optirs-learned 0.3.1

// Search space definition and management for neural architecture search
//
// This module provides comprehensive search space definitions that constrain
// and guide the architecture search process across different domains and requirements.

#[allow(dead_code)]

use scirs2_core::ndarray::{Array1, Array2, Array3};
use scirs2_core::numeric::Float;
use std::fmt::Debug;
use std::collections::{HashMap, HashSet, BTreeMap, VecDeque};

use crate::error::{OptimError, Result};

/// Configuration for search space definition
#[derive(Debug, Clone)]
pub struct SearchSpaceConfig<T: Float + Debug + Send + Sync + 'static> {
    /// Search space type
    pub space_type: SearchSpaceType,
    
    /// Maximum architecture depth
    pub max_depth: usize,
    
    /// Maximum number of operations per layer
    pub max_operations_per_layer: usize,
    
    /// Allowed operation types
    pub allowed_operations: Vec<OperationDef<T>>,
    
    /// Connection constraints
    pub connection_constraints: ConnectionConstraints,
    
    /// Resource constraints
    pub resource_constraints: ResourceConstraints<T>,
    
    /// Domain-specific constraints
    pub domain_constraints: DomainConstraints<T>,
    
    /// Search space complexity limits
    pub complexity_limits: ComplexityLimits<T>,
}

/// Types of search spaces
#[derive(Debug, Clone, Copy)]
pub enum SearchSpaceType {
    /// Chain-structured search space
    Chain,
    
    /// Cell-based search space (NASNet style)
    CellBased,
    
    /// Hierarchical search space
    Hierarchical,
    
    /// Graph-based search space (DARTS style)
    GraphBased,
    
    /// Differentiable search space
    Differentiable,
    
    /// Macro search space
    Macro,
    
    /// Custom search space
    Custom(String),
}

/// Search space definition and management
#[derive(Debug)]
pub struct SearchSpace<T: Float + Debug + Send + Sync + 'static> {
    /// Configuration
    config: SearchSpaceConfig<T>,
    
    /// Operation definitions
    operations: HashMap<String, OperationDef<T>>,
    
    /// Layer definitions
    layers: HashMap<String, LayerDef<T>>,
    
    /// Connection patterns
    connection_patterns: Vec<ConnectionPattern>,
    
    /// Search space graph
    space_graph: SearchSpaceGraph<T>,
    
    /// Constraint checkers
    constraint_checkers: Vec<Box<dyn ConstraintChecker<T>>>,
    
    /// Search space statistics
    statistics: SearchSpaceStatistics<T>,
    
    /// Sampled architectures cache
    architecture_cache: HashMap<String, ArchitectureSample<T>>,
}

/// Operation definition in search space
#[derive(Debug, Clone)]
pub struct OperationDef<T: Float + Debug + Send + Sync + 'static> {
    /// Operation identifier
    pub id: String,
    
    /// Operation name
    pub name: String,
    
    /// Operation type
    pub op_type: OperationType,
    
    /// Parameter space
    pub parameter_space: ParameterSpace<T>,
    
    /// Computational properties
    pub computational_props: ComputationalProperties<T>,
    
    /// Compatibility constraints
    pub compatibility: CompatibilityConstraints,
    
    /// Performance characteristics
    pub performance_chars: PerformanceCharacteristics<T>,
}

/// Types of operations
#[derive(Debug, Clone)]
pub enum OperationType {
    /// Convolution operations
    Conv(ConvType),
    
    /// Dense/Linear operations
    Dense(DenseType),
    
    /// Pooling operations
    Pool(PoolType),
    
    /// Normalization operations
    Norm(NormType),
    
    /// Activation operations
    Activation(ActivationType),
    
    /// Attention operations
    Attention(AttentionType),
    
    /// Recurrent operations
    Recurrent(RecurrentType),
    
    /// Skip/Identity operations
    Skip(SkipType),
    
    /// Custom operations
    Custom(String, HashMap<String, String>),
}

/// Convolution operation types
#[derive(Debug, Clone)]
pub enum ConvType {
    /// Standard convolution
    Standard,
    /// Depthwise convolution
    Depthwise,
    /// Separable convolution
    Separable,
    /// Dilated convolution
    Dilated,
    /// Grouped convolution
    Grouped,
    /// Transposed convolution
    Transposed,
}

/// Dense operation types
#[derive(Debug, Clone)]
pub enum DenseType {
    /// Standard dense layer
    Standard,
    /// Sparse dense layer
    Sparse,
    /// Low-rank dense layer
    LowRank,
    /// Factorized dense layer
    Factorized,
}

/// Pool operation types
#[derive(Debug, Clone)]
pub enum PoolType {
    /// Max pooling
    Max,
    /// Average pooling
    Average,
    /// Global max pooling
    GlobalMax,
    /// Global average pooling
    GlobalAverage,
    /// Adaptive pooling
    Adaptive,
}

/// Normalization types
#[derive(Debug, Clone)]
pub enum NormType {
    /// Batch normalization
    Batch,
    /// Layer normalization
    Layer,
    /// Instance normalization
    Instance,
    /// Group normalization
    Group,
    /// Local response normalization
    LocalResponse,
}

/// Activation types
#[derive(Debug, Clone)]
pub enum ActivationType {
    ReLU, GELU, Swish, Tanh, Sigmoid, LeakyReLU, ELU, Mish,
}

