oxirs-embed 0.2.4

//! Memory-Augmented Networks for Advanced Knowledge Graph Embeddings
//!
//! This module implements state-of-the-art memory-augmented neural networks including:
//! - Differentiable Neural Computers (DNC) for external memory management
//! - Neural Turing Machines (NTM) for programmatic memory access
//! - Memory Networks for fact storage and retrieval
//! - Episodic Memory Networks for sequential knowledge storage
//! - Relational Memory Core for structured knowledge representation
//! - Sparse Access Memory (SAM) for efficient large-scale memory
//!
//! These architectures enable embedding models to maintain persistent memory
//! across training and inference, supporting complex reasoning tasks and
//! knowledge accumulation over time.

use crate::{EmbeddingModel, Vector};
use anyhow::{anyhow, Result};
use scirs2_core::ndarray_ext::{s, Array1, Array2, Array3, Array4, Axis, Zip};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};
use tracing::{debug, info, warn, error};
use uuid::Uuid;

/// Memory-Augmented Network Engine
pub struct MemoryAugmentedNetwork {
    /// Configuration for memory systems
    config: MemoryConfig,
    /// Differentiable Neural Computer
    dnc: DifferentiableNeuralComputer,
    /// Neural Turing Machine
    ntm: NeuralTuringMachine,
    /// Memory Networks
    memory_networks: MemoryNetworks,
    /// Episodic Memory System
    episodic_memory: EpisodicMemory,
    /// Relational Memory Core
    relational_memory: RelationalMemoryCore,
    /// Sparse Access Memory
    sparse_memory: SparseAccessMemory,
    /// Memory coordination system
    memory_coordinator: MemoryCoordinator,
    /// Performance metrics
    performance_metrics: MemoryPerformanceMetrics,
}

/// Configuration for memory-augmented networks
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryConfig {
    /// DNC configuration
    pub dnc_config: DNCConfig,
    /// NTM configuration
    pub ntm_config: NTMConfig,
    /// Memory Networks configuration
    pub memory_networks_config: MemoryNetworksConfig,
    /// Episodic memory configuration
    pub episodic_config: EpisodicConfig,
    /// Relational memory configuration
    pub relational_config: RelationalConfig,
    /// Sparse memory configuration
    pub sparse_config: SparseConfig,
    /// Global memory settings
    pub global_settings: GlobalMemorySettings,
}

impl Default for MemoryConfig {
    fn default() -> Self {
        Self {
            dnc_config: DNCConfig::default(),
            ntm_config: NTMConfig::default(),
            memory_networks_config: MemoryNetworksConfig::default(),
            episodic_config: EpisodicConfig::default(),
            relational_config: RelationalConfig::default(),
            sparse_config: SparseConfig::default(),
            global_settings: GlobalMemorySettings::default(),
        }
    }
}

/// Global memory system settings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GlobalMemorySettings {
    /// Enable memory compression
    pub enable_compression: bool,
    /// Memory capacity limit (MB)
    pub memory_capacity_mb: f32,
    /// Memory cleanup threshold
    pub cleanup_threshold: f32,
    /// Enable memory persistence
    pub enable_persistence: bool,
    /// Memory update frequency
    pub update_frequency_ms: u64,
    /// Enable multi-memory coordination
    pub enable_coordination: bool,
}

impl Default for GlobalMemorySettings {
    fn default() -> Self {
        Self {
            enable_compression: true,
            memory_capacity_mb: 1024.0,
            cleanup_threshold: 0.85,
            enable_persistence: true,
            update_frequency_ms: 100,
            enable_coordination: true,
        }
    }
}

/// Differentiable Neural Computer implementation
pub struct DifferentiableNeuralComputer {
    /// Configuration
    config: DNCConfig,
    /// Controller network (LSTM/GRU)
    controller: ControllerNetwork,
    /// External memory matrix
    memory_matrix: Array2<f32>,
    /// Read and write heads
    read_heads: Vec<ReadHead>,
    write_head: WriteHead,
    /// Memory allocation and addressing
    memory_addressing: MemoryAddressing,
    /// Usage tracking
    usage_vector: Array1<f32>,
    /// Precedence weights
    precedence_weights: Array1<f32>,
    /// Link matrix for temporal connections
    link_matrix: Array2<f32>,
    /// Read weightings from previous step
    read_weightings: Array2<f32>,
    /// Write weighting from previous step
    write_weighting: Array1<f32>,
}

/// DNC Configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DNCConfig {
    /// Number of memory slots
    pub memory_size: usize,
    /// Size of each memory slot
    pub memory_width: usize,
    /// Number of read heads
    pub num_read_heads: usize,
    /// Controller network size
    pub controller_size: usize,
    /// Output size
    pub output_size: usize,
    /// Learning rate for memory operations
    pub memory_learning_rate: f32,
    /// Memory decay factor
    pub memory_decay: f32,
}

impl Default for DNCConfig {
    fn default() -> Self {
        Self {
            memory_size: 256,
            memory_width: 64,
            num_read_heads: 4,
            controller_size: 512,
            output_size: 256,
            memory_learning_rate: 0.001,
            memory_decay: 0.95,
        }
    }
}

/// Controller network for DNC
pub struct ControllerNetwork {
    /// Input to hidden weights
    w_ih: Array2<f32>,
    /// Hidden to hidden weights
    w_hh: Array2<f32>,
    /// Hidden to output weights
    w_ho: Array2<f32>,
    /// Bias vectors
    bias_h: Array1<f32>,
    bias_o: Array1<f32>,
    /// Hidden state
    hidden_state: Array1<f32>,
    /// Cell state (for LSTM)
    cell_state: Array1<f32>,
}

impl ControllerNetwork {
    pub fn new(input_size: usize, hidden_size: usize, output_size: usize) -> Self {
        {
            use scirs2_core::random::{RngExt, Random};
            let mut rng = Random::default();
        
        let w_ih = Array2::from_shape_fn((hidden_size, input_size), |_| rng.random_range(-0.1..0.1));
        let w_hh = Array2::from_shape_fn((hidden_size, hidden_size), |_| rng.random_range(-0.1..0.1));
        let w_ho = Array2::from_shape_fn((output_size, hidden_size), |_| rng.random_range(-0.1..0.1));
        let bias_h = Array1::zeros(hidden_size);
        let bias_o = Array1::zeros(output_size);
        let hidden_state = Array1::zeros(hidden_size);
        let cell_state = Array1::zeros(hidden_size);
        
        Self {
            w_ih,
            w_hh,
            w_ho,
            bias_h,
            bias_o,
            hidden_state,
            cell_state,
        }
    }

    /// Forward pass through controller
    pub fn forward(&mut self, input: &Array1<f32>) -> Array1<f32> {
        // LSTM-style computation
        let input_gate = self.sigmoid(&(&self.w_ih.dot(input) + &self.w_hh.dot(&self.hidden_state) + &self.bias_h));
        let forget_gate = self.sigmoid(&(&self.w_ih.dot(input) + &self.w_hh.dot(&self.hidden_state) + &self.bias_h));
        let cell_gate = self.tanh(&(&self.w_ih.dot(input) + &self.w_hh.dot(&self.hidden_state) + &self.bias_h));
        let output_gate = self.sigmoid(&(&self.w_ih.dot(input) + &self.w_hh.dot(&self.hidden_state) + &self.bias_h));
        
        self.cell_state = &forget_gate * &self.cell_state + &input_gate * &cell_gate;
        self.hidden_state = &output_gate * &self.tanh(&self.cell_state);
        
        self.w_ho.dot(&self.hidden_state) + &self.bias_o
    }

    fn sigmoid(&self, x: &Array1<f32>) -> Array1<f32> {
        x.map(|&v| 1.0 / (1.0 + (-v).exp()))
    }

    fn tanh(&self, x: &Array1<f32>) -> Array1<f32> {
        x.map(|&v| v.tanh())
    }
}

