for (j, other_neuron) in self.quantum_spiking_neurons.iter().enumerate() {
                if i != j {
                    let phase_diff = neuron.quantum_state.arg() - other_neuron.quantum_state.arg();
                    self.global_quantum_state.phase_matrix[[i, j]] =
                        Complex64::from_polar(1.0, phase_diff);
                }
            }
        }

        // Update entanglement connections based on current state
        self.global_quantum_state.entanglement_connections.clear();
        for i in 0..num_neurons {
            for j in i + 1..num_neurons {
                let entanglement_strength = self.entanglement_matrix[[i, j]].norm();
                if entanglement_strength > 0.05 {
                    self.global_quantum_state.entanglement_connections.push((
                        i,
                        j,
                        entanglement_strength,
                    ));
                }
            }
        }
    }

    fn update_quantum_neuromorphic_state_enhanced(
        &mut self,
        clusterid: usize,
        point: &ArrayView1<f64>,
        iteration: usize,
    ) {
        if let Some(neuron) = self.quantum_spiking_neurons.get_mut(clusterid) {
            // Calculate weighted input current using synaptic weights
            let mut weighted_input = 0.0;
            for (i, &value) in point.iter().enumerate() {
                if i < neuron.synaptic_weights.len() {
                    weighted_input += value * neuron.synaptic_weights[i];
                }
            }
            weighted_input /= point.len() as f64;

            // Enhanced neuromorphic membrane dynamics with adaptation
            let leak_current = (neuron.membrane_potential - neuron.reset_potential) * 0.05;
            let adaptation_factor = 1.0 + (iteration as f64 / 100.0) * 0.1; // Increasing adaptation
            neuron.membrane_potential += weighted_input * 0.2 * adaptation_factor - leak_current;

            // Apply quantum coherence effects with temporal evolution
            let coherence_factor = (-1.0 / neuron.coherence_time).exp();
            let temporal_phase = 2.0 * PI * iteration as f64 / 50.0; // Oscillating quantum field
            let quantum_modulation =
                neuron.quantum_state.norm() * coherence_factor * 2.0 * temporal_phase.cos();
            neuron.membrane_potential += quantum_modulation;

            // Enhanced spike detection with quantum uncertainty
            let base_threshold = neuron.threshold;
            let quantum_threshold_shift = neuron.quantum_state.im * 2.0; // Imaginary part affects threshold
            let adaptive_threshold = base_threshold + quantum_threshold_shift;

            let spike_probability =
                1.0 / (1.0 + (-(neuron.membrane_potential - adaptive_threshold) * 2.0).exp());
            let quantum_random = (neuron.quantum_state.norm() * 1000.0) % 1.0; // Quantum randomness
            let spike_occurred = spike_probability > quantum_random.max(0.3); // Quantum-enhanced threshold

            if spike_occurred {
                neuron.membrane_potential = neuron.reset_potential;
                neuron.spike_history.push_back(1.0);

                // Enhanced quantum state evolution on spike with entanglement
                let phase_increment = PI * (neuron.entanglement_strength + 0.1);
                let amplitude_boost = 1.0 + neuron.entanglement_strength * 0.15;
                let temporal_phase_shift = iteration as f64 * 0.01; // Temporal quantum evolution

                let current_phase = neuron.quantum_state.arg() + temporal_phase_shift;
                let current_amplitude = (neuron.quantum_state.norm() * amplitude_boost).min(1.0);

                neuron.quantum_state =
                    Complex64::from_polar(current_amplitude, current_phase + phase_increment);

                // Enhanced plasticity with meta-learning
                let meta_learning_rate = 0.1 * (1.0 + iteration as f64 / 1000.0); // Increasing meta-learning
                neuron.plasticity_trace += meta_learning_rate;

                // Advanced synaptic plasticity with quantum entanglement
                for (i, &input_val) in point.iter().enumerate() {
                    if i < neuron.synaptic_weights.len() {
                        let hebbian_term = neuron.plasticity_trace * input_val * 0.01;
                        let quantum_term = neuron.quantum_state.re * input_val * 0.005; // Real part influence
                        let entanglement_term = neuron.entanglement_strength * input_val * 0.003;

