Struct smartcore::naive_bayes::bernoulli::BernoulliNBParameters

pub struct BernoulliNBParameters<T: Number> {
    pub alpha: f64,
    pub priors: Option<Vec<f64>>,
    pub binarize: Option<T>,
}

BernoulliNB parameters. Use Default::default() for default values.

Fields

alpha: f64

Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).

priors: Option<Vec<f64>>

Prior probabilities of the classes. If specified the priors are not adjusted according to the data

binarize: Option<T>

Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors.

Implementations

impl<T: Number + PartialOrd> BernoulliNBParameters<T>

pub fn with_alpha(self, alpha: f64) -> Self

Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).

pub fn with_priors(self, priors: Vec<f64>) -> Self

Prior probabilities of the classes. If specified the priors are not adjusted according to the data

pub fn with_binarize(self, binarize: T) -> Self

Threshold for binarizing (mapping to booleans) of sample features. If None, input is presumed to already consist of binary vectors.

Trait Implementations

impl<T: Clone + Number> Clone for BernoulliNBParameters<T>

fn clone(&self) -> BernoulliNBParameters<T>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T: Debug + Number> Debug for BernoulliNBParameters<T>

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

Formats the value using the given formatter.

impl<T: Number + PartialOrd> Default for BernoulliNBParameters<T>

fn default() -> Self

Returns the “default value” for a type.

impl<TX: Number + PartialOrd, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, BernoulliNBParameters<TX>> for BernoulliNB<TX, TY, X, Y>

fn new() -> Self

Empty constructor, instantiate an empty estimator. Object is dropped as soon as fit() is called. used to pass around the correct fit() implementation. by calling ::fit(). mostly used to be used with model_selection::cross_validate(...)

fn fit( x: &X, y: &Y, parameters: BernoulliNBParameters<TX> ) -> Result<Self, Failed>

Fit a model to a training dataset, estimate model’s parameters.