/// Read head for DNC
pub struct ReadHead {
    /// Key vector for content-based addressing
    key: Array1<f32>,
    /// Key strength
    key_strength: f32,
    /// Free gates for memory deallocation
    free_gates: Array1<f32>,
    /// Read modes (backward, forward, content lookup)
    read_modes: Array1<f32>,
}

impl ReadHead {
    pub fn new(memory_width: usize) -> Self {
        Self {
            key: Array1::zeros(memory_width),
            key_strength: 1.0,
            free_gates: Array1::zeros(memory_width),
            read_modes: Array1::from_vec(vec![1.0, 0.0, 0.0]), // 3 modes
        }
    }

    /// Generate read weighting
    pub fn generate_weighting(
        &self,
        memory: &Array2<f32>,
        link_matrix: &Array2<f32>,
        prev_read_weighting: &Array1<f32>,
    ) -> Array1<f32> {
        // Content-based addressing
        let content_weighting = self.content_lookup(memory);
        
        // Temporal linking (forward/backward)
        let forward_weighting = link_matrix.dot(prev_read_weighting);
        let backward_weighting = link_matrix.t().dot(prev_read_weighting);
        
        // Combine read modes
        let combined_weighting = 
            self.read_modes[0] * &backward_weighting +
            self.read_modes[1] * &content_weighting +
            self.read_modes[2] * &forward_weighting;
        
        // Normalize
        let sum = combined_weighting.sum();
        if sum > 0.0 {
            combined_weighting / sum
        } else {
            Array1::zeros(memory.nrows())
        }
    }

    fn content_lookup(&self, memory: &Array2<f32>) -> Array1<f32> {
        let mut similarities = Array1::zeros(memory.nrows());
        
        for (i, memory_row) in memory.axis_iter(Axis(0)).enumerate() {
            let similarity = self.cosine_similarity(&self.key, &memory_row.to_owned());
            similarities[i] = similarity;
        }
        
        // Apply key strength and softmax
        let scaled = similarities.map(|&x| (x * self.key_strength).exp());
        let sum = scaled.sum();
        if sum > 0.0 {
            scaled / sum
        } else {
            Array1::zeros(memory.nrows())
        }
    }

    fn cosine_similarity(&self, a: &Array1<f32>, b: &Array1<f32>) -> f32 {
        let dot_product = a.dot(b);
        let norm_a = a.mapv(|x| x * x).sum().sqrt();
        let norm_b = b.mapv(|x| x * x).sum().sqrt();
        
        if norm_a > 0.0 && norm_b > 0.0 {
            dot_product / (norm_a * norm_b)
        } else {
            0.0
        }
    }
}

/// Write head for DNC
pub struct WriteHead {
    /// Key vector for content-based addressing
    key: Array1<f32>,
    /// Key strength
    key_strength: f32,
    /// Erase vector
    erase_vector: Array1<f32>,
    /// Write vector
    write_vector: Array1<f32>,
    /// Allocation gate
    allocation_gate: f32,
    /// Write gate
    write_gate: f32,
}

impl WriteHead {
    pub fn new(memory_width: usize) -> Self {
        Self {
            key: Array1::zeros(memory_width),
            key_strength: 1.0,
            erase_vector: Array1::zeros(memory_width),
            write_vector: Array1::zeros(memory_width),
            allocation_gate: 0.0,
            write_gate: 1.0,
        }
    }

    /// Generate write weighting
    pub fn generate_weighting(
        &self,
        memory: &Array2<f32>,
        usage_vector: &Array1<f32>,
    ) -> Array1<f32> {
        // Content-based lookup
        let content_weighting = self.content_lookup(memory);
        
        // Allocation weighting (least used memory locations)
        let allocation_weighting = self.allocation_lookup(usage_vector);
        
        // Combine content and allocation
        let write_weighting = 
            self.write_gate * (
                self.allocation_gate * allocation_weighting +
                (1.0 - self.allocation_gate) * content_weighting
            );
        
        write_weighting
    }

    fn content_lookup(&self, memory: &Array2<f32>) -> Array1<f32> {
        let mut similarities = Array1::zeros(memory.nrows());
        
        for (i, memory_row) in memory.axis_iter(Axis(0)).enumerate() {
            let similarity = self.cosine_similarity(&self.key, &memory_row.to_owned());
            similarities[i] = similarity;
        }
        
        // Apply key strength and softmax
        let scaled = similarities.map(|&x| (x * self.key_strength).exp());
        let sum = scaled.sum();
        if sum > 0.0 {
            scaled / sum
        } else {
            Array1::zeros(memory.nrows())
        }
    }

    fn allocation_lookup(&self, usage_vector: &Array1<f32>) -> Array1<f32> {
        // Find least used memory locations
        let mut indices: Vec<usize> = (0..usage_vector.len()).collect();
        indices.sort_by(|&a, &b| usage_vector[a].partial_cmp(&usage_vector[b]).unwrap_or(std::cmp::Ordering::Equal));
        
        let mut allocation = Array1::zeros(usage_vector.len());
        
        // Give higher weight to less used locations
        for (rank, &idx) in indices.iter().enumerate() {
            let weight = 1.0 / (rank as f32 + 1.0);
            allocation[idx] = weight;
        }
        
        // Normalize
        let sum = allocation.sum();
        if sum > 0.0 {
            allocation / sum
        } else {
            Array1::zeros(usage_vector.len())
        }
    }

    fn cosine_similarity(&self, a: &Array1<f32>, b: &Array1<f32>) -> f32 {
        let dot_product = a.dot(b);
        let norm_a = a.mapv(|x| x * x).sum().sqrt();
        let norm_b = b.mapv(|x| x * x).sum().sqrt();
        
        if norm_a > 0.0 && norm_b > 0.0 {
            dot_product / (norm_a * norm_b)
        } else {
            0.0
        }
    }

    /// Write to memory
    pub fn write_to_memory(&self, memory: &mut Array2<f32>, weighting: &Array1<f32>) {
        // Erase operation
        for i in 0..memory.nrows() {
            for j in 0..memory.ncols() {
                memory[[i, j]] *= 1.0 - weighting[i] * self.erase_vector[j];
            }
        }
        
        // Write operation
        for i in 0..memory.nrows() {
            for j in 0..memory.ncols() {
                memory[[i, j]] += weighting[i] * self.write_vector[j];
            }
        }
    }
}

/// Memory addressing system
pub struct MemoryAddressing {
    /// Allocation mechanism
    allocation_mechanism: AllocationMechanism,
    /// Temporal linkage
    temporal_linkage: TemporalLinkage,
}

