Struct LinearCurriculum 

pub struct LinearCurriculum {
    pub start_percentage: f64,
    pub sort_by_difficulty: bool,
}

Linear curriculum: gradually increase the percentage of samples used.

Starts with a small percentage of easiest samples and linearly increases to use all samples by the end of training.

Fields

start_percentage: f64

Initial percentage of samples to use (0.0 to 1.0).

sort_by_difficulty: bool

Whether to sort by difficulty (true) or use all samples (false).

Implementations

impl LinearCurriculum

pub fn new(start_percentage: f64) -> TrainResult<Self>

Create a new linear curriculum.

Arguments

• start_percentage - Initial percentage of samples (e.g., 0.1 for 10%)

pub fn without_sorting(self) -> Self

Disable sorting by difficulty (use random subset instead).

Trait Implementations

impl Clone for LinearCurriculum

fn clone(&self) -> LinearCurriculum

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl CurriculumStrategy for LinearCurriculum

fn select_samples( &self, epoch: usize, total_epochs: usize, difficulties: &ArrayView1<'_, f64>, ) -> TrainResult<Vec<usize>>

Get the subset of samples to use for the current training step.

fn compute_difficulty( &self, _data: &Array2<f64>, _labels: &Array2<f64>, predictions: Option<&Array2<f64>>, ) -> TrainResult<Array1<f64>>

Compute difficulty scores for training samples.

impl Debug for LinearCurriculum

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

Formats the value using the given formatter.

impl Default for LinearCurriculum

fn default() -> Self

Returns the “default value” for a type.