scirs2_transform/kernel/
mod.rs

1//! Kernel Methods
2//!
3//! This module provides kernel-based algorithms for nonlinear machine learning:
4//!
5//! - **Kernel Functions** (`kernels`): A library of kernel functions (Linear, Polynomial,
6//!   RBF/Gaussian, Laplacian, Sigmoid), Gram matrix computation, and kernel centering.
7//!
8//! - **Kernel PCA** (`kpca`): Nonlinear dimensionality reduction via the kernel trick,
9//!   with pre-image estimation and automatic parameter selection.
10//!
11//! - **Kernel Ridge Regression** (`kernel_ridge`): Tikhonov-regularized regression in
12//!   kernel space, with closed-form LOO-CV and multi-output support.
13
14/// Kernel functions library (Linear, Polynomial, RBF, Laplacian, Sigmoid)
15pub mod kernels;
16
17/// Kernel PCA for nonlinear dimensionality reduction
18pub mod kpca;
19
20/// Kernel Ridge Regression
21pub mod kernel_ridge;
22
23// Re-exports for convenience
24pub use kernel_ridge::KernelRidgeRegression;
25pub use kernels::{
26    center_kernel_matrix, center_kernel_matrix_test, cross_gram_matrix, estimate_rbf_gamma,
27    gram_matrix, is_positive_semidefinite, kernel_alignment, kernel_diagonal, kernel_eval,
28    KernelType,
29};
30pub use kpca::KernelPCA;
scirs2_transform/kernel/mod.rs

scirs2_transform/kernel/
mod.rs
