Struct Exponential 

pub struct Exponential {
    pub rate: Array1<f64>,
    pub scale: Array1<f64>,
    /* private fields */
}

The Exponential distribution.

Fields

rate: Array1<f64>

The rate parameter (1/scale).

scale: Array1<f64>

The scale parameter (1/rate).

Trait Implementations

impl CensoredScorable<CRPScoreCensored> for Exponential

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

Calculates the censored score for each observation.

fn censored_d_score(&self, y: &SurvivalData) -> Array2<f64>

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

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

Calculates the Riemannian metric tensor for censored data.

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

Calculates the total censored score, averaged over all observations.

fn censored_grad(&self, y: &SurvivalData, natural: bool) -> Array2<f64>

where Self: Sized,

Calculates the gradient for censored data, optionally the natural gradient.

impl CensoredScorable<LogScoreCensored> for Exponential

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

Calculates the censored score for each observation.

fn censored_d_score(&self, y: &SurvivalData) -> Array2<f64>

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

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

Calculates the Riemannian metric tensor for censored data.

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

Calculates the total censored score, averaged over all observations.

fn censored_grad(&self, y: &SurvivalData, natural: bool) -> Array2<f64>

where Self: Sized,

Calculates the gradient for censored data, optionally the natural gradient.

impl Clone for Exponential

fn clone(&self) -> Exponential

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Exponential

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

Formats the value using the given formatter.

impl Distribution for Exponential

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 Exponential

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

Evaluates the log 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 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.

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.

impl Scorable<CRPScore> for Exponential

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 Exponential

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 Exponential