Struct FisherInformation 

pub struct FisherInformation { /* private fields */ }

Fisher Information Matrix calculator

Implementations

impl FisherInformation

pub fn new() -> Self

Create a new FIM calculator

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

Set damping factor (for matrix inversion stability)

pub fn with_samples(self, num_samples: usize) -> Self

Set number of samples for empirical FIM

pub fn empirical_fim(&self, gradients: &[Vec<f64>]) -> Result<Vec<Vec<f64>>>

Compute empirical FIM from gradient samples

F ≈ (1/N) Σᵢ ∇log p(xᵢ|θ) ∇log p(xᵢ|θ)^T

Arguments

• gradients - Sample gradients, each of length d

pub fn diagonal_fim(&self, gradients: &[Vec<f64>]) -> Result<Vec<f64>>

Compute diagonal FIM approximation (much faster)

Only computes diagonal: F_ii ≈ (1/N) Σₙ (∂log p / ∂θᵢ)²

pub fn gaussian_fim(&self, dim: usize, variance: f64) -> Vec<Vec<f64>>

Compute FIM for Gaussian distribution with known variance

For N(μ, σ²I): F(μ) = I/σ²

pub fn categorical_fim(&self, probabilities: &[f64]) -> Result<Vec<Vec<f64>>>

Compute FIM for categorical distribution

For categorical p = (p₁, …, pₖ): F_ij = δᵢⱼ/pᵢ - 1

pub fn invert_fim(&self, fim: &[Vec<f64>]) -> Result<Vec<Vec<f64>>>

Invert FIM using Cholesky decomposition

Returns F⁻¹ for natural gradient computation

pub fn natural_gradient( &self, fim: &[Vec<f64>], gradient: &[f64], ) -> Result<Vec<f64>>

Compute natural gradient: F⁻¹ ∇L

Trait Implementations

impl Clone for FisherInformation

fn clone(&self) -> FisherInformation

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for FisherInformation

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

Formats the value using the given formatter.

impl Default for FisherInformation

fn default() -> Self

Returns the “default value” for a type.