Struct MixtureDiscriminantAnalysis 

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

Mixture Discriminant Analysis

MDA is a semi-supervised extension of Linear/Quadratic Discriminant Analysis that uses both labeled and unlabeled data to learn class-conditional densities. It models each class as a mixture of Gaussians and uses EM algorithm to estimate parameters from both labeled and unlabeled samples.

Parameters

• n_components - Number of mixture components per class
• covariance_type - Type of covariance matrix (‘full’, ‘tied’, ‘diag’, ‘spherical’)
• reg_covar - Regularization added to diagonal of covariance
• max_iter - Maximum number of EM iterations
• tol - Convergence tolerance
• n_init - Number of initializations
• random_state - Random state for reproducible results

Examples

use scirs2_core::array;
use sklears_semi_supervised::MixtureDiscriminantAnalysis;
use sklears_core::traits::{Predict, Fit};


let X = array![[1.0, 2.0], [2.0, 3.0], [3.0, 4.0], [4.0, 5.0]];
let y = array![0, 1, -1, -1]; // -1 indicates unlabeled

let mda = MixtureDiscriminantAnalysis::new()
    .n_components(2)
    .covariance_type("full".to_string())
    .max_iter(100);
let fitted = mda.fit(&X.view(), &y.view()).unwrap();
let predictions = fitted.predict(&X.view()).unwrap();

Implementations

impl MixtureDiscriminantAnalysis<Untrained>

pub fn new() -> Self

Create a new MixtureDiscriminantAnalysis instance

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

Set the number of mixture components per class

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

Set the covariance type

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

Set the covariance regularization

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

Set the maximum number of iterations

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

Set the convergence tolerance

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

Set the number of initializations

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

Set the random state for reproducible results

Trait Implementations

impl<S: Clone> Clone for MixtureDiscriminantAnalysis<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for MixtureDiscriminantAnalysis<S>

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

Formats the value using the given formatter.

impl Default for MixtureDiscriminantAnalysis<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for MixtureDiscriminantAnalysis<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]>>, ArrayBase<ViewRepr<&i32>, Dim<[usize; 1]>>> for MixtureDiscriminantAnalysis<Untrained>

type Fitted = MixtureDiscriminantAnalysis<MixtureDiscriminantAnalysisTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, Float>, y: &ArrayView1<'_, i32>, ) -> 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 MixtureDiscriminantAnalysis<MixtureDiscriminantAnalysisTrained>

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

impl PredictProba<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for MixtureDiscriminantAnalysis<MixtureDiscriminantAnalysisTrained>

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

Predict class probabilities