`shrinkage::DiagonalHessianDatafit`

Shrinkage for Rust · rust · 0.3.0

Source: src/datafit.rs

pub trait DiagonalHessianDatafit: DifferentiableDatafit

A twice-differentiable loss whose predictor Hessian is diagonal.

Off-diagonal Hessian entries must be zero. Entries use exactly the same normalization as DifferentiableDatafit::gradient, including row averaging. Entries may be negative; this capability does not imply convexity or provide the positive working curvature required by crate::Irls.

Methods

`hessian_diagonal`

Source: src/datafit.rs

fn hessian_diagonal(
    &self,
    predictor: &[f64],
    output: &mut [f64]
) -> Result<(), FitError>

Write finite, signed predictor Hessian entries into reusable storage.

Errors

Reject invalid data, predictor values, dimensions, or arithmetic. Output is unspecified after an error.

Implementation of DiagonalHessianDatafit

Source: src/datafit/binomial.rs

impl DiagonalHessianDatafit for Binomial<'_>

Implementation of DiagonalHessianDatafit

Source: src/datafit/gaussian.rs

impl DiagonalHessianDatafit for Gaussian<'_>

Implementation of DiagonalHessianDatafit

Source: src/datafit/linked.rs

impl<L: TwiceDifferentiableLink> DiagonalHessianDatafit for LinkedDatafit<shrinkage::Binomial<'_>, L>

Implementation of DiagonalHessianDatafit

Source: src/datafit/linked.rs

impl<L: TwiceDifferentiableLink> DiagonalHessianDatafit for LinkedDatafit<shrinkage::Gaussian<'_>, L>