Struct rusty_machine::learning::optim::grad_desc::GradientDesc

pub struct GradientDesc {
    pub alpha: f64,
    pub iters: usize,
}

Batch Gradient Descent algorithm

Fields

alpha: f64

The step-size for the gradient descent steps.

iters: usize

The number of iterations to run.

Methods

impl GradientDesc

fn new(alpha: f64, iters: usize) -> GradientDesc

Construct a gradient descent algorithm.

Requires the step size and iteration count to be specified.

Examples

use rusty_machine::learning::optim::grad_desc::GradientDesc;

let gd = GradientDesc::new(0.3, 10000);

Trait Implementations

impl Debug for GradientDesc

fn fmt(&self, __arg_0: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Copy for GradientDesc

impl Clone for GradientDesc

fn clone(&self) -> GradientDesc

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Default for GradientDesc

The default gradient descent algorithm.

The defaults are:

• alpha = 0.3
• iters = 100

fn default() -> GradientDesc

Returns the "default value" for a type.

impl<M: Optimizable> OptimAlgorithm<M> for GradientDesc

fn optimize(&self, model: &M, start: &[f64], inputs: &M::Inputs, targets: &M::Targets) -> Vec<f64>

Return the optimized parameter using gradient optimization.