Struct MiniBatchGMM 

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

Mini-Batch EM Gaussian Mixture Model

Implements EM algorithm with mini-batch processing for scalability. Suitable for datasets with millions of samples.

Examples

use sklears_mixture::large_scale::{MiniBatchGMM, BatchStrategy};
use sklears_core::traits::Fit;
use scirs2_core::ndarray::array;

let model = MiniBatchGMM::builder()
    .n_components(2)
    .batch_strategy(BatchStrategy::Fixed { size: 100 })
    .build();

let X = array![[1.0, 2.0], [1.5, 2.5], [10.0, 11.0], [10.5, 11.5]];
let fitted = model.fit(&X.view(), &()).unwrap();

Implementations

impl MiniBatchGMM<Untrained>

pub fn builder() -> MiniBatchGMMBuilder

Create a new builder

Trait Implementations

impl<S: Clone> Clone for MiniBatchGMM<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for MiniBatchGMM<S>

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

Formats the value using the given formatter.

impl Estimator for MiniBatchGMM<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 MiniBatchGMM<Untrained>

type Fitted = MiniBatchGMM<MiniBatchGMMTrained>

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 MiniBatchGMM<MiniBatchGMMTrained>

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