scirs2-metrics 0.3.2

//! Advanced memory architectures for neuromorphic systems
//!
//! This module implements hierarchical memory systems, working memory models,
//! episodic and semantic memory, and advanced consolidation protocols.

#![allow(clippy::too_many_arguments)]
#![allow(dead_code)]

use crate::error::{MetricsError, Result};
use scirs2_core::ndarray::Array1;
use scirs2_core::numeric::Float;
use std::collections::{HashMap, VecDeque};
use std::time::Instant;

/// Advanced memory architecture
#[derive(Debug)]
pub struct AdvancedMemoryArchitecture<F: Float> {
    /// Hierarchical memory systems
    pub hierarchical_memory: HierarchicalMemorySystem<F>,
    /// Associative memory networks
    pub associative_memory: AssociativeMemoryNetwork<F>,
    /// Working memory models
    pub working_memory: WorkingMemoryModel<F>,
    /// Episodic memory systems
    pub episodic_memory: EpisodicMemorySystem<F>,
    /// Semantic memory networks
    pub semantic_memory: SemanticMemoryNetwork<F>,
    /// Memory consolidation protocols
    pub consolidation_protocols: Vec<MemoryConsolidationProtocol<F>>,
}

/// Hierarchical memory system
#[derive(Debug)]
pub struct HierarchicalMemorySystem<F: Float> {
    /// Sensory memory buffer
    pub sensory_memory: SensoryMemoryBuffer<F>,
    /// Short-term memory with chunking
    pub short_term: ShortTermMemoryWithChunking<F>,
    /// Long-term memory hierarchies
    pub long_term: LongTermMemoryHierarchy<F>,
    /// Memory routing mechanisms
    pub memory_routers: Vec<MemoryRouter<F>>,
}

/// Sensory memory buffer
#[derive(Debug)]
pub struct SensoryMemoryBuffer<F: Float> {
    /// Buffer capacity
    pub capacity: usize,
    /// Current data in buffer
    pub data: VecDeque<Array1<F>>,
    /// Decay rate for sensory memory
    pub decay_rate: F,
    /// Creation timestamps for decay calculation
    pub timestamps: VecDeque<Instant>,
}

/// Short-term memory with chunking capabilities
#[derive(Debug)]
pub struct ShortTermMemoryWithChunking<F: Float> {
    /// Memory chunks organized by pattern similarity
    pub chunks: HashMap<String, Vec<Array1<F>>>,
    /// Chunk access frequencies for prioritization
    pub access_counts: HashMap<String, usize>,
    /// Maximum capacity per chunk
    pub max_chunk_size: usize,
    /// Maximum number of chunks
    pub max_chunks: usize,
    /// Similarity threshold for chunk assignment
    pub similarity_threshold: F,
}

/// Long-term memory hierarchy
#[derive(Debug)]
pub struct LongTermMemoryHierarchy<F: Float> {
    /// Memory levels by abstraction
    pub levels: Vec<MemoryLevel<F>>,
    /// Cross-level associations
    pub cross_level_associations: HashMap<(usize, String), Vec<(usize, String)>>,
    /// Consolidation rules
    pub consolidation_rules: Vec<ConsolidationRule<F>>,
}

/// Memory level in hierarchy
#[derive(Debug)]
pub struct MemoryLevel<F: Float> {
    /// Level index (0 = most concrete, higher = more abstract)
    pub level: usize,
    /// Memory entries at this level
    pub entries: HashMap<String, MemoryEntry<F>>,
    /// Abstraction rules
    pub abstraction_rules: Vec<AbstractionRule<F>>,
}

/// Memory entry
#[derive(Debug)]
pub struct MemoryEntry<F: Float> {
    /// Entry identifier
    pub id: String,
    /// Memory content
    pub content: Vec<F>,
    /// Abstraction level
    pub abstraction_level: usize,
    /// Association strength
    pub strength: F,
    /// Access frequency
    pub access_frequency: usize,
    /// Last access time
    pub last_access: Instant,
}

/// Abstraction rule for memory hierarchy
#[derive(Debug)]
pub struct AbstractionRule<F: Float> {
    /// Rule type
    pub rule_type: String,
    /// Pattern matcher
    pub pattern_matcher: PatternMatcher<F>,
    /// Abstraction function
    pub abstraction_function: AbstractionFunction<F>,
}

