use proteus::{Retina, Config, Result};

fn main() -> Result<()> {
    // Load a pre-trained retina
    let retina = Retina::load("english.retina")?;

    // Generate text fingerprint
    let fp = retina.fingerprint_text("The quick brown fox");
    println!("Active bits: {}", fp.cardinality());
    println!("Sparsity: {:.2}%", fp.sparsity() * 100.0);

    // Compute text similarity
    let sim = retina.text_similarity(
        "machine learning algorithms",
        "deep neural networks"
    );
    println!("Similarity: {:.4}", sim);

    // Find similar words
    let similar = retina.find_similar_words("king", 5)?;
    for (word, score) in similar {
        println!("  {:.4}  {}", score, word);
    }

    Ok(())
}

use proteus::{
    Config, Som, SomConfig, SomTrainer,
    WordFingerprinter, FingerprintConfig, Retina, Tokenizer,
    Result
};
use proteus::som::training::{WordEmbeddings, DEFAULT_EMBEDDING_DIM};
use std::fs::File;
use std::io::{BufRead, BufReader};

fn train_retina(corpus_path: &str, output_path: &str) -> Result<()> {
    // Read corpus (one document per line)
    let file = File::open(corpus_path)?;
    let documents: Vec<String> = BufReader::new(file)
        .lines()
        .filter_map(|l| l.ok())
        .collect();

    // Tokenize and extract context windows
    let tokenizer = Tokenizer::default_config();
    let mut contexts: Vec<(String, Vec<String>)> = Vec::new();

    for doc in &documents {
        for (center, context) in tokenizer.context_windows_as_strings(doc, 2) {
            contexts.push((center, context));
        }
    }

    // Learn word embeddings from context co-occurrence
    let embeddings = WordEmbeddings::from_contexts(
        &contexts,
        DEFAULT_EMBEDDING_DIM,
        Some(42)  // Random seed
    );

    // Configure and train SOM
    let som_config = SomConfig {
        dimension: 128,
        weight_dimension: DEFAULT_EMBEDDING_DIM,
        iterations: 100_000,
        seed: Some(42),
        ..Default::default()
    };

    let mut som = Som::new(&som_config);
    let mut trainer = SomTrainer::new(som_config);
    let word_to_bmus = trainer.train_fast(&mut som, &embeddings, &contexts)?;

    // Generate fingerprints from BMU mappings
    let grid_size = 128 * 128;
    let fp_config = FingerprintConfig::default();
    let mut fingerprinter = WordFingerprinter::new(fp_config.clone(), grid_size);
    fingerprinter.create_fingerprints(&word_to_bmus, None);

    // Create and save retina
    let retina = Retina::with_index(
        fingerprinter.into_fingerprints(),
        128,
        fp_config
    );
    retina.save(output_path)?;

    Ok(())
}

use proteus::{Retina, Result};

fn semantic_operations(retina: &Retina) -> Result<()> {
    // Semantic center: find common ground between concepts
    let center = retina.semantic_center(&["king", "queen", "prince", "princess"]);
    let related = retina.find_similar_to_fingerprint(&center, 5);
    println!("Semantic center of royalty: {:?}", related);

    // Semantic difference: what makes concept A different from B?
    if let Some(diff) = retina.semantic_difference("king", "queen") {
        let unique_to_king = retina.find_similar_to_fingerprint(&diff, 5);
        println!("What 'king' has that 'queen' doesn't: {:?}", unique_to_king);
    }

    // Analogy: king is to queen as man is to ???
    let results = retina.analogy("king", "queen", "man", 5)?;
    println!("king:queen :: man:?");
    for (word, score) in results {
        println!("  {:.4}  {}", score, word);
    }

    Ok(())
}

use proteus::{Tokenizer, SentenceSegmenter, Result};

fn sentence_aware_tokenization() -> Result<()> {
    let text = "Dr. Smith went to Washington. He met with Sen. Johnson.";

    // Create sentence segmenter (downloads model on first use)
    let mut segmenter = SentenceSegmenter::new("sat-3l-sm")?;

    // Segment into sentences
    let sentences = segmenter.segment(text)?;
    for sentence in &sentences {
        println!("Sentence: {}", sentence);
    }

