Trait vikos::Teacher

pub trait Teacher<M: Model> {
    type Training;

    fn new_training(&self, model: &M) -> Self::Training;
    fn teach_event<Y, C>(
        &self,
        training: &mut Self::Training,
        model: &mut M,
        cost: &C,
        features: &M::Features,
        truth: Y
    )
    where
        C: Cost<Y, M::Target>,
        Y: Copy;
}

Algorithms used to adapt Model coefficients

Required Associated Types

type Training

Contains state which changes during the training, but is not part of the expertise

Examples are the velocity of the coefficients (in stochastic gradient descent) or the number of events already learned. This may also be empty

Required Methods

fn new_training(&self, model: &M) -> Self::Training

Creates an instance holding all mutable state of the algorithm

fn teach_event<Y, C>(
    &self,
    training: &mut Self::Training,
    model: &mut M,
    cost: &C,
    features: &M::Features,
    truth: Y
)where
    C: Cost<Y, M::Target>,
    Y: Copy,

Changes models coefficients so they minimize the cost function (hopefully)

Implementors

impl<M> Teacher<M> for Adagardwhere
    M: Model,
    M::Target: Vector,

type Training = Vec<f64, Global>

impl<M> Teacher<M> for GradientDescentwhere
    M: Model,
    M::Target: Vector,

type Training = ()

impl<M> Teacher<M> for GradientDescentAlwhere
    M: Model,
    M::Target: Vector,

type Training = usize

impl<M> Teacher<M> for Momentumwhere
    M: Model,
    M::Target: Vector,

type Training = (usize, Vec<f64, Global>)

impl<M> Teacher<M> for Nesterovwhere
    M: Model,
    M::Target: Vector,

type Training = (usize, Vec<f64, Global>)