Struct LSVMClassifier 

pub struct LSVMClassifier<State = Untrained> { /* private fields */ }

LS-SVM Classifier wrapper for binary classification

Implementations

impl LSVMClassifier<Untrained>

pub fn new() -> Self

Create a new LS-SVM classifier

pub fn gamma(self, gamma: Float) -> Self

Set the regularization parameter gamma

pub fn kernel(self, kernel: KernelType) -> Self

Set the kernel type

pub fn fit_intercept(self, fit_intercept: bool) -> Self

Set whether to fit an intercept

impl LSVMClassifier<Trained>

pub fn decision_function(&self, x: &Array2<Float>) -> Result<Array1<Float>>

Get the decision function values

pub fn lssvm(&self) -> &LSSVM<Trained>

Get the underlying LS-SVM model

Trait Implementations

impl<State: Debug> Debug for LSVMClassifier<State>

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

Formats the value using the given formatter.

impl Default for LSVMClassifier<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>>> for LSVMClassifier<Untrained>

type Fitted = LSVMClassifier<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, y: &Array1<i32>) -> Result<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>>> for LSVMClassifier<Trained>

fn predict(&self, x: &Array2<Float>) -> Result<Array1<i32>>

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals