% Sequential forward input selection using leave-one-out error 

%====== load the iris data set
close all	% Close all figure windows
clear all	% Clear all variables in memory
%loadiris;
loadaba;

% ====== Data normalization to have zero mean and unity variance r.v.
new_feature = normal(feature);
data = [new_feature output];

all_input_n = size(data,2)-1;			% No. of all possible inputs
input_n = 4;					% Select up to input_n inputs
model_n = input_n*(2*all_input_n-input_n+1)/2;	% No. of KNNR models
all_input_index = zeros(model_n, input_n);	% List of selected inputs
all_misclassify = zeros(model_n, 1);		% No. of misclassification
K = 1;						% "K" in KNNR

% ====== You need to replace the following lines to generate 
% "all_input_index" and "all_misclassify" via sequential forward search
all_input_index = [
     1     0     0     0; % input 1 is selected
     2     0     0     0; % input 2 is selected
     3     0     0     0; % input 3 is selected
     4     0     0     0; % input 4 is selected
     5     0     0     0; % input 5 is selected
     6     0     0     0; % input 6 is selected
     7     0     0     0; % input 7 is selected
     8     0     0     0; % input 8 is selected
     5     1     0     0; % inputs 5 and 1 are selected
     5     2     0     0; % inputs 5 and 2 are selected
     5     3     0     0; % inputs 5 and 3 are selected
     5     4     0     0; % inputs 5 and 4 are selected
     5     6     0     0; % inputs 5 and 6 are selected
     5     7     0     0; % inputs 5 and 7 are selected
     5     8     0     0; % inputs 5 and 8 are selected
     5     8     1     0; % inputs 5, 8 and 1 are selected
     5     8     2     0; % inputs 5, 8 and 2 are selected
     5     8     3     0; % inputs 5, 8 and 3 are selected
     5     8     4     0; % inputs 5, 8 and 4 are selected
     5     8     6     0; % inputs 5, 8 and 6 are selected
     5     8     7     0; % inputs 5, 8 and 7 are selected
     5     7     8     1; % inputs 5, 8, 7 and 1 are selected
     5     7     8     2; % inputs 5, 8, 7 and 2 are selected
     5     7     8     3; % inputs 5, 8, 7 and 3 are selected
     5     7     8     4; % inputs 5, 8, 7 and 4 are selected
     5     7     8     6]; % inputs 5, 8, 7 and 6 are selected
all_misclassify = rand(model_n, 1)*100;
% ====== Don't change anything below this line!

% ====== Display the results
figTitle = 'Sequential Input Selection';
figure('Name', figTitle, 'NumberTitle', 'off');
x = (1:model_n)';
error_rate = all_misclassify/size(data,1)*100;
subplot(211);
plot(x, error_rate, '-', x, error_rate, 'co');
tmp = x(:, ones(1, 3))';
X = tmp(:);
tmp = [zeros(model_n, 1) error_rate nan*ones(model_n, 1)]';
Y = tmp(:);
hold on; plot(X, Y, 'g'); hold off;

[a, b] = min(error_rate);
fprintf('Minimal error rate = %.1f%%.\n', a);
fprintf('Selected inputs: ');
tmp = all_input_index(b, :);
tmp(find(tmp==0))=[];
for p = 1:length(tmp),
	fprintf(' %s', deblank(input_name(tmp(p), :)));
end
fprintf('\n');

hold on; plot(b, a, '*'); hold off;
axis([1 model_n -inf inf]);
set(gca, 'xticklabels', []);
ylabel('LOU Error Rates (%)');
grid on
h = findobj(gcf, 'type', 'line');
set(h, 'linewidth', 2)

% ====== Add text of input variables
for k = 1:model_n,
	index = all_input_index(k, :);
	index(find(index==0))=[];
	leng = length(index);
	input_string = [];
	for l=1:leng,
		input_string = [input_string, ...
			deblank(input_name(index(l), :)), ' '];
	end
	text(x(k), 0, input_string);
end
h = findobj(gcf, 'type', 'text');
set(h, 'rot', 90, 'fontsize', 11, 'hori', 'right');
ylabel('LOU Error Rates (%)');
title('LOU Error Rates for Sequential Input Selection');