54 lines
1.6 KiB
Matlab
54 lines
1.6 KiB
Matlab
function [result, prediction] = svr_test_linear_shift(test_labels, test_samples, model)
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prediction = test_samples * model.w(1:end-1)' + model.w(end);
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% prediction = predict(test_labels, test_samples, model);
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prediction(~model.success) = 0;
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if(model.cutoff >= 0)
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% perform shifting here per person
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users = unique(model.vid_ids);
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for i=1:numel(users)
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preds_user = prediction(strcmp(model.vid_ids, users(i)));
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sorted = sort(preds_user);
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% alternative, move to histograms and pick the highest one
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shift = sorted(round(end*model.cutoff)+1);
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prediction(strcmp(model.vid_ids, users(i))) = preds_user - shift;
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end
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end
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% Cap the prediction as well
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prediction(prediction<0)=0;
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prediction(prediction>5)=5;
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% using the average of RMS errors
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% result = mean(sqrt(mean((prediction - test_labels).^2)));
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if(~isfield(model, 'eval_ids'))
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result = corr(test_labels, prediction);
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[ ~, ~, ~, ccc, ~, ~ ] = evaluate_regression_results( prediction, test_labels );
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result = ccc;
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else
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eval_ids = unique(model.eval_ids)';
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ccc = 0;
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fprintf('CCC: ');
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for i=eval_ids
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[ ~, ~, ~, ccc_curr, ~, ~ ] = evaluate_regression_results( prediction(model.eval_ids == i), test_labels(model.eval_ids == i) );
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ccc = ccc + ccc_curr;
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fprintf('%.3f ', ccc_curr);
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end
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ccc = ccc / numel(eval_ids);
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fprintf('mean : %.3f\n', ccc);
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result = ccc;
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end
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if(isnan(result))
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result = 0;
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end
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end |