Struct NaturalGradient 

pub struct NaturalGradient { /* private fields */ }

Natural gradient optimizer state

Implementations

impl NaturalGradient

pub fn new(learning_rate: f64) -> Self

Create a new natural gradient optimizer

Arguments

• learning_rate - Step size (0.01-0.1 typical)

pub fn with_damping(self, damping: f64) -> Self

Set damping factor

pub fn with_diagonal(self, use_diagonal: bool) -> Self

Use diagonal FIM approximation (faster, less memory)

pub fn with_ema(self, ema: f64) -> Self

Set EMA factor for FIM smoothing

pub fn step( &mut self, gradient: &[f64], gradient_samples: Option<&[Vec<f64>]>, ) -> Result<Vec<f64>>

Compute natural gradient step

Arguments

• gradient - Standard gradient ∇L
• gradient_samples - Optional gradient samples for FIM estimation

pub fn apply_update(parameters: &mut [f64], update: &[f64]) -> Result<()>

Apply update to parameters

pub fn optimize_step( &mut self, parameters: &mut [f64], gradient: &[f64], gradient_samples: Option<&[Vec<f64>]>, ) -> Result<f64>

Full optimization step: compute and apply update

pub fn reset(&mut self)

Reset optimizer state

Trait Implementations

impl Clone for NaturalGradient

fn clone(&self) -> NaturalGradient

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for NaturalGradient

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.