optirs-learned 0.3.1

use std::fmt::Debug;
// Architecture encoding and representation for neural architecture search
//
// This module provides methods to encode neural network architectures into
// various representations suitable for different search algorithms.

#[allow(dead_code)]

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

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

/// Configuration for architecture encoding
#[derive(Debug, Clone)]
pub struct ArchitectureEncodingConfig {
    /// Maximum sequence length for sequential encodings
    pub max_sequence_length: usize,
    
    /// Vocabulary size for categorical encodings
    pub vocabulary_size: usize,
    
    /// Embedding dimensions for different encoding types
    pub embedding_dimensions: HashMap<EncodingType, usize>,
    
    /// Whether to include positional encodings
    pub use_positional_encoding: bool,
    
    /// Normalization method
    pub normalization_method: NormalizationMethod,
    
    /// Supported operation types
    pub operation_vocabulary: Vec<String>,
    
    /// Maximum graph depth
    pub max_graph_depth: usize,
}

/// Types of architecture encodings
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum EncodingType {
    /// Sequential encoding (list of operations)
    Sequential,
    
    /// Graph-based encoding (adjacency matrices)
    Graph,
    
    /// Tree-based encoding
    Tree,
    
    /// Path-based encoding
    PathBased,
    
    /// Continuous vector encoding
    ContinuousVector,
    
    /// Categorical encoding
    Categorical,
    
    /// Hybrid encoding (multiple representations)
    Hybrid,
}

/// Normalization methods for encodings
#[derive(Debug, Clone, Copy)]
pub enum NormalizationMethod {
    /// No normalization
    None,
    
    /// Min-max normalization
    MinMax,
    
    /// Z-score normalization
    ZScore,
    
    /// Layer normalization
    LayerNorm,
    
    /// Batch normalization
    BatchNorm,
}

/// Architecture encoder for different representation types
#[derive(Debug)]
pub struct ArchitectureEncoder<T: Float + Debug + Send + Sync + 'static> {
    /// Configuration
    config: ArchitectureEncodingConfig,
    
    /// Encoding mappings
    encodings: HashMap<EncodingType, Box<dyn EncodingStrategy<T>>>,
    
    /// Operation to index mapping
    operation_to_index: HashMap<String, usize>,
    
    /// Index to operation mapping
    index_to_operation: HashMap<usize, String>,
    
    /// Embedding matrices for different encodings
    embedding_matrices: HashMap<EncodingType, Array2<T>>,
    
    /// Normalization statistics
    normalization_stats: HashMap<String, NormalizationStats<T>>,
}

/// Trait for different encoding strategies
pub trait EncodingStrategy<T: Float + Debug + Send + Sync + 'static> {
    /// Encode architecture into the specific representation
    fn encode(&self, architecture: &ArchitectureDefinition<T>) -> Result<EncodedArchitecture<T>>;
    
    /// Decode representation back to architecture
    fn decode(&self, encoded: &EncodedArchitecture<T>) -> Result<ArchitectureDefinition<T>>;
    
    /// Get encoding dimensionality
    fn get_dimensions(&self) -> Vec<usize>;
    
    /// Validate encoding consistency
    fn validate(&self, encoded: &EncodedArchitecture<T>) -> Result<bool>;
}

/// Architecture definition structure
#[derive(Debug, Clone)]
pub struct ArchitectureDefinition<T: Float + Debug + Send + Sync + 'static> {
    /// Architecture identifier
    pub id: String,
    
    /// List of operations/layers
    pub operations: Vec<OperationSpec<T>>,
    
    /// Connection graph
    pub connections: ConnectionGraph,
    
    /// Global architecture parameters
    pub global_parameters: HashMap<String, T>,
    
    /// Input/output specifications
    pub io_specs: IOSpecifications,
    
    /// Architecture metadata
    pub metadata: ArchitectureMetadata,
}

/// Operation specification
#[derive(Debug, Clone)]
pub struct OperationSpec<T: Float + Debug + Send + Sync + 'static> {
    /// Operation identifier
    pub id: String,
    
    /// Operation type
    pub operation_type: String,
    
    /// Operation parameters
    pub parameters: HashMap<String, ParameterValue<T>>,
    
    /// Input shapes
    pub input_shapes: Vec<Vec<usize>>,
    
    /// Output shapes
    pub output_shapes: Vec<Vec<usize>>,
    
    /// Position in architecture
    pub position: Position,
}

/// Parameter values for operations
#[derive(Debug, Clone)]
pub enum ParameterValue<T: Float + Debug + Send + Sync + 'static> {
    /// Integer parameter
    Integer(i64),
    
