Struct WindowedKernelMatrix 

pub struct WindowedKernelMatrix { /* private fields */ }

Sliding window kernel matrix for bounded-memory streaming.

Maintains only the most recent window_size samples, automatically removing oldest samples when the window is full.

Example

use tensorlogic_sklears_kernels::{WindowedKernelMatrix, LinearKernel, Kernel};

let kernel = LinearKernel::new();
let mut windowed = WindowedKernelMatrix::new(Box::new(kernel), 3);

// Add samples (window size = 3)
windowed.add_sample(vec![1.0]).unwrap();
windowed.add_sample(vec![2.0]).unwrap();
windowed.add_sample(vec![3.0]).unwrap();
windowed.add_sample(vec![4.0]).unwrap(); // First sample evicted

assert_eq!(windowed.len(), 3);

Implementations

impl WindowedKernelMatrix

pub fn new(kernel: Box<dyn Kernel>, window_size: usize) -> Self

Create a new windowed kernel matrix.

pub fn add_sample(&mut self, sample: Vec<f64>) -> Result<Option<Vec<f64>>>

Add a sample, evicting oldest if window is full.

pub fn get_matrix(&self) -> &Vec<Vec<f64>>

Get the current kernel matrix.

pub fn get_samples(&self) -> &VecDeque<Vec<f64>>

Get samples in the window.

pub fn window_size(&self) -> usize

Get the window size.

pub fn len(&self) -> usize

Get current number of samples.

pub fn is_empty(&self) -> bool

Check if window is empty.

pub fn is_full(&self) -> bool

Check if window is full.

pub fn stats(&self) -> &OnlineStats

Get statistics.

pub fn clear(&mut self)

Clear the window.

pub fn compute_with_all(&self, query: &[f64]) -> Result<Vec<f64>>

Compute kernel values between query and all samples in window.