% Get the data for Figure 2 of the plot_ras manual page.clear% Method names.methods = {'stupid', 'rbf_rt_1', 'mars'};nmet = length(methods);% Test set from get_data.test.name = 'friedman';test.p = 5000;test.std = 0;[xt, yt] = get_data(test);% Get the variance of the test set outputs.tvar = test.p * std(yt)^2;% Configuration structures for rbf_rt_1.conf = struct('scales', [7 9], 'minmem', [3 4]);% Sizes of test sets.sizes = [100 200];nsiz = length(sizes);% Number of replications of each training set.nrep = 10;% Initialise.results = zeros(nsiz, nrep, 2);% Loop over sizes.t1 = get_tmr(struct('name', 'sizes', 'n', nsiz));for i = 1:nsiz  % What size?  p = sizes(i);  % Loop over replications.  t2 = get_tmr(struct('name', 'replications', 'n', nrep));  for j = 1:nrep    % Get a new training set.    [x, y] = get_data(struct('name', 'friedman', 'p', p));    % Predict with the stupid method.    ft = sum(y) / length(y);    results(i, j, 1) = (yt - ft)' * (yt - ft) / tvar;    % Predict with rbf_rt_1.    [c, r, w, info] = rbf_rt_1(x, y, conf);    H = rbf_dm(xt, c, r, info.dmc);    ft = H * w;    results(i, j, 2) = (yt - ft)' * (yt - ft) / tvar;    % Increment the replication timer.    inc_tmr(t2)  end  % Close the replication timer.  pause(0.5)  close(t2)  % Increment the sizes timer.  inc_tmr(t1)end% Close the sizes timer.pause(0.5)close(t1)% Convert replication results into performance figures.perfs = zeros(nsiz, nmet, 2);for i = 1:nsiz  perfs(i, 1, 1) = mean(results(i, :, 1));  perfs(i, 1, 2) = std(results(i, :, 1));  perfs(i, 2, 1) = mean(results(i, :, 2));  perfs(i, 2, 2) = std(results(i, :, 2));end% Save these results for the fig2b.m script.save fig2 methods nmet sizes nsiz perfs