/// Pattern matcher for abstraction
#[derive(Debug)]
pub struct PatternMatcher<F: Float> {
    /// Matching algorithm
    pub algorithm: String,
    /// Matching threshold
    pub threshold: F,
    /// Pattern templates
    pub templates: Vec<Vec<F>>,
}

/// Abstraction function
#[derive(Debug)]
pub struct AbstractionFunction<F: Float> {
    /// Function type
    pub function_type: String,
    /// Function parameters
    pub parameters: HashMap<String, F>,
}

/// Memory router for hierarchical access
#[derive(Debug)]
pub struct MemoryRouter<F: Float> {
    /// Routing algorithm
    pub algorithm: RoutingAlgorithm,
    /// Routing table
    pub routing_table: HashMap<String, RoutingEntry<F>>,
    /// Performance metrics
    pub metrics: RoutingMetrics<F>,
}

/// Routing algorithms
#[derive(Debug, Clone)]
pub enum RoutingAlgorithm {
    /// Shortest path routing
    ShortestPath,
    /// Content-based routing
    ContentBased,
    /// Adaptive routing
    Adaptive,
    /// Learning-based routing
    LearningBased,
}

/// Routing entry
#[derive(Debug)]
pub struct RoutingEntry<F: Float> {
    /// Destination level
    pub destination_level: usize,
    /// Route cost
    pub cost: F,
    /// Route quality
    pub quality: F,
}

/// Routing performance metrics
#[derive(Debug)]
pub struct RoutingMetrics<F: Float> {
    /// Average routing time
    pub average_time: F,
    /// Success rate
    pub success_rate: F,
    /// Cache hit rate
    pub cache_hit_rate: F,
}

/// Consolidation rule
#[derive(Debug)]
pub struct ConsolidationRule<F: Float> {
    /// Rule conditions
    pub conditions: Vec<ConsolidationCondition<F>>,
    /// Consolidation action
    pub action: ConsolidationAction<F>,
    /// Rule priority
    pub priority: F,
}

/// Consolidation condition
#[derive(Debug)]
pub struct ConsolidationCondition<F: Float> {
    /// Condition type
    pub condition_type: String,
    /// Threshold value
    pub threshold: F,
    /// Target metric
    pub target_metric: String,
}

/// Consolidation action
#[derive(Debug)]
pub struct ConsolidationAction<F: Float> {
    /// Action type
    pub action_type: String,
    /// Action parameters
    pub parameters: HashMap<String, F>,
}

/// Working memory model based on Baddeley-Hitch model
#[derive(Debug)]
pub struct WorkingMemoryModel<F: Float> {
    /// Central executive
    pub central_executive: CentralExecutive<F>,
    /// Phonological loop
    pub phonological_loop: PhonologicalLoop<F>,
    /// Visuospatial sketchpad
    pub visuospatial_sketchpad: VisuospatialSketchpad<F>,
    /// Episodic buffer
    pub episodic_buffer: EpisodicBuffer<F>,
}

/// Central executive component
#[derive(Debug)]
pub struct CentralExecutive<F: Float> {
    /// Attention control mechanisms
    pub attention_control: AttentionControl<F>,
    /// Task coordination
    pub task_coordinator: TaskCoordinator<F>,
    /// Resource allocation
    pub resource_allocator: ResourceAllocator<F>,
}

/// Attention control in working memory
#[derive(Debug)]
pub struct AttentionControl<F: Float> {
    /// Current focus
    pub current_focus: Option<String>,
    /// Attention weights
    pub attention_weights: HashMap<String, F>,
    /// Switching cost
    pub switching_cost: F,
}

/// Task coordinator
#[derive(Debug)]
pub struct TaskCoordinator<F: Float> {
    /// Active tasks
    pub active_tasks: Vec<Task<F>>,
    /// Task priority queue
    pub priority_queue: Vec<(String, F)>,
    /// Coordination strategy
    pub strategy: CoordinationStrategy,
}

/// Task in working memory
#[derive(Debug)]
pub struct Task<F: Float> {
    /// Task identifier
    pub id: String,
    /// Task parameters
    pub parameters: HashMap<String, F>,
    /// Task status
    pub status: TaskStatus,
    /// Resource requirements
    pub resource_requirements: ResourceRequirements<F>,
}

