Struct friedrich::kernel::SquaredExp

pub struct SquaredExp {
    pub ls: f64,
    pub ampl: f64,
}

Squared exponential kernel

Equivalent to a gaussian kernel.

k(x,y) = A exp(-||x-y||² / 2l²)

Where A is the amplitude and l the length scale.

Fields

ls: f64

The length scale of the kernel.

ampl: f64

The amplitude of the kernel.

Methods

impl SquaredExp

pub fn new(ls: f64, ampl: f64) -> SquaredExp

Construct a new squared exponential kernel (gaussian).

Trait Implementations

impl Kernel for SquaredExp

const NB_PARAMETERS: usize

numbers of parameters (such as bandwith and amplitude) of the kernel

fn kernel<S1: Storage<f64, U1, Dynamic>, S2: Storage<f64, U1, Dynamic>>(
    &self,
    x1: &Matrix<f64, U1, Dynamic, S1>,
    x2: &Matrix<f64, U1, Dynamic, S2>
) -> f64

The squared exponential kernel function.

fn gradient<S1: Storage<f64, U1, Dynamic>, S2: Storage<f64, U1, Dynamic>>(
    &self,
    x1: &Matrix<f64, U1, Dynamic, S1>,
    x2: &Matrix<f64, U1, Dynamic, S2>
) -> Vec<f64>

Takes two equal length slices (row vector) and returns a vector containing the value of the gradient for each parameter in an arbitrary order.

fn get_parameters(&self) -> Vec<f64>

Returns a vector containing all the parameters of the kernel in the same order as the outputs of the gradient function

fn set_parameters(&mut self, parameters: &[f64])

Sets all the parameters of the kernel by reading them from a slice where they are in the same order as the outputs of the gradient function

fn heuristic_fit<SM: Storage<f64, Dynamic, Dynamic>, SV: Storage<f64, Dynamic, U1>>(
    &mut self,
    training_inputs: &Matrix<f64, Dynamic, Dynamic, SM>,
    training_outputs: &Matrix<f64, Dynamic, U1, SV>
)

Optional, function that fits the kernel parameters on the training data using fast heuristics. This is used as a starting point for gradient descent.

impl Clone for SquaredExp

fn clone(&self) -> SquaredExp

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Copy for SquaredExp

impl Default for SquaredExp

Constructs the default Squared Exp kernel.

The defaults are:

• ls = 1
• ampl = 1

fn default() -> SquaredExp

Returns the "default value" for a type.

impl Debug for SquaredExp

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

Formats the value using the given formatter.