[p, q] = get_products();

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

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

pred_methods = [
	struct('name', 'mean',     'func', @mean_pred )
	struct('name', 'regress',  'func', @regress_pred )
	struct('name', 'quad',     'func', @quad_regress_pred )
	struct('name', 'log reg.', 'func', @log_regress_pred )
	struct('name', 'sevenday', 'func', @sevenday_pred )
	struct('name', 'random',   'func', @rand_pred )
	struct('name', 'regress2', 'func', @regress_frequency_removal )
];
num_methods = size(pred_methods, 1);

pred_list = {};
for i = 1:num_methods
	pred_list{i} = pred_methods(i).func(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).name, real_data, pred_list{i});
end

opt_data = opt_pred(real_data, pred_list);
[qerr(num_methods+1), terr(num_methods+1), opt_err] = calc_error('optimize', real_data, opt_data);

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=zeros(1, size(err, 1));
for i=1:size(err,1)
	local_min_count(i) = sum(sum(err_idx == i));
	printf(' %d', local_min_count(i));
end
printf('\n');