/// Attention types
#[derive(Debug, Clone)]
pub enum AttentionType {
    /// Self-attention
    SelfAttention,
    /// Multi-head attention
    MultiHead,
    /// Scaled dot-product attention
    ScaledDotProduct,
    /// Additive attention
    Additive,
    /// Spatial attention
    Spatial,
}

/// Recurrent types
#[derive(Debug, Clone)]
pub enum RecurrentType {
    LSTM, GRU, RNN, BiLSTM, BiGRU,
}

/// Skip connection types
#[derive(Debug, Clone)]
pub enum SkipType {
    /// Identity connection
    Identity,
    /// Zero connection (no connection)
    Zero,
    /// Residual connection
    Residual,
    /// Dense connection
    Dense,
}

/// Parameter space definition
#[derive(Debug, Clone)]
pub struct ParameterSpace<T: Float + Debug + Send + Sync + 'static> {
    /// Continuous parameters
    pub continuous_params: HashMap<String, ContinuousParam<T>>,
    
    /// Discrete parameters
    pub discrete_params: HashMap<String, DiscreteParam>,
    
    /// Categorical parameters
    pub categorical_params: HashMap<String, CategoricalParam>,
    
    /// Conditional parameters (depend on other parameters)
    pub conditional_params: HashMap<String, ConditionalParam<T>>,
}

/// Continuous parameter definition
#[derive(Debug, Clone)]
pub struct ContinuousParam<T: Float + Debug + Send + Sync + 'static> {
    /// Parameter name
    pub name: String,
    
    /// Minimum value
    pub min_value: T,
    
    /// Maximum value
    pub max_value: T,
    
    /// Default value
    pub default_value: T,
    
    /// Distribution type for sampling
    pub distribution: DistributionType<T>,
    
    /// Scaling type
    pub scaling: ScalingType,
}

/// Discrete parameter definition
#[derive(Debug, Clone)]
pub struct DiscreteParam {
    /// Parameter name
    pub name: String,
    
    /// Possible values
    pub values: Vec<i64>,
    
    /// Default value
    pub default_value: i64,
    
    /// Sampling weights (optional)
    pub weights: Option<Vec<f64>>,
}

/// Categorical parameter definition
#[derive(Debug, Clone)]
pub struct CategoricalParam {
    /// Parameter name
    pub name: String,
    
    /// Possible categories
    pub categories: Vec<String>,
    
    /// Default category
    pub default_category: String,
    
    /// Sampling probabilities (optional)
    pub probabilities: Option<Vec<f64>>,
}

/// Conditional parameter definition
#[derive(Debug, Clone)]
pub struct ConditionalParam<T: Float + Debug + Send + Sync + 'static> {
    /// Parameter name
    pub name: String,
    
    /// Parent parameter name
    pub parent_param: String,
    
    /// Conditions and corresponding parameter spaces
    pub conditions: HashMap<String, ParameterSpace<T>>,
}

/// Distribution types for continuous parameters
#[derive(Debug, Clone)]
pub enum DistributionType<T: Float + Debug + Send + Sync + 'static> {
    /// Uniform distribution
    Uniform,
    
    /// Normal distribution
    Normal { mean: T, std: T },
    
    /// Log-uniform distribution
    LogUniform,
    
    /// Beta distribution
    Beta { alpha: T, beta: T },
    
    /// Gamma distribution
    Gamma { shape: T, scale: T },
}

/// Scaling types for parameters
#[derive(Debug, Clone, Copy)]
pub enum ScalingType {
    /// Linear scaling
    Linear,
    
    /// Logarithmic scaling
    Logarithmic,
    
    /// Power scaling
    Power(f64),
}

/// Computational properties of operations
#[derive(Debug, Clone)]
pub struct ComputationalProperties<T: Float + Debug + Send + Sync + 'static> {
    /// FLOPS estimate function
    pub flops_estimator: FLOPSEstimator<T>,
    
    /// Memory usage estimate
    pub memory_estimator: MemoryEstimator<T>,
    
    /// Latency estimate
    pub latency_estimator: LatencyEstimator<T>,
    
    /// Parallelizability score
    pub parallelizability: T,
    
    /// Memory access pattern
    pub memory_pattern: MemoryAccessPattern,
}

/// FLOPS estimation function
#[derive(Debug, Clone)]
pub struct FLOPSEstimator<T: Float + Debug + Send + Sync + 'static> {
    /// Base FLOPS count
    pub base_flops: u64,
    
    /// Scaling factors for different dimensions
    pub scaling_factors: HashMap<String, T>,
    
    /// Estimation function type
    pub estimator_type: EstimatorType,
}

/// Memory usage estimator
#[derive(Debug, Clone)]
pub struct MemoryEstimator<T: Float + Debug + Send + Sync + 'static> {
    /// Base memory usage (bytes)
    pub base_memory: usize,
    
    /// Input-dependent scaling
    pub input_scaling: T,
    
    /// Parameter memory overhead
    pub param_overhead: T,
}

/// Latency estimator
#[derive(Debug, Clone)]
pub struct LatencyEstimator<T: Float + Debug + Send + Sync + 'static> {
    /// Base latency (milliseconds)
    pub base_latency: T,
    
    /// Hardware-specific multipliers
    pub hardware_multipliers: HashMap<String, T>,
    
    /// Batch size scaling
    pub batch_scaling: T,
}

/// Types of estimators
#[derive(Debug, Clone, Copy)]
pub enum EstimatorType {
    /// Constant estimate
    Constant,
    
