Struct AdditiveChi2Sampler 

pub struct AdditiveChi2Sampler { /* private fields */ }

Additive Chi-Squared Sampler

Approximate feature map for additive chi-squared kernel using random sampling. The additive chi-squared kernel is defined as: k(x, y) = exp(-gamma * sum_i (x_i - y_i)² / (x_i + y_i))

Parameters

• sample_steps - Number of sampling steps
• sample_interval - Interval for sampling
• n_components - Number of components to generate

Examples

use sklears_feature_extraction::interaction::AdditiveChi2Sampler;
use scirs2_core::ndarray::array;

let X = array![[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]];
let sampler = AdditiveChi2Sampler::new()
    .sample_steps(2)
    .sample_interval(0.5);

let features = sampler.transform(&X.view()).expect("valid input produces transform output");

Implementations

impl AdditiveChi2Sampler

pub fn new() -> Self

Create a new AdditiveChi2Sampler

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

Set the number of sampling steps

pub fn sample_interval(self, sample_interval: Float) -> Self

Set the sampling interval

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

Transform data using additive chi-squared features

pub fn get_n_output_features(&self, n_input_features: usize) -> usize

Get the number of output features

Trait Implementations

impl Clone for AdditiveChi2Sampler

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for AdditiveChi2Sampler

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

Formats the value using the given formatter.

impl Default for AdditiveChi2Sampler

fn default() -> Self

Returns the “default value” for a type.

impl Transform<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for AdditiveChi2Sampler

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

Transform the input data