prophet 0.4.2

A neural network implementation with a focus on cache-efficiency and sequential performance.
Documentation 
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use super::*;

fn create_giant_net() -> NeuralNet {
	use self::Activation::Tanh;
	NeuralNet::from_topology(
		Topology::input(2)
			.layers(&[
				(1000, Tanh),
				(1000, Tanh),
				(1000, Tanh),
				(1000, Tanh),
				(1000, Tanh),
				(1000, Tanh),
				(1000, Tanh),
				(1000, Tanh),
				(1000, Tanh),
				(1000, Tanh)
			])
			.output(1, Tanh))
}

#[bench]
fn predict(bencher: &mut Bencher) {
	let mut net = create_giant_net();
	let (t, f)  = (1.0, -1.0);
	bencher.iter(|| {
		black_box(net.predict(&[f, f]));
		black_box(net.predict(&[f, t]));
		black_box(net.predict(&[t, f]));
		black_box(net.predict(&[t, t]));
	});
}

#[bench]
fn update_gradients(bencher: &mut Bencher) {
	use traits::UpdateGradients;
	let mut net = create_giant_net();
	bencher.iter(|| {
		net.update_gradients(&[1.0]);
	});
}

#[bench]
fn update_weights(bencher: &mut Bencher) {
	use traits::{LearnRate, LearnMomentum, UpdateWeights};
	let mut net = create_giant_net();
	bencher.iter(|| {
		net.update_weights(&[1.0, 1.0], LearnRate::default(), LearnMomentum::default());
	});
}