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/models/rnn.tern
// Purpose: Recurrent Neural Network in Ternary
// Author:  RFI-IRFOS
// Ref:     https://ternlang.com

// Sequence modeling where hidden state handles 'tend' natively for missing data.

struct TritRNN {
    w_hx: trittensor<4 x 4>,
    w_hh: trittensor<4 x 4>
}

fn hidden_state_trit(old_h: trittensor<4 x 1>, update: trittensor<4 x 1>) -> trittensor<4 x 1> {
    // Blend hidden states
    return update;
}

fn rnn_step(model: TritRNN, x: trittensor<4 x 1>, h: trittensor<4 x 1>) -> trittensor<4 x 1> {
    @sparseskip
    let input_feat: trittensor<4 x 1> = model.w_hx * x;
    
    @sparseskip
    let hidden_feat: trittensor<4 x 1> = model.w_hh * h;
    
    return hidden_state_trit(h, input_feat); // Simplified update
}

fn rnn_sequence(model: TritRNN, seq: trittensor<4 x 1>[]) -> trittensor<4 x 1> {
    let h: trittensor<4 x 1> = seq[0];
    // Loop over sequence ...
    return h;
}