    /// Float parameter
    Float(T),
    
    /// Boolean parameter
    Boolean(bool),
    
    /// String parameter
    String(String),
    
    /// Array parameter
    Array(Vec<T>),
    
    /// Tensor parameter
    Tensor(Array3<T>),
}

/// Connection graph representation
#[derive(Debug, Clone)]
pub struct ConnectionGraph {
    /// Adjacency matrix
    pub adjacency_matrix: Array2<f32>,
    
    /// Edge types
    pub edge_types: HashMap<(usize, usize), EdgeType>,
    
    /// Node properties
    pub node_properties: HashMap<usize, NodeProperties>,
    
    /// Graph topology information
    pub topology: GraphTopology,
}

/// Edge types in connection graph
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum EdgeType {
    /// Standard data flow edge
    DataFlow,
    
    /// Skip connection
    Skip,
    
    /// Residual connection
    Residual,
    
    /// Attention connection
    Attention,
    
    /// Dense connection
    Dense,
    
    /// Custom connection type
    Custom(u8),
}

/// Properties of graph nodes
#[derive(Debug, Clone)]
pub struct NodeProperties {
    /// Node type
    pub node_type: NodeType,
    
    /// Node depth in graph
    pub depth: usize,
    
    /// Input degree
    pub in_degree: usize,
    
    /// Output degree
    pub out_degree: usize,
    
    /// Node attributes
    pub attributes: HashMap<String, String>,
}

/// Types of nodes in graph
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum NodeType {
    /// Input node
    Input,
    
    /// Output node
    Output,
    
    /// Operation node
    Operation,
    
    /// Concat/merge node
    Merge,
    
    /// Split node
    Split,
}

/// Graph topology information
#[derive(Debug, Clone)]
pub struct GraphTopology {
    /// Strongly connected components
    pub scc: Vec<Vec<usize>>,
    
    /// Topological ordering
    pub topological_order: Vec<usize>,
    
    /// Longest paths
    pub longest_paths: HashMap<usize, usize>,
    
    /// Graph diameter
    pub diameter: usize,
    
    /// Clustering coefficient
    pub clustering_coefficient: f64,
}

/// Position information for operations
#[derive(Debug, Clone)]
pub struct Position {
    /// Layer index
    pub layer_index: usize,
    
    /// Block index within layer
    pub block_index: usize,
    
    /// Operation index within block
    pub operation_index: usize,
    
    /// Spatial coordinates (if applicable)
    pub coordinates: Option<(f64, f64)>,
}

/// Input/output specifications
#[derive(Debug, Clone)]
pub struct IOSpecifications {
    /// Input shapes
    pub input_shapes: Vec<Vec<usize>>,
    
    /// Output shapes
    pub output_shapes: Vec<Vec<usize>>,
    
    /// Data types
    pub data_types: Vec<String>,
    
    /// Input/output constraints
    pub constraints: Vec<IOConstraint>,
}

/// Input/output constraints
#[derive(Debug, Clone)]
pub struct IOConstraint {
    /// Constraint type
    pub constraint_type: String,
    
    /// Constraint parameters
    pub parameters: HashMap<String, f64>,
}

/// Architecture metadata
#[derive(Debug, Clone)]
pub struct ArchitectureMetadata {
    /// Creation timestamp
    pub created_at: u64,
    
    /// Last modified timestamp
    pub modified_at: u64,
    
    /// Architecture tags
    pub tags: Vec<String>,
    
    /// Performance estimates
    pub performance_estimates: HashMap<String, f64>,
    
    /// Complexity metrics
    pub complexity_metrics: ComplexityMetrics,
}

/// Complexity metrics for architectures
#[derive(Debug, Clone)]
pub struct ComplexityMetrics {
    /// Total parameters
    pub total_parameters: usize,
    
    /// FLOPS estimate
    pub flops: u64,
    
    /// Memory requirement
    pub memory_mb: f64,
    
    /// Graph complexity measures
    pub graph_complexity: f64,
    
    /// Architectural diversity
    pub diversity_score: f64,
}

/// Encoded architecture representation
#[derive(Debug, Clone)]
pub struct EncodedArchitecture<T: Float + Debug + Send + Sync + 'static> {
    /// Encoding type used
    pub encoding_type: EncodingType,
    
