run_cross_validation_classifier.m

% Classifing Neural Network Example
% author: Natanael Junior (natmourajr@gmail.com)
% LPS - Signal Processing Lab.
% UFRJ - Brazil

% Steps
% 1 - Data Aquisition
% 2 - Normalization (data, targets)
% 3 - Split Training Sets (train, test, validation)
% 4 - Training Process with Cross validation
% 5 - Result Analysis
% 

close all;
clear all;
clc;

fprintf('Starting %s.m\n',mfilename('fullpath'));
fprintf('Importing Functions\n');
addpath(genpath('functions'));

% 1 - Data Aquisition
load fisheriris;

str_targets = species;

% 2 - Normalization (target)
fprintf('Normalizing Target\n');
targets =zeros(size(str_targets,1),1); 

for i = 1:size(str_targets,1)
    if strcmp(str_targets(i),'setosa')
        targets(i) = 1;
    end
    
    if strcmp(str_targets(i),'versicolor')
        targets(i) = 0;
    end
    
    if strcmp(str_targets(i),'virginica')
        targets(i) = -1;
    end
end

% selecting just two class
inputs = meas(find(targets == 1 | targets == 0),:);
targets = targets(find(targets == 1 | targets == -1));


% 3 - Split Training Sets (train, test, validation)
fprintf('Split Training Sets\n');
n_tests = 10;
CVO = cvpartition(length(targets),'Kfold',n_tests); % split into n_tests tests

fprintf('Start Cross Validation Process\n');

cv_SP = zeros(n_tests,1);
cv_Pd = zeros(n_tests,1);
cv_Pf = zeros(n_tests,1);

for i_cross_valid = 1:CVO.NumTestSets
    fprintf('Set no %i\n', i_cross_valid);
    trn_id =  CVO.training(i_cross_valid); % taking the first one
    tst_id =  CVO.test(i_cross_valid); % taking the first one
    val_id = tst_id; % test = validation -> small statistics
    
    
    % turn trn_id in integers
    itrn = [];
    itst = [];
    ival = [];
    
    for i = 1:length(trn_id)
        if trn_id(i) == 1
            itrn = [itrn; i];
        else
            itst = [itst; i];
        end
    end
    ival = itst;
    
    % 2 - Using train set to extract normalization factors
    [~,ps] = mapstd(inputs(trn_id,:)'); % ps - normalization factors
    
    % applying normalization in all events
    % mapstd -> mean = 0, std = 1;
    inputs_norm =  mapstd('apply',inputs',ps)';
    
    
    % 4 - Training Process 
    top = 10; % number of neurons in hidden layer
    train_fnc = 'trainbfg'; % weights update function
    perf_fnc = 'mse'; % error function
    act_fnc = {'tansig'}; % activation function
    n_epochs = 100;
    show = false;
    
    [trained_nn, train_description] = train_neural_network(inputs_norm', targets', itrn, ival, itst, top, train_fnc, perf_fnc, act_fnc, n_epochs, show);
    
    nn_output = sim(trained_nn, inputs_norm');
    
    % 5 - Result Analysis
    % separating 2 different class
    c1 = nn_output((ival(find(targets(ival)==1)))); % validation set
    c2 = nn_output((ival(find(targets(ival)==-1)))); % validation set

    % find the maximum
    max_value = max([(-1)*min(nn_output) max(nn_output)]);

    % perform SP
    [SP, pt_SPmax] = calc_sp(c1, c2, max_value);
    
    Pd = length(find(c1 > pt_SPmax))/length(c1); % Detection Probability
    Pf = length(find(c2 > pt_SPmax))/length(c2); % False-Alarm Probability
    
    cv_SP(i_cross_valid) = SP;
    cv_Pd(i_cross_valid) = Pd;
    cv_Pf(i_cross_valid) = Pf;
end

% checking all SP
figure;
plot(1:CVO.NumTestSets, cv_SP,'bx','LineWidth',2.0);
hold on;
plot(1:CVO.NumTestSets, cv_Pd,'ro','LineWidth',2.0);
plot(1:CVO.NumTestSets, cv_Pf,'gs','LineWidth',2.0);
hold off;
ylabel('% SP / % Pd / % Pf','FontSize', 15,'FontWeight', 'bold');
xlabel('Set Id','FontSize', 15,'FontWeight', 'bold');
title(sprintf('Cross Validation Results - %i Sets',CVO.NumTestSets),'FontSize', 15,'FontWeight', 'bold');
legend('% SP', '% Pd', '% Pf');

fig2pdf(gcf,'cv_result.pdf'); close(gcf);

fprintf('Exporting Functions\n');
rmpath(genpath('functions'));

fprintf('THE END!!!\n');