Struct StandardScaler 

#[non_exhaustive]
pub struct StandardScaler { /* private fields */ }

Standardize features by removing the mean and scaling to unit variance.

Each feature is transformed as: x' = (x - mean) / std. Features with zero variance are left unchanged.

Implementations

impl StandardScaler

pub fn new() -> Self

Create a new unfitted scaler.

impl StandardScaler

pub fn fit_sparse(&mut self, features: &CscMatrix) -> Result<()>

Fit on sparse features.

Computes mean and std from sparse columns, correctly accounting for zero entries: mean = sum_nonzero / n_total.

pub fn transform_sparse(&self, features: &CscMatrix) -> Result<CscMatrix>

Transform sparse features, returning a new CscMatrix.

Only scales by std (no centering) to preserve sparsity. Centering would make all zeros become -mean, destroying sparsity.

impl StandardScaler

pub fn is_fitted(&self) -> bool

Whether the scaler has been fitted.

pub fn means(&self) -> &[f64]

Per-feature means computed during fit.

pub fn stds(&self) -> &[f64]

Per-feature standard deviations computed during fit.

Trait Implementations

impl Clone for StandardScaler

fn clone(&self) -> StandardScaler

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for StandardScaler

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

Formats the value using the given formatter.

impl Default for StandardScaler

fn default() -> Self

Returns the “default value” for a type.

impl Transformer for StandardScaler

fn fit(&mut self, data: &Dataset) -> Result<()>

Learn parameters from the training data.

fn transform(&self, data: &mut Dataset) -> Result<()>

Apply the learned transformation to a dataset (in-place).

fn inverse_transform(&self, data: &mut Dataset) -> Result<()>

Reverse the transformation (if invertible).

fn fit_transform(&mut self, data: &mut Dataset) -> Result<()>

Convenience: fit + transform in one call.