    /// Primary encoding (main representation)
    pub primary_encoding: Array2<T>,
    
    /// Secondary encodings (auxiliary representations)
    pub secondary_encodings: HashMap<String, Array2<T>>,
    
    /// Categorical features
    pub categorical_features: HashMap<String, Vec<usize>>,
    
    /// Continuous features
    pub continuous_features: HashMap<String, Array1<T>>,
    
    /// Sequence lengths (for variable-length encodings)
    pub sequence_lengths: Vec<usize>,
    
    /// Mask for padding (if applicable)
    pub padding_mask: Option<Array2<bool>>,
    
    /// Encoding metadata
    pub metadata: EncodingMetadata<T>,
}

/// Metadata for encodings
#[derive(Debug, Clone)]
pub struct EncodingMetadata<T: Float + Debug + Send + Sync + 'static> {
    /// Original architecture ID
    pub architecture_id: String,
    
    /// Encoding timestamp
    pub encoded_at: u64,
    
    /// Encoding quality metrics
    pub quality_metrics: EncodingQualityMetrics<T>,
    
    /// Reconstruction error
    pub reconstruction_error: Option<T>,
    
    /// Encoding version
    pub version: String,
}

/// Quality metrics for encodings
#[derive(Debug, Clone)]
pub struct EncodingQualityMetrics<T: Float + Debug + Send + Sync + 'static> {
    /// Information preservation score
    pub information_preservation: T,
    
    /// Compression ratio
    pub compression_ratio: T,
    
    /// Encoding stability
    pub stability: T,
    
    /// Semantic consistency
    pub semantic_consistency: T,
}

/// Normalization statistics
#[derive(Debug, Clone)]
pub struct NormalizationStats<T: Float + Debug + Send + Sync + 'static> {
    /// Mean values
    pub mean: Array1<T>,
    
    /// Standard deviations
    pub std: Array1<T>,
    
    /// Minimum values
    pub min: Array1<T>,
    
    /// Maximum values
    pub max: Array1<T>,
    
    /// Number of samples used for statistics
    pub sample_count: usize,
}

/// Sequential encoding strategy
#[derive(Debug)]
pub struct SequentialEncoder<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum sequence length
    max_length: usize,
    
    /// Operation vocabulary
    vocabulary: HashMap<String, usize>,
    
    /// Embedding dimension
    embedding_dim: usize,
    
    /// Position encoding
    positional_encoding: Option<Array2<T>>,
}

/// Graph-based encoding strategy
#[derive(Debug)]
pub struct GraphEncoder<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum number of nodes
    max_nodes: usize,
    
    /// Node feature dimension
    node_feature_dim: usize,
    
    /// Edge feature dimension
    edge_feature_dim: usize,
    
    /// Graph normalization method
    normalization: GraphNormalization,
}

/// Graph normalization methods
#[derive(Debug, Clone, Copy)]
pub enum GraphNormalization {
    /// No normalization
    None,
    
    /// Symmetric normalization
    Symmetric,
    
    /// Random walk normalization
    RandomWalk,
    
    /// Spectral normalization
    Spectral,
}

/// Tree-based encoding strategy
#[derive(Debug)]
pub struct TreeEncoder<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum tree depth
    max_depth: usize,
    
    /// Maximum branching factor
    max_branching: usize,
    
    /// Node embedding dimension
    node_dim: usize,
    
    /// Tree traversal order
    traversal_order: TreeTraversalOrder,
}

/// Tree traversal orders
#[derive(Debug, Clone, Copy)]
pub enum TreeTraversalOrder {
    /// Depth-first search
    DepthFirst,
    
    /// Breadth-first search
    BreadthFirst,
    
    /// Pre-order traversal
    PreOrder,
    
    /// Post-order traversal
    PostOrder,
    
    /// In-order traversal
    InOrder,
}

/// Path-based encoding strategy
#[derive(Debug)]
pub struct PathEncoder<T: Float + Debug + Send + Sync + 'static> {
    /// Maximum path length
    max_path_length: usize,
    