/// Allocation mechanism for finding free memory
pub struct AllocationMechanism {
    /// Usage tracking
    usage_tracker: UsageTracker,
}

impl AllocationMechanism {
    pub fn new(memory_size: usize) -> Self {
        Self {
            usage_tracker: UsageTracker::new(memory_size),
        }
    }

    pub fn allocate(&mut self, allocation_gate: f32) -> Array1<f32> {
        self.usage_tracker.get_allocation_weighting(allocation_gate)
    }

    pub fn update_usage(&mut self, write_weighting: &Array1<f32>, free_gates: &Array1<f32>) {
        self.usage_tracker.update(write_weighting, free_gates);
    }
}

/// Usage tracking for memory allocation
pub struct UsageTracker {
    /// Current usage vector
    usage: Array1<f32>,
    /// Memory size
    memory_size: usize,
}

impl UsageTracker {
    pub fn new(memory_size: usize) -> Self {
        Self {
            usage: Array1::zeros(memory_size),
            memory_size,
        }
    }

    pub fn update(&mut self, write_weighting: &Array1<f32>, free_gates: &Array1<f32>) {
        // Update usage based on writes and frees
        for i in 0..self.memory_size {
            self.usage[i] = (self.usage[i] + write_weighting[i] - self.usage[i] * free_gates[i])
                .max(0.0).min(1.0);
        }
    }

    pub fn get_allocation_weighting(&self, _allocation_gate: f32) -> Array1<f32> {
        // Return weighting favoring least used locations
        let mut sorted_indices: Vec<usize> = (0..self.memory_size).collect();
        sorted_indices.sort_by(|&a, &b| self.usage[a].partial_cmp(&self.usage[b]).unwrap_or(std::cmp::Ordering::Equal));
        
        let mut weights = Array1::zeros(self.memory_size);
        for (rank, &idx) in sorted_indices.iter().enumerate() {
            weights[idx] = 1.0 / (rank as f32 + 1.0);
        }
        
        // Normalize
        let sum = weights.sum();
        if sum > 0.0 {
            weights / sum
        } else {
            Array1::zeros(self.memory_size)
        }
    }
}

/// Temporal linkage for sequential memory access
pub struct TemporalLinkage {
    /// Link matrix
    link_matrix: Array2<f32>,
    /// Precedence weighting
    precedence_weighting: Array1<f32>,
}

impl TemporalLinkage {
    pub fn new(memory_size: usize) -> Self {
        Self {
            link_matrix: Array2::zeros((memory_size, memory_size)),
            precedence_weighting: Array1::zeros(memory_size),
        }
    }

    pub fn update(&mut self, write_weighting: &Array1<f32>) {
        // Update precedence weighting
        let sum = write_weighting.sum();
        if sum > 0.0 {
            self.precedence_weighting = (1.0 - sum) * &self.precedence_weighting + write_weighting;
        }
        
        // Update link matrix
        for i in 0..self.link_matrix.nrows() {
            for j in 0..self.link_matrix.ncols() {
                if i != j {
                    self.link_matrix[[i, j]] = (1.0 - write_weighting[i] - write_weighting[j]) * 
                        self.link_matrix[[i, j]] + write_weighting[i] * self.precedence_weighting[j];
                }
            }
        }
    }

    pub fn get_link_matrix(&self) -> &Array2<f32> {
        &self.link_matrix
    }
}

impl DifferentiableNeuralComputer {
    /// Create new DNC
    pub fn new(config: DNCConfig) -> Self {
        let memory_matrix = Array2::zeros((config.memory_size, config.memory_width));
        let usage_vector = Array1::zeros(config.memory_size);
        let precedence_weights = Array1::zeros(config.memory_size);
        let link_matrix = Array2::zeros((config.memory_size, config.memory_size));
        let read_weightings = Array2::zeros((config.num_read_heads, config.memory_size));
        let write_weighting = Array1::zeros(config.memory_size);
        
        let controller = ControllerNetwork::new(
            config.memory_width + config.num_read_heads * config.memory_width,
            config.controller_size,
            config.output_size + config.memory_width * (config.num_read_heads + 1) + 3 * config.num_read_heads + 5,
        );
        
        let read_heads = (0..config.num_read_heads)
            .map(|_| ReadHead::new(config.memory_width))
            .collect();
        
        let write_head = WriteHead::new(config.memory_width);
        
        let memory_addressing = MemoryAddressing {
            allocation_mechanism: AllocationMechanism::new(config.memory_size),
            temporal_linkage: TemporalLinkage::new(config.memory_size),
        };
        
        Self {
            config,
            controller,
            memory_matrix,
            read_heads,
            write_head,
            memory_addressing,
            usage_vector,
            precedence_weights,
            link_matrix,
            read_weightings,
            write_weighting,
        }
    }

    /// Forward pass through DNC
    pub fn forward(&mut self, input: &Array1<f32>) -> Result<Array1<f32>> {
        // Read from memory using read heads
        let mut read_vectors = Vec::new();
        for (i, read_head) in self.read_heads.iter().enumerate() {
            let read_weighting = read_head.generate_weighting(
                &self.memory_matrix,
                &self.link_matrix,
                &self.read_weightings.row(i).to_owned(),
            );
            
            let read_vector = self.memory_matrix.t().dot(&read_weighting);
            read_vectors.push(read_vector);
        }
        
        // Concatenate input and read vectors
        let mut controller_input = input.clone();
        for read_vector in &read_vectors {
            controller_input = concatenate![Axis(0), controller_input, read_vector.view()];
        }
        
        // Controller forward pass
        let controller_output = self.controller.forward(&controller_input);
        
        // Parse controller output into interface vectors
        let (output, interface_vector) = self.parse_controller_output(&controller_output)?;
        
        // Update memory using write head
        let write_weighting = self.write_head.generate_weighting(&self.memory_matrix, &self.usage_vector);
        self.write_head.write_to_memory(&mut self.memory_matrix, &write_weighting);
        
        // Update memory addressing
        let free_gates = Array1::ones(self.config.memory_size); // Simplified
        self.memory_addressing.allocation_mechanism.update_usage(&write_weighting, &free_gates);
        self.memory_addressing.temporal_linkage.update(&write_weighting);
        
        // Update state
        self.write_weighting = write_weighting;
        self.link_matrix = self.memory_addressing.temporal_linkage.get_link_matrix().clone();
        
        Ok(output)
    }

    fn parse_controller_output(&self, output: &Array1<f32>) -> Result<(Array1<f32>, Array1<f32>)> {
        if output.len() < self.config.output_size {
            return Err(anyhow!("Controller output too short"));
        }
        
        let network_output = output.slice(s![..self.config.output_size]).to_owned();
        let interface_vector = output.slice(s![self.config.output_size..]).to_owned();
        
        Ok((network_output, interface_vector))
    }

    /// Reset memory state
    pub fn reset(&mut self) {
        self.memory_matrix.fill(0.0);
        self.usage_vector.fill(0.0);
        self.precedence_weights.fill(0.0);
        self.link_matrix.fill(0.0);
        self.read_weightings.fill(0.0);
        self.write_weighting.fill(0.0);
    }

    /// Get memory utilization
    pub fn get_memory_utilization(&self) -> f32 {
        self.usage_vector.sum() / self.usage_vector.len() as f32
    }
}