    // Create context windows that don't cross sentence boundaries
    let tokenizer = Tokenizer::default_config();
    let windows = tokenizer.sentence_aware_context_windows(
        text,
        2,  // half_window: 2 tokens on each side
        &mut segmenter
    )?;

    for (center, context) in windows {
        println!("{}: {:?}", center, context);
    }

    Ok(())
}

use proteus::{Retina, SemanticSegmenter, SegmentationConfig, Result};

fn segment_into_topics() -> Result<()> {
    let retina = Retina::load("english.retina")?;

    // Create segmenter with custom configuration
    let config = SegmentationConfig {
        block_size: 3,              // Sentences per block
        depth_threshold: 0.5,       // Std deviations above mean for boundaries
        min_segment_sentences: 2,   // Minimum sentences per segment
        smoothing: true,            // Apply Gaussian smoothing
        smoothing_sigma: 1.0,
    };

    let mut segmenter = SemanticSegmenter::with_config(retina, config)?;

    let text = std::fs::read_to_string("article.txt")?;
    let result = segmenter.segment(&text)?;

    // Access detailed analysis
    println!("Found {} segments with {} boundaries",
        result.segments.len(),
        result.boundary_indices.len());
    println!("Depth scores: mean={:.3}, std={:.3}",
        result.depth_mean, result.depth_std);

    // Output segments
    for (i, segment) in result.segments.iter().enumerate() {
        println!("\n=== Segment {} ({} sentences) ===",
            i + 1, segment.sentences.len());
        println!("{}", segment.text);
    }

    Ok(())
}

use proteus::Sdr;

fn sdr_operations() {
    let size = 16384;  // 128x128 grid

    // Create SDRs from active positions
    let sdr_a = Sdr::from_positions(&[100, 200, 300, 400, 500], size);
    let sdr_b = Sdr::from_positions(&[100, 200, 600, 700, 800], size);

    // Basic properties
    println!("Cardinality A: {}", sdr_a.cardinality());  // 5
    println!("Sparsity A: {:.4}", sdr_a.sparsity());     // 0.0003

    // Set operations
    let overlap = &sdr_a & &sdr_b;   // Positions in both
    let union = &sdr_a | &sdr_b;     // Positions in either
    let diff = &sdr_a ^ &sdr_b;      // Positions in one but not both

    println!("Overlap count: {}", overlap.cardinality());  // 2

    // Similarity measures
    println!("Jaccard: {:.4}", sdr_a.jaccard_similarity(&sdr_b));
    println!("Cosine: {:.4}", sdr_a.cosine_similarity(&sdr_b));
    println!("Overlap: {:.4}", sdr_a.overlap_similarity(&sdr_b));

    // Convert to dense representation
    let dense = sdr_a.to_dense();  // Vec<u8> of 0s and 1s

    // Sparsify (limit active bits)
    let mut sdr = Sdr::from_positions(&(0..1000).collect::<Vec<_>>(), size);
    sdr.sparsify(328);  // Keep at most 328 active bits
}

use proteus::index::{HnswIndex, HnswConfig};
use proteus::Sdr;

fn hnsw_search() {
    // Configure HNSW parameters
    let config = HnswConfig {
        m: 32,                  // Max connections per node at layer 0
        m_max: 16,              // Max connections at higher layers
        ef_construction: 200,   // Build-time candidate list size
        ef_search: 50,          // Query-time candidate list size
        ml: 1.0 / (32_f64).ln(), // Level generation factor
        seed: Some(42),
    };

    // Build index from fingerprints
    let fingerprints: Vec<Sdr> = load_fingerprints();
    let index = HnswIndex::build(config, fingerprints);

    // Search for k nearest neighbors
    let query = Sdr::from_positions(&[100, 200, 300], 16384);
    let results = index.search(&query, 10);  // Returns Vec<(id, distance)>

    for (id, dist) in results {
        println!("ID: {}, Distance: {:.4}", id, dist);
    }

    // Get index statistics
    let stats = index.stats();
    println!("Nodes: {}, Max layer: {}", stats.num_nodes, stats.max_layer);
}

use proteus::index::{ProductQuantizer, PqConfig};

fn product_quantization() {
    let config = PqConfig {
        num_subquantizers: 8,   // M partitions (must divide vector dim)
        num_centroids: 256,     // K centroids per subquantizer
        kmeans_iterations: 25,
        seed: Some(42),
    };

    // Train on representative vectors
    let training_vectors: Vec<Vec<f32>> = load_training_data();
    let pq = ProductQuantizer::train(&training_vectors, config);

    // Encode vectors (compression)
    let original = vec![0.1, 0.2, 0.3, /* ... */];
    let codes = pq.encode(&original);  // Vec<u8> - much smaller!