    /// Number of paths to encode
    num_paths: usize,
    
    /// Path sampling strategy
    sampling_strategy: PathSamplingStrategy,
    
    /// Path embedding dimension
    path_dim: usize,
}

/// Path sampling strategies
#[derive(Debug, Clone, Copy)]
pub enum PathSamplingStrategy {
    /// Sample shortest paths
    Shortest,
    
    /// Sample longest paths
    Longest,
    
    /// Random path sampling
    Random,
    
    /// Important paths (based on centrality)
    Important,
    
    /// All simple paths
    AllSimple,
}

/// Continuous vector encoding strategy
#[derive(Debug)]
pub struct ContinuousEncoder<T: Float + Debug + Send + Sync + 'static> {
    /// Vector dimension
    vector_dim: usize,
    
    /// Encoding neural network
    encoder_network: Vec<Array2<T>>,
    
    /// Decoder neural network
    decoder_network: Vec<Array2<T>>,
    
    /// Variational encoding parameters
    variational_params: Option<VariationalParams<T>>,
}

/// Variational encoding parameters
#[derive(Debug, Clone)]
pub struct VariationalParams<T: Float + Debug + Send + Sync + 'static> {
    /// Latent dimension
    latent_dim: usize,
    
    /// KL divergence weight
    kl_weight: T,
    
    /// Reconstruction loss weight
    reconstruction_weight: T,
}

impl<T: Float + Debug + Default + Clone + Send + Sync + 'static> ArchitectureEncoder<T> {
    /// Create new architecture encoder
    pub fn new(config: ArchitectureEncodingConfig) -> Result<Self> {
        let mut encoder = Self {
            config: config.clone(),
            encodings: HashMap::new(),
            operation_to_index: HashMap::new(),
            index_to_operation: HashMap::new(),
            embedding_matrices: HashMap::new(),
            normalization_stats: HashMap::new(),
        };
        
        // Initialize operation vocabulary
        encoder.initialize_vocabulary()?;
        
        // Initialize encoding strategies
        encoder.initialize_encoders()?;
        
        // Initialize embedding matrices
        encoder.initialize_embeddings()?;
        
        Ok(encoder)
    }
    
    /// Initialize operation vocabulary
    fn initialize_vocabulary(&mut self) -> Result<()> {
        for (i, op) in self.config.operation_vocabulary.iter().enumerate() {
            self.operation_to_index.insert(op.clone(), i);
            self.index_to_operation.insert(i, op.clone());
        }
        Ok(())
    }
    
    /// Initialize encoding strategies
    fn initialize_encoders(&mut self) -> Result<()> {
        // Initialize sequential encoder
        if let Some(&dim) = self.config.embedding_dimensions.get(&EncodingType::Sequential) {
            let seq_encoder = SequentialEncoder::new(
                self.config.max_sequence_length,
                self.operation_to_index.clone(),
                dim,
                self.config.use_positional_encoding,
            )?;
            self.encodings.insert(EncodingType::Sequential, Box::new(seq_encoder));
        }
        
        // Initialize graph encoder
        if let Some(&dim) = self.config.embedding_dimensions.get(&EncodingType::Graph) {
            let graph_encoder = GraphEncoder::new(
                self.config.max_sequence_length, // max nodes
                dim,
                dim / 2, // edge feature dim
                GraphNormalization::Symmetric,
            )?;
            self.encodings.insert(EncodingType::Graph, Box::new(graph_encoder));
        }
        
        // Initialize other encoders...
        
        Ok(())
    }
    
    /// Initialize embedding matrices
    fn initialize_embeddings(&mut self) -> Result<()> {
        use scirs2_core::random::Rng;
        let mut rng = scirs2_core::random::thread_rng();
        
        for (&encoding_type, &dim) in &self.config.embedding_dimensions {
            let vocab_size = self.config.vocabulary_size;
            let mut embedding = Array2::zeros((vocab_size, dim));
            
            // Initialize with random values
            for i in 0..vocab_size {
                for j in 0..dim {
                    embedding[[i, j]] = T::from(rng.gen_range(-0.1..0.1)).expect("unwrap failed");
                }
            }
            
            self.embedding_matrices.insert(encoding_type, embedding);
        }
        
        Ok(())
    }
    
    /// Encode architecture using specified encoding type
    pub fn encode(&self, architecture: &ArchitectureDefinition<T>, encoding_type: EncodingType) -> Result<EncodedArchitecture<T>> {
        if let Some(encoder) = self.encodings.get(&encoding_type) {
            let mut encoded = encoder.encode(architecture)?;
            
