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
