%% classifiers
% Introduction of defining, training and evaluating classifiers
%
% <http://37steps.com/software/prtools/ PRTools> and
% <http://37steps.com/software/prdatafiles/ PRDataFiles> should be in the path
%
% <http://37steps.com/exam/classifiers/classifiers.m Download the m-file from
% here>.
% See http://37steps.com/prtools for more. 

%%
%         
prwaitbar off                % waitbar not needed here
delfigs                      % delete existing figures
randreset(1);                % takes care of reproducability
%%        Define a classifier

u = knnc([],3);              % the untrained 3-NN classifier

%%        Define datasets for training and testing

a = gendatb([20 20],2);      % define dataset
a = setlablist(a,[' A ';' B ']); % define class names
[t,s] = gendat(a,0.5);       % split it 50-50 in train set and test set
t = setname(t,'Train Set');  % name the train set 
s = setname(s,'Test Set');   % name the test set

%%        Train the classifier

w = t*u;                     % train the classifier

%%        Show the trained classifier on the training set

figure;
scatterd(t);                 % show training set
axis equal
plotc(w);                    % plot classifier
V = axis;

dt = t*w;                    % apply classifier to the training set
et = dt*testc;               % compute its classification error
fprintf('The apparent error: %4.2f \n',et); % print it
labt = getlabels(t);         % true labels of training set
labtc= dt*labeld;            % estimated labels of classified training set
disp([labt labtc]);          % show them. They correspond to the estimated error
%%
% Compute the apparent error and show the estimated and true labels
% in classifying the training set. They corespond to the apparent error 
% and the classifier in the scatter plot
%%        Show the trained classifier on the test set

figure;
scatterd(s);                 % show test set
axis(V);
plotc(w);                    % plot classifier

ds = s*w;                    % apply classifier on the test set
es = ds*testc;               % compute its classification error
fprintf('The test error: %4.2f \n',es); % print it
labs = getlabels(t);         % true labels of test set
labsc= ds*labeld;            % estimated labels of classified test set
disp([labs labsc]);          % show them. They correspond to the estimated error

%%
% Compute the test error and show the estimated and true labels
% in classifying the test set. They corespond to the test error
% and the classifier in the scatter plot