The library can make very basic feedforward neural networks. Learning hasn't been implemented yet. Library still needs a lot of work.

In brainy a layer struct represents a layer of the neural network (excluding the output layer) and a map to the next layer. For now the activation function is treated as a seperate map from something like matrix multiplication. That way, one can "mix and match". Eventually I'd like to add a convolutional map as well.

``` extern crate brainy; use brainy::matrix::Matrix; use brainy::layer::Map; use brainy::network::Network;

fn main() { let mut network = Network::new(); network.appendlayer(Map::MatrixMultiply(4,3)); network.appendlayer(Map::Sigmoid(3)); network.append_layer(Map::MatrixMultiply(3,2));

let input = vec![0.0, 1.0, 1.0, 0.5];
let x = Matrix { rows: input.len(),  cols: 1, elements: input };

let y = network.feedforward(x);
y.print();

} ```

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