example.m

This is a support vector machine program developed based on quadprog. Polynomial and RBF kernel are

clear all
close all

load data1_X.dat;
load data1_y.dat;
load data2_X.dat;
load data2_y.dat;
load data_train;

% Train : 1/10,  Test : 9/10
data1_X_trn = data1_X(1:length(data1_X)*1/10, :);
data1_X_tst = data1_X(length(data1_X)*1/10+1:length(data1_X), :);
data1_y_trn = data1_y(1:length(data1_y)*1/10, :);
data1_y_tst = data1_y(length(data1_y)*1/10+1:length(data1_y), :);
data2_X_trn = data2_X(1:length(data2_X)/200:length(data2_X), :);
data2_X_tst = data2_X(length(data2_X)*1/10+1:length(data2_X), :);
data2_y_trn = data2_y(1:length(data2_X)/200:length(data2_X), :);
data2_y_tst = data2_y(length(data2_y)*1/10+1:length(data2_y), :);

% Kernel,  K = 1 : Polynomial,  K = 2 : RBF,  K_p : Kernel Parameter
% alpha_s : Support vector
% y_s & x_s : train data according to S.Vs
% b0 : intercept

%{
K = 1;
K_p = 1;
[alpha_s, y_s, x_s, b0] = SVMtrn(data1_X_trn, data1_y_trn, K, K_p);
err_1 = SVMtst(alpha_s, y_s, x_s, b0, K, K_p, data1_X_tst, data1_y_tst)
figure(1)
plotclassdata(data1_X_trn, data1_y_trn, alpha_s, y_s, x_s, b0, K, K_p);
%}
K = 1;
K_p = 1;
[alpha_s, y_s, x_s, b0] = SVMtrn(data2_X_trn, data2_y_trn, K, K_p);
err_2 = SVMtst(alpha_s, y_s, x_s, b0, K, K_p, data2_X_tst, data2_y_tst)
figure(2)
plotclassdata(data2_X_trn, data2_y_trn, alpha_s, y_s, x_s, b0, K, K_p);

%{
K = 1;
K_p = 2;
[alpha_s, y_s, x_s, b0] = SVMtrn(set2_train.X, set2_train.y, K, K_p);
err_2 = SVMtst(alpha_s, y_s, x_s, b0, K, K_p, set2_test.X, set2_test.y)
figure(2)
plotclassdata(set2_test.X, set2_test.y, alpha_s, y_s, x_s, b0, K, K_p);


K = 2;
K_p = 5;
[alpha_s, y_s, x_s, b0] = SVMtrn(set3_train.X, set3_train.y, K, K_p);
err_3 = SVMtst(alpha_s, y_s, x_s, b0, K, K_p, set3_test.X, set3_test.y)
figure(3)
plotclassdata(set3_test.X, set3_test.y, alpha_s, y_s, x_s, b0, K, K_p);
%}