Struct DistributionFeaturesExtractor 

pub struct DistributionFeaturesExtractor { /* private fields */ }

Distribution-Based Feature Extractor

Extracts features based on the empirical distribution of data including quantiles, percentiles, histogram features, and distribution shape measures.

Parameters

• n_quantiles - Number of quantiles to compute
• n_bins - Number of histogram bins
• include_shape_features - Whether to include distribution shape features
• density_estimation - Method for density estimation (“histogram”, “kde”)

Examples

use sklears_feature_extraction::statistical_features::DistributionFeaturesExtractor;
use scirs2_core::ndarray::array;

let data = array![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0];
let extractor = DistributionFeaturesExtractor::new()
    .n_quantiles(5)
    .n_bins(10)
    .include_shape_features(true);

let features = extractor.extract_features(&data.view()).expect("valid data produces features");

Implementations

impl DistributionFeaturesExtractor

pub fn new() -> Self

Create a new DistributionFeaturesExtractor

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

Set the number of quantiles

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

Set the number of histogram bins

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

Set whether to include shape features

pub fn density_estimation(self, method: String) -> Self

Set the density estimation method

pub fn extract_features( &self, data: &ArrayView1<'_, Float>, ) -> SklResult<Array1<Float>>

Extract distribution-based features from data

Trait Implementations

impl Clone for DistributionFeaturesExtractor

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 DistributionFeaturesExtractor

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

Formats the value using the given formatter.

impl Default for DistributionFeaturesExtractor

fn default() -> Self

Returns the “default value” for a type.