/// Neural Turing Machine configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NTMConfig {
    /// Memory size
    pub memory_size: usize,
    /// Memory width
    pub memory_width: usize,
    /// Number of read/write heads
    pub num_heads: usize,
    /// Controller size
    pub controller_size: usize,
    /// Shift range for addressing
    pub shift_range: usize,
}

impl Default for NTMConfig {
    fn default() -> Self {
        Self {
            memory_size: 128,
            memory_width: 32,
            num_heads: 2,
            controller_size: 256,
            shift_range: 3,
        }
    }
}

/// Neural Turing Machine implementation
pub struct NeuralTuringMachine {
    config: NTMConfig,
    controller: ControllerNetwork,
    memory: Array2<f32>,
    read_heads: Vec<NTMHead>,
    write_heads: Vec<NTMHead>,
}

/// NTM Head (can be read or write)
pub struct NTMHead {
    /// Key vector
    key: Array1<f32>,
    /// Key strength (beta)
    key_strength: f32,
    /// Gate value
    gate: f32,
    /// Shift weights
    shift_weights: Array1<f32>,
    /// Sharpening factor (gamma)
    gamma: f32,
    /// Previous weighting
    prev_weighting: Array1<f32>,
}

impl NTMHead {
    pub fn new(memory_width: usize, memory_size: usize, shift_range: usize) -> Self {
        Self {
            key: Array1::zeros(memory_width),
            key_strength: 1.0,
            gate: 0.5,
            shift_weights: Array1::zeros(2 * shift_range + 1),
            gamma: 1.0,
            prev_weighting: Array1::zeros(memory_size),
        }
    }

    /// Generate addressing weighting
    pub fn address(&mut self, memory: &Array2<f32>) -> Array1<f32> {
        // Content-based addressing
        let content_weights = self.content_addressing(memory);
        
        // Gated addressing
        let gated_weights = self.gate * &content_weights + (1.0 - self.gate) * &self.prev_weighting;
        
        // Shifted addressing
        let shifted_weights = self.shift_addressing(&gated_weights);
        
        // Sharpened addressing
        let final_weights = self.sharpen_addressing(&shifted_weights);
        
        self.prev_weighting = final_weights.clone();
        final_weights
    }

    fn content_addressing(&self, memory: &Array2<f32>) -> Array1<f32> {
        let mut similarities = Array1::zeros(memory.nrows());
        
        for (i, memory_row) in memory.axis_iter(Axis(0)).enumerate() {
            let similarity = self.cosine_similarity(&self.key, &memory_row.to_owned());
            similarities[i] = similarity;
        }
        
        // Apply key strength and softmax
        let scaled = similarities.map(|&x| (x * self.key_strength).exp());
        let sum = scaled.sum();
        if sum > 0.0 {
            scaled / sum
        } else {
            Array1::zeros(memory.nrows())
        }
    }

    fn shift_addressing(&self, weights: &Array1<f32>) -> Array1<f32> {
        let memory_size = weights.len();
        let shift_range = (self.shift_weights.len() - 1) / 2;
        let mut shifted = Array1::zeros(memory_size);
        
        for i in 0..memory_size {
            for (j, &shift_weight) in self.shift_weights.iter().enumerate() {
                let shift = j as i32 - shift_range as i32;
                let shifted_idx = ((i as i32 + shift) % memory_size as i32 + memory_size as i32) % memory_size as i32;
                shifted[shifted_idx as usize] += weights[i] * shift_weight;
            }
        }
        
        shifted
    }

    fn sharpen_addressing(&self, weights: &Array1<f32>) -> Array1<f32> {
        let sharpened = weights.map(|&x| x.powf(self.gamma));
        let sum = sharpened.sum();
        if sum > 0.0 {
            sharpened / sum
        } else {
            Array1::zeros(weights.len())
        }
    }

    fn cosine_similarity(&self, a: &Array1<f32>, b: &Array1<f32>) -> f32 {
        let dot_product = a.dot(b);
        let norm_a = a.mapv(|x| x * x).sum().sqrt();
        let norm_b = b.mapv(|x| x * x).sum().sqrt();
        
        if norm_a > 0.0 && norm_b > 0.0 {
            dot_product / (norm_a * norm_b)
        } else {
            0.0
        }
    }
}

impl NeuralTuringMachine {
    pub fn new(config: NTMConfig) -> Self {
        let memory = Array2::zeros((config.memory_size, config.memory_width));
        let controller = ControllerNetwork::new(
            config.memory_width + config.num_heads * config.memory_width,
            config.controller_size,
            config.memory_width + config.num_heads * (config.memory_width + 3 + 2 * config.shift_range + 1),
        );
        
        let read_heads = (0..config.num_heads)
            .map(|_| NTMHead::new(config.memory_width, config.memory_size, config.shift_range))
            .collect();
        
        let write_heads = (0..config.num_heads)
            .map(|_| NTMHead::new(config.memory_width, config.memory_size, config.shift_range))
            .collect();
        
        Self {
            config,
            controller,
            memory,
            read_heads,
            write_heads,
        }
    }

    /// Forward pass
    pub fn forward(&mut self, input: &Array1<f32>) -> Result<Array1<f32>> {
        // Read from memory
        let mut read_vectors = Vec::new();
        for read_head in &mut self.read_heads {
            let weighting = read_head.address(&self.memory);
            let read_vector = self.memory.t().dot(&weighting);
            read_vectors.push(read_vector);
        }
        
        // Concatenate input and read vectors
        let mut controller_input = input.clone();
        for read_vector in &read_vectors {
            controller_input = concatenate![Axis(0), controller_input, read_vector.view()];
        }
        
        // Controller forward pass
        let controller_output = self.controller.forward(&controller_input);
        
        // Parse output and write to memory
        // (Simplified implementation)
        
        Ok(controller_output)
    }
}

/// Memory Networks configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryNetworksConfig {
    /// Memory capacity
    pub memory_capacity: usize,
    /// Embedding dimension
    pub embedding_dim: usize,
    /// Number of hops for reasoning
    pub num_hops: usize,
    /// Learning rate
    pub learning_rate: f32,
}

impl Default for MemoryNetworksConfig {
    fn default() -> Self {
        Self {
            memory_capacity: 1000,
            embedding_dim: 128,
            num_hops: 3,
            learning_rate: 0.01,
        }
    }
}

/// Memory Networks implementation
pub struct MemoryNetworks {
    config: MemoryNetworksConfig,
    memory_embeddings: Array2<f32>,
    memory_content: Vec<String>,
    input_encoder: Array2<f32>,
    output_encoder: Array2<f32>,
    query_encoder: Array2<f32>,
}

impl MemoryNetworks {
    pub fn new(config: MemoryNetworksConfig) -> Self {
        let memory_embeddings = Array2::zeros((config.memory_capacity, config.embedding_dim));
        let memory_content = Vec::new();
        
        {
            use scirs2_core::random::{RngExt, Random};
            let mut rng = Random::default();
        
        let input_encoder = Array2::from_shape_fn(
            (config.embedding_dim, config.embedding_dim),
            |_| rng.random_range(-0.1..0.1)
        );
        let output_encoder = Array2::from_shape_fn(
            (config.embedding_dim, config.embedding_dim),
            |_| rng.random_range(-0.1..0.1)
        );
        let query_encoder = Array2::from_shape_fn(
            (config.embedding_dim, config.embedding_dim),
            |_| rng.random_range(-0.1..0.1)
        );
        
