Enum BaseKernel 

pub enum BaseKernel {
    RBF {
        gamma: f64,
    },
    Polynomial {
        degree: f64,
        gamma: f64,
        coef0: f64,
    },
    Laplacian {
        gamma: f64,
    },
    Linear,
    Sigmoid {
        gamma: f64,
        coef0: f64,
    },
    Custom {
        name: String,
        kernel_fn: fn(&Array1<f64>, &Array1<f64>) -> f64,
    },
}

Multiple Kernel Learning (MKL) methods for kernel approximation

This module provides methods for learning optimal combinations of multiple kernels, enabling automatic kernel selection and weighting for improved machine learning performance.

Base kernel type for multiple kernel learning BaseKernel

Variants

RBF

RBF kernel with specific gamma parameter

Fields

gamma: f64

Polynomial

Polynomial kernel with degree, gamma, and coef0

Fields

degree: f64

gamma: f64

coef0: f64

Laplacian

Laplacian kernel with gamma parameter

Fields

gamma: f64

Linear

Linear kernel

Sigmoid

Sigmoid kernel with gamma and coef0

Fields

gamma: f64

coef0: f64

Custom

Custom kernel with user-defined function

Fields

name: String

kernel_fn: fn(&Array1<f64>, &Array1<f64>) -> f64

Implementations

impl BaseKernel

pub fn evaluate(&self, x: &Array1<f64>, y: &Array1<f64>) -> f64

Evaluate kernel function between two samples

pub fn name(&self) -> String

Get kernel name for identification

Trait Implementations

impl Clone for BaseKernel

fn clone(&self) -> BaseKernel

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for BaseKernel

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

Formats the value using the given formatter.