Struct GaussianNaiveBayes 

pub struct GaussianNaiveBayes { /* private fields */ }

Online Gaussian Naive Bayes classifier.

Assumes features are conditionally independent given the class, and each feature follows a Gaussian distribution per class. Uses Welford’s algorithm for numerically stable variance updates.

Examples

use rill_ml::models::GaussianNaiveBayes;
use rill_ml::OnlineBinaryClassifier;

let mut model = GaussianNaiveBayes::new(2, Default::default()).unwrap();
model.learn(&[1.0, 2.0], true).unwrap();
model.learn(&[-1.0, -2.0], false).unwrap();
let proba = model.predict_proba(&[0.5, 1.0]).unwrap();
assert!(proba > 0.0 && proba < 1.0);

Implementations

impl GaussianNaiveBayes

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

Create a new Gaussian Naive Bayes classifier.

feature_count must be greater than zero.

pub const fn alpha(&self) -> f64

The Laplace smoothing parameter.

Trait Implementations

impl Clone for GaussianNaiveBayes

fn clone(&self) -> GaussianNaiveBayes

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GaussianNaiveBayes

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

Formats the value using the given formatter.

impl OnlineBinaryClassifier for GaussianNaiveBayes

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.