Struct linfa_logistic::FittedLogisticRegression

pub struct FittedLogisticRegression<F: Float, C: PartialOrd + Clone> { /* private fields */ }

A fitted logistic regression which can make predictions

Implementations

impl<F: Float, C: PartialOrd + Clone> FittedLogisticRegression<F, C>

pub fn set_threshold(self, threshold: F) -> FittedLogisticRegression<F, C>

Set the probability threshold for which the ‘positive’ class will be predicted. Defaults to 0.5.

pub fn intercept(&self) -> F

pub fn params(&self) -> &Array1<F>

pub fn predict_probabilities<A: Data<Elem = F>>( &self, x: &ArrayBase<A, Ix2> ) -> Array1<F>

Given a feature matrix, predict the probabilities that a sample should be classified as the larger of the two classes learned when the model was fitted.

Trait Implementations

impl<F: Clone + Float, C: Clone + PartialOrd + Clone> Clone for FittedLogisticRegression<F, C>

fn clone(&self) -> FittedLogisticRegression<F, C>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<F: Debug + Float, C: Debug + PartialOrd + Clone> Debug for FittedLogisticRegression<F, C>

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

Formats the value using the given formatter.

impl<F: PartialEq + Float, C: PartialEq + PartialOrd + Clone> PartialEq for FittedLogisticRegression<F, C>

fn eq(&self, other: &FittedLogisticRegression<F, C>) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<C: PartialOrd + Clone + Default, F: Float, D: Data<Elem = F>> PredictInplace<ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<C>, Dim<[usize; 1]>>> for FittedLogisticRegression<F, C>

fn predict_inplace(&self, x: &ArrayBase<D, Ix2>, y: &mut Array1<C>)

Given a feature matrix, predict the classes learned when the model was fitted.

fn default_target(&self, x: &ArrayBase<D, Ix2>) -> Array1<C>

Create targets that predict_inplace works with.

impl<F: Float, C: PartialOrd + Clone> StructuralPartialEq for FittedLogisticRegression<F, C>