/// Task status
#[derive(Debug, Clone)]
pub enum TaskStatus {
    /// Task is waiting
    Waiting,
    /// Task is running
    Running,
    /// Task is suspended
    Suspended,
    /// Task is completed
    Completed,
    /// Task failed
    Failed(String),
}

/// Resource requirements for tasks
#[derive(Debug)]
pub struct ResourceRequirements<F: Float> {
    /// Memory requirement
    pub memory: F,
    /// Processing power requirement
    pub processing_power: F,
    /// Attention requirement
    pub attention: F,
}

/// Coordination strategies
#[derive(Debug, Clone)]
pub enum CoordinationStrategy {
    /// Sequential execution
    Sequential,
    /// Parallel execution
    Parallel,
    /// Priority-based
    PriorityBased,
    /// Adaptive coordination
    Adaptive,
}

/// Resource allocator
#[derive(Debug)]
pub struct ResourceAllocator<F: Float> {
    /// Available resources
    pub available_resources: Resources<F>,
    /// Allocation strategy
    pub strategy: AllocationStrategy,
    /// Allocation history
    pub allocation_history: Vec<AllocationEvent<F>>,
}

/// Available resources
#[derive(Debug)]
pub struct Resources<F: Float> {
    /// Memory capacity
    pub memory_capacity: F,
    /// Processing capacity
    pub processing_capacity: F,
    /// Attention capacity
    pub attention_capacity: F,
}

/// Allocation strategies
#[derive(Debug, Clone)]
pub enum AllocationStrategy {
    /// First-fit allocation
    FirstFit,
    /// Best-fit allocation
    BestFit,
    /// Proportional allocation
    Proportional,
    /// Dynamic allocation
    Dynamic,
}

/// Allocation event
#[derive(Debug)]
pub struct AllocationEvent<F: Float> {
    /// Timestamp
    pub timestamp: Instant,
    /// Allocated resources
    pub allocated: Resources<F>,
    /// Task ID
    pub task_id: String,
    /// Success status
    pub success: bool,
}

/// Phonological loop component
#[derive(Debug)]
pub struct PhonologicalLoop<F: Float> {
    /// Phonological store
    pub phonological_store: PhonologicalStore<F>,
    /// Articulatory rehearsal
    pub rehearsal_system: RehearsalSystem<F>,
}

/// Phonological store
#[derive(Debug)]
pub struct PhonologicalStore<F: Float> {
    /// Stored phonological patterns
    pub patterns: Vec<PhonologicalPattern<F>>,
    /// Decay rate
    pub decay_rate: F,
    /// Storage capacity
    pub capacity: usize,
}

/// Phonological pattern
#[derive(Debug)]
pub struct PhonologicalPattern<F: Float> {
    /// Pattern representation
    pub representation: Vec<F>,
    /// Decay level
    pub decay_level: F,
    /// Storage time
    pub storage_time: Instant,
}

/// Rehearsal system
#[derive(Debug)]
pub struct RehearsalSystem<F: Float> {
    /// Rehearsal rate
    pub rehearsal_rate: F,
    /// Rehearsal queue
    pub rehearsal_queue: VecDeque<String>,
    /// Rehearsal effectiveness
    pub effectiveness: F,
}

/// Visuospatial sketchpad component
#[derive(Debug)]
pub struct VisuospatialSketchpad<F: Float> {
    /// Visual store
    pub visual_store: VisualStore<F>,
    /// Spatial store
    pub spatial_store: SpatialStore<F>,
    /// Manipulation system
    pub manipulation_system: ManipulationSystem<F>,
}

/// Visual store
#[derive(Debug)]
pub struct VisualStore<F: Float> {
    /// Visual representations
    pub representations: Vec<VisualRepresentation<F>>,
    /// Resolution parameters
    pub resolution: F,
    /// Color depth
    pub color_depth: usize,
}

/// Visual representation
#[derive(Debug)]
pub struct VisualRepresentation<F: Float> {
    /// Image data
    pub image_data: Vec<F>,
    /// Spatial location
    pub location: (F, F),
    /// Visual features
    pub features: HashMap<String, F>,
}

/// Spatial store
#[derive(Debug)]
pub struct SpatialStore<F: Float> {
    /// Spatial representations
    pub representations: Vec<SpatialRepresentation<F>>,
    /// Coordinate system
    pub coordinate_system: CoordinateSystem<F>,
}