    // Compute distances without full decompression (ADC)
    let query = vec![0.15, 0.25, 0.35, /* ... */];
    let distance = pq.asymmetric_distance(&query, &codes);

    // Decode back to approximate vector
    let reconstructed = pq.decode(&codes);
}

use proteus::index::{SimHash, MinHash, BitSamplingLsh, LshIndex};
use proteus::Sdr;

fn lsh_search() {
    // SimHash for cosine similarity on dense vectors
    let simhash = SimHash::new(128, 64, Some(42));  // dim, num_bits, seed
    let hash1 = simhash.hash(&dense_vector1);
    let hash2 = simhash.hash(&dense_vector2);
    let hamming_dist = (hash1 ^ hash2).count_ones();

    // MinHash for Jaccard similarity on sets
    let minhash = MinHash::new(128, Some(42));  // num_hashes, seed
    let sig1 = minhash.signature(&set1);
    let sig2 = minhash.signature(&set2);
    let approx_jaccard = MinHash::estimate_jaccard(&sig1, &sig2);

    // BitSampling for sparse binary vectors (SDRs)
    let bitsampling = BitSamplingLsh::new(16384, 64, Some(42));
    let sdr_hash = bitsampling.hash(&sdr);

    // Multi-table LSH index for fast retrieval
    let sdrs: Vec<Sdr> = load_fingerprints();
    let mut index = LshIndex::new(bitsampling, 10, 4);  // hasher, tables, bands
    index.build(&sdrs);

    let query = Sdr::from_positions(&[100, 200, 300], 16384);
    let candidates = index.query(&query);  // Returns candidate IDs
}

use proteus::som::{BatchSomTrainer, BatchSomConfig, Som, SomConfig};

fn minibatch_training() {
    let config = BatchSomConfig {
        batch_size: 256,
        epochs: 10,
        use_adam: true,           // Adam optimizer
        adam_beta1: 0.9,
        adam_beta2: 0.999,
        use_cosine_annealing: true,
        warm_restarts: 2,         // Restart count for SGDR
        initial_lr: 0.1,
        final_lr: 0.001,
        initial_radius: 32.0,
        final_radius: 1.0,
    };

    let som_config = SomConfig {
        dimension: 64,
        weight_dimension: 100,
        ..Default::default()
    };

    let mut som = Som::new(&som_config);
    let mut trainer = BatchSomTrainer::new(config);

    // Train with monitoring
    let training_data: Vec<Vec<f32>> = load_embeddings();
    let metrics = trainer.train(&mut som, &training_data);

    println!("Final QE: {:.4}", metrics.quantization_error);
    println!("Topographic error: {:.4}", metrics.topographic_error);
}

use proteus::fingerprint::{Hypervector, ItemMemory, SequenceEncoder, AnalogySolver};

fn hyperdimensional_computing() {
    // Create random hypervectors (50% density for HDC operations)
    let hv_king = Hypervector::random(10000, 0.5, Some(1));
    let hv_queen = Hypervector::random(10000, 0.5, Some(2));
    let hv_man = Hypervector::random(10000, 0.5, Some(3));
    let hv_woman = Hypervector::random(10000, 0.5, Some(4));

    // Bundling (⊕): Combine vectors (soft union)
    let royalty = Hypervector::bundle(&[&hv_king, &hv_queen], 0.5);
    assert!(royalty.similarity(&hv_king) > 0.3);  // Similar to both

    // Binding (⊗): Create associations via XOR
    let king_male = hv_king.bind(&hv_man);
    let queen_female = hv_queen.bind(&hv_woman);

    // Unbinding recovers the other component
    let recovered = king_male.unbind(&hv_king);
    assert!(recovered.similarity(&hv_man) > 0.9);

