Struct rusty_machine::learning::gp::GaussianProcess
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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
noise: f64
The observation noise of the GP.
Methods
impl<T: Kernel, U: MeanFunc> GaussianProcess<T, U>
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fn new(ker: T, mean: U, noise: f64) -> GaussianProcess<T, U>
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>
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fn get_posterior(&self,
inputs: &Matrix<f64>)
-> LearningResult<(Vector<f64>, Matrix<f64>)>
inputs: &Matrix<f64>)
-> LearningResult<(Vector<f64>, Matrix<f64>)>
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>
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impl Default for GaussianProcess<SquaredExp, ConstMean>
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Construct a default Gaussian Process
The defaults are:
- Squared Exponential kernel.
- Zero-mean function.
- Zero noise.
Note that zero noise can often lead to numerical instability. A small value for the noise may be a better alternative.
fn default() -> GaussianProcess<SquaredExp, ConstMean>
Returns the "default value" for a type. Read more