Struct MinMaxScaler 

pub struct MinMaxScaler { /* private fields */ }

Scales features to a given range (default [0, 1]).

The transformation is: X_scaled = (X - X_min) / (X_max - X_min)

This transformer is useful for algorithms sensitive to feature scales and when you want bounded outputs (e.g., for neural networks).

Example

use aprender::prelude::*;
use aprender::preprocessing::MinMaxScaler;

let data = Matrix::from_vec(3, 2, vec![
    0.0, 0.0,
    5.0, 10.0,
    10.0, 20.0,
]).expect("valid matrix dimensions");

let mut scaler = MinMaxScaler::new();
let scaled = scaler.fit_transform(&data).expect("fit_transform should succeed");

// Verify scaling to [0, 1]
assert!((scaled.get(0, 0) - 0.0).abs() < 1e-6);
assert!((scaled.get(2, 0) - 1.0).abs() < 1e-6);
assert!((scaled.get(1, 0) - 0.5).abs() < 1e-6);

Implementations

impl MinMaxScaler

pub fn new() -> Self

Creates a new MinMaxScaler with default range [0, 1].

pub fn with_range(self, min: f32, max: f32) -> Self

Sets the target range for scaling.

Example

use aprender::preprocessing::MinMaxScaler;

let scaler = MinMaxScaler::new().with_range(-1.0, 1.0);

pub fn data_min(&self) -> &[f32]

Returns the minimum value of each feature.

Panics

Panics if the scaler is not fitted.

pub fn data_max(&self) -> &[f32]

Returns the maximum value of each feature.

Panics

Panics if the scaler is not fitted.

pub fn is_fitted(&self) -> bool

Returns true if the scaler has been fitted.

pub fn inverse_transform(&self, x: &Matrix<f32>) -> Result<Matrix<f32>>

Transforms data back to original scale.

Errors

Returns an error if the scaler is not fitted or dimensions mismatch.

Trait Implementations

impl Clone for MinMaxScaler

fn clone(&self) -> MinMaxScaler

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for MinMaxScaler

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

Formats the value using the given formatter.

impl Default for MinMaxScaler

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for MinMaxScaler

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for MinMaxScaler

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Transformer for MinMaxScaler

fn fit(&mut self, x: &Matrix<f32>) -> Result<()>

Computes the min and max of each feature.

fn transform(&self, x: &Matrix<f32>) -> Result<Matrix<f32>>

Scales the data to the target range.

fn fit_transform(&mut self, x: &Matrix<f32>) -> Result<Matrix<f32>>

Fits and transforms in one step.