Struct LogisticRegression 

pub struct LogisticRegression { /* private fields */ }

Implementations

impl LogisticRegression

pub fn new(config: LogisticRegressionConfig) -> LogisticRegression

pub fn to_json(&self) -> String

Serialise to JSON for ModelRegistry storage.

pub fn from_json(raw: &str) -> Option<LogisticRegression>

Trait Implementations

impl Clone for LogisticRegression

fn clone(&self) -> LogisticRegression

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for LogisticRegression

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

Formats the value using the given formatter.

impl IncrementalClassifier for LogisticRegression

fn fit(&mut self, examples: &[TrainingExample])

Train from scratch. Any previous weights are discarded.

fn partial_fit(&mut self, examples: &[TrainingExample])

Incrementally update with a mini-batch. Previous weights are preserved; this is the online-learning entrypoint.

fn predict(&self, features: &[f32]) -> Option<u32>

Predict the most likely class for one example.

fn predict_proba(&self, features: &[f32]) -> Vec<f32>

Predict a probability per class (0..num_classes).

fn num_classes(&self) -> usize

Number of distinct classes seen so far.

fn num_features(&self) -> usize

Number of features the model expects. 0 until fit/ partial_fit has been called with at least one example.

fn samples_seen(&self) -> u64

Total number of training examples the model has seen over its lifetime — incremented by both fit (reset to N) and partial_fit (additive). Useful for lineage + rate-limiting.