    // Permutation (ρ): Position encoding for sequences
    let shifted = hv_king.permute(100);
    let restored = shifted.permute_inverse(100);
    assert_eq!(hv_king.similarity(&restored), 1.0);

    // Item Memory: Content-addressable storage
    let mut memory = ItemMemory::new(10000, 0.5);
    memory.encode("apple", Some(1));
    memory.encode("banana", Some(2));
    memory.encode("cherry", Some(3));

    let apple_hv = memory.get("apple").unwrap();
    let (name, similarity) = memory.query(apple_hv).unwrap();
    assert_eq!(name, "apple");

    // Sequence Encoding: Order-preserving representations
    let mut encoder = SequenceEncoder::new(10000, 0.5, 10);
    let seq1 = encoder.encode_sequence(&["the", "cat", "sat"]);
    let seq2 = encoder.encode_sequence(&["the", "dog", "sat"]);
    let seq3 = encoder.encode_sequence(&["a", "bird", "flew"]);

    // Similar sequences have higher similarity
    assert!(seq1.sdr.jaccard_similarity(&seq2.sdr) >
            seq1.sdr.jaccard_similarity(&seq3.sdr));

    // Analogy Solving: king - man + woman ≈ queen
    let mut solver = AnalogySolver::new(10000, 0.5);
    solver.add_concept("king", Some(1));
    solver.add_concept("queen", Some(2));
    solver.add_concept("man", Some(3));
    solver.add_concept("woman", Some(4));

    let results = solver.solve("king", "man", "woman", 3);
    // Returns concepts most similar to (king - man + woman)
}

use proteus::{Config, SomConfig, TextConfig, FingerprintConfig};

fn custom_config() -> Config {
    Config {
        som: SomConfig {
            dimension: 64,              // Smaller grid (64x64 = 4,096 positions)
            iterations: 50_000,         // Fewer training iterations
            initial_learning_rate: 0.1,
            final_learning_rate: 0.01,
            initial_radius: 32.0,       // Half of dimension
            final_radius: 1.0,
            weight_dimension: 100,      // Compact embeddings
            context_window: 5,
            toroidal: true,             // Wrap-around boundaries
            seed: Some(42),             // Reproducibility
            num_threads: 0,             // Use all cores
        },
        text: TextConfig {
            lowercase: true,
            min_token_length: 2,
            max_token_length: 50,
            remove_punctuation: true,
            remove_numbers: false,
            unicode_normalize: true,
        },
        fingerprint: FingerprintConfig {
            sparsity: 0.02,             // 2% active bits
            max_active_bits: 82,        // 2% of 4,096
            min_active_bits: 5,
            weighted_aggregation: true,
        },
        ..Default::default()
    }
}

// Training loop (simplified)
for (word, context) in training_contexts {
    let embedding = compute_embedding(&context);
    let bmu = som.find_bmu(&embedding);
    som.update(&embedding, bmu, learning_rate, neighborhood_radius);
}

// Main semantic model
pub struct Retina { ... }

impl Retina {
    pub fn load(path: &Path) -> Result<Self>;
    pub fn save(&self, path: &Path) -> Result<()>;
    pub fn fingerprint_text(&self, text: &str) -> Sdr;
    pub fn word_similarity(&self, w1: &str, w2: &str) -> Result<f64>;
    pub fn text_similarity(&self, t1: &str, t2: &str) -> f64;
    pub fn find_similar_words(&self, word: &str, k: usize) -> Result<Vec<(String, f64)>>;
    pub fn semantic_center(&self, words: &[&str]) -> Sdr;
    pub fn semantic_difference(&self, w1: &str, w2: &str) -> Option<Sdr>;
    pub fn analogy(&self, a: &str, b: &str, c: &str, k: usize) -> Result<Vec<(String, f64)>>;
}

// Sparse Distributed Representation
pub struct Sdr { ... }

impl Sdr {
    pub fn new(size: u32) -> Self;
    pub fn from_positions(positions: &[u32], size: u32) -> Self;
    pub fn cardinality(&self) -> u64;
    pub fn sparsity(&self) -> f64;
    pub fn overlap(&self, other: &Sdr) -> Sdr;  // AND
    pub fn union(&self, other: &Sdr) -> Sdr;    // OR
    pub fn xor(&self, other: &Sdr) -> Sdr;      // XOR
    pub fn jaccard_similarity(&self, other: &Sdr) -> f64;
    pub fn cosine_similarity(&self, other: &Sdr) -> f64;
    pub fn overlap_similarity(&self, other: &Sdr) -> f64;
}

