Struct MLTSVM 

pub struct MLTSVM<S = Untrained> { /* private fields */ }

MLTSVM (Multi-Label Twin SVM)

MLTSVM is a multi-label classification method that extends Twin SVM to handle multiple labels. Twin SVM finds two non-parallel hyperplanes for binary classification, which often leads to faster training than standard SVM. MLTSVM applies this approach to each label independently in a binary relevance fashion.

Examples

use sklears_core::traits::{Predict, Fit};
use sklears_multioutput::MLTSVM;
// Use SciRS2-Core for arrays and random number generation (SciRS2 Policy)
use scirs2_core::ndarray::array;

let X = array![[1.0, 2.0], [2.0, 3.0], [3.0, 1.0], [4.0, 4.0]];
let y = array![[1, 0], [0, 1], [1, 1], [0, 0]]; // Multi-label binary

let mltsvm = MLTSVM::new().c1(1.0).c2(1.0);
let trained_mltsvm = mltsvm.fit(&X.view(), &y).unwrap();
let predictions = trained_mltsvm.predict(&X.view()).unwrap();

Implementations

impl MLTSVM<Untrained>

pub fn new() -> Self

Create a new MLTSVM instance

pub fn c1(self, c1: Float) -> Self

Set C1 parameter

pub fn c2(self, c2: Float) -> Self

Set C2 parameter

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

Set epsilon parameter

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

Set maximum iterations

impl MLTSVM<MLTSVMTrained>

pub fn n_labels(&self) -> usize

Get the number of labels

pub fn decision_function( &self, x: &ArrayView2<'_, Float>, ) -> SklResult<Array2<Float>>

Get decision function values (distances to hyperplanes)

Trait Implementations

impl<S: Clone> Clone for MLTSVM<S>

fn clone(&self) -> MLTSVM<S>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for MLTSVM<S>

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

Formats the value using the given formatter.

impl Default for MLTSVM<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for MLTSVM<Untrained>

type Config = ()

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Fit<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 2]>>> for MLTSVM<Untrained>

type Fitted = MLTSVM<MLTSVMTrained>

The fitted model type

fn fit( self, x: &ArrayView2<'_, Float>, y: &Array2<i32>, ) -> SklResult<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<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 2]>>> for MLTSVM<MLTSVMTrained>

fn predict(&self, x: &ArrayView2<'_, Float>) -> SklResult<Array2<i32>>

Make predictions on the provided data

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

Make predictions with confidence intervals