[p, q] = get_products();

% quantity sum over a day
%plot(sum(q'));

train_data = q( 1:28, :);
real_data  = q(29:42, :);

pred_methods = {
	{ 'mean',	@mean_pred },
	{ 'regress',	@regress_pred },
	{ 'quad',	@quad_regress_pred },
	{ 'log reg.',	@log_regress_pred },
	{ 'sevenday'	@sevenday_pred },
	{ 'random'	@rand_pred },
	{ 'regress2'	@regress_frequency_removal }
};
num_methods = size(pred_methods, 1);

pred_list = {};
for i = 1:num_methods
	pred_list{i} = pred_methods{i}{2}(p, train_data);
end

qerr = terr = zeros(1, num_methods);
err  = zeros(num_methods, size(real_data,2));
for i = 1:num_methods
	[qerr(i), terr(i), err(i, :)] = calc_error(pred_methods{i}{1}, real_data, pred_list{i});
end

opt_data = opt_pred(real_data, pred_list);
[opqerr, opterr, opt_err] = calc_error('optimize', real_data, opt_data);
qerr = [ qerr opqerr ];
terr = [ terr opterr ];

bar(qerr);
bar(terr);

[min_err, err_idx] = min(err);
printf('global min. error: %d\n', sum(min_err));
printf('local min count:');
local_min_count=[];
for i=1:size(err,1)
	local_min_count = [ local_min_count sum(sum(err_idx == i)) ];
	printf(' %d', local_min_count(i));
end
printf('\n');