    /// Linear scaling
    Linear,
    
    /// Polynomial scaling
    Polynomial(u32),
    
    /// Custom function
    Custom,
}

/// Memory access patterns
#[derive(Debug, Clone)]
pub enum MemoryAccessPattern {
    /// Sequential access
    Sequential,
    
    /// Random access
    Random,
    
    /// Strided access
    Strided { stride: usize },
    
    /// Blocked access
    Blocked { block_size: usize },
}

/// Compatibility constraints for operations
#[derive(Debug, Clone)]
pub struct CompatibilityConstraints {
    /// Input shape constraints
    pub input_shape_constraints: Vec<ShapeConstraint>,
    
    /// Output shape constraints
    pub output_shape_constraints: Vec<ShapeConstraint>,
    
    /// Predecessor operation constraints
    pub predecessor_constraints: Vec<String>,
    
    /// Successor operation constraints
    pub successor_constraints: Vec<String>,
    
    /// Mutual exclusion constraints
    pub exclusion_constraints: Vec<String>,
}

/// Shape constraints
#[derive(Debug, Clone)]
pub enum ShapeConstraint {
    /// Fixed shape
    Fixed(Vec<usize>),
    
    /// Minimum shape
    MinShape(Vec<usize>),
    
    /// Maximum shape
    MaxShape(Vec<usize>),
    
    /// Shape divisibility
    Divisible { dim: usize, factor: usize },
    
    /// Shape relationship
    Relationship { 
        relation: ShapeRelation,
        target_dim: usize,
        factor: f64,
    },
}

/// Shape relationships
#[derive(Debug, Clone, Copy)]
pub enum ShapeRelation {
    Equal, GreaterThan, LessThan, Multiple, Divisor,
}

/// Performance characteristics
#[derive(Debug, Clone)]
pub struct PerformanceCharacteristics<T: Float + Debug + Send + Sync + 'static> {
    /// Expected accuracy contribution
    pub accuracy_impact: T,
    
    /// Training stability impact
    pub stability_impact: T,
    
    /// Convergence speed impact
    pub convergence_impact: T,
    
    /// Generalization impact
    pub generalization_impact: T,
    
    /// Resource efficiency
    pub efficiency_score: T,
}

/// Layer definition in search space
#[derive(Debug, Clone)]
pub struct LayerDef<T: Float + Debug + Send + Sync + 'static> {
    /// Layer identifier
    pub id: String,
    
    /// Layer type
    pub layer_type: LayerType,
    
    /// Allowed operations in this layer
    pub allowed_operations: Vec<String>,
    
    /// Layer-specific constraints
    pub constraints: LayerConstraints<T>,
    
    /// Repetition constraints
    pub repetition: RepetitionConstraints,
}

/// Layer types
#[derive(Debug, Clone)]
pub enum LayerType {
    /// Input layer
    Input,
    
    /// Feature extraction layer
    FeatureExtraction,
    
    /// Attention layer
    Attention,
    
    /// Normalization layer
    Normalization,
    
    /// Activation layer
    Activation,
    
    /// Pooling layer
    Pooling,
    
    /// Output layer
    Output,
    
    /// Custom layer
    Custom(String),
}

/// Layer-specific constraints
#[derive(Debug, Clone)]
pub struct LayerConstraints<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum operations in layer
    pub max_operations: usize,
    
    /// Required operations
    pub required_operations: Vec<String>,
    
    /// Optional operations
    pub optional_operations: Vec<String>,
    
    /// Resource limits for this layer
    pub resource_limits: ResourceConstraints<T>,
}

/// Repetition constraints for layers
#[derive(Debug, Clone)]
pub struct RepetitionConstraints {
    /// Minimum repetitions
    pub min_repeat: usize,
    
    /// Maximum repetitions
    pub max_repeat: usize,
    
    /// Repetition pattern
    pub pattern: RepetitionPattern,
}

/// Repetition patterns
#[derive(Debug, Clone)]
pub enum RepetitionPattern {
    /// Fixed number of repetitions
    Fixed(usize),
    
    /// Variable repetitions
    Variable,
    
    /// Geometric progression
    Geometric { base: usize, ratio: f64 },
    
    /// Arithmetic progression
    Arithmetic { start: usize, step: usize },
}

/// Connection patterns between layers/operations
#[derive(Debug, Clone)]
pub struct ConnectionPattern {
    /// Pattern identifier
    pub id: String,
    
    /// Pattern type
    pub pattern_type: ConnectionPatternType,
    
    /// Source constraints
    pub source_constraints: Vec<NodeConstraint>,
    
    /// Target constraints
    pub target_constraints: Vec<NodeConstraint>,
    
    /// Connection probability/weight
    pub weight: f64,
}

/// Types of connection patterns
#[derive(Debug, Clone)]
pub enum ConnectionPatternType {
    /// Sequential connections
    Sequential,
    
    /// Skip connections
    Skip { max_distance: usize },
    
    /// Dense connections
    Dense,
    
    /// Random connections
    Random { probability: f64 },
    
    /// Attention-based connections
    Attention,
    
    /// Custom pattern
    Custom(String),
}

/// Node constraints for connections
#[derive(Debug, Clone)]
pub enum NodeConstraint {
    /// Node type constraint
    NodeType(String),
    
    /// Layer index constraint
    LayerIndex(std::ops::Range<usize>),
    
    /// Operation type constraint
    OperationType(String),
    
    /// Shape compatibility constraint
    ShapeCompatible,
}

/// Connection constraints
#[derive(Debug, Clone)]
pub struct ConnectionConstraints {
    /// Maximum input degree per node
    pub max_input_degree: usize,
    
