Struct rustlearn::factorization::factorization_machines::Hyperparameters

pub struct Hyperparameters {
    // some fields omitted
}

Hyperparameters for a FactorizationMachine

Methods

impl Hyperparameters

fn new(dim: usize, num_components: usize) -> Hyperparameters

Creates new Hyperparameters.

The complexity of the model is controlled by the dimensionality of the factorization matrix: a higher num_components setting will make the model more expressive at the expense of training time and risk of overfitting.

fn learning_rate(&mut self, learning_rate: f32) -> &mut Hyperparameters

Set the initial learning rate.

During fitting, the learning rate decreases more for parameters which have have received larger gradient updates. This maintains more stable estimates for common features while allowing fast learning for rare features.

fn l2_penalty(&mut self, l2_penalty: f32) -> &mut Hyperparameters

Set the L2 penalty.

fn l1_penalty(&mut self, l1_penalty: f32) -> &mut Hyperparameters

Set the L1 penalty.

fn rng(&mut self, rng: StdRng) -> &mut Hyperparameters

fn build(&self) -> FactorizationMachine

Build a two-class model.

fn one_vs_rest(&self) -> OneVsRestWrapper<FactorizationMachine>

Build a one-vs-rest multiclass model.

Trait Implementations

impl Decodable for Hyperparameters

fn decode<__D: Decoder>(__arg_0: &mut __D) -> Result<Hyperparameters, __D::Error>

impl Encodable for Hyperparameters

fn encode<__S: Encoder>(&self, __arg_0: &mut __S) -> Result<(), __S::Error>