Struct BernoulliNaiveBayes 

pub struct BernoulliNaiveBayes { /* private fields */ }

Online Bernoulli Naive Bayes classifier.

Designed for binary features (0 or 1). Uses Laplace smoothing for probability estimation.

Examples

use rill_ml::models::BernoulliNaiveBayes;
use rill_ml::OnlineBinaryClassifier;

let mut model = BernoulliNaiveBayes::new(3, Default::default()).unwrap();
model.learn(&[1.0, 0.0, 1.0], true).unwrap();
model.learn(&[0.0, 1.0, 0.0], false).unwrap();
let proba = model.predict_proba(&[1.0, 0.0, 1.0]).unwrap();
assert!(proba > 0.5);

Implementations

impl BernoulliNaiveBayes

pub fn new( feature_count: usize, config: NaiveBayesConfig, ) -> Result<Self, RillError>

Create a new Bernoulli Naive Bayes classifier.

feature_count must be greater than zero.

Trait Implementations

impl Clone for BernoulliNaiveBayes

fn clone(&self) -> BernoulliNaiveBayes

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for BernoulliNaiveBayes

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

Formats the value using the given formatter.

impl OnlineBinaryClassifier for BernoulliNaiveBayes

fn feature_count(&self) -> usize

The number of features the model expects.

fn samples_seen(&self) -> u64

How many training samples the model has seen so far.

fn predict_proba(&self, features: &[f64]) -> Result<f64, RillError>

Predict the probability of the positive class.

fn learn(&mut self, features: &[f64], target: bool) -> Result<(), RillError>

Update the model using a single labeled sample.

fn reset(&mut self)

Reset the model to its initial state.

fn predict(&self, features: &[f64]) -> Result<bool, RillError>

Predict the boolean class label using a 0.5 threshold.