    /// Maximum output degree per node
    pub max_output_degree: usize,
    
    /// Forbidden connection patterns
    pub forbidden_patterns: Vec<String>,
    
    /// Required connection patterns
    pub required_patterns: Vec<String>,
    
    /// Cycle constraints
    pub allow_cycles: bool,
}

/// Resource constraints
#[derive(Debug, Clone)]
pub struct ResourceConstraints<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum parameters
    pub max_parameters: Option<usize>,
    
    /// Maximum FLOPS
    pub max_flops: Option<u64>,
    
    /// Maximum memory usage (bytes)
    pub max_memory: Option<usize>,
    
    /// Maximum latency (milliseconds)
    pub max_latency: Option<T>,
    
    /// Maximum energy consumption (joules)
    pub max_energy: Option<T>,
    
    /// Hardware-specific constraints
    pub hardware_constraints: HashMap<String, T>,
}

/// Domain-specific constraints
#[derive(Debug, Clone)]
pub struct DomainConstraints<T: Float + Debug + Send + Sync + 'static> {
    /// Task type constraints
    pub task_constraints: TaskConstraints,
    
    /// Data type constraints
    pub data_constraints: DataConstraints,
    
    /// Performance requirements
    pub performance_requirements: PerformanceRequirements<T>,
    
    /// Regulatory constraints
    pub regulatory_constraints: RegulatoryConstraints,
}

/// Task-specific constraints
#[derive(Debug, Clone)]
pub struct TaskConstraints {
    /// Task type
    pub task_type: TaskType,
    
    /// Required capabilities
    pub required_capabilities: Vec<String>,
    
    /// Prohibited operations
    pub prohibited_operations: Vec<String>,
    
    /// Preferred architectures
    pub preferred_patterns: Vec<String>,
}

/// Task types
#[derive(Debug, Clone)]
pub enum TaskType {
    Classification, Regression, Detection, Segmentation, 
    Generation, Translation, Summarization, Custom(String),
}

/// Data constraints
#[derive(Debug, Clone)]
pub struct DataConstraints {
    /// Input data types
    pub input_types: Vec<DataType>,
    
    /// Output data types
    pub output_types: Vec<DataType>,
    
    /// Data size constraints
    pub size_constraints: SizeConstraints,
    
    /// Privacy requirements
    pub privacy_requirements: PrivacyRequirements,
}

/// Data types
#[derive(Debug, Clone)]
pub enum DataType {
    Image, Video, Audio, Text, Tabular, Graph, TimeSeries, Custom(String),
}

/// Size constraints for data
#[derive(Debug, Clone)]
pub struct SizeConstraints {
    /// Minimum input size
    pub min_size: Vec<usize>,
    
    /// Maximum input size
    pub max_size: Vec<usize>,
    
    /// Batch size constraints
    pub batch_constraints: (usize, usize), // (min, max)
}

/// Privacy requirements
#[derive(Debug, Clone)]
pub struct PrivacyRequirements {
    /// Differential privacy requirements
    pub differential_privacy: bool,
    
    /// Federated learning compatibility
    pub federated_compatible: bool,
    
    /// Data locality requirements
    pub data_locality: bool,
    
    /// Encryption requirements
    pub encryption_required: bool,
}

/// Performance requirements
#[derive(Debug, Clone)]
pub struct PerformanceRequirements<T: Float + Debug + Send + Sync + 'static> {
    /// Minimum accuracy
    pub min_accuracy: Option<T>,
    
    /// Maximum inference time
    pub max_inference_time: Option<T>,
    
    /// Minimum throughput
    pub min_throughput: Option<T>,
    
    /// Real-time requirements
    pub real_time: bool,
}

/// Regulatory constraints
#[derive(Debug, Clone)]
pub struct RegulatoryConstraints {
    /// Explainability requirements
    pub explainability_required: bool,
    
    /// Fairness constraints
    pub fairness_constraints: Vec<String>,
    
    /// Safety requirements
    pub safety_requirements: Vec<String>,
    
    /// Compliance standards
    pub compliance_standards: Vec<String>,
}

/// Complexity limits for search space
#[derive(Debug, Clone)]
pub struct ComplexityLimits<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum search space size
    pub max_space_size: u64,
    
    /// Maximum architecture complexity
    pub max_arch_complexity: T,
    
    /// Sampling complexity limits
    pub sampling_limits: SamplingLimits,
    
    /// Search algorithm constraints
    pub search_constraints: SearchConstraints<T>,
}

/// Sampling complexity limits
#[derive(Debug, Clone)]
pub struct SamplingLimits {
    /// Maximum samples per iteration
    pub max_samples_per_iteration: usize,
    
    /// Maximum total samples
    pub max_total_samples: usize,
    
    /// Sampling timeout (seconds)
    pub sampling_timeout: u32,
}

/// Search algorithm constraints
#[derive(Debug, Clone)]
pub struct SearchConstraints<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum search depth
    pub max_search_depth: usize,
    
    /// Convergence criteria
    pub convergence_criteria: ConvergenceCriteria<T>,
    
    /// Early stopping conditions
    pub early_stopping: EarlyStoppingConditions<T>,
}

/// Convergence criteria
#[derive(Debug, Clone)]
pub struct ConvergenceCriteria<T: Float + Debug + Send + Sync + 'static> {
    /// Performance improvement threshold
    pub improvement_threshold: T,
    
