Struct StandardScaler 

pub struct StandardScaler { /* private fields */ }

A transformer that scales items to have zero mean and unit standard deviation.

The standard score of a sample x is calculated as:

z = (x - mean) / std_dev

where mean is the mean and s is the standard deviation of the data first passed to transform (or provided via with_parameters).

Implementation

This transformer uses Welford’s online algorithm to compute mean and variance in one pass over the data. The standard deviation is calculated using the biased estimator, for parity with the scikit-learn implementation.

Example

Using the default constructor

use augurs_forecaster::transforms::{StandardScaler, Transformer};

let mut data = vec![1.0, 2.0, 3.0];
let mut scaler = StandardScaler::new();
scaler.fit_transform(&mut data);

assert_eq!(data, vec![-1.224744871391589, 0.0, 1.224744871391589]);

Implementations

impl StandardScaler

pub fn new() -> Self

Create a new StandardScaler.

pub fn with_parameters(self, params: StandardScaleParams) -> Self

Set the parameters for the scaler.

This is useful if you know the mean and standard deviation in advance and want to avoid the overhead of fitting the scaler to the data during the initial transform, and instead want to set the parameters manually.

pub fn ignore_nans(self, ignore_nans: bool) -> Self

Whether to ignore NaN values when calculating the scaler parameters.

If true, NaN values will be ignored when calculating the scaler parameters. This can be useful if you have NaN values in your data and want to avoid errors when calculating the scaler parameters.

Defaults to false.

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() -> StandardScaler

Returns the “default value” for a type.

impl Transformer for StandardScaler

fn fit(&mut self, data: &[f64]) -> Result<(), Error>

Fit the transformation to the given time series.

fn transform(&self, data: &mut [f64]) -> Result<(), Error>

Apply the transformation to the given time series.

fn inverse_transform(&self, data: &mut [f64]) -> Result<(), Error>

Apply the inverse transformation to the given time series.

fn fit_transform(&mut self, data: &mut [f64]) -> Result<(), Error>

Fit the transformation to the given time series and then apply it.

fn boxed(self) -> Box<dyn Transformer>

where Self: Sized + 'static,

Create a boxed version of the transformation.