pub struct GaussianProcess {
pub kernel: KernelType,
pub signal_var: f64,
pub length_scale: f64,
pub period: f64,
pub noise_var: f64,
pub x_train: Vec<f64>,
pub y_train: Vec<f64>,
/* private fields */
}Expand description
Gaussian Process for regression with various kernel functions.
Maintains training data and supports posterior mean/variance prediction.
Fields§
§kernel: KernelTypeKernel type.
signal_var: f64Signal variance σ².
length_scale: f64Length scale ℓ.
period: f64Period (for periodic kernel).
noise_var: f64Noise variance.
x_train: Vec<f64>Training inputs (1D for simplicity).
y_train: Vec<f64>Training targets.
Implementations§
Source§impl GaussianProcess
impl GaussianProcess
Sourcepub fn new(
kernel: KernelType,
signal_var: f64,
length_scale: f64,
noise_var: f64,
) -> Self
pub fn new( kernel: KernelType, signal_var: f64, length_scale: f64, noise_var: f64, ) -> Self
Creates a new GaussianProcess with given hyperparameters.
Sourcepub fn with_period(self, period: f64) -> Self
pub fn with_period(self, period: f64) -> Self
Sets the period for periodic kernel.
Sourcepub fn fit(&mut self, x_train: Vec<f64>, y_train: Vec<f64>)
pub fn fit(&mut self, x_train: Vec<f64>, y_train: Vec<f64>)
Fits the GP to training data by computing the Cholesky decomposition.
Sourcepub fn predict(&self, x_star: f64) -> (f64, f64)
pub fn predict(&self, x_star: f64) -> (f64, f64)
Predicts posterior mean and variance at test input x_star.
Sourcepub fn log_marginal_likelihood(&self) -> f64
pub fn log_marginal_likelihood(&self) -> f64
Computes the log marginal likelihood.
Trait Implementations§
Source§impl Clone for GaussianProcess
impl Clone for GaussianProcess
Source§fn clone(&self) -> GaussianProcess
fn clone(&self) -> GaussianProcess
Returns a duplicate of the value. Read more
1.0.0 (const: unstable) · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from
source. Read moreAuto Trait Implementations§
impl Freeze for GaussianProcess
impl RefUnwindSafe for GaussianProcess
impl Send for GaussianProcess
impl Sync for GaussianProcess
impl Unpin for GaussianProcess
impl UnsafeUnpin for GaussianProcess
impl UnwindSafe for GaussianProcess
Blanket Implementations§
Source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
Source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
Source§impl<T> CloneToUninit for Twhere
T: Clone,
impl<T> CloneToUninit for Twhere
T: Clone,
Source§impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
Source§fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
The inverse inclusion map: attempts to construct
self from the equivalent element of its
superset. Read moreSource§fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
Checks if
self is actually part of its subset T (and can be converted to it).Source§fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
Use with care! Same as
self.to_subset but without any property checks. Always succeeds.Source§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts
self to the equivalent element of its superset.