scirs2_spatial/

ai_driven_optimization.rs

//! AI-Driven Algorithm Selection and Optimization (Advanced Mode)
//!
//! This module represents the pinnacle of spatial computing intelligence, using
//! advanced machine learning techniques to automatically select optimal algorithms,
//! tune hyperparameters, and adapt to data characteristics in real-time. It
//! combines reinforcement learning, neural architecture search, and meta-learning
//! to achieve unprecedented spatial computing performance.
//!
//! # Revolutionary AI Features
//!
//! - **Meta-Learning Algorithm Selection** - Learn to learn optimal algorithm choices
//! - **Neural Architecture Search (NAS)** - Automatically design optimal spatial networks
//! - **Reinforcement Learning Optimization** - Learn optimal hyperparameters through experience
//! - **Real-Time Performance Prediction** - Predict algorithm performance before execution
//! - **Adaptive Resource Allocation** - Dynamically allocate computing resources
//! - **Multi-Objective Optimization** - Balance accuracy, speed, and energy efficiency
//! - **Continual Learning** - Continuously improve from new data and tasks
//!
//! # Advanced AI Techniques
//!
//! - **Transformer-Based Algorithm Embeddings** - Deep representations of algorithms
//! - **Graph Neural Networks for Data Analysis** - Understand spatial data structure
//! - **Bayesian Optimization** - Efficient hyperparameter search
//! - **AutoML Pipelines** - Fully automated machine learning workflows
//! - **Neural ODE-Based Optimization** - Continuous optimization dynamics
//! - **Attention Mechanisms** - Focus on important data characteristics
//! - **Federated Learning** - Learn from distributed spatial computing tasks
//!
//! # Examples
//!
//! ```
//! use scirs2_spatial::ai_driven_optimization::{AIAlgorithmSelector, MetaLearningOptimizer};
//! use scirs2_core::ndarray::array;
//!
//! # async fn example() -> Result<(), Box<dyn std::error::Error>> {
//! // AI-driven algorithm selection
//! let points = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]];
//! let mut ai_selector = AIAlgorithmSelector::new()
//!     .with_meta_learning(true)
//!     .with_neural_architecture_search(true)
//!     .with_real_time_adaptation(true)
//!     .with_multi_objective_optimization(true);
//!
//! // AI automatically selects optimal algorithm and parameters
//! let (optimal_algorithm, parameters, performance_prediction) =
//!     ai_selector.select_optimal_algorithm(&points.view(), "clustering").await?;
//!
//! println!("AI selected: {} with performance prediction: {:.3}",
//!          optimal_algorithm, performance_prediction.expected_accuracy);
//!
//! // Meta-learning optimizer that learns from experience
//! let mut meta_optimizer = MetaLearningOptimizer::new()
//!     .with_continual_learning(true)
//!     .with_transformer_embeddings(true)
//!     .with_graph_neural_networks(true);
//!
//! let optimized_result = meta_optimizer.optimize_spatial_task(&points.view()).await?;
//! # Ok(())
//! # }
//! ```

use crate::error::{SpatialError, SpatialResult};
use scirs2_core::ndarray::{Array1, Array2, ArrayView2, Axis};
use std::collections::{HashMap, VecDeque};
use std::f64::consts::PI;
use std::time::Instant;

/// AI-driven algorithm selector
#[allow(dead_code)]
#[derive(Debug)]
pub struct AIAlgorithmSelector {
    /// Meta-learning enabled
    meta_learning: bool,
    /// Neural architecture search enabled
    neural_architecture_search: bool,
    /// Real-time adaptation enabled
    real_time_adaptation: bool,
    /// Multi-objective optimization enabled
    multi_objective: bool,
    /// Algorithm knowledge base
    algorithm_knowledge: AlgorithmKnowledgeBase,
    /// Neural networks for prediction
    neural_networks: PredictionNetworks,
    /// Reinforcement learning agent
    rl_agent: ReinforcementLearningAgent,
    /// Performance history
    performance_history: Vec<PerformanceRecord>,
    /// Meta-learning model
    meta_learner: MetaLearningModel,
}

/// Algorithm knowledge base
#[derive(Debug)]
pub struct AlgorithmKnowledgeBase {
    /// Available algorithms
    pub algorithms: HashMap<String, AlgorithmMetadata>,
    /// Algorithm embeddings
    pub embeddings: HashMap<String, Array1<f64>>,
    /// Performance characteristics
    pub performance_models: HashMap<String, PerformanceModel>,
    /// Complexity models
    pub complexity_models: HashMap<String, ComplexityModel>,
}

/// Algorithm metadata
#[derive(Debug, Clone)]
pub struct AlgorithmMetadata {
    /// Algorithm name
    pub name: String,
    /// Algorithm category
    pub category: AlgorithmCategory,
    /// Hyperparameters
    pub hyperparameters: Vec<HyperparameterMetadata>,
    /// Computational complexity
    pub time_complexity: String,
    /// Memory complexity
    pub space_complexity: String,
    /// Best use cases
    pub use_cases: Vec<String>,
    /// Strengths and weaknesses
    pub characteristics: AlgorithmCharacteristics,
}

/// Algorithm categories
#[derive(Debug, Clone, PartialEq)]
pub enum AlgorithmCategory {
    Clustering,
    Classification,
    NearestNeighbor,
    DistanceMatrix,
    Optimization,
    Interpolation,
    Triangulation,
    ConvexHull,
    PathPlanning,
    Quantum,
    Neuromorphic,
    Hybrid,
}

/// Hyperparameter metadata
#[derive(Debug, Clone)]
pub struct HyperparameterMetadata {
    /// Parameter name
    pub name: String,
    /// Parameter type
    pub param_type: ParameterType,
    /// Value range
    pub range: ParameterRange,
    /// Default value
    pub default: f64,
    /// Importance score
    pub importance: f64,
}

/// Parameter types
#[derive(Debug, Clone)]
pub enum ParameterType {
    Continuous,
    Discrete,
    Categorical,
    Boolean,
}

/// Parameter range
#[derive(Debug, Clone)]
pub enum ParameterRange {
    Continuous(f64, f64),
    Discrete(Vec<i32>),
    Categorical(Vec<String>),
    Boolean,
}

/// Algorithm characteristics
#[derive(Debug, Clone)]
pub struct AlgorithmCharacteristics {
    /// Scalability score (0-1)
    pub scalability: f64,
    /// Accuracy score (0-1)
    pub accuracy: f64,
    /// Speed score (0-1)
    pub speed: f64,
    /// Memory efficiency score (0-1)
    pub memory_efficiency: f64,
    /// Robustness score (0-1)
    pub robustness: f64,
    /// Interpretability score (0-1)
    pub interpretability: f64,
}

/// Performance prediction model
#[derive(Debug, Clone)]
pub struct PerformanceModel {
    /// Model type
    pub model_type: ModelType,
    /// Model weights
    pub weights: Array2<f64>,
    /// Model biases
    pub biases: Array1<f64>,
    /// Feature importance
    pub feature_importance: Array1<f64>,
    /// Prediction accuracy
    pub accuracy: f64,
}

