clc;

clear all;

close all;

file = 'data.csv'; % Dataset

% Reading training file

data = dlmread(file);

label = data(:,end);

% Extracting positive data points

idx = (label==1);

pos_data = data(idx,:); 

row_pos = size(pos_data,1);

% Extracting negative data points

neg_data = data(~idx,:);

row_neg = size(neg_data,1);

% Random permuation of positive and negative data points

p = randperm(row_pos);

n = randperm(row_neg);

% 80-20 split for training and test

tstpf = p(1:round(row_pos/5));

tstnf = n(1:round(row_neg/5));

trpf = setdiff(p, tstpf);

trnf = setdiff(n, tstnf);

train_data = [pos_data(trpf,:);neg_data(trnf,:)];

test_data = [pos_data(tstpf,:);neg_data(tstnf,:)];

% Decision Tree

prediction = SMOTEBoost(train_data,test_data,'tree',false);

disp ('    Label   Probability');

disp ('-----------------------------');

disp (prediction);