Struct rustlearn::linear_models::sgdclassifier::Hyperparameters

pub struct Hyperparameters { /* fields omitted */ }

Hyperparameters for a SGDClassifier model.

Methods

impl Hyperparameters

fn new(dim: usize) -> Hyperparameters

Creates new Hyperparameters.

Examples

use rustlearn::prelude::*;
use rustlearn::linear_models::sgdclassifier::Hyperparameters;


let mut model = Hyperparameters::new(4)
                                .learning_rate(1.0)
                                .l2_penalty(0.5)
                                .l1_penalty(0.0)
                                .build();

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.

Coefficient sparsity is achieved by truncating at zero whenever a coefficient update would change its sign.

fn build(&self) -> SGDClassifier

Build a two-class model.

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

Build a one-vs-rest multiclass model.

Trait Implementations

impl Encodable for Hyperparameters

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

Serialize a value using an Encoder.

impl Decodable for Hyperparameters

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

Deserialize a value using a Decoder.