Struct SparseCoding 

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

Sparse Coding for manifold learning

Sparse coding learns a dictionary of basis vectors such that each data point can be represented as a sparse linear combination of these basis vectors. This is particularly useful for manifold learning when the data lies on a low-dimensional manifold that can be sparsely represented.

Parameters

• n_components - Number of dictionary atoms
• alpha - Sparsity regularization parameter
• max_iter - Maximum number of iterations
• tol - Tolerance for convergence
• random_state - Random seed for reproducibility

Examples

use sklears_manifold::SparseCoding;
use sklears_core::traits::{Transform, Fit};
use scirs2_core::ndarray::array;

let x = array![[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]];

let sc = SparseCoding::new()
    .n_components(2)
    .alpha(0.1);

let fitted = sc.fit(&x.view(), &()).unwrap();
let embedded = fitted.transform(&x.view()).unwrap();

Implementations

impl SparseCoding<Untrained>

pub fn new() -> Self

Create a new SparseCoding instance

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

Set the number of dictionary atoms

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

Set the sparsity 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 random_state(self, random_state: u64) -> Self

Set the random state

Trait Implementations

impl<S: Clone> Clone for SparseCoding<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for SparseCoding<S>

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

Formats the value using the given formatter.

impl Default for SparseCoding<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for SparseCoding<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 SparseCoding<Untrained>

type Fitted = SparseCoding<SCTrained>

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 SparseCoding<SCTrained>

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

Transform the input data