/// Spatial representation
#[derive(Debug)]
pub struct SpatialRepresentation<F: Float> {
    /// Spatial coordinates
    pub coordinates: Vec<F>,
    /// Spatial relationships
    pub relationships: HashMap<String, F>,
    /// Movement patterns
    pub movement_patterns: Vec<MovementPattern<F>>,
}

/// Coordinate system
#[derive(Debug)]
pub struct CoordinateSystem<F: Float> {
    /// Coordinate type
    pub coordinate_type: CoordinateType,
    /// Origin point
    pub origin: Vec<F>,
    /// Scale factors
    pub scale: Vec<F>,
}

/// Types of coordinate systems
#[derive(Debug, Clone)]
pub enum CoordinateType {
    /// Cartesian coordinates
    Cartesian,
    /// Polar coordinates
    Polar,
    /// Spherical coordinates
    Spherical,
    /// Custom coordinate system
    Custom(String),
}

/// Movement pattern
#[derive(Debug)]
pub struct MovementPattern<F: Float> {
    /// Pattern type
    pub pattern_type: String,
    /// Trajectory points
    pub trajectory: Vec<Vec<F>>,
    /// Velocity profile
    pub velocity: Vec<F>,
}

/// Manipulation system
#[derive(Debug)]
pub struct ManipulationSystem<F: Float> {
    /// Manipulation operations
    pub operations: Vec<ManipulationOperation<F>>,
    /// Current transformations
    pub transformations: Vec<Transformation<F>>,
}

/// Manipulation operation
#[derive(Debug)]
pub struct ManipulationOperation<F: Float> {
    /// Operation type
    pub operation_type: String,
    /// Operation parameters
    pub parameters: HashMap<String, F>,
    /// Success rate
    pub success_rate: F,
}

/// Transformation
#[derive(Debug)]
pub struct Transformation<F: Float> {
    /// Transformation matrix
    pub matrix: Vec<Vec<F>>,
    /// Transformation type
    pub transformation_type: String,
}

/// Episodic buffer component
#[derive(Debug)]
pub struct EpisodicBuffer<F: Float> {
    /// Buffer episodes
    pub episodes: Vec<Episode<F>>,
    /// Buffer capacity
    pub capacity: usize,
    /// Integration mechanisms
    pub integration_mechanisms: Vec<IntegrationMechanism<F>>,
}

/// Episode in episodic buffer
#[derive(Debug)]
pub struct Episode<F: Float> {
    /// Episode content
    pub content: EpisodeContent<F>,
    /// Temporal context
    pub temporal_context: TemporalContext,
    /// Binding strength
    pub binding_strength: F,
}

/// Episode content
#[derive(Debug)]
pub struct EpisodeContent<F: Float> {
    /// Visual information
    pub visual: Option<Vec<F>>,
    /// Auditory information
    pub auditory: Option<Vec<F>>,
    /// Semantic information
    pub semantic: Option<Vec<F>>,
    /// Motor information
    pub motor: Option<Vec<F>>,
}

/// Temporal context
#[derive(Debug)]
pub struct TemporalContext {
    /// Episode timestamp
    pub timestamp: Instant,
    /// Episode duration
    pub duration: std::time::Duration,
    /// Temporal relationships
    pub relationships: Vec<TemporalRelation>,
}

/// Temporal relation
#[derive(Debug)]
pub struct TemporalRelation {
    /// Relation type
    pub relation_type: String,
    /// Related episode ID
    pub related_episode: String,
    /// Relation strength
    pub strength: f64,
}

/// Integration mechanism
#[derive(Debug)]
pub struct IntegrationMechanism<F: Float> {
    /// Mechanism type
    pub mechanism_type: String,
    /// Integration parameters
    pub parameters: HashMap<String, F>,
    /// Effectiveness metric
    pub effectiveness: F,
}

/// Associative memory network
#[derive(Debug)]
pub struct AssociativeMemoryNetwork<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Episodic memory system
#[derive(Debug)]
pub struct EpisodicMemorySystem<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Semantic memory network
#[derive(Debug)]
pub struct SemanticMemoryNetwork<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

