You should first understand what is the meaning of each of the inputs:
Here, X is an input matrix of examples with size M x N, where M is the dimension of the feature vector, and N the number of samples.
Since the perceptron model for prediction is Y=w*X+b, you have to supply one extra dimension in X which is constant, usually set to 1, so the b term is "built-in" into X. In the following code for X, I have set the last entry of X to be 1 in all samples.
Y is the classification for each sample from X (the classification you want the perceptron to learn), so it should be an N-dimensional row vector that is one output for each input example.
Since the perceptron is a binary classifier, it should have only 2 distinct possible values. Looking in the code, you see that it checks for the sign of the prediction, which tells you that the allowed values of Y should be -1,+1 (and not 0,1 for example).
w is the weight of the vector that you will be trying to learn.
% input samples X1=[rand(1,100); rand(1,100); ones(1,100)]; % class '+1' X2=[rand(1,100); 1+rand(1,100); ones(1,100)]; % class '-1' X=[X1,X2]; % output class [-1,+1]; Y=[-ones(1,100),ones(1,100)]; % init weigth vector w=[.5 .5 .5]'; % call perceptron wtag=perceptron(X,Y,w); % predict ytag=wtag'*X; % plot prediction over origianl data Figure; hold on plot(X1(1,:),X1(2,:),'b.') plot(X2(1,:),X2(2,:),'r.') 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')