Struct DirichletProcessGaussianMixture 

pub struct DirichletProcessGaussianMixture<S = Untrained> {
    pub alpha: f64,
    pub max_components: usize,
    pub covariance_type: CovarianceType,
    pub tol: f64,
    pub max_iter: usize,
    pub reg_covar: f64,
    pub random_state: Option<u64>,
    pub n_init: usize,
    /* private fields */
}

Dirichlet Process Gaussian Mixture Model

A nonparametric Bayesian mixture model that uses the Dirichlet process as a prior over the mixture weights. Uses stick-breaking construction and variational inference.

Fields

alpha: f64

Concentration parameter of the Dirichlet process

max_components: usize

Maximum number of components to consider

covariance_type: CovarianceType

Type of covariance parameters

tol: f64

Convergence threshold

max_iter: usize

Maximum number of iterations

reg_covar: f64

Regularization added to diagonal of covariance

random_state: Option<u64>

Random state for reproducible results

n_init: usize

Number of random initializations

Implementations

impl DirichletProcessGaussianMixture<Untrained>

pub fn new() -> Self

Create a new Dirichlet Process Gaussian Mixture Model

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

Set the concentration parameter

pub fn max_components(self, max_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 threshold

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

Set the maximum number of iterations

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

Set the regularization parameter

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

Set the random state

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

Set the number of initializations

impl DirichletProcessGaussianMixture<DirichletProcessGaussianMixtureTrained>

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

Predict class probabilities

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

Score samples using the lower bound

Trait Implementations

impl<S: Clone> Clone for DirichletProcessGaussianMixture<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for DirichletProcessGaussianMixture<S>

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

Formats the value using the given formatter.

impl Default for DirichletProcessGaussianMixture<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for DirichletProcessGaussianMixture<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 DirichletProcessGaussianMixture<Untrained>

type Fitted = DirichletProcessGaussianMixture<DirichletProcessGaussianMixtureTrained>

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 DirichletProcessGaussianMixture<DirichletProcessGaussianMixtureTrained>

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