Enum NaiveBayesError 

pub enum NaiveBayesError {
    Stats(MinMaxError),
    InvalidSmoothing(f64),
    BaseCrate(Error),
}

Error variants from hyper-parameter construction or model estimation

Variants

Stats(MinMaxError)

Error when performing Max operation on data

InvalidSmoothing(f64)

Invalid smoothing parameter

BaseCrate(Error)

Trait Implementations

impl Debug for NaiveBayesError

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

Formats the value using the given formatter.

impl Display for NaiveBayesError

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

Formats the value using the given formatter.

impl Error for NaiveBayesError

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<F, L, D, T> Fit<ArrayBase<D, Dim<[usize; 2]>>, T, NaiveBayesError> for BernoulliNbValidParams<F, L>

where F: Float, L: Label + Ord, D: Data<Elem = F>, T: AsSingleTargets<Elem = L> + Labels<Elem = L>,

type Object = BernoulliNb<F, L>

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

impl<F, L, D, T> Fit<ArrayBase<D, Dim<[usize; 2]>>, T, NaiveBayesError> for GaussianNbValidParams<F, L>

where F: Float, L: Label + Ord, D: Data<Elem = F>, T: AsSingleTargets<Elem = L> + Labels<Elem = L>,

type Object = GaussianNb<F, L>

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

impl<F, L, D, T> Fit<ArrayBase<D, Dim<[usize; 2]>>, T, NaiveBayesError> for MultinomialNbValidParams<F, L>

where F: Float, L: Label + Ord, D: Data<Elem = F>, T: AsSingleTargets<Elem = L> + Labels<Elem = L>,

type Object = MultinomialNb<F, L>

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

impl<'a, F, L, D, T> FitWith<'a, ArrayBase<D, Dim<[usize; 2]>>, T, NaiveBayesError> for BernoulliNbValidParams<F, L>

where F: Float, L: Label + 'a, D: Data<Elem = F>, T: AsSingleTargets<Elem = L> + Labels<Elem = L>,

type ObjectIn = Option<BernoulliNb<F, L>>

type ObjectOut = BernoulliNb<F, L>

fn fit_with( &self, model_in: Self::ObjectIn, dataset: &DatasetBase<ArrayBase<D, Ix2>, T>, ) -> Result<Self::ObjectOut>

impl<'a, F, L, D, T> FitWith<'a, ArrayBase<D, Dim<[usize; 2]>>, T, NaiveBayesError> for GaussianNbValidParams<F, L>

where F: Float, L: Label + 'a, D: Data<Elem = F>, T: AsSingleTargets<Elem = L> + Labels<Elem = L>,

type ObjectIn = Option<GaussianNb<F, L>>

type ObjectOut = GaussianNb<F, L>

fn fit_with( &self, model_in: Self::ObjectIn, dataset: &DatasetBase<ArrayBase<D, Ix2>, T>, ) -> Result<Self::ObjectOut>

impl<'a, F, L, D, T> FitWith<'a, ArrayBase<D, Dim<[usize; 2]>>, T, NaiveBayesError> for MultinomialNbValidParams<F, L>

where F: Float, L: Label + 'a, D: Data<Elem = F>, T: AsSingleTargets<Elem = L> + Labels<Elem = L>,

type ObjectIn = Option<MultinomialNb<F, L>>

type ObjectOut = MultinomialNb<F, L>

fn fit_with( &self, model_in: Self::ObjectIn, dataset: &DatasetBase<ArrayBase<D, Ix2>, T>, ) -> Result<Self::ObjectOut>

impl From<Error> for NaiveBayesError

fn from(source: Error) -> Self

Converts to this type from the input type.

impl From<MinMaxError> for NaiveBayesError

fn from(source: MinMaxError) -> Self

Converts to this type from the input type.