// Module: 11_training_loop.tern
// Purpose: Supervised training in ternary neural networks
// Author: RFI-IRFOS

// Supervised training involves a forward pass, loss calculation, 
// and weight updates. In ternary networks, this is approximated 
// through trit cross-entropy.

fn main() {
    // 1. Initial Weights (Small 2x2 for example)
    let weights: trittensor<2 x 2> = {
        [tend, tend],
        [tend, tend]
    };

    // 2. Training Data (Input, Target)
    let input: trittensor<2 x 1> = { [affirm], [reject] };
    let target: trit = affirm;

    // 3. Epoch Loop (Simplified 3 epochs)
    let epoch: int = 0;
    while epoch < 3 {
        // Forward Pass
        @sparseskip
        let output: trittensor<2 x 1> = weights * input;
        let prediction: trit = output[0, 0];

        // Loss Calculation: Trit Cross-Entropy Approximation
        // If prediction == target, loss is affirm (+1) - No change needed.
        // If prediction != target, loss is reject (-1) - Update weights.
        // If prediction is tend, loss is tend (0) - Needs more training.
        let loss: trit = tend;
        if prediction == target {
            loss = affirm;
            print("Epoch: Complete - Loss: Low (affirm)");
        } else {
            loss = reject;
            print("Epoch: Complete - Loss: High (reject)");
            
            // 4. Simple weight update (simulation)
            // Weight = Weight + (Loss * Input)
            // In practice, this uses gradient-based updates.
            weights = weights; // Simulation of update logic
        }
        
        epoch = epoch + 1;
    }

    print("Training loop complete. Weights converged.");
}
