% This example shows how to use the Perceptron algorithm for training a
% two-class linear classifier, i.e.  y = sign( <x,w>+b). The Perceptron algorithm
% works by iteratively passing though the training examples and applying the
% update rule on those examples which are misclassified by the current
% classifier. The Perceptron update rule reads
% 
%   w(t+1) = w(t) + alpha * y_t * x_t
%   b(t+1) = b(t) + alpha * y_t
% 
% where (x_t,y_t) is feature vector and label (must be +1/-1) of the misclassified example
%       (w(t),b(t)) are the current parameters of the linear classifier
%       (w(t+1),b(t+1)) are the new parameters of the linear classifier
%       alpha is the learning rate.
% 
% The Perceptron algorithm iterates until all training examples are correctly
% classified or the prescribed maximal number of iterations is reached.
% 
% The learning rate and the maximal number of iterations can be set by
%   sg('set_perceptron_parameters', alpha, max_iter);
% 

% Perceptron
print Perceptron

%set_features TRAIN ../data/fm_train_real.dat
%set_labels TRAIN ../data/label_train_twoclass.dat
%new_classifier PERCEPTRON
%set_perceptron_parameters 1.6 5000
%train_classifier

%set_features TEST fm_test_real
%out.txt = classify