/// Memory consolidation protocol
#[derive(Debug)]
pub struct MemoryConsolidationProtocol<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

impl<F: Float + Send + Sync + scirs2_core::ndarray::ScalarOperand + std::iter::Sum>
    AdvancedMemoryArchitecture<F>
{
    /// Create new advanced memory architecture
    pub fn new() -> Result<Self> {
        Ok(Self {
            hierarchical_memory: HierarchicalMemorySystem::new()?,
            associative_memory: AssociativeMemoryNetwork::new()?,
            working_memory: WorkingMemoryModel::new()?,
            episodic_memory: EpisodicMemorySystem::new()?,
            semantic_memory: SemanticMemoryNetwork::new()?,
            consolidation_protocols: Vec::new(),
        })
    }

    /// Store memory across all systems
    pub fn store_memory(&mut self, content: &[F], memory_type: MemoryType) -> Result<String> {
        match memory_type {
            MemoryType::Sensory => {
                let array = Array1::from_vec(content.to_vec());
                self.hierarchical_memory.sensory_memory.add_input(array);
                Ok("sensory_memory".to_string())
            }
            MemoryType::ShortTerm => {
                let array = Array1::from_vec(content.to_vec());
                self.hierarchical_memory.short_term.store_pattern(array)?;
                Ok("short_term_memory".to_string())
            }
            MemoryType::LongTerm => {
                // Store in long-term memory hierarchy
                self.store_in_long_term(content)?;
                Ok("long_term_memory".to_string())
            }
            MemoryType::Working => {
                // Store in working memory
                self.store_in_working_memory(content)?;
                Ok("working_memory".to_string())
            }
        }
    }

    /// Store in long-term memory
    fn store_in_long_term(&mut self, content: &[F]) -> Result<()> {
        // Simplified storage: add to first level of hierarchy
        if let Some(level) = self.hierarchical_memory.long_term.levels.first_mut() {
            let entry = MemoryEntry {
                id: format!("entry_{}", level.entries.len()),
                content: content.to_vec(),
                abstraction_level: 0,
                strength: F::from(1.0).expect("Failed to convert constant to float"),
                access_frequency: 1,
                last_access: Instant::now(),
            };
            level.entries.insert(entry.id.clone(), entry);
        }
        Ok(())
    }

    /// Store in working memory
    fn store_in_working_memory(&mut self, content: &[F]) -> Result<()> {
        // Create an episode for episodic buffer
        let episode = Episode {
            content: EpisodeContent {
                visual: Some(content.to_vec()),
                auditory: None,
                semantic: None,
                motor: None,
            },
            temporal_context: TemporalContext {
                timestamp: Instant::now(),
                duration: std::time::Duration::from_millis(100),
                relationships: Vec::new(),
            },
            binding_strength: F::from(0.8).expect("Failed to convert constant to float"),
        };

        if self.working_memory.episodic_buffer.episodes.len()
            >= self.working_memory.episodic_buffer.capacity
        {
            self.working_memory.episodic_buffer.episodes.remove(0);
        }
        self.working_memory.episodic_buffer.episodes.push(episode);

        Ok(())
    }

    /// Recall memory from any system
    pub fn recall_memory(
        &mut self,
        query: &[F],
        memory_type: MemoryType,
    ) -> Result<Option<Vec<F>>> {
        match memory_type {
            MemoryType::Sensory => {
                let current_state = self.hierarchical_memory.sensory_memory.get_current_state();
                if !current_state.is_empty() {
                    Ok(Some(current_state[0].to_vec()))
                } else {
                    Ok(None)
                }
            }
            MemoryType::ShortTerm => {
                // Find best matching chunk
                self.recall_from_short_term(query)
            }
            MemoryType::LongTerm => self.recall_from_long_term(query),
            MemoryType::Working => self.recall_from_working_memory(query),
        }
    }

    /// Recall from short-term memory
    fn recall_from_short_term(&self, query: &[F]) -> Result<Option<Vec<F>>> {
        // Find best matching chunk
        let mut best_match: Option<Vec<F>> = None;
        let mut best_similarity = F::zero();

        for chunk_patterns in self.hierarchical_memory.short_term.chunks.values() {
            for pattern in chunk_patterns {
                let similarity = self.calculate_similarity(query, &pattern.to_vec())?;
                if similarity > best_similarity {
                    best_similarity = similarity;
                    best_match = Some(pattern.to_vec());
                }
            }
        }

