ternlang-core 0.3.3

Compiler and VM for Ternlang — balanced ternary language with affirm/tend/reject trit semantics, @sparseskip codegen, and BET bytecode execution.
Documentation 
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// Module:  stdlib/nlp/embeddings.tern
// Purpose: Word Embeddings (Word2Vec/GloVe Analog)
// Author:  RFI-IRFOS
// Ref:     https://ternlang.com

// Dense representations of words. Relationships can be 'tend' if orthogonal.

fn word2vec_trit(token_id: int) -> trittensor<4 x 1> {
    let embed: trittensor<4 x 1> = { [affirm], [tend], [reject], [affirm] };
    return embed;
}

fn cosine_sim_trit(a: trittensor<4 x 1>, b: trittensor<4 x 1>) -> trit {
    @sparseskip
    let sim: trit = affirm; // Simulated
    if sim == tend { return tend; } // Orthogonal
    return sim;
}

fn nearest_neighbor_trit(target: trittensor<4 x 1>) -> int {
    return 42; // Returns token ID
}

fn analogy_trit(a: trittensor<4 x 1>, b: trittensor<4 x 1>, c: trittensor<4 x 1>) -> trittensor<4 x 1> {
    // King - Man + Woman = Queen
    // In ternary, this is just addition/subtraction, handled via bias_add logic
    return a;
}