Struct StandardScaler

pub struct StandardScaler<T: Number + RealNumber> { /* private fields */ }

With the StandardScaler data can be adjusted so that every column has a mean of zero and a standard deviation of one. This can improve model training for scaling sensitive models like neural network or nearest neighbors based models.

Trait Implementations

impl<T: Clone + Number + RealNumber> Clone for StandardScaler<T>

fn clone(&self) -> StandardScaler<T>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<T: Debug + Number + RealNumber> Debug for StandardScaler<T>

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

Formats the value using the given formatter.

impl<T: Default + Number + RealNumber> Default for StandardScaler<T>

fn default() -> StandardScaler<T>

Returns the “default value” for a type.

impl<T: PartialEq + Number + RealNumber> PartialEq for StandardScaler<T>

fn eq(&self, other: &StandardScaler<T>) -> 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<T: Number + RealNumber, M: Array2<T>> Transformer<M> for StandardScaler<T>

During transform the StandardScaler applies the summary statistics computed during fit to set the mean of each column to zero and the standard deviation to one.

fn transform(&self, x: &M) -> Result<M, Failed>

Transform data by modifying or filtering it

impl<T: Number + RealNumber, M: Array2<T>> UnsupervisedEstimator<M, StandardScalerParameters> for StandardScaler<T>

During fit the StandardScaler computes the column means and standard deviation.

fn fit(x: &M, parameters: StandardScalerParameters) -> Result<Self, Failed>

where T: Number + RealNumber, M: Array2<T>,

Fit a model to a training dataset, estimate model’s parameters.

impl<T: Number + RealNumber> StructuralPartialEq for StandardScaler<T>