Struct rusty_machine::learning::gp::GaussianProcess [] [src]

pub struct GaussianProcess<T: Kernel, U: MeanFunc> {
    pub noise: f64,
    // some fields omitted
}

Gaussian Process struct

Gaussian process with generic kernel and deterministic mean function. Can be used for gaussian process regression with noise. Currently does not support classification.

Fields

The observation noise of the GP.

Methods

impl<T: Kernel, U: MeanFunc> GaussianProcess<T, U>
[src]

Construct a new Gaussian Process.

Examples

use rusty_machine::learning::gp;
use rusty_machine::learning::toolkit::kernel;

let ker = kernel::SquaredExp::default();
let mean = gp::ConstMean::default();
let gaussp = gp::GaussianProcess::new(ker, mean, 1e-3f64);

impl<T: Kernel, U: MeanFunc> GaussianProcess<T, U>
[src]

Compute the posterior distribution [UNSTABLE]

Requires the model to be trained first.

Outputs the posterior mean and covariance matrix.

Trait Implementations

impl<T: Debug + Kernel, U: Debug + MeanFunc> Debug for GaussianProcess<T, U>
[src]

Formats the value using the given formatter.

impl Default for GaussianProcess<SquaredExp, ConstMean>
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Construct a default Gaussian Process

The defaults are:

Note that zero noise can often lead to numerical instability. A small value for the noise may be a better alternative.

Returns the "default value" for a type. Read more

impl<T: Kernel, U: MeanFunc> SupModel<Matrix<f64>, Vector<f64>> for GaussianProcess<T, U>
[src]

Predict output from inputs.

Train the model using data and outputs.