Enum linfa_linear::LinearError

#[non_exhaustive]
pub enum LinearError<F: Float> {
    Argmin(Error),
    BaseCrate(Error),
    NotEnoughSamples,
    NotEnoughTargets,
    InvalidPenalty(F),
    InvalidTweediePower(F),
    InvalidTargetRange(F),
    LinalgError(LinalgError),
}

An error when modeling a Linear algorithm

Variants (Non-exhaustive)

Non-exhaustive enums could have additional variants added in future. Therefore, when matching against variants of non-exhaustive enums, an extra wildcard arm must be added to account for any future variants.

Argmin(Error)

Errors encountered when using argmin’s solver

BaseCrate(Error)

NotEnoughSamples

NotEnoughTargets

InvalidPenalty(F)

InvalidTweediePower(F)

InvalidTargetRange(F)

LinalgError(LinalgError)

Trait Implementations

impl<F: Debug + Float> Debug for LinearError<F>

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

Formats the value using the given formatter.

impl<F> Display for LinearError<F>

where F: Display + Float,

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

Formats the value using the given formatter.

impl<F: Float> Error for LinearError<F>

where Self: Debug + Display,

fn source(&self) -> Option<&(dyn Error + 'static)>

The lower-level source of this error, if any.

fn description(&self) -> &str

👎Deprecated since 1.42.0: use the Display impl or to_string()

fn cause(&self) -> Option<&dyn Error>

👎Deprecated since 1.33.0: replaced by Error::source, which can support downcasting

fn provide<'a>(&'a self, request: &mut Request<'a>)

🔬This is a nightly-only experimental API. (error_generic_member_access)

Provides type based access to context intended for error reports.

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

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

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

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

The target variable y must have shape (n_samples)

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

type Object = FittedLinearRegression<F>

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

where Array1<F>: ArgminAdd<Array1<F>, Array1<F>> + ArgminSub<Array1<F>, Array1<F>> + ArgminSub<F, Array1<F>> + ArgminAdd<F, Array1<F>> + ArgminMul<F, Array1<F>> + ArgminMul<Array1<F>, Array1<F>> + ArgminDot<Array1<F>, F> + ArgminL2Norm<F> + ArgminL1Norm<F> + ArgminSignum + ArgminMinMax, F: ArgminMul<Array1<F>, Array1<F>> + ArgminZero + Float,

type Object = TweedieRegressor<F>

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

impl<F: Float> From<Error> for LinearError<F>

fn from(source: Error) -> Self

Converts to this type from the input type.

impl<F: Float> From<Error> for LinearError<F>

fn from(source: Error) -> Self

Converts to this type from the input type.

impl<F: Float> From<LinalgError> for LinearError<F>

fn from(source: LinalgError) -> Self

Converts to this type from the input type.