// Self-Organizing Map
pub struct Som { ... }

impl Som {
    pub fn new(config: &SomConfig) -> Self;
    pub fn find_bmu(&self, input: &[f64]) -> Result<usize>;
    pub fn find_bmu_parallel(&self, input: &[f64]) -> Result<usize>;
    pub fn update(&mut self, input: &[f64], bmu: usize, lr: f64, radius: f64);
}

// Sentence segmentation
pub struct SentenceSegmenter { ... }

impl SentenceSegmenter {
    pub fn new(model: &str) -> Result<Self>;  // "sat-3l-sm", "sat-3l", "sat-6l", "sat-12l"
    pub fn segment(&mut self, text: &str) -> Result<Vec<String>>;
}

// Semantic segmentation (topic detection)
pub struct SemanticSegmenter { ... }

impl SemanticSegmenter {
    pub fn new(retina: Retina) -> Result<Self>;
    pub fn with_config(retina: Retina, config: SegmentationConfig) -> Result<Self>;
    pub fn segment(&mut self, text: &str) -> Result<SegmentationResult>;
    pub fn segment_text(&mut self, text: &str) -> Result<Vec<SemanticSegment>>;
}

pub struct SegmentationResult {
    pub segments: Vec<SemanticSegment>,
    pub boundary_indices: Vec<usize>,
    pub similarity_scores: Vec<f32>,
    pub depth_scores: Vec<f32>,
}

// HNSW Graph Index
pub struct HnswIndex { ... }

impl HnswIndex {
    pub fn build(config: HnswConfig, data: Vec<Sdr>) -> Self;
    pub fn search(&self, query: &Sdr, k: usize) -> Vec<(u32, f32)>;
    pub fn search_ef(&self, query: &Sdr, k: usize, ef: usize) -> Vec<(u32, f32)>;
    pub fn stats(&self) -> HnswStats;
}

pub struct HnswConfig {
    pub m: usize,              // Max connections at layer 0
    pub m_max: usize,          // Max connections at higher layers
    pub ef_construction: usize, // Build-time candidate list
    pub ef_search: usize,       // Query-time candidate list
    pub ml: f64,               // Level multiplier
    pub seed: Option<u64>,
}

// Product Quantization
pub struct ProductQuantizer { ... }

impl ProductQuantizer {
    pub fn train(vectors: &[Vec<f32>], config: PqConfig) -> Self;
    pub fn encode(&self, vector: &[f32]) -> Vec<u8>;
    pub fn decode(&self, codes: &[u8]) -> Vec<f32>;
    pub fn asymmetric_distance(&self, query: &[f32], codes: &[u8]) -> f32;
}

// Locality-Sensitive Hashing
pub trait LshHasher: Clone {
    fn hash(&self, data: &Sdr) -> u64;
    fn hash_band(&self, data: &Sdr, band: usize) -> u64;
}

pub struct SimHash { ... }      // Cosine similarity
pub struct MinHash { ... }      // Jaccard similarity
pub struct BitSamplingLsh { ... } // Binary vector hashing

pub struct LshIndex<H: LshHasher> { ... }

impl<H: LshHasher> LshIndex<H> {
    pub fn new(hasher: H, num_tables: usize, bands_per_table: usize) -> Self;
    pub fn build(&mut self, data: &[Sdr]);
    pub fn query(&self, query: &Sdr) -> Vec<u32>;
}

// Hyperdimensional Computing
pub struct Hypervector { ... }

impl Hypervector {
    pub fn random(dimension: u32, sparsity: f64, seed: Option<u64>) -> Self;
    pub fn bundle(vectors: &[&Hypervector], threshold: f64) -> Self;
    pub fn bind(&self, other: &Hypervector) -> Self;
    pub fn unbind(&self, other: &Hypervector) -> Self;
    pub fn permute(&self, amount: i32) -> Self;
    pub fn similarity(&self, other: &Hypervector) -> f64;
}

pub struct ItemMemory { ... }
pub struct SequenceEncoder { ... }
pub struct NgramEncoder { ... }
pub struct AnalogySolver { ... }

