Struct L1RegularizedGMM 

pub struct L1RegularizedGMM<S = Untrained> { /* private fields */ }

L1 Regularized Gaussian Mixture Model

Implements sparse Gaussian mixture modeling using L1 (LASSO) regularization. This promotes sparsity in the parameter estimates, which is useful for feature selection and high-dimensional data.

Examples

use sklears_mixture::regularization::L1RegularizedGMM;
use sklears_core::traits::Fit;
use scirs2_core::ndarray::array;

let X = array![[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]];

let model = L1RegularizedGMM::builder()
    .n_components(2)
    .lambda(0.01)
    .build();

let fitted = model.fit(&X.view(), &()).unwrap();

Implementations

impl L1RegularizedGMM<Untrained>

pub fn builder() -> L1RegularizedGMMBuilder

Create a new builder

Trait Implementations

impl<S: Clone> Clone for L1RegularizedGMM<S>

fn clone(&self) -> L1RegularizedGMM<S>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for L1RegularizedGMM<S>

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

Formats the value using the given formatter.

impl Estimator for L1RegularizedGMM<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]>>, ()> for L1RegularizedGMM<Untrained>

type Fitted = L1RegularizedGMM<L1RegularizedGMMTrained>

The fitted model type

fn fit(self, X: &ArrayView2<'_, Float>, _y: &()) -> 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<usize>, Dim<[usize; 1]>>> for L1RegularizedGMM<L1RegularizedGMMTrained>

fn predict(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array1<usize>>

Make predictions on the provided data

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

Make predictions with confidence intervals