% CSCI 5521 Introduction to Machine Learning
% Rui Kuang
% Run perceptron on random data points in two classes 

n = 20; %set the number of data points
mydata = rand(n,2);

shiftidx = abs(mydata(:,1)-mydata(:,2))>0.05;
mydata = mydata(shiftidx,:); 
myclasses = mydata(:,1)>mydata(:,2); % labels
n = size(mydata,1);
X = [mydata ones(1,n)']'; Y=myclasses;
Y = Y * 2 -1; 

% init weigth vector
w = [mean(mydata) 0]';

for i = 1:1
    w=rand(1,3)';
    w(3,1)=0;%go through the origin for visualization
    % call perceptron
    wtag=perceptron(X,Y,w,10);
end

% call perceptron
% wtag=perceptron(X,Y,w);
% predict
ytag=wtag'*X;

% plot prediction over origianl data

%plot(X(1,ytag<0),X(2,ytag<0),'bo')
%plot(X(1,ytag>0),X(2,ytag>0),'ro')
%legend('class -1','class +1','pred -1','pred +1')