Struct VariationalBayesianGMM 

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

Variational Bayesian Gaussian Mixture Model

This implementation uses variational inference to perform Bayesian parameter estimation for Gaussian mixture models. Unlike standard EM, this approach provides uncertainty estimates and automatic model selection by effectively “turning off” unnecessary components.

Parameters

• n_components - Maximum number of mixture components (actual number determined automatically)
• covariance_type - Type of covariance parameters
• tol - Convergence threshold
• reg_covar - Regularization added to the diagonal of covariance
• max_iter - Maximum number of variational iterations
• random_state - Random state for reproducibility
• weight_concentration_prior - Prior on the weight concentration parameter
• mean_precision_prior - Prior precision for component means
• degrees_of_freedom_prior - Prior degrees of freedom for covariance matrices

Examples

use sklears_mixture::{VariationalBayesianGMM, 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], [10.0, 10.0], [11.0, 11.0], [12.0, 12.0]];

let vbgmm = VariationalBayesianGMM::new()
    .n_components(5)  // Will automatically determine optimal number
    .covariance_type(CovarianceType::Diagonal)
    .max_iter(100);
let fitted = vbgmm.fit(&X.view(), &()).unwrap();
let labels = fitted.predict(&X.view()).unwrap();

Implementations

impl VariationalBayesianGMM<Untrained>

pub fn new() -> Self

Create a new VariationalBayesianGMM instance

pub fn builder() -> Self

Create a new VariationalBayesianGMM instance using builder pattern (alias for new)

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

Set the maximum 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 random_state(self, random_state: u64) -> Self

Set the random state

pub fn weight_concentration_prior(self, prior: f64) -> Self

Set the weight concentration prior

pub fn mean_precision_prior(self, prior: f64) -> Self

Set the mean precision prior

pub fn degrees_of_freedom_prior(self, prior: f64) -> Self

Set the degrees of freedom prior

pub fn build(self) -> Self

Build the VariationalBayesianGMM (builder pattern completion)

impl VariationalBayesianGMM<VariationalBayesianGMMTrained>

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

Get the fitted weights

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

Get the fitted means

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

Get the fitted covariances

pub fn lower_bound(&self) -> f64

Get the variational lower bound

pub fn effective_components(&self) -> usize

Get the number of effective components

pub fn converged(&self) -> bool

Check if the model converged

pub fn n_iter(&self) -> usize

Get the number of iterations performed

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

Get the weight concentration parameters

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

Get the mean precision parameters

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

Get the degrees of freedom parameters

Trait Implementations

impl<S: Clone> Clone for VariationalBayesianGMM<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for VariationalBayesianGMM<S>

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

Formats the value using the given formatter.

impl Default for VariationalBayesianGMM<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for VariationalBayesianGMM<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 VariationalBayesianGMM<Untrained>

type Fitted = VariationalBayesianGMM<VariationalBayesianGMMTrained>

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 VariationalBayesianGMM<VariationalBayesianGMMTrained>

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