use nn::{NN, HaltCondition};
// create examples of the XOR function
// the network is trained on tuples of vectors where the first vector
// is the inputs and the second vector is the expected outputs
let examples = [
    (vec![0f64, 0f64], vec![0f64]),
    (vec![0f64, 1f64], vec![1f64]),
    (vec![1f64, 0f64], vec![1f64]),
    (vec![1f64, 1f64], vec![0f64]),
];
// create a new neural network by passing a pointer to an array
// that specifies the number of layers and the number of nodes in each layer
// in this case we have an input layer with 2 nodes, one hidden layer
// with 3 nodes and the output layer has 1 node
let mut net = NN::new(&[2, 3, 1]);
    
// train the network on the examples of the XOR function
// all methods seen here are optional except go() which must be called to begin training
// see the documentation for the Trainer struct for more info on what each method does
net.train(&examples)
    .halt_condition( HaltCondition::Epochs(10000) )
    .log_interval( Some(100) )
    .momentum( 0.1 )
    .rate( 0.3 )
    .go();
    
// evaluate the network to see if it learned the XOR function
for &(ref inputs, ref outputs) in examples.iter() {
    let results = net.run(inputs);
    let (result, key) = (results[0].round(), outputs[0]);
    assert!(result == key);
}