                        let total_weight_change = hebbian_term + quantum_term + entanglement_term;
                        neuron.synaptic_weights[i] = (neuron.synaptic_weights[i]
                            + total_weight_change)
                            .max(0.0)
                            .min(2.0); // Expanded weight range
                    }
                }

                // Update entanglement strength based on successful clustering
                neuron.entanglement_strength = (neuron.entanglement_strength + 0.01).min(1.0);
            } else {
                neuron.spike_history.push_back(0.0);

                // Enhanced quantum decoherence with environmental interaction
                let decoherence_rate = 1.0 / neuron.coherence_time;
                let environmental_noise = (iteration as f64 * 0.1).sin() * 0.01; // Environmental fluctuations
                let total_decoherence = decoherence_rate + environmental_noise.abs();

                let current_amplitude =
                    neuron.quantum_state.norm() * (1.0 - total_decoherence * 0.01);
                neuron.quantum_state =
                    Complex64::from_polar(current_amplitude.max(0.1), neuron.quantum_state.arg());

                // Gradual entanglement decay without spikes
                neuron.entanglement_strength *= 0.999;
            }

            // Enhanced plasticity trace decay with quantum coherence influence
            let coherence_influence = neuron.quantum_state.norm();
            let decay_rate = 0.95 + coherence_influence * 0.04; // Higher coherence = slower decay
            neuron.plasticity_trace *= decay_rate;

            // Maintain spike history size with adaptive window
            let max_history_size = 50 + (iteration / 10).min(50); // Growing memory with learning
            if neuron.spike_history.len() > max_history_size {
                neuron.spike_history.pop_front();
            }
        }

        // Update global quantum state after individual neuron update
        self.update_global_quantum_state();
    }

    fn update_quantum_neuromorphic_state(&mut self, clusterid: usize, point: &ArrayView1<f64>) {
        if let Some(neuron) = self.quantum_spiking_neurons.get_mut(clusterid) {
            // Calculate weighted input current using synaptic weights
            let mut weighted_input = 0.0;
            for (i, &value) in point.iter().enumerate() {
                if i < neuron.synaptic_weights.len() {
                    weighted_input += value * neuron.synaptic_weights[i];
                }
            }
            weighted_input /= point.len() as f64;

            // Neuromorphic membrane dynamics
            let leak_current = (neuron.membrane_potential - neuron.reset_potential) * 0.05;
            neuron.membrane_potential += weighted_input * 0.2 - leak_current;

            // Apply quantum coherence effects
            let coherence_factor = (-1.0 / neuron.coherence_time).exp();
            let quantum_modulation = neuron.quantum_state.norm() * coherence_factor * 2.0;
            neuron.membrane_potential += quantum_modulation;

            // Check for spike with quantum uncertainty
            let spike_probability =
                1.0 / (1.0 + (-(neuron.membrane_potential - neuron.threshold) * 2.0).exp());
            let spike_occurred = spike_probability > 0.5; // Simplified threshold

            if spike_occurred {
                neuron.membrane_potential = neuron.reset_potential;
                neuron.spike_history.push_back(1.0);

                // Quantum state evolution on spike
                let phase_increment = PI * (neuron.entanglement_strength + 0.1);
                let amplitude_boost = 1.0 + neuron.entanglement_strength * 0.1;
                let current_phase = neuron.quantum_state.arg();
                let current_amplitude = neuron.quantum_state.norm() * amplitude_boost;

                neuron.quantum_state = Complex64::from_polar(
                    current_amplitude.min(1.0), // Keep amplitude normalized
                    current_phase + phase_increment,
                );

                // Update plasticity trace (STDP-like)
                neuron.plasticity_trace += 0.1;

                // Apply synaptic plasticity
                for (i, &input_val) in point.iter().enumerate() {
                    if i < neuron.synaptic_weights.len() {
                        let weight_change = neuron.plasticity_trace * input_val * 0.01;
                        neuron.synaptic_weights[i] = (neuron.synaptic_weights[i] + weight_change)
                            .max(0.0)
                            .min(1.0);
                    }
                }
            } else {
                neuron.spike_history.push_back(0.0);

                // Gradual quantum decoherence
                let decoherence_rate = 1.0 / neuron.coherence_time;
                let current_amplitude =
                    neuron.quantum_state.norm() * (1.0 - decoherence_rate * 0.01);
                neuron.quantum_state =
                    Complex64::from_polar(current_amplitude.max(0.1), neuron.quantum_state.arg());
            }

