Struct GaussianRandomProjection 

pub struct GaussianRandomProjection<F> { /* private fields */ }

Gaussian random projection transformer.

Projects data into a lower-dimensional space using a random matrix drawn from N(0, 1/n_components).

Examples

use ferrolearn_preprocess::random_projection::GaussianRandomProjection;
use ferrolearn_core::traits::{Fit, Transform};
use ndarray::Array2;

let x = Array2::<f64>::ones((10, 50));
let proj = GaussianRandomProjection::<f64>::new(5);
let fitted = proj.fit(&x, &()).unwrap();
let out = fitted.transform(&x).unwrap();
assert_eq!(out.shape(), &[10, 5]);

Implementations

impl<F: Float + Send + Sync + 'static> GaussianRandomProjection<F>

pub fn new(n_components: usize) -> Self

Create a new Gaussian random projection with an explicit n_components number of output dimensions (NComponents::Fixed).

pub fn new_auto() -> Self

Create a new Gaussian random projection with n_components='auto' (sklearn default): the target dimension is resolved at fit time from the Johnson-Lindenstrauss bound for the configured eps (random_projection.py:388-391).

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

Set the distortion-rate eps used by the Auto JL bound (sklearn eps, random_projection.py:328).

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

Set the random seed for reproducibility.

Trait Implementations

impl<F: Clone> Clone for GaussianRandomProjection<F>

fn clone(&self) -> GaussianRandomProjection<F>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug> Debug for GaussianRandomProjection<F>

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

Formats the value using the given formatter.

impl<F: Float + Send + Sync + 'static> Fit<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, ()> for GaussianRandomProjection<F>

fn fit( &self, x: &Array2<F>, _y: &(), ) -> Result<FittedGaussianRandomProjection<F>, FerroError>

Fit the projection by generating a random matrix R ~ N(0, 1/n_components).

When n_components is NComponents::Auto the target dimension is resolved from the Johnson-Lindenstrauss bound for eps (random_projection.py:388-391).

Errors

Returns FerroError::InvalidParameter if the resolved n_components is 0 (explicit, or the JL bound resolving to <= 0, random_projection.py:393-397), or if eps is outside (0, 1) for the Auto path. Returns FerroError::InsufficientSamples if x has zero rows.

type Fitted = FittedGaussianRandomProjection<F>

The fitted model type returned by fit.

type Error = FerroError

The error type returned by fit.

impl<F: Float + Send + Sync + 'static> FitTransform<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>> for GaussianRandomProjection<F>

fn fit_transform(&self, x: &Array2<F>) -> Result<Array2<F>, FerroError>

Fit and transform in one step.

type FitError = FerroError

The error type for the combined fit-transform operation.

impl<F: Float + Send + Sync + 'static> PipelineTransformer<F> for GaussianRandomProjection<F>

fn fit_pipeline( &self, x: &Array2<F>, _y: &Array1<F>, ) -> Result<Box<dyn FittedPipelineTransformer<F>>, FerroError>

Fit this transformer on the given data.

impl<F: Float + Send + Sync + 'static> Transform<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>> for GaussianRandomProjection<F>

fn transform(&self, _x: &Array2<F>) -> Result<Array2<F>, FerroError>

Always returns an error — the projection must be fitted first.

type Output = ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>

The transformed output type.

type Error = FerroError

The error type returned by transform.