Struct IsotonicRegression 

pub struct IsotonicRegression {}

An isotonic regression model.

IsotonicRegression solves an isotonic regression problem using the pool adjacent violators algorithm.

Examples

Here’s an example on how to train an isotonic regression model on the first feature from the diabetes dataset.

use linfa::{traits::Fit, traits::Predict, Dataset};
use linfa_linear::IsotonicRegression;
use linfa::prelude::SingleTargetRegression;

let diabetes = linfa_datasets::diabetes();
let dataset = diabetes.feature_iter().next().unwrap(); // get first 1D feature
let model = IsotonicRegression::default().fit(&dataset).unwrap();
let pred = model.predict(&dataset);
let r2 = pred.r2(&dataset).unwrap();
println!("r2 from prediction: {}", r2);

References

Best, M.J., Chakravarti, N. Active set algorithms for isotonic regression; A unifying framework. Mathematical Programming 47, 425–439 (1990).

Implementations

impl IsotonicRegression

pub fn new() -> IsotonicRegression

Create a default isotonic regression model.

Trait Implementations

impl Clone for IsotonicRegression

fn clone(&self) -> IsotonicRegression

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for IsotonicRegression

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

Formats the value using the given formatter.

impl Default for IsotonicRegression

fn default() -> IsotonicRegression

Returns the “default value” for a type.

impl<F: Float, D: Data<Elem = F>, T: AsSingleTargets<Elem = F>> Fit<ArrayBase<D, Dim<[usize; 2]>>, T, LinearError<F>> for IsotonicRegression

fn fit( &self, dataset: &DatasetBase<ArrayBase<D, Ix2>, T>, ) -> Result<Self::Object, F>

Fit an isotonic regression model given a feature matrix X and a target variable y.

The feature matrix X must have shape (n_samples, 1)

The target variable y must have shape (n_samples)

Returns a FittedIsotonicRegression object which contains the fitted parameters and can be used to predict values of the target variable for new feature values.

type Object = FittedIsotonicRegression<F>

impl PartialEq for IsotonicRegression

fn eq(&self, other: &IsotonicRegression) -> 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 Eq for IsotonicRegression

impl StructuralPartialEq for IsotonicRegression