Struct WeightedSumKernel 

pub struct WeightedSumKernel { /* private fields */ }

Weighted sum of multiple kernels: K(x,y) = Σ_i w_i * K_i(x,y)

Combines multiple kernels using weighted averaging. Weights should sum to 1.0 for proper normalization.

Example

use tensorlogic_sklears_kernels::{
    LinearKernel, RbfKernel, RbfKernelConfig,
    WeightedSumKernel, Kernel
};

let linear = Box::new(LinearKernel::new()) as Box<dyn Kernel>;
let rbf = Box::new(RbfKernel::new(RbfKernelConfig::new(0.5)).unwrap()) as Box<dyn Kernel>;

let weights = vec![0.7, 0.3];
let composite = WeightedSumKernel::new(vec![linear, rbf], weights).unwrap();

let x = vec![1.0, 2.0, 3.0];
let y = vec![4.0, 5.0, 6.0];
let sim = composite.compute(&x, &y).unwrap();
// sim = 0.7 * linear(x,y) + 0.3 * rbf(x,y)

Implementations

impl WeightedSumKernel

pub fn new(kernels: Vec<Box<dyn Kernel>>, weights: Vec<f64>) -> Result<Self>

Create a new weighted sum kernel

pub fn new_normalized( kernels: Vec<Box<dyn Kernel>>, weights: Vec<f64>, ) -> Result<Self>

Create with normalized weights (sum to 1.0)

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

Create with uniform weights

Trait Implementations

impl Kernel for WeightedSumKernel

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.