Module svm 

Support Vector Machine (ISL ch 9, PRML ch 7) — soft-margin binary classifier trained by simplified Sequential Minimal Optimization (SMO) on the dual, with a linear or RBF (Gaussian) kernel. Kernel SVM separates classes a linear boundary cannot. Labels are boolean (true = +1, false = −1). Kernel-class DenseLinear (the kernel matrix) + Divergent (the SMO working-set loop) → CPU here.

Structs

Svm

A fitted SVM: the support vectors (the training points with non-zero α) plus the bias and kernel.

Enums

Kernel

The kernel K(a, b).

Functions

fit

Fit a soft-margin SVM by simplified SMO. c is the regularization (box) bound, max_passes the number of consecutive no-change sweeps to declare convergence. Fails closed on shape mismatch / a single-class target.