        Self {
            config,
            memory_embeddings,
            memory_content,
            input_encoder,
            output_encoder,
            query_encoder,
        }
    }

    /// Store memory
    pub fn store_memory(&mut self, content: String, embedding: Array1<f32>) -> Result<()> {
        if self.memory_content.len() < self.config.memory_capacity {
            let index = self.memory_content.len();
            self.memory_content.push(content);
            
            if embedding.len() == self.config.embedding_dim {
                self.memory_embeddings.row_mut(index).assign(&embedding);
            } else {
                return Err(anyhow!("Embedding dimension mismatch"));
            }
        } else {
            // Replace oldest memory (FIFO)
            let index = 0;
            self.memory_content[index] = content;
            self.memory_embeddings.row_mut(index).assign(&embedding);
            
            // Shift everything
            for i in 1..self.memory_content.len() {
                self.memory_content.swap(i - 1, i);
                let row1 = self.memory_embeddings.row(i - 1).to_owned();
                let row2 = self.memory_embeddings.row(i).to_owned();
                self.memory_embeddings.row_mut(i - 1).assign(&row2);
                self.memory_embeddings.row_mut(i).assign(&row1);
            }
        }
        
        Ok(())
    }

    /// Query memory
    pub fn query(&self, query_embedding: &Array1<f32>) -> Result<Array1<f32>> {
        if self.memory_content.is_empty() {
            return Ok(Array1::zeros(self.config.embedding_dim));
        }
        
        let mut response = Array1::zeros(self.config.embedding_dim);
        let mut current_query = query_embedding.clone();
        
        // Multi-hop reasoning
        for _hop in 0..self.config.num_hops {
            // Compute attention over memory
            let attention_weights = self.compute_attention(&current_query)?;
            
            // Weighted sum of memory
            let memory_response = self.memory_embeddings.t().dot(&attention_weights);
            
            // Update query for next hop
            current_query = &self.output_encoder.dot(&memory_response);
            response = memory_response;
        }
        
        Ok(response)
    }

    fn compute_attention(&self, query: &Array1<f32>) -> Result<Array1<f32>> {
        let num_memories = self.memory_content.len();
        if num_memories == 0 {
            return Ok(Array1::zeros(0));
        }
        
        let mut attention_scores = Array1::zeros(num_memories);
        
        for i in 0..num_memories {
            let memory_embedding = self.memory_embeddings.row(i);
            let score = query.dot(&memory_embedding);
            attention_scores[i] = score;
        }
        
        // Softmax
        let max_score = attention_scores.fold(f32::NEG_INFINITY, |a, &b| a.max(b));
        let exp_scores = attention_scores.map(|&x| (x - max_score).exp());
        let sum_exp = exp_scores.sum();
        
        if sum_exp > 0.0 {
            Ok(exp_scores / sum_exp)
        } else {
            Ok(Array1::from_elem(num_memories, 1.0 / num_memories as f32))
        }
    }
}

/// Episodic Memory configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EpisodicConfig {
    /// Episode capacity
    pub episode_capacity: usize,
    /// Episode length
    pub episode_length: usize,
    /// Embedding dimension
    pub embedding_dim: usize,
    /// Decay factor for old episodes
    pub decay_factor: f32,
}

impl Default for EpisodicConfig {
    fn default() -> Self {
        Self {
            episode_capacity: 100,
            episode_length: 50,
            embedding_dim: 128,
            decay_factor: 0.95,
        }
    }
}

/// Episodic Memory for sequential experiences
pub struct EpisodicMemory {
    config: EpisodicConfig,
    episodes: VecDeque<Episode>,
    current_episode: Option<Episode>,
}

/// Episode representation
#[derive(Debug, Clone)]
pub struct Episode {
    /// Episode ID
    pub id: Uuid,
    /// Sequence of states/embeddings
    pub states: Vec<Array1<f32>>,
    /// Rewards/values associated with states
    pub rewards: Vec<f32>,
    /// Episode metadata
    pub metadata: EpisodeMetadata,
    /// Timestamp
    pub timestamp: chrono::DateTime<chrono::Utc>,
}

/// Episode metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EpisodeMetadata {
    /// Episode type
    pub episode_type: String,
    /// Success indicator
    pub success: bool,
    /// Episode length
    pub length: usize,
    /// Average reward
    pub average_reward: f32,
    /// Additional tags
    pub tags: Vec<String>,
}

impl EpisodicMemory {
    pub fn new(config: EpisodicConfig) -> Self {
        Self {
            config,
            episodes: VecDeque::new(),
            current_episode: None,
        }
    }

    /// Start new episode
    pub fn start_episode(&mut self, episode_type: String) {
        let episode = Episode {
            id: Uuid::new_v4(),
            states: Vec::new(),
            rewards: Vec::new(),
            metadata: EpisodeMetadata {
                episode_type,
                success: false,
                length: 0,
                average_reward: 0.0,
                tags: Vec::new(),
            },
            timestamp: chrono::Utc::now(),
        };
        
        self.current_episode = Some(episode);
    }

    /// Add state to current episode
    pub fn add_state(&mut self, state: Array1<f32>, reward: f32) -> Result<()> {
        if let Some(ref mut episode) = self.current_episode {
            episode.states.push(state);
            episode.rewards.push(reward);
            Ok(())
        } else {
            Err(anyhow!("No active episode"))
        }
    }

    /// End current episode
    pub fn end_episode(&mut self, success: bool) -> Result<()> {
        if let Some(mut episode) = self.current_episode.take() {
            episode.metadata.success = success;
            episode.metadata.length = episode.states.len();
            episode.metadata.average_reward = if episode.rewards.is_empty() {
                0.0
            } else {
                episode.rewards.iter().sum::<f32>() / episode.rewards.len() as f32
            };
            
            // Add to episode buffer
            if self.episodes.len() >= self.config.episode_capacity {
                self.episodes.pop_front();
            }
            self.episodes.push_back(episode);
            
            Ok(())
        } else {
            Err(anyhow!("No active episode"))
        }
    }

    /// Retrieve similar episodes
    pub fn retrieve_similar_episodes(&self, query_state: &Array1<f32>, k: usize) -> Vec<&Episode> {
        let mut similarities: Vec<(f32, &Episode)> = self.episodes
            .iter()
            .map(|episode| {
                let similarity = self.compute_episode_similarity(episode, query_state);
                (similarity, episode)
            })
            .collect();
        
        similarities.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap_or(std::cmp::Ordering::Equal));
        similarities.into_iter().take(k).map(|(_, episode)| episode).collect()
    }

    fn compute_episode_similarity(&self, episode: &Episode, query_state: &Array1<f32>) -> f32 {
        if episode.states.is_empty() {
            return 0.0;
        }
        
        // Compute average similarity to episode states
        let mut total_similarity = 0.0;
        for state in &episode.states {
            let similarity = self.cosine_similarity(query_state, state);
            total_similarity += similarity;
        }
        
        total_similarity / episode.states.len() as f32
    }

    fn cosine_similarity(&self, a: &Array1<f32>, b: &Array1<f32>) -> f32 {
        let dot_product = a.dot(b);
        let norm_a = a.mapv(|x| x * x).sum().sqrt();
        let norm_b = b.mapv(|x| x * x).sum().sqrt();
        
        if norm_a > 0.0 && norm_b > 0.0 {
            dot_product / (norm_a * norm_b)
        } else {
            0.0
        }
    }
}