// Mini-batch SOM Training
pub struct BatchSomTrainer { ... }

impl BatchSomTrainer {
    pub fn new(config: BatchSomConfig) -> Self;
    pub fn train(&mut self, som: &mut Som, data: &[Vec<f32>]) -> TrainingMetrics;
}

pub struct TrainingMetrics {
    pub quantization_error: f32,
    pub topographic_error: f32,
    pub learning_rate_history: Vec<f32>,
}

pub trait SimilarityMeasure {
    fn similarity(&self, a: &Sdr, b: &Sdr) -> f64;
    fn distance(&self, a: &Sdr, b: &Sdr) -> f64 { 1.0 - self.similarity(a, b) }
}

pub struct CosineSimilarity;    // |A ∩ B| / √(|A| × |B|)
pub struct JaccardSimilarity;   // |A ∩ B| / |A ∪ B|
pub struct OverlapSimilarity;   // |A ∩ B| / min(|A|, |B|)

// Current approach - O(corpus_size) memory
let documents: Vec<String> = reader.lines().collect();
for doc in &documents {
    for (center, context) in tokenizer.context_windows(doc, 2) {
        contexts.push((center, context));  // All contexts in RAM
    }
}

/// Streaming iterator over sharded context files
trait ContextStream: Iterator<Item = (String, Vec<String>)> {
    fn shuffle_window(&mut self, buffer_size: usize);
    fn sample(&mut self, rate: f64) -> impl ContextStream;
}

/// Phase 1: Extract contexts to sharded files on disk
struct ContextExtractor {
    output_dir: PathBuf,
    shard_size: usize,  // e.g., 10M contexts per shard
    current_shard: BufWriter<File>,
}

/// Phase 2: Memory-mapped streaming with shuffle buffer
struct ShardedContextReader {
    shards: Vec<PathBuf>,
    shuffle_buffer: Vec<(String, Vec<String>)>,
    buffer_size: usize,  // e.g., 1M contexts for local shuffling
}

/// Same word always gets same vector - no coordination needed
fn word_to_random_vector(word: &str, dim: usize, global_seed: u64) -> Vec<f32> {
    let seed = hash(global_seed, word);
    let mut rng = ChaCha8Rng::seed_from_u64(seed);
    (0..dim).map(|_| rng.gen_range(-1.0..1.0)).collect()
}

struct HierarchicalSom {
    /// Level 0: Coarse clusters (32×32 = 1K neurons)
    level0: Som,

    /// Level 1: Per-cluster refinement (each has 64×64 local SOM)
    level1: HashMap<usize, Som>,

    // Final fingerprint = [level0_pos * 4096 + level1_pos]
    // Provides 1K × 4K = 4M effective positions
}

impl Retina {
    /// Expand vocabulary from additional corpus
    fn incremental_train(
        &mut self,
        new_contexts: impl ContextStream,
        frozen_som: bool,  // If true, only assign BMUs (no weight updates)
    ) -> Result<()> {
        // 1. For new words: assign BMUs based on context similarity
        // 2. For existing words: update BMU statistics
        // 3. Optionally fine-tune SOM with small learning rate
        // 4. Recompute fingerprints for affected words
    }
}

/// Orthogonal Procrustes alignment between SOM spaces
struct SomAlignment {
    rotation: Matrix,      // Rotation/reflection (dim × dim)
    translation: Vec<f64>, // Translation vector
    scale: f64,            // Scaling factor
}

impl SomAlignment {
    /// Learn alignment from bilingual dictionary
    fn from_anchors(
        source_retina: &Retina,
        target_retina: &Retina,
        anchors: &[(String, String)],  // e.g., [("king", "roi"), ("water", "eau"), ...]
    ) -> Result<Self> {
        // 1. Extract centroid positions for anchor words
        let source_positions = anchors.iter()
            .filter_map(|(src, _)| source_retina.get_centroid(src))
            .collect();
        let target_positions = anchors.iter()
            .filter_map(|(_, tgt)| target_retina.get_centroid(tgt))
            .collect();

