Struct GaussianKernel 

pub struct GaussianKernel {
    pub sigma: f32,
}

Gaussian kernel, defined as $\exp(-\frac{||x_1 - x_2||^2}{2\sigma^2})$. Also known as RBF kernel.

Examples

use pr_ml::{RowVector, svm::{Kernel, GaussianKernel}};
let kernel = GaussianKernel::new(1.0);
let x1 = RowVector::from([1.0, 2.0]);
let x2 = RowVector::from([2.0, 3.0]);
let result = kernel.compute(&x1, &x2);
assert!((result - 0.3679).abs() < 1e-4); // exp(-1) = 0.3679

Fields

sigma: f32

Standard deviation parameter.

Implementations

impl GaussianKernel

pub fn new(sigma: f32) -> Self

Creates a new GaussianKernel with the given sigma.

Trait Implementations

impl Clone for GaussianKernel

fn clone(&self) -> GaussianKernel

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for GaussianKernel

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

Formats the value using the given formatter.

impl Default for GaussianKernel

fn default() -> Self

Returns the “default value” for a type.

impl<const D: usize> Kernel<D> for GaussianKernel

fn compute(&self, x1: &RowVector<D>, x2: &RowVector<D>) -> f32

Computes the kernel function between two vectors.

impl PartialEq for GaussianKernel

fn eq(&self, other: &GaussianKernel) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Copy for GaussianKernel

impl StructuralPartialEq for GaussianKernel