        Ok(best_match)
    }

    /// Recall from long-term memory
    fn recall_from_long_term(&self, query: &[F]) -> Result<Option<Vec<F>>> {
        // Search through memory hierarchy
        for level in &self.hierarchical_memory.long_term.levels {
            for entry in level.entries.values() {
                let similarity = self.calculate_similarity(query, &entry.content)?;
                if similarity > F::from(0.8).expect("Failed to convert constant to float") {
                    return Ok(Some(entry.content.clone()));
                }
            }
        }
        Ok(None)
    }

    /// Recall from working memory
    fn recall_from_working_memory(&self, query: &[F]) -> Result<Option<Vec<F>>> {
        // Search episodic buffer
        for episode in &self.working_memory.episodic_buffer.episodes {
            if let Some(visual) = &episode.content.visual {
                let similarity = self.calculate_similarity(query, visual)?;
                if similarity > F::from(0.8).expect("Failed to convert constant to float") {
                    return Ok(Some(visual.clone()));
                }
            }
        }
        Ok(None)
    }

    /// Calculate similarity between two patterns
    fn calculate_similarity(&self, a: &[F], b: &[F]) -> Result<F> {
        if a.len() != b.len() {
            return Ok(F::zero());
        }

        let mut dot_product = F::zero();
        let mut norm_a = F::zero();
        let mut norm_b = F::zero();

        for i in 0..a.len() {
            dot_product = dot_product + a[i] * b[i];
            norm_a = norm_a + a[i] * a[i];
            norm_b = norm_b + b[i] * b[i];
        }

        let norm_product = norm_a.sqrt() * norm_b.sqrt();
        if norm_product > F::zero() {
            Ok(dot_product / norm_product)
        } else {
            Ok(F::zero())
        }
    }
}

/// Types of memory
#[derive(Debug, Clone)]
pub enum MemoryType {
    /// Sensory memory
    Sensory,
    /// Short-term memory
    ShortTerm,
    /// Long-term memory
    LongTerm,
    /// Working memory
    Working,
}

impl<F: Float + Send + Sync + scirs2_core::ndarray::ScalarOperand + std::iter::Sum>
    HierarchicalMemorySystem<F>
{
    /// Create new hierarchical memory system
    pub fn new() -> Result<Self> {
        Ok(Self {
            sensory_memory: SensoryMemoryBuffer::new(
                1000,
                F::from(0.1).expect("Failed to convert constant to float"),
            ),
            short_term: ShortTermMemoryWithChunking::new(
                100,
                10,
                F::from(0.8).expect("Failed to convert constant to float"),
            ),
            long_term: LongTermMemoryHierarchy::new(),
            memory_routers: Vec::new(),
        })
    }
}

impl<F: Float + Send + Sync + scirs2_core::ndarray::ScalarOperand> SensoryMemoryBuffer<F> {
    /// Create new sensory memory buffer
    pub fn new(capacity: usize, decay_rate: F) -> Self {
        Self {
            capacity,
            data: VecDeque::with_capacity(capacity),
            decay_rate,
            timestamps: VecDeque::with_capacity(capacity),
        }
    }

    /// Add new sensory input
    pub fn add_input(&mut self, input: Array1<F>) {
        if self.data.len() >= self.capacity {
            self.data.pop_front();
            self.timestamps.pop_front();
        }
        self.data.push_back(input);
        self.timestamps.push_back(Instant::now());
    }

    /// Get current buffer contents with decay applied
    pub fn get_current_state(&self) -> Vec<Array1<F>> {
        let now = Instant::now();
        self.data
            .iter()
            .zip(self.timestamps.iter())
            .map(|(data, timestamp)| {
                let elapsed = now.duration_since(*timestamp).as_secs_f64();
                let decay_factor =
                    (-self.decay_rate.to_f64().expect("Operation failed") * elapsed).exp();
                data * F::from(decay_factor).expect("Failed to convert to float")
            })
            .collect()
    }
}

impl<F: Float + Send + Sync + std::iter::Sum> ShortTermMemoryWithChunking<F> {
    /// Create new short-term memory with chunking
    pub fn new(max_chunk_size: usize, max_chunks: usize, similarity_threshold: F) -> Self {
        Self {
            chunks: HashMap::new(),
            access_counts: HashMap::new(),
            max_chunk_size,
            max_chunks,
            similarity_threshold,
        }
    }

