Struct LinearSVR 

#[non_exhaustive]
pub struct LinearSVR { /* private fields */ }

Linear Support Vector Regressor.

Uses ε-insensitive loss with L2 penalty, solved by SGD. Predictions within epsilon of the true value incur no loss.

Example

use scry_learn::dataset::Dataset;
use scry_learn::svm::LinearSVR;

let features = vec![vec![1.0, 2.0, 3.0, 4.0, 5.0]];
let target = vec![2.0, 4.0, 6.0, 8.0, 10.0];
let data = Dataset::new(features, target, vec!["x".into()], "y");

let mut svr = LinearSVR::new().c(1.0).epsilon(0.1);
svr.fit(&data).unwrap();

let preds = svr.predict(&[vec![3.0]]).unwrap();
assert!((preds[0] - 6.0).abs() < 1.0);

Implementations

impl LinearSVR

pub fn new() -> Self

Create a new LinearSVR with default parameters.

Defaults: C = 1.0, epsilon = 0.1, max_iter = 1000, tol = 1e-4.

pub fn c(self, c: f64) -> Self

Set the regularisation parameter C.

pub fn epsilon(self, e: f64) -> Self

Set the epsilon tube width.

Predictions within epsilon of the true value incur zero loss.

pub fn max_iter(self, n: usize) -> Self

Set the maximum number of SGD epochs.

pub fn tol(self, t: f64) -> Self

Set convergence tolerance on the max weight change per epoch.

pub fn fit(&mut self, data: &Dataset) -> Result<()>

Train the SVR on the given dataset.

Auto-dispatches to sparse kernels when the dataset uses sparse storage.

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

Predict continuous target values.

pub fn predict_sparse(&self, csr: &CsrMatrix) -> Result<Vec<f64>>

Predict continuous target values from sparse input (CSR format).

Trait Implementations

impl Clone for LinearSVR

fn clone(&self) -> LinearSVR

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Default for LinearSVR

fn default() -> Self

Returns the “default value” for a type.

impl PipelineModel for LinearSVR

fn fit(&mut self, data: &Dataset) -> Result<()>

Train the model on a dataset.

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

Predict on row-major feature matrix.

impl Tunable for LinearSVR

fn set_param(&mut self, name: &str, _value: ParamValue) -> Result<()>

Apply a named hyperparameter.

fn clone_box(&self) -> Box<dyn Tunable>

Clone this model into a boxed trait object.

fn fit(&mut self, data: &Dataset) -> Result<()>

Train on a dataset.

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

Predict on row-major features.