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[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);
[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=[];
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');
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