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

% n = 200; %set the number of data points
% mydata = rand(n,2);
% 
% shiftidx = abs(mydata(:,1)-mydata(:,2))>0.00005;
% 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]';
% w = [1 0 0];

for i = 1:1
    %%% w=rand(1,3)'
    w =   [0.6842
    0.5148
         0]
    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')