shrinkage::Gaussian

fits_intercept

Source: src/datafit/gaussian.rs

pub fn fits_intercept(&self) -> bool

Whether fitting includes a free, unpenalized intercept.

response

Source: src/datafit/gaussian.rs

pub fn response(&self) -> &[f64]

Borrow the observed response.

fit_intercept

Source: src/datafit/gaussian.rs

pub fn fit_intercept(self, enabled: bool) -> Self

Enable or disable the unpenalized fitted intercept.

Explicit column centering can still induce an original-scale intercept.

inverse_link

Source: src/datafit/gaussian.rs

pub fn inverse_link(&self, eta: f64) -> Result<f64, FitError>

Transform a predictor to a mean with the default link.

Errors

Reject nonfinite predictors or arithmetic.

link

Source: src/datafit/gaussian.rs

pub fn link(&self, mu: f64) -> Result<f64, FitError>

Transform a mean with the default identity link.

Errors

Reject means outside the link domain or nonfinite arithmetic.

new

Source: src/datafit/gaussian.rs

pub fn new(response: &'a [f64]) -> Self

Borrow a response, fitting an intercept by default.

with_link

Source: src/datafit/gaussian.rs

pub fn with_link<L: shrinkage::Link>(
    self,
    link: L
) -> shrinkage::LinkedDatafit<Self, L>

Compose this family with a link for Fisher-scoring IRLS.

clone

Source: src/datafit/gaussian.rs

fn clone(&self) -> Self

WithResponse

Source: src/cross_validation/metric.rs

type WithResponse<'a> = Gaussian<'a>;

response

Source: src/cross_validation/metric.rs

fn response(&self) -> &[f64]

supports_metric

Source: src/cross_validation/metric.rs

fn supports_metric(&self, metric: ValidationMetric) -> bool

validate_training

Source: src/cross_validation/metric.rs

fn validate_training(&self) -> Result<(), FitError>

validation_metric

Source: src/cross_validation/metric.rs

fn validation_metric(&self) -> ValidationMetric

with_response

Source: src/cross_validation/metric.rs

fn with_response<'a>(&self, response: &'a [f64]) -> Gaussian<'a>

Error

Source: src/datafit/gaussian.rs

type Error = FitError;

value

Source: src/datafit/gaussian.rs

fn value(&self, predictor: &[f64]) -> Result<f64, <Self as Datafit>::Error>

Evaluate ||response - predictor||² / (2n).

fmt

Source: src/datafit/gaussian.rs

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

hessian_diagonal

Source: src/datafit/gaussian.rs

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

fits_intercept

Source: src/datafit/gaussian.rs

fn fits_intercept(&self) -> bool

gradient

Source: src/datafit/gaussian.rs

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

n_obs

Source: src/datafit/gaussian.rs

fn n_obs(&self) -> usize

from

Source: src/solver/hybrid/quadratic.rs

fn from(value: Gaussian<'a>) -> Self

dual_candidate

Source: src/datafit/gaussian.rs

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

dual_objective

Source: src/datafit/gaussian.rs

fn dual_objective(&self, dual: &[f64]) -> Result<f64, FitError>

fenchel_gap

Source: src/datafit/gaussian.rs

fn fenchel_gap(&self, predictor: &[f64], dual: &[f64]) -> Result<f64, FitError>

finalize_dual

Source: src/datafit/gaussian.rs

fn finalize_dual(&self, dual: &mut [f64]) -> Result<(), FitError>

strong_concavity

Source: src/datafit/gaussian.rs

fn strong_concavity(&self) -> f64

Outer

Source: src/solver/hybrid/path.rs

type Outer = ();

fit

Source: src/solver/hybrid/path.rs

fn fit<M, V, P>(
    &self,
    design: &mut NativeDesign<M, V>,
    penalty: &P,
    solver: &HybridCoordinateDescent<V>,
    threshold: f64,
    work: (&mut Workspace<P>, &mut ())
) -> Result<Solution, FitError<<M as >::Error>> where M: RawColumns<f64> + MatVecInto<V> + MatTransposeVecInto<V>, V: ProximalVector, P: ReducedCoordinatePenalty

initial_intercept

Source: src/solver/hybrid/path.rs

fn initial_intercept(&self) -> Result<f64, FitError>

outer

Source: src/solver/hybrid/path.rs

fn outer(_: usize, _: usize)

path_deviance

Source: src/solver/hybrid/path.rs

fn path_deviance<P: ReducedCoordinatePenalty>(
    &self,
    work: &Workspace<P>,
    _: &()
) -> Result<Option<f64>, FitError>

deviance

Source: src/path/datafit.rs

fn deviance(&self, predictor: &[f64]) -> Result<Option<f64>, FitError>

null_intercepts

Source: src/path/datafit.rs

fn null_intercepts(&self) -> Result<Vec<f64>, FitError>

working_curvature

Source: src/datafit/gaussian.rs

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

gap_safe

Source: src/datafit/gaussian.rs

fn gap_safe(&self) -> Option<&dyn shrinkage::GapSafeDatafit>