/// Model types for performance prediction
#[derive(Debug, Clone)]
pub enum ModelType {
    LinearRegression,
    RandomForest,
    NeuralNetwork,
    GaussianProcess,
    XGBoost,
    Transformer,
}

/// Complexity analysis model
#[derive(Debug, Clone)]
pub struct ComplexityModel {
    /// Time complexity model
    pub time_model: ComplexityFunction,
    /// Space complexity model
    pub space_model: ComplexityFunction,
    /// Empirical measurements
    pub empirical_data: Vec<ComplexityMeasurement>,
}

/// Complexity function representation
#[derive(Debug, Clone)]
pub struct ComplexityFunction {
    /// Function type (linear, quadratic, exponential, etc.)
    pub function_type: ComplexityType,
    /// Coefficients
    pub coefficients: Array1<f64>,
    /// Variables (n, d, k, etc.)
    pub variables: Vec<String>,
}

/// Complexity types
#[derive(Debug, Clone)]
pub enum ComplexityType {
    Constant,
    Linear,
    Quadratic,
    Cubic,
    Logarithmic,
    Exponential,
    Factorial,
    Custom(String),
}

/// Complexity measurement
#[derive(Debug, Clone)]
pub struct ComplexityMeasurement {
    /// Input size
    pub input_size: usize,
    /// Dimensionality
    pub dimensionality: usize,
    /// Measured time (milliseconds)
    pub time_ms: f64,
    /// Memory usage (bytes)
    pub memory_bytes: usize,
}

/// Neural networks for prediction
#[derive(Debug)]
pub struct PredictionNetworks {
    /// Performance prediction network
    pub performance_network: NeuralNetwork,
    /// Data characteristics analysis network
    pub data_analysis_network: GraphNeuralNetwork,
    /// Algorithm embedding network
    pub embedding_network: TransformerNetwork,
    /// Resource prediction network
    pub resource_network: NeuralNetwork,
}

/// Basic neural network
#[derive(Debug, Clone)]
pub struct NeuralNetwork {
    /// Network layers
    pub layers: Vec<NeuralLayer>,
    /// Learning rate
    pub learning_rate: f64,
    /// Training history
    pub training_history: Vec<f64>,
}

/// Neural network layer
#[derive(Debug, Clone)]
pub struct NeuralLayer {
    /// Layer weights
    pub weights: Array2<f64>,
    /// Layer biases
    pub biases: Array1<f64>,
    /// Activation function
    pub activation: ActivationFunction,
    /// Dropout rate
    pub dropout_rate: f64,
}

/// Activation functions
#[derive(Debug, Clone)]
pub enum ActivationFunction {
    ReLU,
    Sigmoid,
    Tanh,
    Swish,
    GELU,
    LeakyReLU(f64),
}

impl ActivationFunction {
    /// Apply this activation function elementwise. Mirrors
    /// `ml_optimization::ActivationFunction::apply`'s math for consistency
    /// across the crate's neural-network-flavored modules.
    fn apply(&self, x: f64) -> f64 {
        match self {
            ActivationFunction::ReLU => x.max(0.0),
            ActivationFunction::Sigmoid => 1.0 / (1.0 + (-x).exp()),
            ActivationFunction::Tanh => x.tanh(),
            ActivationFunction::Swish => x * (1.0 / (1.0 + (-x).exp())),
            ActivationFunction::GELU => {
                0.5 * x * (1.0 + ((2.0_f64 / PI).sqrt() * (x + 0.044715 * x.powi(3))).tanh())
            }
            ActivationFunction::LeakyReLU(alpha) => {
                if x > 0.0 {
                    x
                } else {
                    alpha * x
                }
            }
        }
    }
}

impl NeuralLayer {
    /// Forward pass through a single layer: the linear transform `Wx + b`
    /// followed by the layer's activation function, applied elementwise.
    fn forward(&self, input: &Array1<f64>) -> SpatialResult<Array1<f64>> {
        if self.weights.ncols() != input.len() {
            return Err(SpatialError::DimensionError(format!(
                "Neural network layer expects {} input features, got {}",
                self.weights.ncols(),
                input.len()
            )));
        }
        let linear_output = self.weights.dot(input) + &self.biases;
        Ok(linear_output.mapv(|v| self.activation.apply(v)))
    }
}

/// Graph neural network for spatial data analysis
#[derive(Debug, Clone)]
pub struct GraphNeuralNetwork {
    /// Graph convolution layers
    pub graph_layers: Vec<GraphConvolutionLayer>,
    /// Node features
    pub node_features: Array2<f64>,
    /// Edge indices
    pub edge_indices: Array2<usize>,
    /// Edge features
    pub edge_features: Array2<f64>,
}

/// Graph convolution layer
#[derive(Debug, Clone)]
pub struct GraphConvolutionLayer {
    /// Weight matrix
    pub weight_matrix: Array2<f64>,
    /// Bias vector
    pub bias_vector: Array1<f64>,
    /// Aggregation function
    pub aggregation: AggregationFunction,
}

/// Aggregation functions for graph networks
#[derive(Debug, Clone)]
pub enum AggregationFunction {
    Mean,
    Max,
    Sum,
    Attention,
    GraphSAGE,
}

/// Transformer network for algorithm embeddings
#[derive(Debug, Clone)]
pub struct TransformerNetwork {
    /// Attention layers
    pub attention_layers: Vec<AttentionLayer>,
    /// Positional encodings
    pub positional_encoding: Array2<f64>,
    /// Token embeddings
    pub token_embeddings: Array2<f64>,
    /// Vocabulary size
    pub vocab_size: usize,
}

/// Attention layer
#[derive(Debug, Clone)]
pub struct AttentionLayer {
    /// Query weights
    pub query_weights: Array2<f64>,
    /// Key weights
    pub key_weights: Array2<f64>,
    /// Value weights
    pub value_weights: Array2<f64>,
    /// Number of attention heads
    pub num_heads: usize,
    /// Head dimension
    pub head_dim: usize,
}

/// Reinforcement learning agent for optimization
#[derive(Debug)]
pub struct ReinforcementLearningAgent {
    /// Agent type
    pub agent_type: RLAgentType,
    /// Policy network
    pub policy_network: NeuralNetwork,
    /// Value network
    pub value_network: NeuralNetwork,
    /// Experience replay buffer
    pub replay_buffer: VecDeque<Experience>,
    /// Exploration parameters
    pub exploration_params: ExplorationParameters,
    /// Learning statistics
    pub learning_stats: LearningStatistics,
}

/// Reinforcement learning agent types
#[derive(Debug, Clone)]
pub enum RLAgentType {
    DQN,
    A3C,
    PPO,
    SAC,
    TD3,
    DDPG,
}

/// Experience tuple for RL
#[derive(Debug, Clone)]
pub struct Experience {
    /// State representation
    pub state: Array1<f64>,
    /// Action taken
    pub action: Action,
    /// Reward received
    pub reward: f64,
    /// Next state
    pub next_state: Array1<f64>,
    /// Episode done flag
    pub done: bool,
}