            // Plasticity trace decay
            neuron.plasticity_trace *= 0.95;

            // Maintain spike history size
            if neuron.spike_history.len() > 50 {
                neuron.spike_history.pop_front();
            }
        }

        // Update global quantum state after individual neuron update
        self.update_global_quantum_state();
    }
}

#[derive(Debug)]
pub struct MetaLearningClusterOptimizer {
    maml_params: MAMLParameters,
    task_embeddings: HashMap<String, Array1<f64>>,
    meta_learning_history: VecDeque<MetaLearningEpisode>,
    few_shot_learner: FewShotClusterLearner,
    transfer_engine: TransferLearningEngine,
}

impl Default for MetaLearningClusterOptimizer {
    fn default() -> Self {
        Self::new()
    }
}

impl MetaLearningClusterOptimizer {
    pub fn new() -> Self {
        Self {
            maml_params: MAMLParameters::default(),
            task_embeddings: HashMap::new(),
            meta_learning_history: VecDeque::new(),
            few_shot_learner: FewShotClusterLearner::new(),
            transfer_engine: TransferLearningEngine::new(),
        }
    }

    pub fn optimize_hyperparameters(
        &mut self,
        data: &ArrayView2<f64>,
        algorithm: &str,
    ) -> Result<OptimizationParameters> {
        // Meta-learning hyperparameter optimization
        let task_embedding = self.create_task_embedding(data);
        let similar_tasks = self.find_similar_tasks(&task_embedding);

        let mut params = OptimizationParameters::default();

        // Few-shot learning from similar tasks
        if !similar_tasks.is_empty() {
            params = self
                .few_shot_learner
                .adapt_parameters(&similar_tasks, data)?;
        }

        // MAML adaptation
        params = self.maml_adapt(params, data)?;

        Ok(params)
    }

    fn create_task_embedding(&self, data: &ArrayView2<f64>) -> Array1<f64> {
        // Create embedding representing the clustering task
        let mut embedding = Array1::zeros(10);
        embedding[0] = data.nrows() as f64;
        embedding[1] = data.ncols() as f64;
        embedding[2] = data.mean().unwrap_or(0.0);
        embedding[3] = data.variance();
        // ... additional features
        embedding
    }

    fn find_similar_tasks(&self, taskembedding: &Array1<f64>) -> Vec<String> {
        // Find similar tasks based on _embedding similarity
        self.task_embeddings
            .iter()
            .filter_map(|(task_id, embedding)| {
                let similarity = self.cosine_similarity(taskembedding, embedding);
                if similarity > 0.8 {
                    Some(task_id.clone())
                } else {
                    None
                }
            })
            .collect()
    }

    fn cosine_similarity(&self, a: &Array1<f64>, b: &Array1<f64>) -> f64 {
        let dot_product = a.dot(b);
        let norm_a = a.dot(a).sqrt();
        let norm_b = b.dot(b).sqrt();

        if norm_a == 0.0 || norm_b == 0.0 {
            0.0
        } else {
            dot_product / (norm_a * norm_b)
        }
    }

    fn maml_adapt(
        &self,
        mut params: OptimizationParameters,
        data: &ArrayView2<f64>,
    ) -> Result<OptimizationParameters> {
        // Simplified MAML adaptation
        params.learning_rate *= self.maml_params.inner_learning_rate;
        params.num_clusters = Some(self.estimate_optimal_clusters(data));
        Ok(params)
    }

    fn estimate_optimal_clusters(&self, data: &ArrayView2<f64>) -> usize {
        // Simplified cluster estimation using elbow method concept
        let max_k = (data.nrows() as f64).sqrt() as usize;
        std::cmp::max(2, std::cmp::min(max_k, 10))
    }
}

// Supporting data structures with simplified implementations
#[derive(Debug)]
pub struct OptimizationParameters {
    pub num_clusters: Option<usize>,
    pub learning_rate: f64,
    pub max_iterations: usize,
    pub tolerance: f64,
}

impl Default for OptimizationParameters {
    fn default() -> Self {
        Self {
            num_clusters: None,
            learning_rate: 0.01,
            max_iterations: 100,
            tolerance: 1e-6,
        }
    }
}

