Struct VariationalLinearRegression

pub struct VariationalLinearRegression {
    pub noise_precision: GammaDistribution,
    pub bound: f64,
    /* private fields */
}

Represents a linear regression model trained via variational inference

Fields

noise_precision: GammaDistribution

Noise precision distribution

bound: f64

Variational lower bound

Implementations

impl VariationalLinearRegression

pub fn train( features: impl Features, labels: impl RealLabels, config: &LinearTrainConfig, ) -> Result<VariationalLinearRegression, RegressionError>

Trains the model on the provided data

Arguments

features - The feature values (in row-major orientation) labels - The vector of corresponding labels config - The training configuration

Trait Implementations

impl Clone for VariationalLinearRegression

fn clone(&self) -> VariationalLinearRegression

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<'de> Deserialize<'de> for VariationalLinearRegression

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for VariationalLinearRegression

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