Enum linfa_linear::LinearError[−][src]

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

Expand description

An error when modeling a Linear algorithm

Variants

Argmin(Error)

Errors encountered when using argmin’s solver

Tuple Fields of Argmin

0: Error

BaseCrate(Error)

Tuple Fields of BaseCrate

0: Error

NotEnoughSamples

NotEnoughTargets

InvalidPenalty(F)

Tuple Fields of InvalidPenalty

0: F

InvalidTweediePower(F)

Tuple Fields of InvalidTweediePower

0: F

InvalidTargetRange(F)

Tuple Fields of InvalidTargetRange

0: F

LinalgError(LinalgError)

Tuple Fields of LinalgError

0: LinalgError

Trait Implementations

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

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

Formats the value using the given formatter. Read more

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impl<F: Float> Display for LinearError<F> where
F: Display,

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

Formats the value using the given formatter. Read more

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impl<F: Float> Error for LinearError<F> where
Self: Debug + Display,

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

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

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fn backtrace(&self) -> Option<&Backtrace>

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

Returns a stack backtrace, if available, of where this error occurred. Read more

1.0.0[src]

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

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

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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.