    /// Patience (iterations without improvement)
    pub patience: usize,
    
    /// Minimum iterations
    pub min_iterations: usize,
    
    /// Maximum iterations
    pub max_iterations: usize,
}

/// Early stopping conditions
#[derive(Debug, Clone)]
pub struct EarlyStoppingConditions<T: Float + Debug + Send + Sync + 'static> {
    /// Performance threshold for early stopping
    pub performance_threshold: Option<T>,
    
    /// Resource usage threshold
    pub resource_threshold: Option<T>,
    
    /// Time limit (seconds)
    pub time_limit: Option<u32>,
}

/// Search space graph representation
#[derive(Debug, Clone)]
pub struct SearchSpaceGraph<T: Float + Debug + Send + Sync + 'static> {
    /// Nodes (operations/layers)
    pub nodes: HashMap<String, GraphNode<T>>,
    
    /// Edges (connections)
    pub edges: HashMap<String, GraphEdge<T>>,
    
    /// Adjacency lists
    pub adjacency: HashMap<String, Vec<String>>,
    
    /// Graph properties
    pub properties: GraphProperties<T>,
}

/// Graph node representation
#[derive(Debug, Clone)]
pub struct GraphNode<T: Float + Debug + Send + Sync + 'static> {
    /// Node identifier
    pub id: String,
    
    /// Node type
    pub node_type: String,
    
    /// Node features
    pub features: HashMap<String, T>,
    
    /// Node constraints
    pub constraints: Vec<String>,
}

/// Graph edge representation
#[derive(Debug, Clone)]
pub struct GraphEdge<T: Float + Debug + Send + Sync + 'static> {
    /// Edge identifier
    pub id: String,
    
    /// Source node
    pub source: String,
    
    /// Target node
    pub target: String,
    
    /// Edge weight
    pub weight: T,
    
    /// Edge type
    pub edge_type: String,
}

/// Graph properties
#[derive(Debug, Clone)]
pub struct GraphProperties<T: Float + Debug + Send + Sync + 'static> {
    /// Number of nodes
    pub num_nodes: usize,
    
    /// Number of edges
    pub num_edges: usize,
    
    /// Graph density
    pub density: T,
    
    /// Average clustering coefficient
    pub clustering_coefficient: T,
    
    /// Graph diameter
    pub diameter: usize,
}

/// Constraint checker trait
pub trait ConstraintChecker<T: Float + Debug + Send + Sync + 'static> {
    /// Check if architecture satisfies constraints
    fn check(&self, architecture: &ArchitectureSample<T>) -> Result<bool>;
    
    /// Get constraint violation details
    fn get_violations(&self, architecture: &ArchitectureSample<T>) -> Result<Vec<ConstraintViolation>>;
    
    /// Get constraint severity
    fn get_severity(&self) -> ConstraintSeverity;
}

/// Constraint violation details
#[derive(Debug, Clone)]
pub struct ConstraintViolation {
    /// Constraint type
    pub constraint_type: String,
    
    /// Violation description
    pub description: String,
    
    /// Severity level
    pub severity: ConstraintSeverity,
    
    /// Suggested fix
    pub suggested_fix: Option<String>,
}

/// Constraint severity levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum ConstraintSeverity {
    Info, Warning, Error, Critical,
}

/// Search space statistics
#[derive(Debug, Clone)]
pub struct SearchSpaceStatistics<T: Float + Debug + Send + Sync + 'static> {
    /// Total number of possible architectures
    pub total_architectures: u64,
    
    /// Sampled architectures count
    pub sampled_count: usize,
    
    /// Valid architectures count
    pub valid_count: usize,
    
    /// Average architecture complexity
    pub avg_complexity: T,
    
    /// Complexity distribution
    pub complexity_distribution: HashMap<String, usize>,
    
    /// Operation usage statistics
    pub operation_usage: HashMap<String, usize>,
}

/// Architecture sample from search space
#[derive(Debug, Clone)]
pub struct ArchitectureSample<T: Float + Debug + Send + Sync + 'static> {
    /// Sample identifier
    pub id: String,
    
    /// Architecture definition
    pub architecture: Vec<String>, // Simplified as operation sequence
    
    /// Parameter values
    pub parameters: HashMap<String, T>,
    
    /// Constraint satisfaction status
    pub is_valid: bool,
    
    /// Complexity metrics
    pub complexity: T,
    
    /// Estimated performance
    pub estimated_performance: HashMap<String, T>,
    
    /// Sampling metadata
    pub metadata: SamplingMetadata,
}

/// Sampling metadata
#[derive(Debug, Clone)]
pub struct SamplingMetadata {
    /// Sampling method used
    pub sampling_method: String,
    
    /// Sampling timestamp
    pub timestamp: u64,
    
    /// Sampling parameters
    pub parameters: HashMap<String, String>,
    
    /// Parent architectures (for mutation/crossover)
    pub parents: Vec<String>,
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> SearchSpace<T> {
    /// Create new search space
    pub fn new(config: SearchSpaceConfig<T>) -> Result<Self> {
        let mut space = Self {
            config: config.clone(),
            operations: HashMap::new(),
            layers: HashMap::new(),
            connection_patterns: Vec::new(),
            space_graph: SearchSpaceGraph::new(),
            constraint_checkers: Vec::new(),
            statistics: SearchSpaceStatistics::new(),
            architecture_cache: HashMap::new(),
        };
        
        // Initialize operations
        space.initialize_operations()?;
        
        // Initialize layers
        space.initialize_layers()?;
        
        // Initialize connection patterns
        space.initialize_connections()?;
        
        // Initialize constraint checkers
        space.initialize_constraints()?;
        