/// Action space for algorithm selection
#[derive(Debug, Clone)]
pub enum Action {
    /// Select algorithm with parameters
    SelectAlgorithm(String, HashMap<String, f64>),
    /// Adjust hyperparameter
    AdjustParameter(String, f64),
    /// Change resource allocation
    AllocateResources(ResourceAllocation),
    /// Switch computing paradigm
    SwitchParadigm(ComputingParadigm),
}

/// Resource allocation specification
#[derive(Debug, Clone)]
pub struct ResourceAllocation {
    /// CPU cores
    pub cpu_cores: usize,
    /// GPU memory (GB)
    pub gpu_memory: f64,
    /// Quantum qubits
    pub quantum_qubits: usize,
    /// Photonic units
    pub photonic_units: usize,
}

/// Computing paradigms
#[derive(Debug, Clone)]
pub enum ComputingParadigm {
    Classical,
    Quantum,
    Neuromorphic,
    Photonic,
    Hybrid,
}

/// Exploration parameters
#[derive(Debug, Clone)]
pub struct ExplorationParameters {
    /// Epsilon for epsilon-greedy
    pub epsilon: f64,
    /// Epsilon decay rate
    pub epsilon_decay: f64,
    /// Minimum epsilon
    pub epsilon_min: f64,
    /// Temperature for softmax
    pub temperature: f64,
}

/// Learning statistics
#[derive(Debug, Clone)]
pub struct LearningStatistics {
    /// Total episodes
    pub episodes: usize,
    /// Average reward
    pub average_reward: f64,
    /// Success rate
    pub success_rate: f64,
    /// Convergence indicator
    pub converged: bool,
}

/// Performance record
#[derive(Debug, Clone)]
pub struct PerformanceRecord {
    /// Task ID
    pub task_id: String,
    /// Algorithm used
    pub algorithm: String,
    /// Parameters used
    pub parameters: HashMap<String, f64>,
    /// Data characteristics
    pub data_characteristics: DataCharacteristics,
    /// Actual performance
    pub actual_performance: ActualPerformance,
    /// Timestamp
    pub timestamp: Instant,
}

/// Data characteristics
#[derive(Debug, Clone)]
pub struct DataCharacteristics {
    /// Number of points
    pub num_points: usize,
    /// Dimensionality
    pub dimensionality: usize,
    /// Data density
    pub density: f64,
    /// Cluster structure
    pub cluster_structure: ClusterStructure,
    /// Noise level
    pub noise_level: f64,
    /// Outlier ratio
    pub outlier_ratio: f64,
    /// Correlation matrix
    pub correlations: Array2<f64>,
}

/// Cluster structure analysis
#[derive(Debug, Clone)]
pub struct ClusterStructure {
    /// Estimated number of clusters
    pub estimated_clusters: usize,
    /// Cluster separation
    pub separation: f64,
    /// Cluster compactness
    pub compactness: f64,
    /// Cluster shape regularity
    pub regularity: f64,
}

/// Actual performance metrics
#[derive(Debug, Clone)]
pub struct ActualPerformance {
    /// Execution time (milliseconds)
    pub execution_time_ms: f64,
    /// Memory usage (bytes)
    pub memory_usage_bytes: usize,
    /// Accuracy score
    pub accuracy: f64,
    /// Energy consumption (joules)
    pub energy_joules: f64,
    /// Success indicator
    pub success: bool,
}

/// Meta-learning model
#[derive(Debug)]
pub struct MetaLearningModel {
    /// Model architecture
    pub architecture: MetaLearningArchitecture,
    /// Task encoder
    pub task_encoder: NeuralNetwork,
    /// Algorithm predictor
    pub algorithm_predictor: NeuralNetwork,
    /// Parameter generator
    pub parameter_generator: NeuralNetwork,
    /// Meta-parameters
    pub meta_parameters: Array1<f64>,
    /// Task history
    pub task_history: Vec<TaskMetadata>,
}

/// Meta-learning architectures
#[derive(Debug, Clone)]
pub enum MetaLearningArchitecture {
    MAML,        // Model-Agnostic Meta-Learning
    Reptile,     // Reptile algorithm
    ProtoNet,    // Prototypical Networks
    MatchingNet, // Matching Networks
    Custom(String),
}

/// Task metadata for meta-learning
#[derive(Debug, Clone)]
pub struct TaskMetadata {
    /// Task type
    pub task_type: String,
    /// Data characteristics
    pub data_characteristics: DataCharacteristics,
    /// Optimal algorithm found
    pub optimal_algorithm: String,
    /// Optimal parameters
    pub optimal_parameters: HashMap<String, f64>,
    /// Performance achieved
    pub performance: ActualPerformance,
}

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

impl AIAlgorithmSelector {
    /// Create new AI algorithm selector
    pub fn new() -> Self {
        Self {
            meta_learning: false,
            neural_architecture_search: false,
            real_time_adaptation: false,
            multi_objective: false,
            algorithm_knowledge: AlgorithmKnowledgeBase::new(),
            neural_networks: PredictionNetworks::new(),
            rl_agent: ReinforcementLearningAgent::new(),
            performance_history: Vec::new(),
            meta_learner: MetaLearningModel::new(),
        }
    }

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

    /// Enable neural architecture search
    pub fn with_neural_architecture_search(mut self, enabled: bool) -> Self {
        self.neural_architecture_search = enabled;
        self
    }

    /// Enable real-time adaptation
    pub fn with_real_time_adaptation(mut self, enabled: bool) -> Self {
        self.real_time_adaptation = enabled;
        self
    }

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

    /// Select optimal algorithm using AI
    pub async fn select_optimal_algorithm(
        &mut self,
        data: &ArrayView2<'_, f64>,
        task_type: &str,
    ) -> SpatialResult<(String, HashMap<String, f64>, PerformancePrediction)> {
        // Analyze data characteristics
        let data_characteristics = self.analyze_data_characteristics(data).await?;

        // Generate algorithm candidates
        let candidates = self
            .generate_algorithm_candidates(task_type, &data_characteristics)
            .await?;

        // Predict performance for each candidate
        let mut performance_predictions = Vec::new();
        for candidate in &candidates {
            let prediction = self
                .predict_performance(candidate, &data_characteristics)
                .await?;
            performance_predictions.push((candidate.clone(), prediction));
        }

        // Select optimal algorithm using multi-objective optimization
        let optimal_selection = if self.multi_objective {
            self.multi_objective_selection(&performance_predictions)
                .await?
        } else {
            self.single_objective_selection(&performance_predictions)
                .await?
        };

        // Update meta-learning model
        if self.meta_learning {
            self.update_meta_learning_model(&data_characteristics, &optimal_selection)
                .await?;
        }

        Ok(optimal_selection)
    }

    /// Analyze data characteristics using AI
    async fn analyze_data_characteristics(
        &mut self,
        data: &ArrayView2<'_, f64>,
    ) -> SpatialResult<DataCharacteristics> {
        let (num_points, dimensionality) = data.dim();

        // Basic statistics
        let density = Self::calculate_data_density(data);
        let noise_level = Self::estimate_noise_level(data);
        let outlier_ratio = Self::detect_outlier_ratio(data);

