const VOTE_DIM: usize = 2;
const RESULTS: usize = 1;
const EPOCHS: usize = 20;
const LAYER1_OUT_SIZE: usize = 4;
const LAYER2_OUT_SIZE: usize = 2;
const LEARNING_RATE: f64 = 0.05;

#[derive(Clone)]
pub struct Dataset {
    pub train_votes: Tensor,
    pub train_results: Tensor,
    pub test_votes: Tensor,
    pub test_results: Tensor,
}

struct MultiLevelPerceptron {
    ln1: Linear,
    ln2: Linear,
    ln3: Linear,
}

impl MultiLevelPerceptron {
    fn new(vs: VarBuilder) -> Result<Self> {
        let ln1 = candle_nn::linear(VOTE_DIM, LAYER1_OUT_SIZE, vs.pp("ln1"))?;
        let ln2 = candle_nn::linear(LAYER1_OUT_SIZE, LAYER2_OUT_SIZE, vs.pp("ln2"))?;
        let ln3 = candle_nn::linear(LAYER2_OUT_SIZE, RESULTS + 1, vs.pp("ln3"))?;
        Ok(Self { ln1, ln2, ln3 })
    }

    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
        let xs = self.ln1.forward(xs)?;
        let xs = xs.relu()?;
        let xs = self.ln2.forward(&xs)?;
        let xs = xs.relu()?;
        self.ln3.forward(&xs)
    }
}


pub fn main() -> anyhow::Result<()> {
    let dev = Device::cuda_if_available(0)?;

    let train_votes_vec: Vec<u32> = vec![
        15, 10,
        10, 15,
        5, 12,
        30, 20,
        16, 12,
        13, 25,
        6, 14,
        31, 21,
    ];
    let train_votes_tensor = Tensor::from_vec(train_votes_vec.clone(), (train_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;

    let train_results_vec: Vec<u32> = vec![
        1,
        0,
        0,
        1,
        1,
        0,
        0,
        1,
    ];
    let train_results_tensor = Tensor::from_vec(train_results_vec, train_votes_vec.len() / VOTE_DIM, &dev)?;

    let test_votes_vec: Vec<u32> = vec![
        13, 9,
        8, 14,
        3, 10,
    ];
    let test_votes_tensor = Tensor::from_vec(test_votes_vec.clone(), (test_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;

    let test_results_vec: Vec<u32> = vec![
        1,
        0,
        0,
    ];
    let test_results_tensor = Tensor::from_vec(test_results_vec.clone(), test_results_vec.len(), &dev)?;

    let m = Dataset {
        train_votes: train_votes_tensor,
        train_results: train_results_tensor,
        test_votes: test_votes_tensor,
        test_results: test_results_tensor,
    };

    let trained_model: MultiLevelPerceptron;
    loop {
        println!("Trying to train neural network.");
        match train(m.clone(), &dev) {
            Ok(model) => {
                trained_model = model;
                break;
            },
            Err(e) => {
                println!("Error: {:?}", e);
                continue;
            }
        }

    }

    let real_world_votes: Vec<u32> = vec![
        13, 22,
    ];

    let tensor_test_votes = Tensor::from_vec(real_world_votes.clone(), (1, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;

    let final_result = trained_model.forward(&tensor_test_votes)?;

    let result = final_result
        .argmax(D::Minus1)?
        .to_dtype(DType::F32)?
        .get(0).map(|x| x.to_scalar::<f32>())??;
    println!("real_life_votes: {:?}", real_world_votes);
    println!("neural_network_prediction_result: {:?}", result);

    Ok(())
}

Trying to train neural network.
Epoch:   1 Train loss:  4.42555 Test accuracy:  0.00%
Epoch:   2 Train loss:  0.84677 Test accuracy: 33.33%
Epoch:   3 Train loss:  2.54335 Test accuracy: 33.33%
Epoch:   4 Train loss:  0.37806 Test accuracy: 33.33%
Epoch:   5 Train loss:  0.36647 Test accuracy: 100.00%
real_life_votes: [13, 22]
neural_network_prediction_result: 0.0