Support Vector Machine

Simple SVM from scratch using CVXOPT.

Based on: D. J. Sebald and J. A. Bucklew, “Support vector machine techniques for nonlinear equalization,” in IEEE Transactions on Signal Processing, vol. 48, no. 11, pp. 3217-3226, Nov. 2000.

Dependency:
numpy,
cvxopt,
matplotlib,
sklearn (For convenience)

usage:

main.py [-h] [—test_type TEST_TYPE] [—test_number TEST_NUMBER]
[—kernel_type KERNEL_TYPE] [—dataset_path DATASET_PATH]
[—dataset_name DATASET_NAME]

Support Vector Machine from Scratch

optional arguments:
-h, —help show this help message and exit

—kernel_group KERNEL_GROUP
Select kernel’s group from: [linear, non_linear]

—dataset_number DATASET_NUMBER
Insert ID for LINEAR dataset: [1, 2].
Insert ID for NON LINEAR dataset: [1:RandomNonLinear, 2:XDataset,
3:MoonDataset, 4:CirclesDataset, 6:IrisDataset]

—kernel_type KERNEL_TYPE
[ONLY FOR NON LINEAR] Select kernel’s type from:
[polynomial, gaussian]

—dataset_path DATASET_PATH
Insert path of your own dataset

—dataset_name DATASET_NAME
Insert name of your own dataset

Example:

python3 main.py —kernel_group linear

python3 main.py —kernel_group non_linear —dataset_number 1 —kernel_type polynomial