        // Compute statistics
        space.compute_statistics()?;
        
        Ok(space)
    }
    
    /// Initialize operations from config
    fn initialize_operations(&mut self) -> Result<()> {
        for op_def in &self.config.allowed_operations {
            self.operations.insert(op_def.id.clone(), op_def.clone());
        }
        Ok(())
    }
    
    /// Initialize layers
    fn initialize_layers(&mut self) -> Result<()> {
        // Create default layer definitions based on operations
        let layer_types = vec![
            ("input", LayerType::Input),
            ("feature", LayerType::FeatureExtraction),
            ("attention", LayerType::Attention),
            ("norm", LayerType::Normalization),
            ("output", LayerType::Output),
        ];
        
        for (layer_id, layer_type) in layer_types {
            let layer_def = LayerDef {
                id: layer_id.to_string(),
                layer_type,
                allowed_operations: self.operations.keys().cloned().collect(),
                constraints: LayerConstraints {
                    max_operations: self.config.max_operations_per_layer,
                    required_operations: Vec::new(),
                    optional_operations: self.operations.keys().cloned().collect(),
                    resource_limits: self.config.resource_constraints.clone(),
                },
                repetition: RepetitionConstraints {
                    min_repeat: 1,
                    max_repeat: 5,
                    pattern: RepetitionPattern::Variable,
                },
            };
            self.layers.insert(layer_id.to_string(), layer_def);
        }
        
        Ok(())
    }
    
    /// Initialize connection patterns
    fn initialize_connections(&mut self) -> Result<()> {
        // Sequential connections
        self.connection_patterns.push(ConnectionPattern {
            id: "sequential".to_string(),
            pattern_type: ConnectionPatternType::Sequential,
            source_constraints: Vec::new(),
            target_constraints: Vec::new(),
            weight: 1.0,
        });
        
        // Skip connections
        self.connection_patterns.push(ConnectionPattern {
            id: "skip".to_string(),
            pattern_type: ConnectionPatternType::Skip { max_distance: 3 },
            source_constraints: Vec::new(),
            target_constraints: Vec::new(),
            weight: 0.3,
        });
        
        Ok(())
    }
    
    /// Initialize constraint checkers
    fn initialize_constraints(&mut self) -> Result<()> {
        // Add resource constraint checker
        self.constraint_checkers.push(Box::new(ResourceConstraintChecker::new(
            self.config.resource_constraints.clone()
        )));
        
        // Add compatibility constraint checker
        self.constraint_checkers.push(Box::new(CompatibilityConstraintChecker::new(
            self.operations.clone()
        )));
        
        Ok(())
    }
    
    /// Sample architecture from search space
    pub fn sample_architecture(&mut self, method: &str) -> Result<ArchitectureSample<T>> {
        match method {
            "random" => self.sample_random_architecture(),
            "uniform" => self.sample_uniform_architecture(),
            "guided" => self.sample_guided_architecture(),
            _ => Err(OptimError::InvalidInput(
                format!("Unknown sampling method: {}", method)
            ))
        }
    }
    
    /// Sample random architecture
    fn sample_random_architecture(&mut self) -> Result<ArchitectureSample<T>> {
        use scirs2_core::random::Rng;
        let mut rng = scirs2_core::random::thread_rng();
        
        let num_ops = rng.gen_range(1..=self.config.max_depth);
        let mut architecture = Vec::new();
        let mut parameters = HashMap::new();
        
        for i in 0..num_ops {
            let op_names: Vec<&String> = self.operations.keys().collect();
            let op_name = op_names[rng.gen_range(0..op_names.len())];
            architecture.push(op_name.clone());
            
            // Sample parameters for this operation
            if let Some(op_def) = self.operations.get(op_name) {
                for (param_name, continuous_param) in &op_def.parameter_space.continuous_params {
                    let value = match continuous_param.distribution {
                        DistributionType::Uniform => {
                            let range = continuous_param.max_value - continuous_param.min_value;
                            continuous_param.min_value + T::from(rng.random::<f64>()).expect("unwrap failed") * range
                        }
                        _ => continuous_param.default_value,
                    };
                    parameters.insert(format!("{}_{}", i, param_name), value);
                }
            }
        }
        
        let sample = ArchitectureSample {
            id: format!("sample_{}", scirs2_core::random::random::<u64>()),
            architecture,
            parameters,
            is_valid: true, // Will be validated later
            complexity: scirs2_core::numeric::NumCast::from(num_ops as f64).unwrap_or_else(|| T::zero()),
            estimated_performance: HashMap::new(),
            metadata: SamplingMetadata {
                sampling_method: "random".to_string(),
                timestamp: std::time::SystemTime::now()
                    .duration_since(std::time::UNIX_EPOCH)
                    .unwrap_or_default()
                    .as_secs(),
                parameters: HashMap::new(),
                parents: Vec::new(),
            },
        };
        
        // Validate architecture
        let is_valid = self.validate_architecture(&sample)?;
        let mut validated_sample = sample;
        validated_sample.is_valid = is_valid;
        
        // Cache the sample
        self.architecture_cache.insert(validated_sample.id.clone(), validated_sample.clone());
        
        Ok(validated_sample)
    }
    
    /// Sample uniform architecture
    fn sample_uniform_architecture(&self) -> Result<ArchitectureSample<T>> {
        // Simplified uniform sampling - in practice would be more sophisticated
        self.sample_random_architecture()
    }
    
