Trait Predict 

pub trait Predict<X, Output> {
    // Required method
    fn predict(&self, x: &X) -> Result<Output, SklearsError>;

    // Provided method
    fn predict_with_uncertainty(
        &self,
        x: &X,
    ) -> Result<(Output, UncertaintyMeasure), SklearsError> { ... }
}

Enhanced trait for models that can make predictions

Required Methods

fn predict(&self, x: &X) -> Result<Output, SklearsError>

Make predictions on the provided data

Provided Methods

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals

Trait Implementations

impl<X, Output> StableApi for dyn Predict<X, Output>

const STABLE_SINCE: &'static str = "0.1.0"

API version this type was stabilized in

const HAS_EXPERIMENTAL_FEATURES: bool = false

Whether this API has any experimental features

Implementors

impl Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for KernelRidgeRegression<Trained>

impl Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for OnlineKernelRidgeRegression<Trained>

impl Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for RobustKernelRidgeRegression<Trained>

impl Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for MultiTaskKernelRidgeRegression<Trained>

impl<'a> Predict<ArrayBase<ViewRepr<&'a f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for FittedScikitLearnModel

impl<'a> Predict<ArrayBase<ViewRepr<&'a f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for TrainedMockEstimator

impl<K: SparseKernel> Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for FittedSKI<K>

Prediction implementation for fitted SKI

impl<K: SparseKernel> Predict<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>>> for FittedSparseGP<K>

Prediction implementation for fitted sparse GP