            // Apply normalization if configured
            if let NormalizationMethod::MinMax | NormalizationMethod::ZScore = self.config.normalization_method {
                self.apply_normalization(&mut encoded)?;
            }
            
            Ok(encoded)
        } else {
            Err(OptimError::UnsupportedOperation(
                format!("Encoding type {:?} not supported", encoding_type)
            ))
        }
    }
    
    /// Decode encoded architecture back to definition
    pub fn decode(&self, encoded: &EncodedArchitecture<T>) -> Result<ArchitectureDefinition<T>> {
        if let Some(encoder) = self.encodings.get(&encoded.encoding_type) {
            encoder.decode(encoded)
        } else {
            Err(OptimError::UnsupportedOperation(
                format!("Encoding type {:?} not supported", encoded.encoding_type)
            ))
        }
    }
    
    /// Encode architecture using multiple encoding types
    pub fn encode_multi(&self, architecture: &ArchitectureDefinition<T>, 
                       encoding_types: &[EncodingType]) -> Result<HashMap<EncodingType, EncodedArchitecture<T>>> {
        let mut results = HashMap::new();
        
        for &encoding_type in encoding_types {
            let encoded = self.encode(architecture, encoding_type)?;
            results.insert(encoding_type, encoded);
        }
        
        Ok(results)
    }
    
    /// Apply normalization to encoded representation
    fn apply_normalization(&self, encoded: &mut EncodedArchitecture<T>) -> Result<()> {
        match self.config.normalization_method {
            NormalizationMethod::MinMax => {
                self.apply_minmax_normalization(&mut encoded.primary_encoding)?;
            }
            NormalizationMethod::ZScore => {
                self.apply_zscore_normalization(&mut encoded.primary_encoding)?;
            }
            _ => {} // Other normalization methods not implemented
        }
        
        Ok(())
    }
    
    /// Apply min-max normalization
    fn apply_minmax_normalization(&self, data: &mut Array2<T>) -> Result<()> {
        for j in 0..data.ncols() {
            let mut col = data.column_mut(j);
            let min_val = col.iter().cloned().fold(scirs2_core::numeric::NumCast::from(f64::INFINITY).unwrap_or_else(|| T::zero()), T::min);
            let max_val = col.iter().cloned().fold(scirs2_core::numeric::NumCast::from(f64::NEG_INFINITY).unwrap_or_else(|| T::zero()), T::max);
            
            if max_val > min_val {
                let range = max_val - min_val;
                for val in col.iter_mut() {
                    *val = (*val - min_val) / range;
                }
            }
        }
        
        Ok(())
    }
    
    /// Apply z-score normalization
    fn apply_zscore_normalization(&self, data: &mut Array2<T>) -> Result<()> {
        for j in 0..data.ncols() {
            let mut col = data.column_mut(j);
            let n = T::from(col.len() as f64).expect("unwrap failed");
            
            // Compute mean
            let mean = col.iter().cloned().fold(T::zero(), |acc, x| acc + x) / n;
            
            // Compute standard deviation
            let variance = col.iter().map(|&x| (x - mean) * (x - mean)).fold(T::zero(), |acc, x| acc + x) / n;
            let std_dev = variance.sqrt();
            
            if std_dev > T::zero() {
                for val in col.iter_mut() {
                    *val = (*val - mean) / std_dev;
                }
            }
        }
        
        Ok(())
    }
    
    /// Compute similarity between two encoded architectures
    pub fn compute_similarity(&self, encoded1: &EncodedArchitecture<T>, 
                             encoded2: &EncodedArchitecture<T>) -> Result<T> {
        if encoded1.encoding_type != encoded2.encoding_type {
            return Err(OptimError::InvalidInput(
                "Cannot compute similarity between different encoding types".to_string()
            ));
        }
        
