Struct LinearSVC 

pub struct LinearSVC<F> {
    pub c: F,
    pub max_iter: usize,
    pub tol: F,
    pub loss: LinearSVCLoss,
}

Linear Support Vector Classifier (primal formulation).

Solves the L2-regularized hinge or squared-hinge loss via coordinate descent in the primal. Supports binary and multiclass (one-vs-rest) classification.

Type Parameters

• F: The floating-point type (f32 or f64).

Fields

c: F

Inverse regularization strength. Larger values allow more misclassification.

max_iter: usize

Maximum number of coordinate descent iterations.

tol: F

Convergence tolerance on the change in weight vector.

loss: LinearSVCLoss

Loss function to use.

Implementations

impl<F: Float> LinearSVC<F>

pub fn new() -> Self

Create a new LinearSVC with default settings.

Defaults: C = 1.0, max_iter = 1000, tol = 1e-4, loss = SquaredHinge.

pub fn with_c(self, c: F) -> Self

Set the regularization parameter C.

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

Set the maximum number of iterations.

pub fn with_tol(self, tol: F) -> Self

Set the convergence tolerance.

pub fn with_loss(self, loss: LinearSVCLoss) -> Self

Set the loss function.

Trait Implementations

impl<F: Clone> Clone for LinearSVC<F>

fn clone(&self) -> LinearSVC<F>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for LinearSVC<F>

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

Formats the value using the given formatter.

impl<F: Float> Default for LinearSVC<F>

fn default() -> Self

Returns the “default value” for a type.

impl<F: Float + Send + Sync + ScalarOperand + 'static> Fit<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>>> for LinearSVC<F>

fn fit( &self, x: &Array2<F>, y: &Array1<usize>, ) -> Result<FittedLinearSVC<F>, FerroError>

Fit the linear SVC model using coordinate descent.

Errors

• FerroError::ShapeMismatch — sample count mismatch.
• FerroError::InvalidParameter — C not positive.
• FerroError::InsufficientSamples — fewer than 2 distinct classes.

type Fitted = FittedLinearSVC<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.