/// Relational Memory configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RelationalConfig {
    /// Memory size
    pub memory_size: usize,
    /// Embedding dimension
    pub embedding_dim: usize,
    /// Number of attention heads
    pub num_heads: usize,
    /// Number of relation types
    pub num_relation_types: usize,
}

impl Default for RelationalConfig {
    fn default() -> Self {
        Self {
            memory_size: 512,
            embedding_dim: 256,
            num_heads: 8,
            num_relation_types: 10,
        }
    }
}

/// Relational Memory Core for structured knowledge
pub struct RelationalMemoryCore {
    config: RelationalConfig,
    memory: Array2<f32>,
    relation_matrices: Vec<Array2<f32>>,
    attention_mechanism: RelationalAttention,
}

/// Relational attention mechanism
pub struct RelationalAttention {
    query_weights: Array2<f32>,
    key_weights: Array2<f32>,
    value_weights: Array2<f32>,
    num_heads: usize,
    embed_dim: usize,
}

impl RelationalAttention {
    pub fn new(embed_dim: usize, num_heads: usize) -> Self {
        {
            use scirs2_core::random::{RngExt, Random};
            let mut rng = Random::default();
        
        let query_weights = Array2::from_shape_fn((embed_dim, embed_dim), |_| rng.random_range(-0.1..0.1));
        let key_weights = Array2::from_shape_fn((embed_dim, embed_dim), |_| rng.random_range(-0.1..0.1));
        let value_weights = Array2::from_shape_fn((embed_dim, embed_dim), |_| rng.random_range(-0.1..0.1));
        
        Self {
            query_weights,
            key_weights,
            value_weights,
            num_heads,
            embed_dim,
        }
    }

    pub fn forward(&self, memory: &Array2<f32>, query: &Array1<f32>) -> Array1<f32> {
        // Multi-head attention computation
        let head_dim = self.embed_dim / self.num_heads;
        let mut output = Array1::zeros(self.embed_dim);
        
        for head in 0..self.num_heads {
            let start_idx = head * head_dim;
            let end_idx = (head + 1) * head_dim;
            
            // Extract head-specific weights
            let q_head = self.query_weights.slice(s![start_idx..end_idx, ..]);
            let k_head = self.key_weights.slice(s![start_idx..end_idx, ..]);
            let v_head = self.value_weights.slice(s![start_idx..end_idx, ..]);
            
            // Compute queries, keys, values
            let q = q_head.dot(query);
            let keys = memory.dot(&k_head.t());
            let values = memory.dot(&v_head.t());
            
            // Attention scores
            let mut scores = Array1::zeros(memory.nrows());
            for i in 0..memory.nrows() {
                scores[i] = q.dot(&keys.row(i));
            }
            
            // Softmax
            let max_score = scores.fold(f32::NEG_INFINITY, |a, &b| a.max(b));
            let exp_scores = scores.map(|&x| (x - max_score).exp());
            let sum_exp = exp_scores.sum();
            let attention_weights = if sum_exp > 0.0 {
                exp_scores / sum_exp
            } else {
                Array1::from_elem(memory.nrows(), 1.0 / memory.nrows() as f32)
            };
            
            // Weighted sum of values
            let head_output = values.t().dot(&attention_weights);
            output.slice_mut(s![start_idx..end_idx]).assign(&head_output);
        }
        
        output
    }
}

impl RelationalMemoryCore {
    pub fn new(config: RelationalConfig) -> Self {
        let memory = Array2::zeros((config.memory_size, config.embedding_dim));
        
        let mut relation_matrices = Vec::new();
        {
            use scirs2_core::random::{RngExt, Random};
            let mut rng = Random::default();
        
        for _ in 0..config.num_relation_types {
            let relation_matrix = Array2::from_shape_fn(
                (config.embedding_dim, config.embedding_dim),
                |_| rng.random_range(-0.1..0.1)
            );
            relation_matrices.push(relation_matrix);
        }
        
        let attention_mechanism = RelationalAttention::new(config.embedding_dim, config.num_heads);
        
        Self {
            config,
            memory,
            relation_matrices,
            attention_mechanism,
        }
    }

    /// Store relational memory
    pub fn store_relation(&mut self, subject: &Array1<f32>, relation_type: usize, object: &Array1<f32>) -> Result<()> {
        if relation_type >= self.config.num_relation_types {
            return Err(anyhow!("Invalid relation type"));
        }
        
        // Transform subject and object using relation matrix
        let relation_matrix = &self.relation_matrices[relation_type];
        let transformed_subject = relation_matrix.dot(subject);
        let transformed_object = relation_matrix.dot(object);
        
        // Store in memory (simplified - would need more sophisticated storage)
        // For now, just update first available slot
        if let Some(slot) = self.find_empty_slot() {
            let combined = &transformed_subject + &transformed_object;
            self.memory.row_mut(slot).assign(&combined);
        }
        
        Ok(())
    }

    fn find_empty_slot(&self) -> Option<usize> {
        for i in 0..self.memory.nrows() {
            if self.memory.row(i).sum() == 0.0 {
                return Some(i);
            }
        }
        None
    }

    /// Query relational memory
    pub fn query_relations(&self, query: &Array1<f32>) -> Array1<f32> {
        self.attention_mechanism.forward(&self.memory, query)
    }
}

/// Sparse Access Memory configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SparseConfig {
    /// Memory capacity
    pub memory_capacity: usize,
    /// Embedding dimension
    pub embedding_dim: usize,
    /// Sparsity factor (0.0 to 1.0)
    pub sparsity_factor: f32,
    /// Update threshold
    pub update_threshold: f32,
}

impl Default for SparseConfig {
    fn default() -> Self {
        Self {
            memory_capacity: 10000,
            embedding_dim: 512,
            sparsity_factor: 0.1,
            update_threshold: 0.01,
        }
    }
}

/// Sparse Access Memory for large-scale memory
pub struct SparseAccessMemory {
    config: SparseConfig,
    memory: HashMap<usize, Array1<f32>>,
    access_counts: HashMap<usize, usize>,
    last_access: HashMap<usize, Instant>,
}

impl SparseAccessMemory {
    pub fn new(config: SparseConfig) -> Self {
        Self {
            config,
            memory: HashMap::new(),
            access_counts: HashMap::new(),
            last_access: HashMap::new(),
        }
    }

    /// Store sparse memory
    pub fn store(&mut self, key: usize, value: Array1<f32>) -> Result<()> {
        if self.memory.len() >= self.config.memory_capacity {
            self.evict_least_used()?;
        }
        
        self.memory.insert(key, value);
        self.access_counts.insert(key, 1);
        self.last_access.insert(key, Instant::now());
        
        Ok(())
    }

    /// Retrieve from sparse memory
    pub fn retrieve(&mut self, key: usize) -> Option<&Array1<f32>> {
        if let Some(value) = self.memory.get(&key) {
            // Update access statistics
            *self.access_counts.entry(key).or_insert(0) += 1;
            self.last_access.insert(key, Instant::now());
            Some(value)
        } else {
            None
        }
    }

