Struct Linear

pub struct Linear<A = f64, K = Biased, D = Ix2, S = OwnedRepr<A>>
where
    D: Dimension,
    S: RawData<Elem = A>,
{ /* private fields */ }

An implementation of a linear model.

In an effort to streamline the api, the Linear model relies upon a ParamMode type (Biased or Unbiased) which enables the model to automatically determine whether or not to include a bias term. Doing so allows the model to inherit several methods familar to the underlying ndarray crate.

Implementations

impl<A, K> Linear<A, K, Ix2, OwnedRepr<A>>

where K: ParamMode,

pub fn std(inputs: usize, outputs: usize) -> Self

where A: Default,

impl<A, S, D, K> Linear<A, K, D, S>

where D: RemoveAxis, K: ParamMode, S: RawData<Elem = A>,

pub fn new<Sh>(shape: Sh) -> Self

where Sh: ShapeBuilder<Dim = D>, A: Default, S: DataOwned,

pub fn ones<Sh>(shape: Sh) -> Self

where Sh: ShapeBuilder<Dim = D>, A: Clone + One, S: DataOwned,

pub fn zeros<Sh>(shape: Sh) -> Self

where Sh: ShapeBuilder<Dim = D>, A: Clone + Zero, S: DataOwned,

pub fn from_config(config: Config<K, D>) -> Self

where A: Clone + Default, K: ParamMode, S: DataOwned,

pub fn from_layout(layout: Layout<D>) -> Self

where A: Clone + Default, K: ParamMode, S: DataOwned,

pub fn from_params(params: ParamsBase<S, D, K>) -> Self

pub fn activate<X, Y, F>(&self, args: &X, func: F) -> Result<Y>

where F: Fn(Y) -> Y, Self: Predict<X, Output = Y>,

Applies an activcation function onto the prediction of the model.

pub const fn config(&self) -> &Config<K, D>

pub fn weights(&self) -> &ArrayBase<S, D>

pub fn weights_mut(&mut self) -> &mut ArrayBase<S, D>

pub const fn params(&self) -> &ParamsBase<S, D, K>

pub fn params_mut(&mut self) -> &mut ParamsBase<S, D, K>

pub fn into_biased(self) -> Linear<A, Biased, D, S>

where A: Default, K: 'static, S: DataOwned,

pub fn into_unbiased(self) -> Linear<A, Unbiased, D, S>

where A: Default, K: 'static, S: DataOwned,

pub fn is_biased(&self) -> bool

where K: 'static,

pub fn with_params<E>(self, params: LinearParams<A, K, E>) -> Linear<A, K, E>

where E: RemoveAxis,

pub fn with_name(self, name: impl ToString) -> Self

pub fn dim(&self) -> D::Pattern

Return the pattern of the dimension

pub fn ndim(&self) -> usize

Returns rank (ndim) of the dimension

pub fn raw_dim(&self) -> D

Returns the raw dimension D

pub fn shape(&self) -> &[usize]

Returns a reference to the current dimension, as a slice.

impl<A, S, D> Linear<A, Biased, D, S>

where D: RemoveAxis, S: RawData<Elem = A>,

pub fn biased<Sh>(shape: Sh) -> Self

where A: Default, S: DataOwned, Sh: ShapeBuilder<Dim = D>,

pub fn bias(&self) -> &ArrayBase<S, D::Smaller>

pub fn bias_mut(&mut self) -> &mut ArrayBase<S, D::Smaller>

impl<A, S, D> Linear<A, Unbiased, D, S>

where D: RemoveAxis, S: RawData<Elem = A>,

pub fn unbiased<Sh>(shape: Sh) -> Self

where A: Default, S: DataOwned, Sh: ShapeBuilder<Dim = D>,

impl<A, S, D, K> Linear<A, K, D, S>

where A: Clone + Float, D: RemoveAxis, K: ParamMode, S: DataOwned<Elem = A>, StandardNormal: Distribution<A>,

pub fn uniform(self) -> Linear<A, K, D, OwnedRepr<A>>

where A: SampleUniform, <A as SampleUniform>::Sampler: Clone,

impl<A, K, S> Linear<A, K, Ix2, S>

where K: ParamMode, S: RawData<Elem = A>,

pub fn from_features(inputs: usize, outputs: usize) -> Self

where A: Clone + Default, S: DataOwned,

Trait Implementations

impl<A, S, D, K> Borrow<Config<K, D>> for Linear<A, K, D, S>

where D: RemoveAxis, S: RawData<Elem = A>,

fn borrow(&self) -> &Config<K, D>

Immutably borrows from an owned value.

impl<A, S, D, K> Borrow<ParamsBase<S, D, K>> for Linear<A, K, D, S>

where D: RemoveAxis, S: RawData<Elem = A>,

fn borrow(&self) -> &ParamsBase<S, D, K>

Immutably borrows from an owned value.

impl<A, S, D, K> BorrowMut<ParamsBase<S, D, K>> for Linear<A, K, D, S>

where D: RemoveAxis, S: RawData<Elem = A>,

fn borrow_mut(&mut self) -> &mut ParamsBase<S, D, K>

Mutably borrows from an owned value.

impl<A, S, D, K> Initialize<A, D> for Linear<A, K, D, S>

where D: RemoveAxis, K: ParamMode, S: DataOwned<Elem = A>, StandardNormal: Distribution<A>,

type Data = OwnedRepr<A>

fn rand<Sh, Ds>(shape: Sh, distr: Ds) -> Self

where Sh: ShapeBuilder<Dim = D>, Ds: Clone + Distribution<A>,

Generate a random array using the given distribution

fn rand_with<Sh, Ds, R>(shape: Sh, distr: Ds, rng: &mut R) -> Self

where R: Rng + ?Sized, Ds: Clone + Distribution<A>, Sh: ShapeBuilder<Dim = D>,

Generate a random array using the given distribution and random number generator

fn init_rand<Ds>(self, distr: Ds) -> Self

where Ds: Clone + Distribution<A>, Self: Sized,

Initialize an array with random values using the given distribution and current shape

fn init_rand_with<Ds, R>(self, distr: Ds, rng: &mut R) -> Self

where R: Rng + ?Sized, Ds: Clone + Distribution<A>,

Initialize an array with random values from the current shape using the given distribution and random number generator

impl<A, K, D> Module for Linear<A, K, D>

where D: RemoveAxis,

type Config = Config<K, D>

type Params = ParamsBase<OwnedRepr<A>, D, K>

fn config(&self) -> &Self::Config

fn params(&self) -> &Self::Params

fn params_mut(&mut self) -> &mut Self::Params

impl<U, V, A, K, D> Predict<U> for Linear<A, K, D>

where D: RemoveAxis, LinearParams<A, K, D>: Predict<U, Output = V>,

type Output = V

fn predict(&self, input: &U) -> Result<Self::Output, PredictError>