Struct lfa::TransformedLFA

pub struct TransformedLFA<P, E, T = Identity> {
    pub projector: P,
    pub evaluator: E,
    pub transform: T,
}

Transformed linear function approximator.

Fields

projector: P

evaluator: E

transform: T

Methods

impl<P, E, T> TransformedLFA<P, E, T>

pub fn new(projector: P, evaluator: E, transform: T) -> Self

impl<P: Space, T> TransformedLFA<P, ScalarFunction, T>

pub fn scalar(projector: P, transform: T) -> Self

impl<P: Space, T> TransformedLFA<P, PairFunction, T>

pub fn pair(projector: P, transform: T) -> Self

impl<P: Space, T> TransformedLFA<P, TripleFunction, T>

pub fn triple(projector: P, transform: T) -> Self

impl<P: Space, T> TransformedLFA<P, VectorFunction, T>

pub fn vector(projector: P, n_outputs: usize, transform: T) -> Self

Trait Implementations

impl<P, E, T> Parameterised for TransformedLFA<P, E, T> where
    E: Parameterised, 

fn weights(&self) -> Matrix<f64>

Return a clone of the weights.

fn weights_dim(&self) -> (usize, usize)

Return the dimensions of the weight matrix.

fn weights_view(&self) -> MatrixView<f64>

Return a read-only view of the weights.

fn weights_view_mut(&mut self) -> MatrixViewMut<f64>

Return a mutable view of the weights.

impl<I: ?Sized, P, E, T> Embedding<I> for TransformedLFA<P, E, T> where
    P: Projector<I>, 

fn n_features(&self) -> usize

Return the number of features in the embedded representation.

fn embed(&self, input: &I) -> Features

Embed the input I in the form of a Features vector.

impl<P, E, T> Approximator for TransformedLFA<P, E, T> where
    E: Approximator,
    T: Transform<E::Output, Output = E::Output>,
    T::Output: ElementwiseMul<T::Output> + IntoVector, 

type Output = T::Output

The type of value being approximated.

fn n_outputs(&self) -> usize

Return the dimensionality of the output value Approximator::Output.

fn evaluate(&self, features: &Features) -> EvaluationResult<Self::Output>

Evaluate the approximator and return its value.

fn jacobian(&self, features: &Features) -> Matrix<f64>

Return the Jacobian matrix df/dw for a given features vector.

fn update_grad(
    &mut self,
    grad: &Matrix<f64>,
    update: Self::Output
) -> UpdateResult<()>

Update the approximator's estimate for a given gradient matrix.

fn update(
    &mut self,
    features: &Features,
    update: Self::Output
) -> UpdateResult<()>

Update the approximator's estimate for the given input.

impl<P: Copy, E: Copy, T: Copy> Copy for TransformedLFA<P, E, T>

impl<P: Clone, E: Clone, T: Clone> Clone for TransformedLFA<P, E, T>

fn clone(&self) -> TransformedLFA<P, E, T>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<P: Debug, E: Debug, T: Debug> Debug for TransformedLFA<P, E, T>

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

Formats the value using the given formatter.