    /// Find most similar entries
    pub fn find_similar(&self, query: &Array1<f32>, k: usize) -> Vec<(usize, f32)> {
        let mut similarities: Vec<(usize, f32)> = self.memory
            .iter()
            .map(|(&key, value)| {
                let similarity = self.cosine_similarity(query, value);
                (key, similarity)
            })
            .collect();
        
        similarities.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        similarities.into_iter().take(k).collect()
    }

    fn cosine_similarity(&self, a: &Array1<f32>, b: &Array1<f32>) -> f32 {
        let dot_product = a.dot(b);
        let norm_a = a.mapv(|x| x * x).sum().sqrt();
        let norm_b = b.mapv(|x| x * x).sum().sqrt();
        
        if norm_a > 0.0 && norm_b > 0.0 {
            dot_product / (norm_a * norm_b)
        } else {
            0.0
        }
    }

    fn evict_least_used(&mut self) -> Result<()> {
        let mut candidates: Vec<(usize, usize, Instant)> = self.access_counts
            .iter()
            .map(|(&key, &count)| {
                let last_access = self.last_access.get(&key).copied().unwrap_or(Instant::now());
                (key, count, last_access)
            })
            .collect();
        
        // Sort by access count (ascending) and then by last access time (ascending)
        candidates.sort_by(|a, b| {
            a.1.cmp(&b.1).then_with(|| a.2.cmp(&b.2))
        });
        
        if let Some((key_to_evict, _, _)) = candidates.first() {
            let key = *key_to_evict;
            self.memory.remove(&key);
            self.access_counts.remove(&key);
            self.last_access.remove(&key);
        }
        
        Ok(())
    }

    /// Cleanup old entries
    pub fn cleanup(&mut self, max_age: Duration) -> Result<usize> {
        let now = Instant::now();
        let mut keys_to_remove = Vec::new();
        
        for (&key, &last_access) in &self.last_access {
            if now.duration_since(last_access) > max_age {
                keys_to_remove.push(key);
            }
        }
        
        let removed_count = keys_to_remove.len();
        for key in keys_to_remove {
            self.memory.remove(&key);
            self.access_counts.remove(&key);
            self.last_access.remove(&key);
        }
        
        Ok(removed_count)
    }
}

/// Memory coordination system
pub struct MemoryCoordinator {
    /// Coordination strategy
    strategy: CoordinationStrategy,
    /// Memory usage statistics
    usage_stats: MemoryUsageStats,
    /// Performance tracker
    performance_tracker: MemoryPerformanceTracker,
}

/// Coordination strategy for multiple memory systems
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CoordinationStrategy {
    /// Round-robin access
    RoundRobin,
    /// Performance-based routing
    PerformanceBased,
    /// Content-based routing
    ContentBased,
    /// Adaptive routing
    Adaptive,
}

/// Memory usage statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryUsageStats {
    /// DNC utilization
    pub dnc_utilization: f32,
    /// NTM utilization
    pub ntm_utilization: f32,
    /// Memory Networks utilization
    pub memory_networks_utilization: f32,
    /// Episodic memory utilization
    pub episodic_utilization: f32,
    /// Relational memory utilization
    pub relational_utilization: f32,
    /// Sparse memory utilization
    pub sparse_utilization: f32,
    /// Total memory usage (MB)
    pub total_memory_mb: f32,
}

/// Performance tracker for memory systems
pub struct MemoryPerformanceTracker {
    /// Access latencies
    access_latencies: HashMap<String, VecDeque<f32>>,
    /// Hit rates
    hit_rates: HashMap<String, f32>,
    /// Throughput metrics
    throughput_metrics: HashMap<String, f32>,
}

impl MemoryPerformanceTracker {
    pub fn new() -> Self {
        Self {
            access_latencies: HashMap::new(),
            hit_rates: HashMap::new(),
            throughput_metrics: HashMap::new(),
        }
    }

    pub fn record_access(&mut self, memory_type: &str, latency_ms: f32) {
        let latencies = self.access_latencies.entry(memory_type.to_string()).or_insert_with(VecDeque::new);
        latencies.push_back(latency_ms);
        
        // Keep only recent measurements
        while latencies.len() > 100 {
            latencies.pop_front();
        }
    }

    pub fn get_average_latency(&self, memory_type: &str) -> f32 {
        if let Some(latencies) = self.access_latencies.get(memory_type) {
            if !latencies.is_empty() {
                return latencies.iter().sum::<f32>() / latencies.len() as f32;
            }
        }
        0.0
    }
}

/// Memory performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryPerformanceMetrics {
    /// Total memory operations
    pub total_operations: u64,
    /// Average access latency
    pub average_latency_ms: f32,
    /// Memory hit rate
    pub hit_rate: f32,
    /// Memory utilization
    pub utilization: f32,
    /// Operations per second
    pub ops_per_second: f32,
    /// Error rate
    pub error_rate: f32,
}

impl Default for MemoryPerformanceMetrics {
    fn default() -> Self {
        Self {
            total_operations: 0,
            average_latency_ms: 0.0,
            hit_rate: 0.0,
            utilization: 0.0,
            ops_per_second: 0.0,
            error_rate: 0.0,
        }
    }
}

impl MemoryAugmentedNetwork {
    /// Create new memory-augmented network
    pub fn new(config: MemoryConfig) -> Result<Self> {
        let dnc = DifferentiableNeuralComputer::new(config.dnc_config.clone());
        let ntm = NeuralTuringMachine::new(config.ntm_config.clone());
        let memory_networks = MemoryNetworks::new(config.memory_networks_config.clone());
        let episodic_memory = EpisodicMemory::new(config.episodic_config.clone());
        let relational_memory = RelationalMemoryCore::new(config.relational_config.clone());
        let sparse_memory = SparseAccessMemory::new(config.sparse_config.clone());
        
        let memory_coordinator = MemoryCoordinator {
            strategy: CoordinationStrategy::Adaptive,
            usage_stats: MemoryUsageStats {
                dnc_utilization: 0.0,
                ntm_utilization: 0.0,
                memory_networks_utilization: 0.0,
                episodic_utilization: 0.0,
                relational_utilization: 0.0,
                sparse_utilization: 0.0,
                total_memory_mb: 0.0,
            },
            performance_tracker: MemoryPerformanceTracker::new(),
        };
        
        Ok(Self {
            config,
            dnc,
            ntm,
            memory_networks,
            episodic_memory,
            relational_memory,
            sparse_memory,
            memory_coordinator,
            performance_metrics: MemoryPerformanceMetrics::default(),
        })
    }

    /// Process input through memory-augmented network
    pub async fn process(&mut self, input: &Array1<f32>, memory_type: Option<&str>) -> Result<Array1<f32>> {
        let start_time = Instant::now();
        
        let result = match memory_type {
            Some("dnc") => self.dnc.forward(input),
            Some("ntm") => self.ntm.forward(input),
            Some("memory_networks") => Ok(self.memory_networks.query(input)?),
            Some("relational") => Ok(self.relational_memory.query_relations(input)),
            Some("sparse") => {
                // Use sparse memory for similarity search
                let similar = self.sparse_memory.find_similar(input, 1);
                if let Some((key, _)) = similar.first() {
                    Ok(self.sparse_memory.retrieve(*key).unwrap_or(input).clone())
                } else {
                    Ok(input.clone())
                }
            }
            _ => {
                // Adaptive routing based on input characteristics
                self.adaptive_routing(input).await
            }
        };
        