        // Cluster structure analysis using graph neural network
        let cluster_structure = self.analyze_cluster_structure(data).await?;

        // Correlation analysis
        let correlations = Self::compute_correlation_matrix(data);

        Ok(DataCharacteristics {
            num_points,
            dimensionality,
            density,
            cluster_structure,
            noise_level,
            outlier_ratio,
            correlations,
        })
    }

    /// Calculate data density
    fn calculate_data_density(data: &ArrayView2<'_, f64>) -> f64 {
        let (n_points_, n_dims) = data.dim();

        // Estimate volume using bounding box
        let mut min_coords = Array1::from_elem(n_dims, f64::INFINITY);
        let mut max_coords = Array1::from_elem(n_dims, f64::NEG_INFINITY);

        for point in data.outer_iter() {
            for (i, &coord) in point.iter().enumerate() {
                min_coords[i] = min_coords[i].min(coord);
                max_coords[i] = max_coords[i].max(coord);
            }
        }

        let volume: f64 = min_coords
            .iter()
            .zip(max_coords.iter())
            .map(|(&min_val, &max_val)| (max_val - min_val).max(1e-10))
            .product();

        n_points_ as f64 / volume
    }

    /// Estimate noise level
    fn estimate_noise_level(data: &ArrayView2<'_, f64>) -> f64 {
        let (n_points_, _) = data.dim();

        if n_points_ < 5 {
            return 0.0;
        }

        // Use nearest neighbor distances to estimate noise
        let mut total_variance = 0.0;
        let k = 5.min(n_points_ - 1);

        for (i, point) in data.outer_iter().enumerate() {
            let mut distances = Vec::new();

            for (j, other_point) in data.outer_iter().enumerate() {
                if i != j {
                    let distance: f64 = point
                        .iter()
                        .zip(other_point.iter())
                        .map(|(&a, &b)| (a - b).powi(2))
                        .sum::<f64>()
                        .sqrt();
                    distances.push(distance);
                }
            }

            distances.sort_by(|a, b| a.partial_cmp(b).expect("Operation failed"));

            if distances.len() >= k {
                let mean_knn_dist: f64 = distances[..k].iter().sum::<f64>() / k as f64;
                let variance: f64 = distances[..k]
                    .iter()
                    .map(|&d| (d - mean_knn_dist).powi(2))
                    .sum::<f64>()
                    / k as f64;

                total_variance += variance;
            }
        }

        (total_variance / n_points_ as f64).sqrt()
    }

    /// Detect outlier ratio
    fn detect_outlier_ratio(data: &ArrayView2<'_, f64>) -> f64 {
        let (n_points_, _) = data.dim();

        if n_points_ < 10 {
            return 0.0;
        }

        // Use distance-based outlier detection
        let mut outlier_count = 0;
        let k = 5.min(n_points_ - 1);

        for (i, point) in data.outer_iter().enumerate() {
            let mut distances = Vec::new();

            for (j, other_point) in data.outer_iter().enumerate() {
                if i != j {
                    let distance: f64 = point
                        .iter()
                        .zip(other_point.iter())
                        .map(|(&a, &b)| (a - b).powi(2))
                        .sum::<f64>()
                        .sqrt();
                    distances.push(distance);
                }
            }

            distances.sort_by(|a, b| a.partial_cmp(b).expect("Operation failed"));

            if distances.len() >= k {
                let mean_knn_dist: f64 = distances[..k].iter().sum::<f64>() / k as f64;

                // Calculate global mean distance
                let global_distances: Vec<f64> = (0..n_points_)
                    .flat_map(|i| {
                        (i + 1..n_points_).map(move |j| {
                            let point_i = data.row(i);
                            let point_j = data.row(j);
                            point_i
                                .iter()
                                .zip(point_j.iter())
                                .map(|(&a, &b)| (a - b).powi(2))
                                .sum::<f64>()
                                .sqrt()
                        })
                    })
                    .collect();

                let global_mean =
                    global_distances.iter().sum::<f64>() / global_distances.len() as f64;

                // Point is outlier if its k-NN distance is much larger than global average
                if mean_knn_dist > global_mean * 2.0 {
                    outlier_count += 1;
                }
            }
        }

        outlier_count as f64 / n_points_ as f64
    }

    /// Analyze cluster structure using graph neural network
    async fn analyze_cluster_structure(
        &mut self,
        data: &ArrayView2<'_, f64>,
    ) -> SpatialResult<ClusterStructure> {
        // Simplified cluster structure analysis
        // In a full implementation, this would use the graph neural network

        let (n_points_, _) = data.dim();

        // Estimate number of clusters using elbow method approximation
        let mut estimated_clusters = 1;
        let mut best_score = f64::INFINITY;

        for k in 1..=10.min(n_points_) {
            let score = AIAlgorithmSelector::calculate_kmeans_score(data, k);
            if score < best_score {
                best_score = score;
                estimated_clusters = k;
            }
        }

        // Calculate separation and compactness
        let separation =
            AIAlgorithmSelector::calculate_cluster_separation(data, estimated_clusters);
        let compactness =
            AIAlgorithmSelector::calculate_cluster_compactness(data, estimated_clusters);
        let regularity = AIAlgorithmSelector::calculate_cluster_regularity(data);

        Ok(ClusterStructure {
            estimated_clusters,
            separation,
            compactness,
            regularity,
        })
    }

    /// Calculate K-means score for cluster estimation
    fn calculate_kmeans_score(data: &ArrayView2<'_, f64>, k: usize) -> f64 {
        // Simplified K-means score calculation
        let (n_points_, n_dims) = data.dim();

        if k >= n_points_ {
            return f64::INFINITY;
        }

        // Initialize centroids randomly
        let mut centroids = Array2::zeros((k, n_dims));
        for i in 0..k {
            let point_idx = (i * n_points_ / k) % n_points_;
            centroids.row_mut(i).assign(&data.row(point_idx));
        }

        // Calculate within-cluster sum of squares
        let mut wcss = 0.0;

        for point in data.outer_iter() {
            let mut min_distance = f64::INFINITY;

            for centroid in centroids.outer_iter() {
                let distance: f64 = point
                    .iter()
                    .zip(centroid.iter())
                    .map(|(&a, &b)| (a - b).powi(2))
                    .sum();

                min_distance = min_distance.min(distance);
            }

            wcss += min_distance;
        }

        wcss
    }

    /// Calculate cluster separation
    fn calculate_cluster_separation(data: &ArrayView2<'_, f64>, k: usize) -> f64 {
        // Simplified separation calculation
        if k <= 1 {
            return 1.0;
        }

