Struct MultipleKernelLearning 

pub struct MultipleKernelLearning { /* private fields */ }

Multiple Kernel Learning approximation

Learns optimal weights for combining multiple kernel approximations to create a single, improved kernel approximation.

Implementations

impl MultipleKernelLearning

pub fn new(base_kernels: Vec<BaseKernel>) -> Self

Create a new multiple kernel learning instance

pub fn with_config(self, config: MultiKernelConfig) -> Self

Set configuration

pub fn with_random_state(self, random_state: u64) -> Self

Set random state for reproducibility

pub fn fit(&mut self, x: &Array2<f64>, y: Option<&Array1<f64>>) -> Result<()>

Fit the multiple kernel learning model

pub fn transform(&self, x: &Array2<f64>) -> Result<Array2<f64>>

Transform data using learned kernel combination

pub fn kernel_weights(&self) -> Option<&Array1<f64>>

Get learned kernel weights

pub fn kernel_stats(&self) -> &HashMap<String, KernelStatistics>

Get kernel statistics

pub fn important_kernels( &self, threshold: f64, ) -> Vec<(usize, &BaseKernel, f64)>

Get most important kernels based on weights