    /// Sample guided architecture
    fn sample_guided_architecture(&self) -> Result<ArchitectureSample<T>> {
        // Simplified guided sampling - in practice would use learned priors
        self.sample_random_architecture()
    }
    
    /// Validate architecture against constraints
    pub fn validate_architecture(&self, architecture: &ArchitectureSample<T>) -> Result<bool> {
        for checker in &self.constraint_checkers {
            if !checker.check(architecture)? {
                return Ok(false);
            }
        }
        Ok(true)
    }
    
    /// Get constraint violations for architecture
    pub fn get_constraint_violations(&self, architecture: &ArchitectureSample<T>) -> Result<Vec<ConstraintViolation>> {
        let mut violations = Vec::new();
        
        for checker in &self.constraint_checkers {
            violations.extend(checker.get_violations(architecture)?);
        }
        
        Ok(violations)
    }
    
    /// Compute search space statistics
    fn compute_statistics(&mut self) -> Result<()> {
        // Simplified statistics computation
        self.statistics.total_architectures = 1000000; // Estimate
        self.statistics.avg_complexity = scirs2_core::numeric::NumCast::from(5.0).unwrap_or_else(|| T::zero()); // Average depth
        
        Ok(())
    }
    
    /// Get search space size estimate
    pub fn get_space_size(&self) -> u64 {
        self.statistics.total_architectures
    }
    
    /// Get operation definitions
    pub fn get_operations(&self) -> &HashMap<String, OperationDef<T>> {
        &self.operations
    }
    
    /// Get layer definitions
    pub fn get_layers(&self) -> &HashMap<String, LayerDef<T>> {
        &self.layers
    }
    
    /// Get connection patterns
    pub fn get_connection_patterns(&self) -> &[ConnectionPattern] {
        &self.connection_patterns
    }
    
    /// Get cached architectures
    pub fn get_cached_architectures(&self) -> &HashMap<String, ArchitectureSample<T>> {
        &self.architecture_cache
    }
}

/// Resource constraint checker
#[derive(Debug)]
pub struct ResourceConstraintChecker<T: Float + Debug + Send + Sync + 'static> {
    constraints: ResourceConstraints<T>,
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> ResourceConstraintChecker<T> {
    pub fn new(constraints: ResourceConstraints<T>) -> Self {
        Self { constraints }
    }
}

impl<T: Float + Debug + Send + Sync + 'static> ConstraintChecker<T> for ResourceConstraintChecker<T> {
    fn check(&self, architecture: &ArchitectureSample<T>) -> Result<bool> {
        // Check parameter count
        if let Some(max_params) = self.constraints.max_parameters {
            let param_count = architecture.parameters.len();
            if param_count > max_params {
                return Ok(false);
            }
        }
        
        // Check complexity
        if let Some(max_flops) = self.constraints.max_flops {
            let estimated_flops = architecture.complexity.to_f64().unwrap_or(0.0) * 1000000.0; // Rough estimate
            if estimated_flops > max_flops as f64 {
                return Ok(false);
            }
        }
        
        Ok(true)
    }
    
    fn get_violations(&self, architecture: &ArchitectureSample<T>) -> Result<Vec<ConstraintViolation>> {
        let mut violations = Vec::new();
        
        if let Some(max_params) = self.constraints.max_parameters {
            let param_count = architecture.parameters.len();
            if param_count > max_params {
                violations.push(ConstraintViolation {
                    constraint_type: "resource".to_string(),
                    description: format!("Parameter count {} exceeds maximum {}", param_count, max_params),
                    severity: ConstraintSeverity::Error,
                    suggested_fix: Some("Reduce architecture complexity".to_string()),
                });
            }
        }
        
        Ok(violations)
    }
    
    fn get_severity(&self) -> ConstraintSeverity {
        ConstraintSeverity::Error
    }
}

/// Compatibility constraint checker
#[derive(Debug)]
pub struct CompatibilityConstraintChecker<T: Float + Debug + Send + Sync + 'static> {
    operations: HashMap<String, OperationDef<T>>,
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> CompatibilityConstraintChecker<T> {
    pub fn new(operations: HashMap<String, OperationDef<T>>) -> Self {
        Self { operations }
    }
}

impl<T: Float + Debug + Send + Sync + 'static> ConstraintChecker<T> for CompatibilityConstraintChecker<T> {
    fn check(&self, architecture: &ArchitectureSample<T>) -> Result<bool> {
        // Check if all operations are valid
        for op_name in &architecture.architecture {
            if !self.operations.contains_key(op_name) {
                return Ok(false);
            }
        }
        
        Ok(true)
    }
    
    fn get_violations(&self, architecture: &ArchitectureSample<T>) -> Result<Vec<ConstraintViolation>> {
        let mut violations = Vec::new();
        
        for op_name in &architecture.architecture {
            if !self.operations.contains_key(op_name) {
                violations.push(ConstraintViolation {
                    constraint_type: "compatibility".to_string(),
                    description: format!("Unknown operation: {}", op_name),
                    severity: ConstraintSeverity::Critical,
                    suggested_fix: Some("Replace with valid operation".to_string()),
                });
            }
        }
        
        Ok(violations)
    }
    
    fn get_severity(&self) -> ConstraintSeverity {
        ConstraintSeverity::Critical
    }
}