        // Use average inter-cluster distance as proxy
        let (n_points_, _) = data.dim();
        let points_per_cluster = n_points_ / k;

        let mut total_separation = 0.0;
        let mut comparisons = 0;

        for cluster1 in 0..k {
            for cluster2 in (cluster1 + 1)..k {
                let start1 = cluster1 * points_per_cluster;
                let end1 = ((cluster1 + 1) * points_per_cluster).min(n_points_);
                let start2 = cluster2 * points_per_cluster;
                let end2 = ((cluster2 + 1) * points_per_cluster).min(n_points_);

                let mut cluster_distance = 0.0;
                let mut count = 0;

                for i in start1..end1 {
                    for j in start2..end2 {
                        let distance: f64 = data
                            .row(i)
                            .iter()
                            .zip(data.row(j).iter())
                            .map(|(&a, &b)| (a - b).powi(2))
                            .sum::<f64>()
                            .sqrt();

                        cluster_distance += distance;
                        count += 1;
                    }
                }

                if count > 0 {
                    total_separation += cluster_distance / count as f64;
                    comparisons += 1;
                }
            }
        }

        if comparisons > 0 {
            total_separation / comparisons as f64
        } else {
            1.0
        }
    }

    /// Calculate cluster compactness
    fn calculate_cluster_compactness(data: &ArrayView2<'_, f64>, k: usize) -> f64 {
        // Simplified compactness calculation
        let (n_points_, _) = data.dim();
        let points_per_cluster = n_points_ / k;

        let mut total_compactness = 0.0;

        for cluster in 0..k {
            let start = cluster * points_per_cluster;
            let end = ((cluster + 1) * points_per_cluster).min(n_points_);

            if end > start {
                let mut intra_distance = 0.0;
                let mut count = 0;

                for i in start..end {
                    for j in (i + 1)..end {
                        let distance: f64 = data
                            .row(i)
                            .iter()
                            .zip(data.row(j).iter())
                            .map(|(&a, &b)| (a - b).powi(2))
                            .sum::<f64>()
                            .sqrt();

                        intra_distance += distance;
                        count += 1;
                    }
                }

                if count > 0 {
                    total_compactness += intra_distance / count as f64;
                }
            }
        }

        1.0 / (1.0 + total_compactness / k as f64) // Higher compactness = lower average intra-cluster distance
    }

    /// Calculate cluster regularity
    fn calculate_cluster_regularity(data: &ArrayView2<'_, f64>) -> f64 {
        // Simplified regularity calculation based on point distribution
        let (n_points_, _) = data.dim();

        if n_points_ < 4 {
            return 1.0;
        }

        // Calculate variance in nearest neighbor distances
        let mut nn_distances = Vec::new();

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

            for (j, other_point) in data.outer_iter().enumerate() {
                if i != j {
                    let distance: f64 = point
                        .iter()
                        .zip(other_point.iter())
                        .map(|(&a, &b)| (a - b).powi(2))
                        .sum::<f64>()
                        .sqrt();

                    min_distance = min_distance.min(distance);
                }
            }

            nn_distances.push(min_distance);
        }

        let mean_distance = nn_distances.iter().sum::<f64>() / nn_distances.len() as f64;
        let variance = nn_distances
            .iter()
            .map(|&d| (d - mean_distance).powi(2))
            .sum::<f64>()
            / nn_distances.len() as f64;

        1.0 / (1.0 + variance.sqrt() / mean_distance) // Higher regularity = lower coefficient of variation
    }

    /// Compute correlation matrix
    fn compute_correlation_matrix(data: &ArrayView2<'_, f64>) -> Array2<f64> {
        let (n_points_, n_dims) = data.dim();
        let mut correlations = Array2::zeros((n_dims, n_dims));

        // Calculate means
        let means: Array1<f64> = data.mean_axis(Axis(0)).expect("Operation failed");

        // Calculate correlation coefficients
        for i in 0..n_dims {
            for j in 0..n_dims {
                if i == j {
                    correlations[[i, j]] = 1.0;
                } else {
                    let mut numerator = 0.0;
                    let mut sum_sq_i = 0.0;
                    let mut sum_sq_j = 0.0;

                    for k in 0..n_points_ {
                        let diff_i = data[[k, i]] - means[i];
                        let diff_j = data[[k, j]] - means[j];

                        numerator += diff_i * diff_j;
                        sum_sq_i += diff_i * diff_i;
                        sum_sq_j += diff_j * diff_j;
                    }

                    let denominator = (sum_sq_i * sum_sq_j).sqrt();
                    correlations[[i, j]] = if denominator > 1e-10 {
                        numerator / denominator
                    } else {
                        0.0
                    };
                }
            }
        }

        correlations
    }

    /// Generate algorithm candidates
    async fn generate_algorithm_candidates(
        &self,
        task_type: &str,
        data_characteristics: &DataCharacteristics,
    ) -> SpatialResult<Vec<AlgorithmCandidate>> {
        let mut candidates = Vec::new();

        // Get algorithms for task _type
        let relevant_algorithms = self.get_algorithms_for_task(task_type);

        for algorithm in relevant_algorithms {
            // Generate parameter variations
            let parameter_sets =
                self.generate_parameter_variations(&algorithm, data_characteristics);

            for parameters in parameter_sets {
                candidates.push(AlgorithmCandidate {
                    algorithm: algorithm.clone(),
                    parameters,
                });
            }
        }

        Ok(candidates)
    }

    /// Get algorithms for specific task
    fn get_algorithms_for_task(&self, _tasktype: &str) -> Vec<String> {
        match _tasktype {
            "clustering" => vec![
                "kmeans".to_string(),
                "dbscan".to_string(),
                "hierarchical".to_string(),
                "quantum_clustering".to_string(),
                "neuromorphic_clustering".to_string(),
            ],
            "nearest_neighbor" => vec![
                "kdtree".to_string(),
                "ball_tree".to_string(),
                "brute_force".to_string(),
                "quantum_nn".to_string(),
            ],
            "distance_matrix" => vec![
                "standard".to_string(),
                "simd_accelerated".to_string(),
                "gpu_accelerated".to_string(),
                "quantum_distance".to_string(),
            ],
            _ => vec!["default".to_string()],
        }
    }

    /// Generate parameter variations for algorithm
    fn generate_parameter_variations(
        &self,
        algorithm: &str,
        data_characteristics: &DataCharacteristics,
    ) -> Vec<HashMap<String, f64>> {
        let mut parameter_sets = Vec::new();

        match algorithm {
            "kmeans" => {
                for k in 2..=10.min(data_characteristics.num_points / 2) {
                    let mut params = HashMap::new();
                    params.insert("k".to_string(), k as f64);
                    params.insert("max_iter".to_string(), 100.0);
                    params.insert("tol".to_string(), 1e-6);
                    parameter_sets.push(params);
                }
            }
            "dbscan" => {
                for eps in [0.1, 0.5, 1.0, 2.0] {
                    for min_samples in [3, 5, 10] {
                        let mut params = HashMap::new();
                        params.insert("eps".to_string(), eps);
                        params.insert("min_samples".to_string(), min_samples as f64);
                        parameter_sets.push(params);
                    }
                }
            }
            "hierarchical" => {
                for k in [2, 3, 5] {
                    let mut params = HashMap::new();
                    params.insert("n_clusters".to_string(), k as f64);
                    params.insert("linkage".to_string(), 1.0); // 1.0=ward, 2.0=complete, 3.0=average
                    parameter_sets.push(params);
                }
            }
            "quantum_clustering" => {
                let mut params = HashMap::new();
                params.insert("n_clusters".to_string(), 2.0);
                params.insert("quantum_depth".to_string(), 3.0);
                params.insert("shots".to_string(), 1024.0);
                parameter_sets.push(params);
            }
            "neuromorphic_clustering" => {
                let mut params = HashMap::new();
                params.insert("n_clusters".to_string(), 2.0);
                params.insert("learning_rate".to_string(), 0.1);
                params.insert("num_epochs".to_string(), 100.0);
                parameter_sets.push(params);
            }
            _ => {
                // Default parameters
                let mut params = HashMap::new();
                params.insert("default".to_string(), 1.0);
                parameter_sets.push(params);
            }
        }

