Struct friedrich::kernel::Exponential

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

The Exponential Kernel.

k(x,y) = A exp(-||x-y|| / 2l²)

Where A is the amplitude and l is the length scale.

Fields

ls: f64

The length scale of the kernel.

ampl: f64

The amplitude of the kernel.

Implementations

impl Exponential

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

Construct a new squared exponential kernel.

Trait Implementations

impl Clone for Exponential

fn clone(&self) -> Exponential

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Exponential

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

Formats the value using the given formatter.

impl Default for Exponential

Constructs the default Exponential kernel.

The defaults are:

• ls = 1
• amplitude = 1

fn default() -> Exponential

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for Exponential

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>where
    __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Kernel for Exponential

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

The squared exponential kernel function.

fn nb_parameters(&self) -> usize

Numbers of parameters (such as bandwidth and amplitude) of the kernel.

fn is_scalable(&self) -> bool

Can the kernel be rescaled (see the rescale function) ? This value is false by default.

fn gradient<S1: Storage<f64, U1, Dynamic>, S2: Storage<f64, U1, Dynamic>>(
    &self,
    x1: &SRowVector<S1>,
    x2: &SRowVector<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 rescale(&mut self, scale: f64)

Multiplies the amplitude of the kernel by the scale parameter such that a kernel a*K(x,y) becomes scale*a*K(x,y).

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: &SMatrix<SM>,
    training_outputs: &SVector<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 Serialize for Exponential

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>where
    __S: Serializer,

Serialize this value into the given Serde serializer.

impl Copy for Exponential