Struct ProductKernel 

pub struct ProductKernel { /* private fields */ }

Product of multiple kernels: K(x,y) = Π_i K_i(x,y)

Combines kernels through multiplication. The resulting kernel corresponds to the tensor product of feature spaces.

Example

use tensorlogic_sklears_kernels::{
    LinearKernel, CosineKernel,
    ProductKernel, Kernel
};

let linear = Box::new(LinearKernel::new()) as Box<dyn Kernel>;
let cosine = Box::new(CosineKernel::new()) as Box<dyn Kernel>;

let product = ProductKernel::new(vec![linear, cosine]).unwrap();

let x = vec![1.0, 2.0, 3.0];
let y = vec![4.0, 5.0, 6.0];
let sim = product.compute(&x, &y).unwrap();
// sim = linear(x,y) * cosine(x,y)

Implementations

impl ProductKernel

pub fn new(kernels: Vec<Box<dyn Kernel>>) -> Result<Self>

Create a new product kernel

Trait Implementations

impl Kernel for ProductKernel

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 is_psd(&self) -> bool

Check if kernel is positive semi-definite.

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

Compute kernel matrix for a set of inputs.