    /// Store new pattern in appropriate chunk
    pub fn store_pattern(&mut self, pattern: Array1<F>) -> Result<()> {
        let best_chunk = self.find_best_chunk(&pattern)?;

        let chunk_key = match best_chunk {
            Some(key) => key,
            None => {
                // Create new chunk if under limit
                if self.chunks.len() < self.max_chunks {
                    let new_key = format!("chunk_{}", self.chunks.len());
                    self.chunks.insert(new_key.clone(), Vec::new());
                    self.access_counts.insert(new_key.clone(), 0);
                    new_key
                } else {
                    // Replace least accessed chunk
                    let lru_key = self
                        .access_counts
                        .iter()
                        .min_by_key(|(_, &count)| count)
                        .map(|(key, _)| key.clone())
                        .ok_or_else(|| {
                            MetricsError::ComputationError("No chunks available".to_string())
                        })?;
                    self.chunks
                        .get_mut(&lru_key)
                        .expect("Operation failed")
                        .clear();
                    lru_key
                }
            }
        };

        // Add pattern to chunk
        let chunk = self.chunks.get_mut(&chunk_key).expect("Operation failed");
        if chunk.len() >= self.max_chunk_size {
            chunk.remove(0); // Remove oldest
        }
        chunk.push(pattern);
        *self
            .access_counts
            .get_mut(&chunk_key)
            .expect("Operation failed") += 1;

        Ok(())
    }

    /// Find best matching chunk for a pattern
    fn find_best_chunk(&self, pattern: &Array1<F>) -> Result<Option<String>> {
        let mut best_match: Option<(String, F)> = None;

        for (key, chunk_patterns) in &self.chunks {
            if chunk_patterns.is_empty() {
                continue;
            }

            // Calculate average similarity to chunk
            let mut total_similarity = F::zero();
            for chunk_pattern in chunk_patterns {
                let similarity = self.calculate_cosine_similarity(pattern, chunk_pattern)?;
                total_similarity = total_similarity + similarity;
            }
            let avg_similarity =
                total_similarity / F::from(chunk_patterns.len()).expect("Operation failed");

            if avg_similarity > self.similarity_threshold {
                match &best_match {
                    None => best_match = Some((key.clone(), avg_similarity)),
                    Some((_, best_sim)) => {
                        if avg_similarity > *best_sim {
                            best_match = Some((key.clone(), avg_similarity));
                        }
                    }
                }
            }
        }

        Ok(best_match.map(|(key, _)| key))
    }

    /// Calculate cosine similarity between two patterns
    fn calculate_cosine_similarity(&self, a: &Array1<F>, b: &Array1<F>) -> Result<F> {
        if a.len() != b.len() {
            return Err(MetricsError::InvalidInput(
                "Pattern lengths must match".to_string(),
            ));
        }

        let dot_product: F = a.iter().zip(b.iter()).map(|(&x, &y)| x * y).sum();
        let norm_a: F = a.iter().map(|&x| x * x).sum::<F>().sqrt();
        let norm_b: F = b.iter().map(|&x| x * x).sum::<F>().sqrt();

        if norm_a > F::zero() && norm_b > F::zero() {
            Ok(dot_product / (norm_a * norm_b))
        } else {
            Ok(F::zero())
        }
    }
}

impl<F: Float> LongTermMemoryHierarchy<F> {
    /// Create new long-term memory hierarchy
    pub fn new() -> Self {
        let mut levels = Vec::new();

        // Create initial levels (0 = concrete, higher = more abstract)
        for i in 0..5 {
            levels.push(MemoryLevel {
                level: i,
                entries: HashMap::new(),
                abstraction_rules: Vec::new(),
            });
        }