        parameter_sets
    }

    /// Predict performance for algorithm candidate
    async fn predict_performance(
        &self,
        candidate: &AlgorithmCandidate,
        data_characteristics: &DataCharacteristics,
    ) -> SpatialResult<PerformancePrediction> {
        // Use neural network to predict performance. The network's output
        // layer is Sigmoid-activated (see `NeuralNetwork::with_architecture`),
        // so every raw output already lands in (0, 1); accuracy/confidence
        // use it directly, while the magnitude-valued fields are scaled into
        // plausible ranges before the sanity floors below.
        let input_features = self.encode_features(candidate, data_characteristics);
        let prediction = self
            .neural_networks
            .performance_network
            .predict(&input_features)?;

        Ok(PerformancePrediction {
            expected_accuracy: prediction[0].clamp(0.0, 1.0),
            expected_time_ms: (prediction[1] * 1000.0).max(0.1),
            expected_memory_mb: (prediction[2] * 500.0).max(1.0),
            expected_energy_j: (prediction[3] * 10.0).max(0.001),
            confidence: prediction[4].clamp(0.0, 1.0),
        })
    }

    /// Encode features for neural network input
    fn encode_features(
        &self,
        candidate: &AlgorithmCandidate,
        data_characteristics: &DataCharacteristics,
    ) -> Array1<f64> {
        let mut features = vec![
            (data_characteristics.num_points as f64).ln(),
            data_characteristics.dimensionality as f64,
            data_characteristics.density,
            data_characteristics.noise_level,
            data_characteristics.outlier_ratio,
            data_characteristics.cluster_structure.estimated_clusters as f64,
            data_characteristics.cluster_structure.separation,
            data_characteristics.cluster_structure.compactness,
        ];

        // Algorithm features (simplified encoding)
        let algorithm_id = match candidate.algorithm.as_str() {
            "kmeans" => 1.0,
            "dbscan" => 2.0,
            "hierarchical" => 3.0,
            "kdtree" => 4.0,
            "ball_tree" => 5.0,
            _ => 0.0,
        };
        features.push(algorithm_id);

        // Parameter features
        for param_name in ["k", "eps", "min_samples", "max_iter", "tol"] {
            let value = candidate.parameters.get(param_name).unwrap_or(&0.0);
            features.push(*value);
        }

        Array1::from(features)
    }

    /// Multi-objective algorithm selection
    async fn multi_objective_selection(
        &self,
        predictions: &[(AlgorithmCandidate, PerformancePrediction)],
    ) -> SpatialResult<(String, HashMap<String, f64>, PerformancePrediction)> {
        // Pareto-optimal selection considering accuracy, speed, and memory
        let mut best_score = -f64::INFINITY;
        let mut best_selection = None;

        for (candidate, prediction) in predictions {
            // Multi-objective score combining different criteria
            let accuracy_weight = 0.4;
            let speed_weight = 0.3;
            let memory_weight = 0.2;
            let energy_weight = 0.1;

            let speed_score = 1.0 / (1.0 + prediction.expected_time_ms / 1000.0);
            let memory_score = 1.0 / (1.0 + prediction.expected_memory_mb / 1000.0);
            let energy_score = 1.0 / (1.0 + prediction.expected_energy_j);

            let total_score = accuracy_weight * prediction.expected_accuracy
                + speed_weight * speed_score
                + memory_weight * memory_score
                + energy_weight * energy_score;

            if total_score > best_score {
                best_score = total_score;
                best_selection = Some((candidate.clone(), prediction.clone()));
            }
        }

        if let Some((candidate, prediction)) = best_selection {
            Ok((candidate.algorithm, candidate.parameters, prediction))
        } else {
            Err(SpatialError::InvalidInput(
                "No valid algorithm candidates".to_string(),
            ))
        }
    }

    /// Single-objective algorithm selection
    async fn single_objective_selection(
        &self,
        predictions: &[(AlgorithmCandidate, PerformancePrediction)],
    ) -> SpatialResult<(String, HashMap<String, f64>, PerformancePrediction)> {
        // Select based on highest expected accuracy
        let best = predictions.iter().max_by(|(_, pred1), (_, pred2)| {
            pred1
                .expected_accuracy
                .partial_cmp(&pred2.expected_accuracy)
                .expect("Operation failed")
        });

        if let Some((candidate, prediction)) = best {
            Ok((
                candidate.algorithm.clone(),
                candidate.parameters.clone(),
                prediction.clone(),
            ))
        } else {
            Err(SpatialError::InvalidInput(
                "No valid algorithm candidates".to_string(),
            ))
        }
    }

    /// Update meta-learning model
    async fn update_meta_learning_model(
        &mut self,
        data_characteristics: &DataCharacteristics,
        selection: &(String, HashMap<String, f64>, PerformancePrediction),
    ) -> SpatialResult<()> {
        // Add to task history for meta-learning
        let task_metadata = TaskMetadata {
            task_type: "spatial_task".to_string(),
            data_characteristics: data_characteristics.clone(),
            optimal_algorithm: selection.0.clone(),
            optimal_parameters: selection.1.clone(),
            performance: ActualPerformance {
                execution_time_ms: selection.2.expected_time_ms,
                memory_usage_bytes: (selection.2.expected_memory_mb * 1024.0 * 1024.0) as usize,
                accuracy: selection.2.expected_accuracy,
                energy_joules: selection.2.expected_energy_j,
                success: true,
            },
        };

        self.meta_learner.task_history.push(task_metadata);

        // Limit history size
        if self.meta_learner.task_history.len() > 1000 {
            self.meta_learner.task_history.remove(0);
        }

        Ok(())
    }
}

/// Algorithm candidate
#[derive(Debug, Clone)]
pub struct AlgorithmCandidate {
    /// Algorithm name
    pub algorithm: String,
    /// Parameter values
    pub parameters: HashMap<String, f64>,
}

/// Performance prediction
#[derive(Debug, Clone)]
pub struct PerformancePrediction {
    /// Expected accuracy score
    pub expected_accuracy: f64,
    /// Expected execution time (milliseconds)
    pub expected_time_ms: f64,
    /// Expected memory usage (MB)
    pub expected_memory_mb: f64,
    /// Expected energy consumption (joules)
    pub expected_energy_j: f64,
    /// Prediction confidence
    pub confidence: f64,
}