        // Cosine similarity
        let flat1 = encoded1.primary_encoding.as_slice().expect("unwrap failed");
        let flat2 = encoded2.primary_encoding.as_slice().expect("unwrap failed");
        
        if flat1.len() != flat2.len() {
            return Err(OptimError::InvalidInput(
                "Encoded architectures have different dimensions".to_string()
            ));
        }
        
        let dot_product = flat1.iter().zip(flat2.iter()).map(|(&a, &b)| a * b).fold(T::zero(), |acc, x| acc + x);
        let norm1 = flat1.iter().map(|&x| x * x).fold(T::zero(), |acc, x| acc + x).sqrt();
        let norm2 = flat2.iter().map(|&x| x * x).fold(T::zero(), |acc, x| acc + x).sqrt();
        
        if norm1 > T::zero() && norm2 > T::zero() {
            Ok(dot_product / (norm1 * norm2))
        } else {
            Ok(T::zero())
        }
    }
    
    /// Update normalization statistics
    pub fn update_normalization_stats(&mut self, encoded: &EncodedArchitecture<T>) -> Result<()> {
        let stats_key = format!("{:?}", encoded.encoding_type);
        
        // Update running statistics (simplified implementation)
        let stats = self.normalization_stats.entry(stats_key).or_insert_with(|| NormalizationStats {
            mean: Array1::zeros(encoded.primary_encoding.ncols()),
            std: Array1::ones(encoded.primary_encoding.ncols()),
            min: Array1::from_elem(encoded.primary_encoding.ncols(), scirs2_core::numeric::NumCast::from(f64::INFINITY).unwrap_or_else(|| T::zero())),
            max: Array1::from_elem(encoded.primary_encoding.ncols(), scirs2_core::numeric::NumCast::from(f64::NEG_INFINITY).unwrap_or_else(|| T::zero())),
            sample_count: 0,
        });
        
        // Update statistics
        for j in 0..encoded.primary_encoding.ncols() {
            let col = encoded.primary_encoding.column(j);
            let col_min = col.iter().cloned().fold(scirs2_core::numeric::NumCast::from(f64::INFINITY).unwrap_or_else(|| T::zero()), T::min);
            let col_max = col.iter().cloned().fold(scirs2_core::numeric::NumCast::from(f64::NEG_INFINITY).unwrap_or_else(|| T::zero()), T::max);
            
            stats.min[j] = stats.min[j].min(col_min);
            stats.max[j] = stats.max[j].max(col_max);
        }
        
        stats.sample_count += 1;
        
        Ok(())
    }
    
    /// Get supported encoding types
    pub fn get_supported_encoding_types(&self) -> Vec<EncodingType> {
        self.encodings.keys().copied().collect()
    }
    
    /// Get encoding dimensions
    pub fn get_encoding_dimensions(&self, encoding_type: EncodingType) -> Option<Vec<usize>> {
        self.encodings.get(&encoding_type).map(|encoder| encoder.get_dimensions())
    }
    
    /// Validate encoded architecture
    pub fn validate_encoding(&self, encoded: &EncodedArchitecture<T>) -> Result<bool> {
        if let Some(encoder) = self.encodings.get(&encoded.encoding_type) {
            encoder.validate(encoded)
        } else {
            Ok(false)
        }
    }
}

// Implementations for specific encoding strategies

impl<T: Float + Debug + Default + Clone + Send + Sync + 'static> SequentialEncoder<T> {
    pub fn new(max_length: usize, vocabulary: HashMap<String, usize>, 
              embedding_dim: usize, use_positional: bool) -> Result<Self> {
        let positional_encoding = if use_positional {
            Some(Self::create_positional_encoding(max_length, embedding_dim)?)
        } else {
            None
        };
        
        Ok(Self {
            max_length,
            vocabulary,
            embedding_dim,
            positional_encoding,
        })
    }
    
    fn create_positional_encoding(max_length: usize, embedding_dim: usize) -> Result<Array2<T>> {
        let mut pos_encoding = Array2::zeros((max_length, embedding_dim));
        
        for pos in 0..max_length {
            for i in 0..(embedding_dim / 2) {
                let angle = scirs2_core::numeric::NumCast::from(pos as f64).unwrap_or_else(|| T::zero()) / 
                           T::from(10000.0_f64.powf(2.0 * i as f64 / embedding_dim as f64)).expect("unwrap failed");
                pos_encoding[[pos, 2 * i]] = angle.sin();
                pos_encoding[[pos, 2 * i + 1]] = angle.cos();
            }
        }
        
        Ok(pos_encoding)
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + 'static> EncodingStrategy<T> for SequentialEncoder<T> {
    fn encode(&self, architecture: &ArchitectureDefinition<T>) -> Result<EncodedArchitecture<T>> {
        let seq_len = architecture.operations.len().min(self.max_length);
        let mut encoding = Array2::zeros((seq_len, self.embedding_dim));
        