        // 2. Solve: min ||s * R * source + t - target||²
        //    Subject to: R is orthogonal
        procrustes_align(&source_positions, &target_positions)
    }

    /// Transform fingerprint from source to aligned target space
    fn transform(&self, fp: &Sdr, grid_dim: u32) -> Sdr {
        let transformed: Vec<u32> = fp.iter()
            .map(|pos| {
                let (row, col) = (pos / grid_dim, pos % grid_dim);
                let (new_row, new_col) = self.transform_point(row, col);
                (new_row * grid_dim + new_col).clamp(0, grid_dim * grid_dim - 1)
            })
            .collect();
        Sdr::from_positions(&transformed, grid_dim * grid_dim)
    }
}

struct MultilingualTrainer {
    /// Shared SOM - all languages map to same grid
    shared_som: Som,

    /// Language-specific tokenizers
    tokenizers: HashMap<LangCode, Tokenizer>,

    /// Parallel corpus provides alignment signal
    parallel_corpus: Vec<ParallelSentence>,
}

struct ParallelSentence {
    // Same meaning expressed in different languages
    sentences: HashMap<LangCode, String>,
}

impl MultilingualTrainer {
    fn train(&mut self, corpora: HashMap<LangCode, impl ContextStream>) {
        // Phase 1: Interleaved monolingual training
        // - Sample contexts from all languages uniformly
        // - Train shared SOM with mixed input
        // - Words cluster by meaning, not by language

        // Phase 2: Parallel corpus alignment
        for parallel in &self.parallel_corpus {
            // Extract contexts from translation pairs
            // Apply soft constraint: translations → similar BMUs
            self.align_translations(parallel);
        }

        // Phase 3: Cross-lingual analogy preservation
        // Verify: king:queen :: roi:reine :: könig:königin
    }

    fn align_translations(&mut self, parallel: &ParallelSentence) {
        // Cross-lingual loss: penalize BMU distance between translations
        // Modified SOM update pulls translation pairs together
    }
}

/// Loss function during training
fn cross_lingual_loss(
    word_en: &str,
    word_fr: &str,  // Translation of word_en
    bmu_en: usize,
    bmu_fr: usize,
    som_dimension: usize,
) -> f64 {
    let (row_en, col_en) = (bmu_en / som_dimension, bmu_en % som_dimension);
    let (row_fr, col_fr) = (bmu_fr / som_dimension, bmu_fr % som_dimension);

    // Squared distance on SOM grid
    (row_en as f64 - row_fr as f64).powi(2) +
    (col_en as f64 - col_fr as f64).powi(2)
}

struct MultilingualRetina {
    /// Shared semantic grid (all languages)
    grid_dimension: u32,

    /// Per-language vocabularies mapped to shared grid
    languages: HashMap<LangCode, LanguageData>,

    /// Unified inverted index (positions → words from all languages)
    shared_index: InvertedIndex,
}

struct LanguageData {
    fingerprints: HashMap<String, WordFingerprint>,
    tokenizer_config: TokenizerConfig,
    stopwords: HashSet<String>,
}

impl MultilingualRetina {
    /// Fingerprint text in any supported language
    fn fingerprint(&self, text: &str, lang: LangCode) -> Sdr {
        let lang_data = &self.languages[&lang];
        // Language-specific tokenization, shared fingerprint space
        self.fingerprint_with_tokenizer(text, &lang_data.tokenizer_config)
    }

    /// Cross-lingual similarity (e.g., English query vs French document)
    fn cross_lingual_similarity(
        &self,
        text1: &str, lang1: LangCode,
        text2: &str, lang2: LangCode,
    ) -> f64 {
        let fp1 = self.fingerprint(text1, lang1);
        let fp2 = self.fingerprint(text2, lang2);
        fp1.cosine_similarity(&fp2)  // Works because shared space!
    }

    /// Find translations (words in target language with similar fingerprints)
    fn find_translations(
        &self,
        word: &str,
        source_lang: LangCode,
        target_lang: LangCode,
        k: usize
    ) -> Vec<(String, f64)> {
        let source_fp = self.languages[&source_lang].fingerprints.get(word)?;
        self.search_in_language(&source_fp.fingerprint, target_lang, k)
    }
}