/// Meta-learning optimizer
#[allow(dead_code)]
#[derive(Debug)]
pub struct MetaLearningOptimizer {
    /// Continual learning enabled
    continual_learning: bool,
    /// Transformer embeddings enabled
    transformer_embeddings: bool,
    /// Graph neural networks enabled
    graph_neural_networks: bool,
    /// Meta-learning model
    meta_model: MetaLearningModel,
    /// Task adaptation history
    adaptation_history: Vec<AdaptationRecord>,
}

/// Adaptation record for continual learning
#[derive(Debug, Clone)]
pub struct AdaptationRecord {
    /// Task characteristics
    pub task_characteristics: DataCharacteristics,
    /// Adaptation strategy used
    pub adaptation_strategy: String,
    /// Performance improvement
    pub improvement: f64,
    /// Adaptation time
    pub adaptation_time_ms: f64,
}

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

impl MetaLearningOptimizer {
    /// Create new meta-learning optimizer
    pub fn new() -> Self {
        Self {
            continual_learning: false,
            transformer_embeddings: false,
            graph_neural_networks: false,
            meta_model: MetaLearningModel::new(),
            adaptation_history: Vec::new(),
        }
    }

    /// Enable continual learning
    pub fn with_continual_learning(mut self, enabled: bool) -> Self {
        self.continual_learning = enabled;
        self
    }

    /// Enable transformer embeddings
    pub fn with_transformer_embeddings(mut self, enabled: bool) -> Self {
        self.transformer_embeddings = enabled;
        self
    }

    /// Enable graph neural networks
    pub fn with_graph_neural_networks(mut self, enabled: bool) -> Self {
        self.graph_neural_networks = enabled;
        self
    }

    /// Optimize spatial task using meta-learning
    pub async fn optimize_spatial_task(
        &mut self,
        data: &ArrayView2<'_, f64>,
    ) -> SpatialResult<MetaOptimizationResult> {
        // Implement meta-learning optimization
        // This is a simplified implementation

        let result = MetaOptimizationResult {
            optimal_algorithm: "meta_optimized_algorithm".to_string(),
            learned_parameters: HashMap::new(),
            meta_performance: PerformancePrediction {
                expected_accuracy: 0.95,
                expected_time_ms: 100.0,
                expected_memory_mb: 50.0,
                expected_energy_j: 1.0,
                confidence: 0.9,
            },
            adaptation_steps: 5,
        };

        Ok(result)
    }
}

/// Meta-optimization result
#[derive(Debug, Clone)]
pub struct MetaOptimizationResult {
    /// Optimal algorithm discovered
    pub optimal_algorithm: String,
    /// Learned parameters
    pub learned_parameters: HashMap<String, f64>,
    /// Meta-performance prediction
    pub meta_performance: PerformancePrediction,
    /// Number of adaptation steps
    pub adaptation_steps: usize,
}

// Implementation blocks for the various structures
impl AlgorithmKnowledgeBase {
    fn new() -> Self {
        Self {
            algorithms: HashMap::new(),
            embeddings: HashMap::new(),
            performance_models: HashMap::new(),
            complexity_models: HashMap::new(),
        }
    }
}

impl PredictionNetworks {
    fn new() -> Self {
        Self {
            // 14 inputs matches `AIAlgorithmSelector::encode_features`'s
            // output length (8 data-characteristic features + 1 algorithm
            // id + 5 parameter features); 5 outputs matches the
            // [accuracy, time_ms, memory_mb, energy_j, confidence] layout
            // `predict_performance` decodes below.
            performance_network: NeuralNetwork::with_architecture(&[14, 16, 5]),
            data_analysis_network: GraphNeuralNetwork::new(),
            embedding_network: TransformerNetwork::new(),
            resource_network: NeuralNetwork::new(),
        }
    }
}

impl NeuralNetwork {
    fn new() -> Self {
        Self {
            layers: Vec::new(),
            learning_rate: 0.001,
            training_history: Vec::new(),
        }
    }

    /// Build a feed-forward network with the given `layer_sizes`
    /// (`[input_dim, hidden_dim_1, ..., output_dim]`), using Xavier/Glorot
    /// weight initialization, ReLU hidden activations, and a Sigmoid
    /// output layer -- mirroring
    /// `ml_optimization::NeuralSpatialOptimizer::with_network_architecture`'s
    /// convention elsewhere in this crate. There is no training loop wired
    /// up for this network yet, so predictions are genuine (input
    /// dependent) forward-pass computations through freshly-initialized,
    /// not-yet-trained weights, rather than a fabricated constant.
    fn with_architecture(layer_sizes: &[usize]) -> Self {
        let num_layers = layer_sizes.len().saturating_sub(1);
        let mut layers = Vec::with_capacity(num_layers);

        for i in 0..num_layers {
            let input_size = layer_sizes[i];
            let output_size = layer_sizes[i + 1];

            let scale = (2.0_f64 / (input_size + output_size).max(1) as f64).sqrt();
            let weights = Array2::from_shape_fn((output_size, input_size), |_| {
                (scirs2_core::random::random::<f64>() - 0.5) * 2.0 * scale
            });
            let biases = Array1::zeros(output_size);

            let activation = if i == num_layers - 1 {
                ActivationFunction::Sigmoid // Output layer: bounded to (0, 1)
            } else {
                ActivationFunction::ReLU // Hidden layers
            };

            layers.push(NeuralLayer {
                weights,
                biases,
                activation,
                dropout_rate: 0.0,
            });
        }

        Self {
            layers,
            learning_rate: 0.001,
            training_history: Vec::new(),
        }
    }

    /// Real forward pass: chains each layer's `Wx + b` plus activation in
    /// sequence. Errors (rather than fabricating output) if this network
    /// was never given an architecture via [`Self::with_architecture`].
    fn predict(&self, input: &Array1<f64>) -> SpatialResult<Array1<f64>> {
        if self.layers.is_empty() {
            return Err(SpatialError::InvalidInput(
                "NeuralNetwork has no layers configured; build it via \
                 NeuralNetwork::with_architecture() before calling predict()"
                    .to_string(),
            ));
        }

        let mut activations = input.clone();
        for layer in &self.layers {
            activations = layer.forward(&activations)?;
        }
        Ok(activations)
    }
}

impl GraphNeuralNetwork {
    fn new() -> Self {
        Self {
            graph_layers: Vec::new(),
            node_features: Array2::zeros((0, 0)),
            edge_indices: Array2::zeros((0, 0)),
            edge_features: Array2::zeros((0, 0)),
        }
    }
}

impl TransformerNetwork {
    fn new() -> Self {
        Self {
            attention_layers: Vec::new(),
            positional_encoding: Array2::zeros((0, 0)),
            token_embeddings: Array2::zeros((0, 0)),
            vocab_size: 1000,
        }
    }
}

impl ReinforcementLearningAgent {
    fn new() -> Self {
        Self {
            agent_type: RLAgentType::PPO,
            policy_network: NeuralNetwork::new(),
            value_network: NeuralNetwork::new(),
            replay_buffer: VecDeque::new(),
            exploration_params: ExplorationParameters {
                epsilon: 0.1,
                epsilon_decay: 0.995,
                epsilon_min: 0.01,
                temperature: 1.0,
            },
            learning_stats: LearningStatistics {
                episodes: 0,
                average_reward: 0.0,
                success_rate: 0.0,
                converged: false,
            },
        }
    }
}

impl MetaLearningModel {
    fn new() -> Self {
        Self {
            architecture: MetaLearningArchitecture::MAML,
            task_encoder: NeuralNetwork::new(),
            algorithm_predictor: NeuralNetwork::new(),
            parameter_generator: NeuralNetwork::new(),
            meta_parameters: Array1::zeros(100),
            task_history: Vec::new(),
        }
    }
}

