Struct NormalFixedMean 

pub struct NormalFixedMean {
    pub loc: Array1<f64>,
    pub scale: Array1<f64>,
    pub var: Array1<f64>,
    /* private fields */
}

Normal distribution with mean fixed at 0.

Has one parameter: log(scale).

Fields

loc: Array1<f64>

The location parameter (fixed at 0.0).

scale: Array1<f64>

The scale parameter.

var: Array1<f64>

The variance.

Trait Implementations

impl Clone for NormalFixedMean

fn clone(&self) -> NormalFixedMean

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for NormalFixedMean

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

Formats the value using the given formatter.

impl Distribution for NormalFixedMean

fn from_params(params: &Array2<f64>) -> Self

Creates a new distribution from a set of parameters.

fn fit(y: &Array1<f64>) -> Array1<f64>

Fits the distribution to the data y and returns the initial parameters.

fn n_params(&self) -> usize

Returns the number of parameters for this distribution.

fn predict(&self) -> Array1<f64>

Returns a point prediction.

fn params(&self) -> &Array2<f64>

Returns the parameters of the distribution.

fn grad<S: Score>( &self, y: &Array1<f64>, _score: S, natural: bool, ) -> Array2<f64>

where Self: Scorable<S>,

Calculates the gradient of the score with respect to the distribution’s parameters.

fn total_score<S: Score>(&self, y: &Array1<f64>, _score: S) -> f64

where Self: Scorable<S>,

impl DistributionMethods for NormalFixedMean

fn mean(&self) -> Array1<f64>

Returns the mean of the distribution for each observation.

fn variance(&self) -> Array1<f64>

Returns the variance of the distribution for each observation.

fn std(&self) -> Array1<f64>

Returns the standard deviation of the distribution for each observation.

fn pdf(&self, y: &Array1<f64>) -> Array1<f64>

Evaluates the probability density function at point y for each observation.

fn cdf(&self, y: &Array1<f64>) -> Array1<f64>

Evaluates the cumulative distribution function at point y for each observation.

fn ppf(&self, q: &Array1<f64>) -> Array1<f64>

Evaluates the percent point function (inverse CDF / quantile function). Returns the value y such that P(Y <= y) = q.

fn sample(&self, n_samples: usize) -> Array2<f64>

Generates random samples from the distribution.

fn logpdf(&self, y: &Array1<f64>) -> Array1<f64>

Evaluates the log probability density function at point y for each observation.

fn interval(&self, alpha: f64) -> (Array1<f64>, Array1<f64>)

Returns the confidence interval for each observation.

fn sf(&self, y: &Array1<f64>) -> Array1<f64>

Returns the survival function (1 - CDF) at point y for each observation.

fn median(&self) -> Array1<f64>

Returns the median of the distribution for each observation.

fn mode(&self) -> Array1<f64>

Returns the mode of the distribution for each observation (if well-defined). Default implementation returns the mean; override for distributions where mode != mean.

impl Scorable<CRPScore> for NormalFixedMean

fn score(&self, y: &Array1<f64>) -> Array1<f64>

Calculates the score for each observation.

fn d_score(&self, y: &Array1<f64>) -> Array2<f64>

Calculates the gradient of the score with respect to the distribution’s parameters.

fn metric(&self) -> Array3<f64>

Calculates the Riemannian metric tensor of the score for each observation.

fn total_score( &self, y: &Array1<f64>, sample_weight: Option<&Array1<f64>>, ) -> f64

Calculates the total score, averaged over all observations.

fn grad(&self, y: &Array1<f64>, natural: bool) -> Array2<f64>

Calculates the gradient, optionally the natural gradient. Uses the same fallback strategy as Python’s NGBoost:

impl Scorable<LogScore> for NormalFixedMean

fn score(&self, y: &Array1<f64>) -> Array1<f64>

Calculates the score for each observation.

fn d_score(&self, y: &Array1<f64>) -> Array2<f64>

Calculates the gradient of the score with respect to the distribution’s parameters.

fn metric(&self) -> Array3<f64>

Calculates the Riemannian metric tensor of the score for each observation.

fn total_score( &self, y: &Array1<f64>, sample_weight: Option<&Array1<f64>>, ) -> f64

Calculates the total score, averaged over all observations.

fn grad(&self, y: &Array1<f64>, natural: bool) -> Array2<f64>

Calculates the gradient, optionally the natural gradient. Uses the same fallback strategy as Python’s NGBoost:

impl RegressionDistn for NormalFixedMean