Struct LinearScaler 

pub struct LinearScaler<F: Float> { /* private fields */ }

The result of fitting a linear scaler. Scales datasets with the parameters learned during fitting.

Implementations

impl<F: Float> LinearScaler<F>

pub fn standard() -> LinearScalerParams<F>

Initializes a Standard scaler

pub fn standard_no_mean() -> LinearScalerParams<F>

Initializes a Standard scaler that does not subract the mean to the features

pub fn standard_no_std() -> LinearScalerParams<F>

Initializes a Stadard scaler that does not scale the features by the inverse of the standard deviation

pub fn min_max() -> LinearScalerParams<F>

Initializes a MinMax scaler with range 0..=1

pub fn min_max_range(min: F, max: F) -> LinearScalerParams<F>

Initializes a MinMax scaler with the specified minimum and maximum values for the range.

If min is bigger than max then fitting will return an error on any input.

pub fn max_abs() -> LinearScalerParams<F>

Initializes a MaxAbs scaler

impl<F: Float> LinearScaler<F>

pub fn offsets(&self) -> &Array1<F>

Array of size n_features that contains the offset that will be subtracted to each feature

pub fn scales(&self) -> &Array1<F>

Array of size n_features that contains the scale that will be applied to each feature

pub fn method(&self) -> &ScalingMethod<F>

Returns the method used for fitting. Useful for printing, since ScalingMethod implements Display

Trait Implementations

impl<F: Clone + Float> Clone for LinearScaler<F>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<F: Debug + Float> Debug for LinearScaler<F>

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

Formats the value using the given formatter.

impl<F: Eq + Float> Eq for LinearScaler<F>

impl<F: PartialEq + Float> PartialEq for LinearScaler<F>

fn eq(&self, other: &LinearScaler<F>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<F: Float> StructuralPartialEq for LinearScaler<F>

impl<F: Float> Transformer<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>> for LinearScaler<F>

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

Scales an array of size (nsamples, nfeatures) according to the scaler’s offsets and scales. Panics if the shape of the input array is not compatible with the shape of the dataset used for fitting.

impl<F: Float, D: Data<Elem = F>, T: AsTargets> Transformer<DatasetBase<ArrayBase<D, Dim<[usize; 2]>>, T>, DatasetBase<ArrayBase<OwnedRepr<F>, Dim<[usize; 2]>>, T>> for LinearScaler<F>

fn transform( &self, x: DatasetBase<ArrayBase<D, Ix2>, T>, ) -> DatasetBase<Array2<F>, T>

Substitutes the records of the dataset with their scaled version. Panics if the shape of the records is not compatible with the shape of the dataset used for fitting.