Struct GaussianDistribution

#[non_exhaustive]
pub struct GaussianDistribution {
    pub mean: f64,
    pub variance: f64,
}

Represents a Gaussian (Normal) distribution

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

mean: f64

Mean parameter

variance: f64

Variance parameter

Implementations

impl GaussianDistribution

pub fn new( mean: f64, variance: f64, ) -> Result<GaussianDistribution, RegressionError>

Constructs a new Gaussian distribution from the provided parameters

Arguments

mean - The mean parameter variance - The variance parameter

Trait Implementations

impl Clone for GaussianDistribution

fn clone(&self) -> GaussianDistribution

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for GaussianDistribution

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for GaussianDistribution

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl ScalarDistribution for GaussianDistribution

fn mean(&self) -> f64

Computes the mean of the distribution

fn variance(&self) -> f64

Computes the variance of the distribution

fn std_dev(&self) -> f64

Computes the standard deviation of the distribution

impl Serialize for GaussianDistribution

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl VariationalRegression<GaussianDistribution> for VariationalLinearRegression

fn predict( &self, features: &[f64], ) -> Result<GaussianDistribution, RegressionError>

Computes the predictive distribution for the provided features

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

Provides the estimated model weights

fn bias(&self) -> Option<f64>

Provides the estimated model bias, if specified for training

impl Copy for GaussianDistribution