Struct MinMaxScaler 

pub struct MinMaxScaler { /* private fields */ }

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

Formula

For each feature column x:

z = (x - x_min) / (x_max - x_min) * (max_range - min_range) + min_range

where:

• x_min is the minimum value seen during fitting
• x_max is the maximum value seen during fitting
• max_range and min_range are the target range bounds

When to Use

• When you need bounded feature ranges
• For neural networks (input normalization)
• When preserving zero values is important
• For visualization purposes

Handling Constant Features

If a feature has zero range (all values identical), the scaler sets the scale factor to 1.0 to avoid division by zero.

Implementations

impl MinMaxScaler

pub fn new() -> Self

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

pub fn with_range(min: f64, max: f64) -> Self

Creates a new MinMaxScaler with custom output range.

Parameters

• min: Lower bound of output range
• max: Upper bound of output range

Panics

Will cause transformation errors if min >= max.

pub fn is_fitted(&self) -> bool

Returns true if the scaler has been fitted.

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 Scaler for MinMaxScaler

fn fit(&mut self, data: &FeatureMatrix) -> DataResult<()>

Learns scaling parameters from the training data.

fn transform(&self, data: &FeatureMatrix) -> DataResult<FeatureMatrix>

Applies learned transformation to data.

fn fit_transform(&mut self, data: &FeatureMatrix) -> DataResult<FeatureMatrix>

Fits and transforms in a single operation.

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.