Struct peroxide::numerical::optimize::Optimizer

pub struct Optimizer<F>where
    F: Fn(&Vec<f64>, Vec<AD>) -> Option<Vec<AD>>,{ /* private fields */ }

Optimizer for optimization (non-linear regression)

Methods

• Gradient Descent
• Gauss Newton
• Levenberg Marquardt

Caution

• func should be boxed. (This allows more generic function)

Implementations

impl<F> Optimizer<F>where
    F: Fn(&Vec<f64>, Vec<AD>) -> Option<Vec<AD>>,

pub fn new(data: Matrix, func: F) -> Self

pub fn get_domain(&self) -> Vec<f64>

Get domain

pub fn get_error(&self) -> f64

Get error

pub fn get_hyperparam(&self, key: &str) -> Option<&f64>

Get hyperparameter (learning rate or lambda or etc.)

pub fn set_init_param(&mut self, p: Vec<f64>) -> &mut Self

Set initial parameter

pub fn set_max_iter(&mut self, n: usize) -> &mut Self

Set maximum iteration

pub fn set_method(&mut self, method: OptMethod) -> &mut Self

Set optimization method

pub fn set_lr(&mut self, lr: f64) -> &mut Self

Set learning rate for GradientDescent

pub fn set_lambda_init(&mut self, lambda_init: f64) -> &mut Self

Set initial lambda for LevenbergMarquardt

pub fn set_lambda_max(&mut self, lambda_max: f64) -> &mut Self

Set maximum lambda for LevenbergMarquardt

pub fn optimize(&mut self) -> Vec<f64>

Main function for optimization