Implementation-of-Support-Vector-Machine

Contains code involving Implementation of Support vector Machine

In this experiment, we implement Support Vector Machine using CVOXPT package. We
then implement Linear Kernel, Polynomial Kernel and Gaussian Kernel. We Create random
dataset based on Linearly Separable data, Linear separable Overlapping data, and Circular
Data. Later we split the data into Train and Test data based on Fraction 0.75. Later we
call Linear SVM, Kernel SVM and Soft SVM based on Different Kernel Function and Kernel
Hyper Parameters.

Required packages :

• Numpy

• CVOXPT

• Matplotlib

• Python3

• math

• Random

• math

• pylab

• copy

To execute code in /code directory run following code :

python3 answer.py
Or
python3 answer.py > result.txt

In case of first command results will be displayed on terminal. In case of second command results will be stored in result.txt file. Plots generated are stored in figures folder.