Struct TrainingSample 

pub struct TrainingSample {
    pub features: Vec<f64>,
    pub label: f64,
}

A single labelled training example for online learning.

For binary classification the label must be +1.0 or −1.0. For regression the label may be any finite f64.

Fields

features: Vec<f64>

Input feature vector.

label: f64

Target label. Binary classifiers expect ±1.0.

Implementations

impl TrainingSample

pub fn new(features: Vec<f64>, label: f64) -> Self

Construct a new training sample.

pub fn is_valid_binary_label(&self) -> bool

Return true if the label is a valid binary classification label (±1.0).

Trait Implementations

impl Clone for TrainingSample

fn clone(&self) -> TrainingSample

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for TrainingSample

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

Formats the value using the given formatter.

impl PartialEq for TrainingSample

fn eq(&self, other: &TrainingSample) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for TrainingSample