41 lines
1.1 KiB
Mathematica
41 lines
1.1 KiB
Mathematica
|
function [result, prediction] = svr_test_linear_shift(test_labels, test_samples, model)
|
||
|
|
|
||
|
|
prediction = test_samples * model.w(1:end-1)' + model.w(end);
|
||
|
|
% prediction = predict(test_labels, test_samples, model);
|
||
|
|
|
||
|
|
prediction(~model.success) = 0;
|
||
|
|
|
||
|
|
if(model.cutoff >= 0)
|
||
|
|
% perform shifting here per person
|
||
|
|
users = unique(model.vid_ids);
|
||
|
|
|
||
|
|
for i=1:numel(users)
|
||
|
|
|
||
|
|
preds_user = prediction(strcmp(model.vid_ids, users(i)));
|
||
|
|
sorted = sort(preds_user);
|
||
|
|
|
||
|
|
% alternative, move to histograms and pick the highest one
|
||
|
|
|
||
|
|
shift = sorted(round(end*model.cutoff)+1);
|
||
|
|
|
||
|
|
prediction(strcmp(model.vid_ids, users(i))) = preds_user - shift;
|
||
|
|
|
||
|
|
end
|
||
|
|
end
|
||
|
|
|
||
|
|
% Cap the prediction as well
|
||
|
|
prediction(prediction<0)=0;
|
||
|
|
prediction(prediction>5)=5;
|
||
|
|
|
||
|
|
% using the average of RMS errors
|
||
|
|
% result = mean(sqrt(mean((prediction - test_labels).^2)));
|
||
|
|
result = corr(test_labels, prediction);
|
||
|
|
[ ~, ~, ~, ccc, ~, ~ ] = evaluate_regression_results( prediction, test_labels );
|
||
|
|
|
||
|
|
result = ccc;
|
||
|
|
|
||
|
|
if(isnan(result))
|
||
|
|
result = 0;
|
||
|
|
end
|
||
|
|
|
||
|
|
end
|