// Implementations for various data structures

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> SearchSpaceGraph<T> {
    fn new() -> Self {
        Self {
            nodes: HashMap::new(),
            edges: HashMap::new(),
            adjacency: HashMap::new(),
            properties: GraphProperties {
                num_nodes: 0,
                num_edges: 0,
                density: T::zero(),
                clustering_coefficient: T::zero(),
                diameter: 0,
            },
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> SearchSpaceStatistics<T> {
    fn new() -> Self {
        Self {
            total_architectures: 0,
            sampled_count: 0,
            valid_count: 0,
            avg_complexity: T::zero(),
            complexity_distribution: HashMap::new(),
            operation_usage: HashMap::new(),
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> Default for SearchSpaceConfig<T> {
    fn default() -> Self {
        Self {
            space_type: SearchSpaceType::Chain,
            max_depth: 10,
            max_operations_per_layer: 5,
            allowed_operations: vec![
                OperationDef {
                    id: "conv2d".to_string(),
                    name: "2D Convolution".to_string(),
                    op_type: OperationType::Conv(ConvType::Standard),
                    parameter_space: ParameterSpace {
                        continuous_params: HashMap::new(),
                        discrete_params: HashMap::new(),
                        categorical_params: HashMap::new(),
                        conditional_params: HashMap::new(),
                    },
                    computational_props: ComputationalProperties {
                        flops_estimator: FLOPSEstimator {
                            base_flops: 1000000,
                            scaling_factors: HashMap::new(),
                            estimator_type: EstimatorType::Linear,
                        },
                        memory_estimator: MemoryEstimator {
                            base_memory: 1024,
                            input_scaling: T::one(),
                            param_overhead: scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()),
                        },
                        latency_estimator: LatencyEstimator {
                            base_latency: scirs2_core::numeric::NumCast::from(10.0).unwrap_or_else(|| T::zero()),
                            hardware_multipliers: HashMap::new(),
                            batch_scaling: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
                        },
                        parallelizability: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
                        memory_pattern: MemoryAccessPattern::Sequential,
                    },
                    compatibility: CompatibilityConstraints {
                        input_shape_constraints: Vec::new(),
                        output_shape_constraints: Vec::new(),
                        predecessor_constraints: Vec::new(),
                        successor_constraints: Vec::new(),
                        exclusion_constraints: Vec::new(),
                    },
                    performance_chars: PerformanceCharacteristics {
                        accuracy_impact: scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero()),
                        stability_impact: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
                        convergence_impact: scirs2_core::numeric::NumCast::from(0.6).unwrap_or_else(|| T::zero()),
                        generalization_impact: scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero()),
                        efficiency_score: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
                    },
                },
            ],
            connection_constraints: ConnectionConstraints {
                max_input_degree: 5,
                max_output_degree: 5,
                forbidden_patterns: Vec::new(),
                required_patterns: Vec::new(),
                allow_cycles: false,
            },
            resource_constraints: ResourceConstraints {
                max_parameters: Some(10_000_000),
                max_flops: Some(1_000_000_000),
                max_memory: Some(1_000_000_000), // 1GB
                max_latency: Some(scirs2_core::numeric::NumCast::from(100.0).unwrap_or_else(|| T::zero())), // 100ms
                max_energy: Some(scirs2_core::numeric::NumCast::from(1.0).unwrap_or_else(|| T::zero())), // 1J
                hardware_constraints: HashMap::new(),
            },
            domain_constraints: DomainConstraints {
                task_constraints: TaskConstraints {
                    task_type: TaskType::Classification,
                    required_capabilities: Vec::new(),
                    prohibited_operations: Vec::new(),
                    preferred_patterns: Vec::new(),
                },
                data_constraints: DataConstraints {
                    input_types: vec![DataType::Image],
                    output_types: vec![DataType::Tabular],
                    size_constraints: SizeConstraints {
                        min_size: vec![224, 224, 3],
                        max_size: vec![1024, 1024, 3],
                        batch_constraints: (1, 128),
                    },
                    privacy_requirements: PrivacyRequirements {
                        differential_privacy: false,
                        federated_compatible: false,
                        data_locality: false,
                        encryption_required: false,
                    },
                },
                performance_requirements: PerformanceRequirements {
                    min_accuracy: Some(scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero())),
                    max_inference_time: Some(scirs2_core::numeric::NumCast::from(100.0).unwrap_or_else(|| T::zero())),
                    min_throughput: Some(scirs2_core::numeric::NumCast::from(10.0).unwrap_or_else(|| T::zero())),
                    real_time: false,
                },
                regulatory_constraints: RegulatoryConstraints {
                    explainability_required: false,
                    fairness_constraints: Vec::new(),
                    safety_requirements: Vec::new(),
                    compliance_standards: Vec::new(),
                },
            },
            complexity_limits: ComplexityLimits {
                max_space_size: 1_000_000_000,
                max_arch_complexity: scirs2_core::numeric::NumCast::from(100.0).unwrap_or_else(|| T::zero()),
                sampling_limits: SamplingLimits {
                    max_samples_per_iteration: 100,
                    max_total_samples: 10000,
                    sampling_timeout: 3600, // 1 hour
                },
                search_constraints: SearchConstraints {
                    max_search_depth: 100,
                    convergence_criteria: ConvergenceCriteria {
                        improvement_threshold: scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()),
                        patience: 10,
                        min_iterations: 5,
                        max_iterations: 1000,
                    },
                    early_stopping: EarlyStoppingConditions {
                        performance_threshold: Some(scirs2_core::numeric::NumCast::from(0.95).unwrap_or_else(|| T::zero())),
                        resource_threshold: Some(scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero())),
                        time_limit: Some(7200), // 2 hours
                    },
                },
            },
        }
    }
}