Struct HomogeneousPolynomialFeatures 

pub struct HomogeneousPolynomialFeatures<State = Untrained> {
    pub degree: u32,
    pub interaction_only: bool,
    pub normalization: NormalizationMethod,
    pub coefficient_method: CoefficientMethod,
    /* private fields */
}

Homogeneous Polynomial Features

Generates polynomial features where all terms have exactly the same total degree. This is useful for creating features that capture specific order interactions without lower-order contamination.

For degree d and features [x₁, x₂, …, xₙ], generates all terms of the form: x₁^(i₁) * x₂^(i₂) * … * xₙ^(iₙ) where i₁ + i₂ + … + iₙ = d

Parameters

• degree - The fixed total degree for all polynomial terms
• interaction_only - Include only interaction terms (all powers ≤ 1)
• normalization - Normalization method for features
• coefficient_method - Method for computing multinomial coefficients

Examples

use sklears_kernel_approximation::homogeneous_polynomial::HomogeneousPolynomialFeatures;
use sklears_core::traits::{Transform, Fit, Untrained}
use scirs2_core::ndarray::array;

let X = array![[1.0, 2.0], [3.0, 4.0]];

let homo_poly = HomogeneousPolynomialFeatures::new(2);
let fitted_homo = homo_poly.fit(&X, &()).unwrap();
let X_transformed = fitted_homo.transform(&X).unwrap();

HomogeneousPolynomialFeatures

Fields

degree: u32

The fixed total degree

interaction_only: bool

Include only interaction terms

normalization: NormalizationMethod

Normalization method

coefficient_method: CoefficientMethod

Coefficient computation method

Implementations

impl HomogeneousPolynomialFeatures<Untrained>

pub fn new(degree: u32) -> Self

Create a new homogeneous polynomial features transformer

pub fn interaction_only(self, interaction_only: bool) -> Self

Set interaction_only parameter

pub fn normalization(self, method: NormalizationMethod) -> Self

Set normalization method

pub fn coefficient_method(self, method: CoefficientMethod) -> Self

Set coefficient method

impl HomogeneousPolynomialFeatures<Trained>

pub fn n_input_features(&self) -> usize

Get the number of input features

pub fn n_output_features(&self) -> usize

Get the number of output features

pub fn power_combinations(&self) -> &[Vec<u32>]

Get the power combinations

pub fn coefficients(&self) -> &[Float]

Get the coefficients

pub fn normalization_params(&self) -> Option<&(Array1<Float>, Array1<Float>)>

Get normalization parameters (if standard normalization is used)

pub fn count_homogeneous_terms( degree: u32, n_features: usize, interaction_only: bool, ) -> usize

Count the number of terms for a given degree and number of features

Trait Implementations

impl<State: Clone> Clone for HomogeneousPolynomialFeatures<State>

fn clone(&self) -> HomogeneousPolynomialFeatures<State>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for HomogeneousPolynomialFeatures<State>

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

Formats the value using the given formatter.

impl Estimator for HomogeneousPolynomialFeatures<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 HomogeneousPolynomialFeatures<Untrained>

type Fitted = HomogeneousPolynomialFeatures<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, _y: &()) -> Result<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<OwnedRepr<f64>, Dim<[usize; 2]>>> for HomogeneousPolynomialFeatures<Trained>

fn transform(&self, x: &Array2<Float>) -> Result<Array2<Float>>

Transform the input data