Struct SparseImputer 

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

Sparse Imputation methods for high-dimensional data

Imputation designed for high-dimensional sparse data where most values are zero or missing. Uses compressed sensing and sparse coding techniques.

Implementations

impl SparseImputer<Untrained>

pub fn new() -> Self

Create a new SparseImputer instance

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

Set the expected sparsity level

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

Set the L1 regularization parameter

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

Set the maximum number of iterations

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

Set the tolerance for convergence

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

Set the missing values placeholder

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

Set the random state

Trait Implementations

impl<S: Clone> Clone for SparseImputer<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for SparseImputer<S>

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

Formats the value using the given formatter.

impl Default for SparseImputer<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for SparseImputer<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 SparseImputer<Untrained>

type Fitted = SparseImputer<SparseImputerTrained>

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 Transform<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for SparseImputer<SparseImputerTrained>

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

Transform the input data