Struct linfa_logistic::MultiFittedLogisticRegression

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

A fitted multinomial logistic regression which can make predictions

Implementations

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

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

pub fn params(&self) -> &Array2<F>

pub fn predict_probabilities<A: Data<Elem = F>>(
    &self,
    x: &ArrayBase<A, Ix2>
) -> Array2<F>

Return normalized probabilities for each output class. The output dimensions are (n_samples

• n_classes).

pub fn classes(&self) -> &[C]

Get the list of class labels, which maps the numerical class indices to the labels

Trait Implementations

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

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

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

fn ne(&self, other: &MultiFittedLogisticRegression<F, C>) -> bool

This method tests for !=.

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