Struct StructuredVariationalGMMTrained 

pub struct StructuredVariationalGMMTrained { /* private fields */ }

Trained Structured Variational Gaussian Mixture Model

Implementations

impl StructuredVariationalGMMTrained

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

Predict class probabilities

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

Compute log-likelihood of the data

pub fn model_selection(&self) -> &ModelSelection

Get model selection criteria

pub fn lower_bound(&self) -> f64

Get the lower bound (ELBO)

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

Get the final responsibilities

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

Get the variational mean parameters

pub fn precision_values(&self) -> &Array3<f64>

Get the variational precision parameters

pub fn structured_cov(&self) -> &Array3<f64>

Get the structured covariance parameters

pub fn structured_family(&self) -> StructuredFamily

Get the structured approximation family

Trait Implementations

impl Clone for StructuredVariationalGMMTrained

fn clone(&self) -> StructuredVariationalGMMTrained

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for StructuredVariationalGMMTrained

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

Formats the value using the given formatter.

impl Estimator<Trained> for StructuredVariationalGMMTrained

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 Predict<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>>> for StructuredVariationalGMMTrained

fn predict(&self, X: &ArrayView2<'_, f64>) -> 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