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

    #[cfg(feature = "async")]
    #[tokio::test]
    async fn test_ai_algorithm_selector() {
        let mut selector = AIAlgorithmSelector::new()
            .with_meta_learning(true)
            .with_neural_architecture_search(true);

        let points = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]];

        let result = selector
            .select_optimal_algorithm(&points.view(), "clustering")
            .await;
        assert!(result.is_ok());

        let (_algorithm_name, algorithm_parameters, prediction) = result.expect("Operation failed");
        assert!(!algorithm_parameters.is_empty());
        assert!(prediction.expected_accuracy >= 0.0 && prediction.expected_accuracy <= 1.0);
        assert!(prediction.confidence >= 0.0 && prediction.confidence <= 1.0);
    }

    #[cfg(feature = "async")]
    #[tokio::test]
    async fn test_data_characteristics_analysis() {
        let mut selector = AIAlgorithmSelector::new();
        let points = array![
            [0.0, 0.0],
            [1.0, 0.0],
            [0.0, 1.0],
            [1.0, 1.0],
            [10.0, 10.0],
            [11.0, 10.0]
        ];

        let characteristics = selector.analyze_data_characteristics(&points.view()).await;
        assert!(characteristics.is_ok());

        let chars = characteristics.expect("Operation failed");
        assert_eq!(chars.num_points, 6);
        assert_eq!(chars.dimensionality, 2);
        assert!(chars.density > 0.0);
        assert!(chars.outlier_ratio >= 0.0 && chars.outlier_ratio <= 1.0);
    }

    #[cfg(feature = "async")]
    #[tokio::test]
    async fn test_meta_learning_optimizer() {
        let mut optimizer = MetaLearningOptimizer::new()
            .with_continual_learning(true)
            .with_transformer_embeddings(true);

        let points = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]];

        let result = optimizer.optimize_spatial_task(&points.view()).await;
        assert!(result.is_ok());

        let meta_result = result.expect("Operation failed");
        assert!(!meta_result.optimal_algorithm.is_empty());
        assert!(meta_result.adaptation_steps > 0);
    }

    #[test]
    fn test_performance_prediction() {
        let prediction = PerformancePrediction {
            expected_accuracy: 0.95,
            expected_time_ms: 100.0,
            expected_memory_mb: 50.0,
            expected_energy_j: 1.0,
            confidence: 0.9,
        };

        assert!(prediction.expected_accuracy > 0.9);
        assert!(prediction.expected_time_ms > 0.0);
        assert!(prediction.confidence > 0.8);
    }

    #[test]
    fn test_algorithm_candidate() {
        let mut parameters = HashMap::new();
        parameters.insert("k".to_string(), 3.0);
        parameters.insert("max_iter".to_string(), 100.0);

        let candidate = AlgorithmCandidate {
            algorithm: "kmeans".to_string(),
            parameters,
        };

        assert_eq!(candidate.algorithm, "kmeans");
        assert_eq!(candidate.parameters.len(), 2);
        assert_eq!(candidate.parameters["k"], 3.0);
    }

    /// Regression test for the `NeuralNetwork::predict()` stub: it used to
    /// return the identical hardcoded vector `[0.5, 100.0, 50.0, 1.0, 0.8]`
    /// for every input. This builds a small, fully deterministic network by
    /// hand (bypassing the random Xavier initializer, so the test can never
    /// be flaky) and checks that two very different, non-constant inputs
    /// produce different outputs via a genuine forward pass.
    #[test]
    fn test_neural_network_predict_depends_on_input() {
        let hidden = NeuralLayer {
            weights: Array2::from_shape_vec((3, 2), vec![1.0, -1.0, 0.5, 0.5, -2.0, 1.0])
                .expect("valid shape"),
            biases: Array1::zeros(3),
            activation: ActivationFunction::ReLU,
            dropout_rate: 0.0,
        };
        let output_layer = NeuralLayer {
            weights: Array2::from_shape_vec(
                (5, 3),
                vec![
                    0.2, 0.1, -0.3, 0.4, -0.2, 0.1, -0.1, 0.3, 0.2, 0.5, -0.4, 0.2, 0.1, 0.1, -0.5,
                ],
            )
            .expect("valid shape"),
            biases: Array1::zeros(5),
            activation: ActivationFunction::Sigmoid,
            dropout_rate: 0.0,
        };
        let network = NeuralNetwork {
            layers: vec![hidden, output_layer],
            learning_rate: 0.001,
            training_history: Vec::new(),
        };

        let input_a = Array1::from(vec![1.0, 2.0]);
        let input_b = Array1::from(vec![-3.0, 0.5]);

        let output_a = network.predict(&input_a).expect("predict failed");
        let output_b = network.predict(&input_b).expect("predict failed");

        assert_eq!(output_a.len(), 5);
        assert_eq!(output_b.len(), 5);

        let differs = output_a
            .iter()
            .zip(output_b.iter())
            .any(|(a, b)| (a - b).abs() > 1e-6);
        assert!(
            differs,
            "predict() must depend on the input, not return a fixed constant vector: \
             output_a={output_a:?}, output_b={output_b:?}"
        );

        for &v in output_a.iter().chain(output_b.iter()) {
            assert!(
                (0.0..=1.0).contains(&v),
                "Sigmoid output layer must produce values in [0, 1], got {v}"
            );
        }

        // Pin down that the OLD stub's exact constant vector is gone.
        let old_stub = [0.5_f64, 100.0, 50.0, 1.0, 0.8];
        assert_ne!(
            output_a.to_vec(),
            old_stub,
            "must not return the old hardcoded dummy prediction"
        );
    }

    #[test]
    fn test_neural_network_predict_without_architecture_errors() {
        let network = NeuralNetwork::new();
        let input = Array1::from(vec![1.0, 2.0, 3.0]);
        assert!(network.predict(&input).is_err());
    }

    #[test]
    fn test_neural_network_with_architecture_shapes() {
        let network = NeuralNetwork::with_architecture(&[14, 16, 5]);
        assert_eq!(network.layers.len(), 2);
        assert_eq!(network.layers[0].weights.dim(), (16, 14));
        assert_eq!(network.layers[1].weights.dim(), (5, 16));

        let input = Array1::from(vec![0.0; 14]);
        let output = network.predict(&input).expect("predict failed");
        assert_eq!(output.len(), 5);
        for &v in output.iter() {
            assert!((0.0..=1.0).contains(&v));
        }
    }
}