Enum Error

pub enum Error {
    LinfaError(Error),
    TooManyClasses,
    TooFewClasses,
    ArgMinError(Error),
    MismatchedShapes(usize, usize),
    InvalidValues,
    InitialParameterFeaturesMismatch {
        rows: usize,
        n_features: usize,
    },
    InitialParameterClassesMismatch {
        cols: usize,
        n_classes: usize,
    },
    InvalidGradientTolerance,
    InvalidAlpha,
    InvalidInitialParameters,
}

Variants

LinfaError(Error)

TooManyClasses

TooFewClasses

ArgMinError(Error)

MismatchedShapes(usize, usize)

InvalidValues

InitialParameterFeaturesMismatch

Fields

rows: usize

n_features: usize

InitialParameterClassesMismatch

Fields

cols: usize

n_classes: usize

InvalidGradientTolerance

InvalidAlpha

InvalidInitialParameters

Trait Implementations

impl Debug for Error

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

Formats the value using the given formatter.

impl Display for Error

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

Formats the value using the given formatter.

impl Error for Error

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

Returns 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<C: Ord + Clone, F: Float, D: Data<Elem = F>, T: AsSingleTargets<Elem = C>> Fit<ArrayBase<D, Dim<[usize; 2]>>, T, Error> for ValidLogisticRegression<F>

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

Given a 2-dimensional feature matrix array x with shape (n_samples, n_features) and an array of target classes to predict, create a FittedLinearRegression object which allows making predictions.

The array of target classes y must have exactly two discrete values, (e.g. 0 and 1, “cat” and “dog”, …), which represent the two different classes the model is supposed to predict.

The array y must also have exactly n_samples items, i.e. exactly as many items as there are rows in the feature matrix x.

This method returns an error if any of the preconditions are violated, i.e. any values are Inf or NaN, y doesn’t have as many items as x has rows, or if other parameters (gradient_tolerance, alpha) have been set to inalid values.

type Object = FittedLogisticRegression<F, C>

impl<C: Ord + Clone, F: Float, D: Data<Elem = F>, T: AsSingleTargets<Elem = C>> Fit<ArrayBase<D, Dim<[usize; 2]>>, T, Error> for ValidMultiLogisticRegression<F>

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

Given a 2-dimensional feature matrix array x with shape (n_samples, n_features) and an array of target classes to predict, create a MultiFittedLogisticRegression object which allows making predictions. The target classes can have any number of discrete values.

This method returns an error if any of the preconditions are violated, i.e. any values are Inf or NaN, y doesn’t have as many items as x has rows, or if other parameters (gradient_tolerance, alpha) have been set to inalid values. The input features are also strongly recommended to be normalized to ensure numerical stability.

type Object = MultiFittedLogisticRegression<F, C>

impl From<Error> for Error

fn from(source: Error) -> Self

Converts to this type from the input type.

impl From<Error> for Error

fn from(source: Error) -> Self

Converts to this type from the input type.