Enum linfa_logistic::error::Error

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

Variants

`LinfaError(Error)`

`WrongNumberOfClasses`

`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

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impl Debug for Error

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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 Display for Error

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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 Error for Error

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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 · source

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<'a, C: 'a + Ord + Clone, F: Float, D: Data<Elem = F>, T: AsTargets<Elem = C>> Fit<ArrayBase<D, Dim<[usize ; 2]>>, T, Error> for ValidLogisticRegression<F>

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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>

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impl<'a, C: 'a + Ord + Clone, F: Float, D: Data<Elem = F>, T: AsTargets<Elem = C>> Fit<ArrayBase<D, Dim<[usize ; 2]>>, T, Error> for ValidMultiLogisticRegression<F>

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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.