Struct MarkovRandomFieldMixture 

pub struct MarkovRandomFieldMixture<S = Untrained> {
    pub n_components: usize,
    pub covariance_type: CovarianceType,
    pub interaction_strength: f64,
    pub neighborhood_size: usize,
    pub max_iter: usize,
    pub tol: f64,
    pub random_state: Option<u64>,
    /* private fields */
}

Markov Random Field Mixture Model

A mixture model based on Markov Random Fields that models spatial dependencies through local neighborhoods.

Fields

n_components: usize

n_components

covariance_type: CovarianceType

covariance_type

interaction_strength: f64

interaction_strength

neighborhood_size: usize

neighborhood_size

max_iter: usize

max_iter

tol: f64

tol

random_state: Option<u64>

random_state

Trait Implementations

impl<S: Clone> Clone for MarkovRandomFieldMixture<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for MarkovRandomFieldMixture<S>

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

Formats the value using the given formatter.

impl Estimator for MarkovRandomFieldMixture<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<OwnedRepr<f64>, Dim<[usize; 2]>>, ()> for MarkovRandomFieldMixture<Untrained>

type Fitted = MarkovRandomFieldMixture<MarkovRandomFieldMixtureTrained>

The fitted model type

fn fit(self, X: &Array2<f64>, _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<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>>> for MarkovRandomFieldMixture<MarkovRandomFieldMixtureTrained>

fn predict(&self, X: &Array2<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