Struct DotProductKernel 

pub struct DotProductKernel { /* private fields */ }

Dot Product kernel (Linear kernel with variance and shift).

K(x, y) = σ² + σ_b² + x · y

Useful for Bayesian linear regression. The offset parameter allows the linear model to have a non-zero mean.

Implementations

impl DotProductKernel

pub fn new(variance: f64, variance_bias: f64) -> Result<Self>

Create a new dot product kernel.

pub fn simple() -> Self

Create a simple dot product kernel (variance=1, no bias).

pub fn variance(&self) -> f64

Get the variance.

pub fn variance_bias(&self) -> f64

Get the bias variance.

Trait Implementations

impl Clone for DotProductKernel

fn clone(&self) -> DotProductKernel

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for DotProductKernel

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

Formats the value using the given formatter.

impl Kernel for DotProductKernel

fn compute(&self, x: &[f64], y: &[f64]) -> Result<f64>

Compute kernel value between two inputs.

fn name(&self) -> &str

Get kernel name for identification.

fn compute_matrix(&self, inputs: &[Vec<f64>]) -> Result<Vec<Vec<f64>>>

Compute kernel matrix for a set of inputs.

fn is_psd(&self) -> bool

Check if kernel is positive semi-definite.