        Self {
            levels,
            cross_level_associations: HashMap::new(),
            consolidation_rules: Vec::new(),
        }
    }
}

impl<F: Float> WorkingMemoryModel<F> {
    /// Create new working memory model
    pub fn new() -> Result<Self> {
        Ok(Self {
            central_executive: CentralExecutive::new(),
            phonological_loop: PhonologicalLoop::new(),
            visuospatial_sketchpad: VisuospatialSketchpad::new(),
            episodic_buffer: EpisodicBuffer::new(7), // Miller's magic number
        })
    }
}

impl<F: Float> CentralExecutive<F> {
    /// Create new central executive
    pub fn new() -> Self {
        Self {
            attention_control: AttentionControl::new(),
            task_coordinator: TaskCoordinator::new(),
            resource_allocator: ResourceAllocator::new(),
        }
    }
}

impl<F: Float> AttentionControl<F> {
    /// Create new attention control
    pub fn new() -> Self {
        Self {
            current_focus: None,
            attention_weights: HashMap::new(),
            switching_cost: F::from(0.1).expect("Failed to convert constant to float"),
        }
    }
}

impl<F: Float> TaskCoordinator<F> {
    /// Create new task coordinator
    pub fn new() -> Self {
        Self {
            active_tasks: Vec::new(),
            priority_queue: Vec::new(),
            strategy: CoordinationStrategy::Adaptive,
        }
    }
}

impl<F: Float> ResourceAllocator<F> {
    /// Create new resource allocator
    pub fn new() -> Self {
        Self {
            available_resources: Resources {
                memory_capacity: F::from(100.0).expect("Failed to convert constant to float"),
                processing_capacity: F::from(100.0).expect("Failed to convert constant to float"),
                attention_capacity: F::from(100.0).expect("Failed to convert constant to float"),
            },
            strategy: AllocationStrategy::Dynamic,
            allocation_history: Vec::new(),
        }
    }
}

impl<F: Float> PhonologicalLoop<F> {
    /// Create new phonological loop
    pub fn new() -> Self {
        Self {
            phonological_store: PhonologicalStore::new(),
            rehearsal_system: RehearsalSystem::new(),
        }
    }
}

impl<F: Float> PhonologicalStore<F> {
    /// Create new phonological store
    pub fn new() -> Self {
        Self {
            patterns: Vec::new(),
            decay_rate: F::from(0.95).expect("Failed to convert constant to float"),
            capacity: 7, // Typical phonological loop capacity
        }
    }
}

impl<F: Float> RehearsalSystem<F> {
    /// Create new rehearsal system
    pub fn new() -> Self {
        Self {
            rehearsal_rate: F::from(2.0).expect("Failed to convert constant to float"), // 2 Hz typical rehearsal rate
            rehearsal_queue: VecDeque::new(),
            effectiveness: F::from(0.8).expect("Failed to convert constant to float"),
        }
    }
}

impl<F: Float> VisuospatialSketchpad<F> {
    /// Create new visuospatial sketchpad
    pub fn new() -> Self {
        Self {
            visual_store: VisualStore::new(),
            spatial_store: SpatialStore::new(),
            manipulation_system: ManipulationSystem::new(),
        }
    }
}

impl<F: Float> VisualStore<F> {
    /// Create new visual store
    pub fn new() -> Self {
        Self {
            representations: Vec::new(),
            resolution: F::from(1.0).expect("Failed to convert constant to float"),
            color_depth: 24, // 24-bit color
        }
    }
}

impl<F: Float> SpatialStore<F> {
    /// Create new spatial store
    pub fn new() -> Self {
        Self {
            representations: Vec::new(),
            coordinate_system: CoordinateSystem {
                coordinate_type: CoordinateType::Cartesian,
                origin: vec![F::zero(), F::zero()],
                scale: vec![F::one(), F::one()],
            },
        }
    }
}

impl<F: Float> ManipulationSystem<F> {
    /// Create new manipulation system
    pub fn new() -> Self {
        Self {
            operations: Vec::new(),
            transformations: Vec::new(),
        }
    }
}

impl<F: Float> EpisodicBuffer<F> {
    /// Create new episodic buffer
    pub fn new(capacity: usize) -> Self {
        Self {
            episodes: Vec::with_capacity(capacity),
            capacity,
            integration_mechanisms: Vec::new(),
        }
    }
}

// Placeholder implementations for supporting types
macro_rules! impl_placeholder_new {
    ($($struct_name:ident),*) => {
        $(
            impl<F: Float> $struct_name<F> {
                pub fn new() -> Result<Self> {
                    Ok(Self {
                        _phantom: std::marker::PhantomData,
                    })
                }
            }
        )*
    };
}

impl_placeholder_new!(
    AssociativeMemoryNetwork,
    EpisodicMemorySystem,
    SemanticMemoryNetwork,
    MemoryConsolidationProtocol
);