By Andrew Tulloch (http://tullo.ch)

This is a basic implementation of a soft-margin kernel SVM solver in Python using numpy and cvxopt.

See http://tullo.ch/articles/svm-py/ for a description of the algorithm used and the general theory behind SVMs.

Run bin/svm-py-demo --help.

∴ bin/svm-py-demo --help usage: svm-py-demo [-h] [--num-samples NUM_SAMPLES] [--num-features NUM_FEATURES] [-g GRID_SIZE] [-f FILENAME] optional arguments: -h, --help show this help message and exit --num-samples NUM_SAMPLES --num-features NUM_FEATURES -g GRID_SIZE, --grid-size GRID_SIZE -f FILENAME, --filename FILENAME 

For example,

bin/svm-py-demo --num-samples=100 --num-features=2 --grid-size=500 --filename=svm500.pdf 

yields the image