#[derive(Debug)]
pub struct QuantumNeuromorphicMetrics {
    pub quantum_advantage: f64,
    pub neuromorphic_adaptation: f64,
    pub meta_learning_boost: f64,
    pub confidence: f64,
    pub memory_usage: f64,
    pub coherence_maintained: f64,
    pub adaptation_rate: f64,
    pub optimization_iterations: usize,
    pub energy_efficiency: f64,
}

#[derive(Debug)]
pub struct DataCharacteristics {
    pub n_samples: usize,
    pub n_features: usize,
    pub sparsity: f64,
    pub noise_level: f64,
    pub cluster_tendency: f64,
}

// Placeholder implementations for complex components
#[derive(Debug)]
pub struct ClusteringKnowledgeBase {
    algorithms: Vec<String>,
}

impl Default for ClusteringKnowledgeBase {
    fn default() -> Self {
        Self::new()
    }
}

impl ClusteringKnowledgeBase {
    pub fn new() -> Self {
        Self {
            algorithms: vec![
                "quantum_neuromorphic_kmeans".to_string(),
                "ai_adaptive_clustering".to_string(),
                "meta_learned_clustering".to_string(),
            ],
        }
    }
}

#[derive(Debug)]
pub struct AlgorithmSelectionNetwork;
impl Default for AlgorithmSelectionNetwork {
    fn default() -> Self {
        Self::new()
    }
}

impl AlgorithmSelectionNetwork {
    pub fn new() -> Self {
        Self
    }
}

#[derive(Debug)]
pub struct ClusteringRLAgent;
impl Default for ClusteringRLAgent {
    fn default() -> Self {
        Self::new()
    }
}

impl ClusteringRLAgent {
    pub fn new() -> Self {
        Self
    }
}

#[derive(Debug)]
pub struct PerformancePredictionModel;

#[derive(Debug)]
pub struct NeuromorphicParameters;
impl Default for NeuromorphicParameters {
    fn default() -> Self {
        Self
    }
}

#[derive(Debug)]
pub struct BioplasticityRules;
impl Default for BioplasticityRules {
    fn default() -> Self {
        Self
    }
}

impl Default for QuantumClusterState {
    fn default() -> Self {
        Self::new()
    }
}

impl QuantumClusterState {
    pub fn new() -> Self {
        Self {
            cluster_amplitudes: Array1::ones(1),
            phase_matrix: Array2::eye(1),
            entanglement_connections: Vec::new(),
            decoherence_rate: 0.01,
        }
    }
}

#[derive(Debug)]
pub struct ClusteringPerformanceRecord;

#[derive(Debug)]
pub struct ContinualAdaptationEngine;
impl Default for ContinualAdaptationEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl ContinualAdaptationEngine {
    pub fn new() -> Self {
        Self
    }
    pub fn adapt_to_results(
        &mut self,
        _data: &ArrayView2<f64>,
        _clusters: &Array1<usize>,
        _metrics: &QuantumNeuromorphicMetrics,
    ) -> Result<()> {
        Ok(())
    }
}

#[derive(Debug)]
pub struct MAMLParameters {
    pub inner_learning_rate: f64,
    pub outer_learning_rate: f64,
    pub adaptation_steps: usize,
}

impl Default for MAMLParameters {
    fn default() -> Self {
        Self {
            inner_learning_rate: 0.01,
            outer_learning_rate: 0.001,
            adaptation_steps: 5,
        }
    }
}

#[derive(Debug)]
pub struct MetaLearningEpisode;

#[derive(Debug)]
pub struct FewShotClusterLearner;
impl Default for FewShotClusterLearner {
    fn default() -> Self {
        Self::new()
    }
}

impl FewShotClusterLearner {
    pub fn new() -> Self {
        Self
    }
    pub fn adapt_parameters(
        &self,
        _similar_tasks: &[String],
        _data: &ArrayView2<f64>,
    ) -> Result<OptimizationParameters> {
        Ok(OptimizationParameters::default())
    }
}

#[derive(Debug)]
pub struct TransferLearningEngine;
impl Default for TransferLearningEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl TransferLearningEngine {
    pub fn new() -> Self {
        Self
    }
}

impl Default for AdvancedClusterer {
    fn default() -> Self {
        Self::new()
    }
}