// In this example we will teach our NeuralNetwork to solve XOR problem

// we will create a 3 layer neural network with 2 inputs 2 hidden and 1 output node

use vinyana::NeuralNetwork;

let mut nn = NeuralNetwork::new(2, 2, 1, 0.05);


// We will train this network 
let scenarios = vec![
    (vec![1.0, 1.0], vec![0.0f32]),
    (vec![0.0, 1.0], vec![1.0]),
    (vec![1.0, 0.0], vec![1.0]),
    (vec![0.0, 0.0], vec![0.0]),
];

let mut rng = thread_rng();
for _ in 0..300000 {
    let random = rng.gen_range(0..4) as usize;
    let (train_data, target_data) = scenarios.get(random).unwrap();

    // we will pick a random scenario from the dataset and feed it to the network with the expected target
    nn.train(train_data, target_data)
}

// we can store our trained model and play with it later
nn.save(Path::new("xor_model.bin")).unwrap();

// Load your model from file

let nn = NeuralNetwork::load(Path::new("xor_model.bin")).unwrap();

let result = nn.predict(&vec![1.0, 1.0]);
println!("{:?}", result);