        // Encode operations as sequence
        for (i, operation) in architecture.operations.iter().enumerate().take(seq_len) {
            if let Some(&op_idx) = self.vocabulary.get(&operation.operation_type) {
                // Simple one-hot encoding (in practice would use learned embeddings)
                if op_idx < self.embedding_dim {
                    encoding[[i, op_idx]] = T::one();
                }
            }
        }
        
        // Add positional encoding if enabled
        if let Some(ref pos_enc) = self.positional_encoding {
            for i in 0..seq_len {
                for j in 0..self.embedding_dim.min(pos_enc.ncols()) {
                    encoding[[i, j]] = encoding[[i, j]] + pos_enc[[i, j]];
                }
            }
        }
        
        Ok(EncodedArchitecture {
            encoding_type: EncodingType::Sequential,
            primary_encoding: encoding,
            secondary_encodings: HashMap::new(),
            categorical_features: HashMap::new(),
            continuous_features: HashMap::new(),
            sequence_lengths: vec![seq_len],
            padding_mask: None,
            metadata: EncodingMetadata {
                architecture_id: architecture.id.clone(),
                encoded_at: std::time::SystemTime::now()
                    .duration_since(std::time::UNIX_EPOCH)
                    .unwrap_or_default()
                    .as_secs(),
                quality_metrics: EncodingQualityMetrics {
                    information_preservation: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
                    compression_ratio: T::from(seq_len as f64 / architecture.operations.len() as f64).expect("unwrap failed"),
                    stability: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
                    semantic_consistency: scirs2_core::numeric::NumCast::from(0.85).unwrap_or_else(|| T::zero()),
                },
                reconstruction_error: None,
                version: "1.0".to_string(),
            },
        })
    }
    
    fn decode(&self, encoded: &EncodedArchitecture<T>) -> Result<ArchitectureDefinition<T>> {
        // Simplified decoding - in practice would be more sophisticated
        let mut operations = Vec::new();
        
        for i in 0..encoded.primary_encoding.nrows() {
            // Find the operation with highest activation
            let row = encoded.primary_encoding.row(i);
            if let Some((max_idx, _)) = row.indexed_iter().max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)) {
                if let Some(op_name) = self.vocabulary.iter().find(|(_, &idx)| idx == max_idx).map(|(name, _)| name) {
                    operations.push(OperationSpec {
                        id: format!("op_{}", i),
                        operation_type: op_name.clone(),
                        parameters: HashMap::new(),
                        input_shapes: vec![vec![224, 224, 3]], // Default
                        output_shapes: vec![vec![224, 224, 3]], // Default
                        position: Position {
                            layer_index: i,
                            block_index: 0,
                            operation_index: 0,
                            coordinates: None,
                        },
                    });
                }
            }
        }
        
        Ok(ArchitectureDefinition {
            id: encoded.metadata.architecture_id.clone(),
            operations,
            connections: ConnectionGraph {
                adjacency_matrix: Array2::eye(operations.len()),
                edge_types: HashMap::new(),
                node_properties: HashMap::new(),
                topology: GraphTopology {
                    scc: vec![vec![0]],
                    topological_order: (0..operations.len()).collect(),
                    longest_paths: HashMap::new(),
                    diameter: operations.len(),
                    clustering_coefficient: 0.0,
                },
            },
            global_parameters: HashMap::new(),
            io_specs: IOSpecifications {
                input_shapes: vec![vec![224, 224, 3]],
                output_shapes: vec![vec![1000]],
                data_types: vec!["float32".to_string()],
                constraints: Vec::new(),
            },
            metadata: ArchitectureMetadata {
                created_at: encoded.metadata.encoded_at,
                modified_at: encoded.metadata.encoded_at,
                tags: Vec::new(),
                performance_estimates: HashMap::new(),
                complexity_metrics: ComplexityMetrics {
                    total_parameters: operations.len() * 1000, // Estimate
                    flops: operations.len() as u64 * 1000000, // Estimate
                    memory_mb: operations.len() as f64 * 10.0, // Estimate
                    graph_complexity: operations.len() as f64,
                    diversity_score: 0.5,
                },
            },
        })
    }
    
