Struct RobustGaussianMixture 

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

Robust Gaussian Mixture Model

A robust version of Gaussian mixture model that is resistant to outliers. This implementation uses trimmed likelihood estimation and outlier detection to provide robust parameter estimates even in the presence of outliers.

Parameters

• n_components - Number of mixture components
• covariance_type - Type of covariance parameters
• tol - Convergence threshold
• reg_covar - Regularization added to the diagonal of covariance
• max_iter - Maximum number of EM iterations
• n_init - Number of initializations to perform
• outlier_fraction - Expected fraction of outliers in the data (0.0 to 0.5)
• outlier_threshold - Threshold for outlier detection (in standard deviations)
• robust_covariance - Whether to use robust covariance estimation
• random_state - Random state for reproducibility

Examples

use sklears_mixture::{RobustGaussianMixture, CovarianceType};
use sklears_core::traits::{Predict, Fit};
use scirs2_core::ndarray::array;

let X = array![[0.0, 0.0], [1.0, 1.0], [2.0, 2.0], [100.0, 100.0], [11.0, 11.0], [12.0, 12.0]];

let rgmm = RobustGaussianMixture::new()
    .n_components(2)
    .outlier_fraction(0.15)
    .covariance_type(CovarianceType::Diagonal)
    .max_iter(100);
let fitted = rgmm.fit(&X.view(), &()).unwrap();
let labels = fitted.predict(&X.view()).unwrap();

Implementations

impl RobustGaussianMixture<Untrained>

pub fn new() -> Self

Create a new RobustGaussianMixture instance

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

Set the number of components

pub fn covariance_type(self, covariance_type: CovarianceType) -> Self

Set the covariance type

pub fn tol(self, tol: f64) -> Self

Set the convergence tolerance

pub fn reg_covar(self, reg_covar: f64) -> Self

Set the regularization parameter

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

Set the maximum number of iterations

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

Set the number of initializations

pub fn outlier_fraction(self, outlier_fraction: f64) -> Self

Set the expected fraction of outliers

pub fn outlier_threshold(self, outlier_threshold: f64) -> Self

Set the outlier detection threshold (in standard deviations)

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

Set whether to use robust covariance estimation

pub fn random_state(self, random_state: u64) -> Self

Set the random state

impl RobustGaussianMixture<RobustGaussianMixtureTrained>

pub fn weights(&self) -> &Array1<f64>

Get the mixture weights

pub fn means(&self) -> &Array2<f64>

Get the component means

pub fn covariances(&self) -> &[Array2<f64>]

Get the component covariances

pub fn log_likelihood(&self) -> f64

Get the log likelihood of the fitted model

pub fn n_iter(&self) -> usize

Get the number of iterations performed

pub fn converged(&self) -> bool

Check if the algorithm converged

pub fn bic(&self) -> f64

Get the Bayesian Information Criterion

pub fn aic(&self) -> f64

Get the Akaike Information Criterion

pub fn outlier_mask(&self) -> &Array1<bool>

Get the outlier mask

pub fn n_outliers(&self) -> usize

Get the number of detected outliers

pub fn predict_proba(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<f64>>

Predict probabilities for each component

pub fn score_samples(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array1<f64>>

Compute the per-sample log-likelihood

pub fn score(&self, X: &ArrayView2<'_, Float>) -> SklResult<f64>

Compute the average log-likelihood

pub fn detect_outliers( &self, X: &ArrayView2<'_, Float>, ) -> SklResult<Array1<bool>>

Detect outliers in new data

Trait Implementations

impl<S: Clone> Clone for RobustGaussianMixture<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for RobustGaussianMixture<S>

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

Formats the value using the given formatter.

impl Default for RobustGaussianMixture<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for RobustGaussianMixture<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 RobustGaussianMixture<Untrained>

type Fitted = RobustGaussianMixture<RobustGaussianMixtureTrained>

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<i32>, Dim<[usize; 1]>>> for RobustGaussianMixture<RobustGaussianMixtureTrained>

fn predict(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array1<i32>>

Make predictions on the provided data

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

Make predictions with confidence intervals