Struct linfa_preprocessing::linear_scaling::LinearScalerParams

pub struct LinearScalerParams<F: Float> { /* fields omitted */ }

Linear Scaler: learns scaling parameters, according to the specified method, from a dataset, producing a fitted linear scaler that can be used to scale different datasets using the same parameters.

Example

use linfa::traits::{Fit, Transformer};
use linfa_preprocessing::linear_scaling::LinearScaler;

// Load dataset
let dataset = linfa_datasets::diabetes();
// Learn scaling parameters
let scaler = LinearScaler::standard().fit(&dataset).unwrap();
// scale dataset according to parameters
let dataset = scaler.transform(dataset);

Implementations

impl<F: Float> LinearScalerParams<F>

pub fn new(method: ScalingMethod<F>) -> Self

Initializes the scaler with the specified method.

pub fn method(self, method: ScalingMethod<F>) -> Self

Setter for the scaler method

Trait Implementations

impl<F: Float, D: Data<Elem = F>, T: AsTargets> Fit<ArrayBase<D, Dim<[usize; 2]>>, T, PreprocessingError> for LinearScalerParams<F>

fn fit(&self, x: &DatasetBase<ArrayBase<D, Ix2>, T>) -> Result<Self::Object>

Fits the input dataset accordng to the scaler method. Will return an error if the dataset does not contain any samples or (in the case of MinMax scaling) if the specified range is not valid.

type Object = LinearScaler<F>