Struct ElasticNet

pub struct ElasticNet {
    pub features: Value<NDArray<f64>>,
    pub outputs: Value<NDArray<f64>>,
    pub weights: Value<NDArray<f64>>,
    pub bias: Value<NDArray<f64>>,
    /* private fields */
}

Fields

features: Value<NDArray<f64>>

outputs: Value<NDArray<f64>>

weights: Value<NDArray<f64>>

bias: Value<NDArray<f64>>

Implementations

impl ElasticNet

pub fn new( features: &NDArray<f64>, y: &NDArray<f64>, lambda: f64, learning_rate: f64, ) -> Result<ElasticNet, String>

Create new instance of elastic net regression

pub fn predict(&mut self, inputs: NDArray<f64>) -> NDArray<f64>

Predict features for elastic net regression

pub fn save(&self, filepath: &str) -> Result<()>

Save parameters for elastic net regression

pub fn load( filepath: &str, features: &NDArray<f64>, y: &NDArray<f64>, learning_rate: f64, lambda: f64, ) -> Result<ElasticNet>

Load parameters for elastic net regression

pub fn train(&mut self, epochs: usize, log_output: bool)

Train elastic net regression model

pub fn sgd(&mut self, epochs: usize, log_output: bool, batch_size: usize)

Train elastic net regression model with batch gradient descent