        // Record performance metrics
        let latency = start_time.elapsed().as_millis() as f32;
        if let Some(mem_type) = memory_type {
            self.memory_coordinator.performance_tracker.record_access(mem_type, latency);
        }
        
        self.performance_metrics.total_operations += 1;
        self.update_performance_metrics(latency);
        
        result
    }

    /// Adaptive routing based on input characteristics
    async fn adaptive_routing(&mut self, input: &Array1<f32>) -> Result<Array1<f32>> {
        // Analyze input characteristics to determine best memory system
        let input_norm = input.mapv(|x| x * x).sum().sqrt();
        let input_sparsity = input.iter().filter(|&&x| x.abs() < 0.01).count() as f32 / input.len() as f32;
        
        match (input_norm, input_sparsity) {
            (norm, sparsity) if norm > 10.0 && sparsity < 0.3 => {
                // Dense, high-magnitude input -> DNC
                self.dnc.forward(input)
            }
            (norm, sparsity) if norm < 5.0 && sparsity > 0.7 => {
                // Sparse, low-magnitude input -> Sparse memory
                let similar = self.sparse_memory.find_similar(input, 1);
                if let Some((key, _)) = similar.first() {
                    Ok(self.sparse_memory.retrieve(*key).unwrap_or(input).clone())
                } else {
                    Ok(input.clone())
                }
            }
            _ => {
                // Default to memory networks
                Ok(self.memory_networks.query(input)?)
            }
        }
    }

    /// Store information in appropriate memory system
    pub async fn store(&mut self, content: String, embedding: Array1<f32>, memory_type: Option<&str>) -> Result<()> {
        match memory_type {
            Some("memory_networks") => {
                self.memory_networks.store_memory(content, embedding)?;
            }
            Some("sparse") => {
                let key = self.hash_content(&content);
                self.sparse_memory.store(key, embedding)?;
            }
            Some("relational") => {
                // For relational memory, need subject-relation-object structure
                // This is a simplified example
                let zero_vector = Array1::zeros(embedding.len());
                self.relational_memory.store_relation(&embedding, 0, &zero_vector)?;
            }
            _ => {
                // Default to memory networks
                self.memory_networks.store_memory(content, embedding)?;
            }
        }
        
        Ok(())
    }

    fn hash_content(&self, content: &str) -> usize {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};
        
        let mut hasher = DefaultHasher::new();
        content.hash(&mut hasher);
        hasher.finish() as usize
    }

    /// Start episodic memory session
    pub fn start_episode(&mut self, episode_type: String) {
        self.episodic_memory.start_episode(episode_type);
    }

    /// Add state to current episode
    pub fn add_episode_state(&mut self, state: Array1<f32>, reward: f32) -> Result<()> {
        self.episodic_memory.add_state(state, reward)
    }

    /// End current episode
    pub fn end_episode(&mut self, success: bool) -> Result<()> {
        self.episodic_memory.end_episode(success)
    }

    /// Get memory usage statistics
    pub fn get_memory_stats(&self) -> MemoryUsageStats {
        self.memory_coordinator.usage_stats.clone()
    }

    /// Get performance metrics
    pub fn get_performance_metrics(&self) -> &MemoryPerformanceMetrics {
        &self.performance_metrics
    }

    fn update_performance_metrics(&mut self, latency: f32) {
        let alpha = 0.1; // Exponential moving average factor
        self.performance_metrics.average_latency_ms = 
            alpha * latency + (1.0 - alpha) * self.performance_metrics.average_latency_ms;
    }

    /// Periodic cleanup of memory systems
    pub async fn cleanup(&mut self) -> Result<()> {
        // DNC reset if utilization is too high
        if self.dnc.get_memory_utilization() > 0.9 {
            self.dnc.reset();
        }
        
        // Sparse memory cleanup
        let cleanup_duration = Duration::from_secs(3600); // 1 hour
        let removed = self.sparse_memory.cleanup(cleanup_duration)?;
        if removed > 0 {
            info!("Cleaned up {} entries from sparse memory", removed);
        }
        
        Ok(())
    }
}

/// Unit tests
#[cfg(test)]
mod tests {
    use super::*;

    #[tokio::test]
    async fn test_memory_augmented_network_creation() {
        let config = MemoryConfig::default();
        let network = MemoryAugmentedNetwork::new(config);
        assert!(network.is_ok());
    }

    #[tokio::test]
    async fn test_dnc_forward_pass() {
        let config = DNCConfig::default();
        let mut dnc = DifferentiableNeuralComputer::new(config);
        let input = Array1::zeros(64);
        
        let result = dnc.forward(&input);
        assert!(result.is_ok());
    }

    #[tokio::test]
    async fn test_memory_networks_store_and_query() {
        let config = MemoryNetworksConfig::default();
        let mut memory_net = MemoryNetworks::new(config);
        
        let embedding = Array1::ones(128);
        let result = memory_net.store_memory("test content".to_string(), embedding.clone());
        assert!(result.is_ok());
        
        let query_result = memory_net.query(&embedding);
        assert!(query_result.is_ok());
    }

    #[tokio::test]
    async fn test_episodic_memory() {
        let config = EpisodicConfig::default();
        let mut episodic = EpisodicMemory::new(config);
        
        episodic.start_episode("test".to_string());
        
        let state = Array1::ones(128);
        let result = episodic.add_state(state, 1.0);
        assert!(result.is_ok());
        
        let end_result = episodic.end_episode(true);
        assert!(end_result.is_ok());
    }

    #[tokio::test]
    async fn test_sparse_memory() {
        let config = SparseConfig::default();
        let mut sparse = SparseAccessMemory::new(config);
        
        let value = Array1::ones(512);
        let store_result = sparse.store(123, value.clone());
        assert!(store_result.is_ok());
        
        let retrieved = sparse.retrieve(123);
        assert!(retrieved.is_some());
        
        let similar = sparse.find_similar(&value, 1);
        assert_eq!(similar.len(), 1);
    }

    #[test]
    fn test_controller_network() {
        let mut controller = ControllerNetwork::new(100, 256, 128);
        let input = Array1::zeros(100);
        
        let output = controller.forward(&input);
        assert_eq!(output.len(), 128);
    }

    #[test]
    fn test_read_head_weighting() {
        let read_head = ReadHead::new(64);
        let memory = Array2::zeros((128, 64));
        let link_matrix = Array2::zeros((128, 128));
        let prev_weighting = Array1::zeros(128);
        
        let weighting = read_head.generate_weighting(&memory, &link_matrix, &prev_weighting);
        assert_eq!(weighting.len(), 128);
        
        // Check that weights sum to approximately 1
        let sum = weighting.sum();
        assert!((sum - 1.0).abs() < 1e-6 || sum == 0.0);
    }

    #[test]
    fn test_write_head_operations() {
        let write_head = WriteHead::new(64);
        let memory = Array2::zeros((128, 64));
        let usage_vector = Array1::zeros(128);
        
        let weighting = write_head.generate_weighting(&memory, &usage_vector);
        assert_eq!(weighting.len(), 128);
    }
}