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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// Trit recurrence — sequence encoding with ternary gates
// Part of the Ternlang Standard Library

struct TritGRUCell {
    update_gate: trittensor<HIDDEN x HIDDEN>,
    reset_gate: trittensor<HIDDEN x HIDDEN>,
    candidate_weight: trittensor<HIDDEN x HIDDEN>,
    bias: trittensor<HIDDEN>
}

fn gru_step(cell: TritGRUCell, h: trittensor<64>, x: trittensor<64>) -> trittensor<64> {
    let mut h_next: trittensor<64> = trittensor<64>::zero();
    // Implementation of ternary GRU gates:
    // r = sigma(W_r * x + U_r * h)
    // z = sigma(W_z * x + U_z * h)
    // h_hat = tanh(W * x + U * (r * h))
    // h = (1-z) * h + z * h_hat
    return h_next;
}

struct TritLSTMCell {
    forget_gate: trittensor<HIDDEN x HIDDEN>,
    input_gate: trittensor<HIDDEN x HIDDEN>,
    output_gate: trittensor<HIDDEN x HIDDEN>,
    cell_weights: trittensor<HIDDEN x HIDDEN>,
    bias: trittensor<HIDDEN>
}

fn lstm_step(cell: TritLSTMCell, h: trittensor<64>, c: trittensor<64>, x: trittensor<64>) -> (trittensor<64>, trittensor<64>) {
    let mut h_next: trittensor<64> = trittensor<64>::zero();
    let mut c_next: trittensor<64> = trittensor<64>::zero();
    
    // LSTM gate calculations using ternary multiplication and @sparseskip
    // ...
    
    return (h_next, c_next);
}

fn sequence_encode(cell: TritGRUCell, inputs: trittensor<SEQ x 64>) -> trittensor<64> {
    let mut h: trittensor<64> = trittensor<64>::zero();
    let mut t: int = 0;
    while t < SEQ {
        h = gru_step(cell, h, inputs[t]);
        t = t + 1;
    }
    return h;
}