    fn get_dimensions(&self) -> Vec<usize> {
        vec![self.max_length, self.embedding_dim]
    }
    
    fn validate(&self, encoded: &EncodedArchitecture<T>) -> Result<bool> {
        Ok(encoded.primary_encoding.nrows() <= self.max_length &&
           encoded.primary_encoding.ncols() == self.embedding_dim)
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + 'static> GraphEncoder<T> {
    pub fn new(max_nodes: usize, node_feature_dim: usize, 
              edge_feature_dim: usize, normalization: GraphNormalization) -> Result<Self> {
        Ok(Self {
            max_nodes,
            node_feature_dim,
            edge_feature_dim,
            normalization,
        })
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + 'static> EncodingStrategy<T> for GraphEncoder<T> {
    fn encode(&self, architecture: &ArchitectureDefinition<T>) -> Result<EncodedArchitecture<T>> {
        let num_nodes = architecture.operations.len().min(self.max_nodes);
        let mut node_features = Array2::zeros((self.max_nodes, self.node_feature_dim));
        
        // Encode node features
        for (i, operation) in architecture.operations.iter().enumerate().take(num_nodes) {
            // Simple encoding - in practice would be more sophisticated
            node_features[[i, 0]] = scirs2_core::numeric::NumCast::from(i as f64).unwrap_or_else(|| T::zero()); // Node index
            node_features[[i, 1]] = T::from(operation.parameters.len() as f64).expect("unwrap failed"); // Parameter count
        }
        
        Ok(EncodedArchitecture {
            encoding_type: EncodingType::Graph,
            primary_encoding: node_features,
            secondary_encodings: {
                let mut secondary = HashMap::new();
                secondary.insert("adjacency_matrix".to_string(), 
                               architecture.connections.adjacency_matrix.mapv(|x| scirs2_core::numeric::NumCast::from(x).unwrap_or_else(|| T::zero())));
                secondary
            },
            categorical_features: HashMap::new(),
            continuous_features: HashMap::new(),
            sequence_lengths: vec![num_nodes],
            padding_mask: None,
            metadata: EncodingMetadata {
                architecture_id: architecture.id.clone(),
                encoded_at: std::time::SystemTime::now()
                    .duration_since(std::time::UNIX_EPOCH)
                    .unwrap_or_default()
                    .as_secs(),
                quality_metrics: EncodingQualityMetrics {
                    information_preservation: scirs2_core::numeric::NumCast::from(0.95).unwrap_or_else(|| T::zero()),
                    compression_ratio: T::from(num_nodes as f64 / architecture.operations.len() as f64).expect("unwrap failed"),
                    stability: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
                    semantic_consistency: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
                },
                reconstruction_error: None,
                version: "1.0".to_string(),
            },
        })
    }
    
    fn decode(&self, _encoded: &EncodedArchitecture<T>) -> Result<ArchitectureDefinition<T>> {
        // Simplified decoding implementation
        Err(OptimError::UnsupportedOperation("Graph decoding not implemented".to_string()))
    }
    
    fn get_dimensions(&self) -> Vec<usize> {
        vec![self.max_nodes, self.node_feature_dim]
    }
    
    fn validate(&self, encoded: &EncodedArchitecture<T>) -> Result<bool> {
        Ok(encoded.primary_encoding.nrows() <= self.max_nodes &&
           encoded.primary_encoding.ncols() == self.node_feature_dim)
    }
}

impl Default for ArchitectureEncodingConfig {
    fn default() -> Self {
        let mut embedding_dimensions = HashMap::new();
        embedding_dimensions.insert(EncodingType::Sequential, 128);
        embedding_dimensions.insert(EncodingType::Graph, 64);
        embedding_dimensions.insert(EncodingType::ContinuousVector, 256);
        
        Self {
            max_sequence_length: 50,
            vocabulary_size: 1000,
            embedding_dimensions,
            use_positional_encoding: true,
            normalization_method: NormalizationMethod::MinMax,
            operation_vocabulary: vec![
                "conv2d".to_string(),
                "dense".to_string(),
                "batch_norm".to_string(),
                "relu".to_string(),
                "max_pool".to_string(),
                "avg_pool".to_string(),
                "dropout".to_string(),
                "attention".to_string(),
                "lstm".to_string(),
                "gru".to_string(),
            ],
            max_graph_depth: 20,
        }
    }
}