62 lines
1.8 KiB
Matlab
62 lines
1.8 KiB
Matlab
function [model] = svr_train_linear_shift(train_labels, train_samples, hyper)
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% Change to your downloaded location
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addpath('C:\liblinear\matlab')
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comm = sprintf('-s 11 -B 1 -p %.10f -c %.10f -q', hyper.p, hyper.c);
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model = train(train_labels, train_samples, comm);
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% Try predicting on the valid samples data and shifting it
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cutoffs = 0:0.05:0.8;
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results = zeros(numel(cutoffs)+1, 1);
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prediction = hyper.valid_samples * model.w(1:end-1)' + model.w(end);
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prediction(~hyper.success) = 0;
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for c=1:numel(cutoffs)
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% perform shifting here per person
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users = unique(hyper.vid_ids);
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prediction_curr = prediction;
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for i=1:numel(users)
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preds_user = prediction_curr(strcmp(hyper.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*cutoffs(c))+1);
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prediction_curr(strcmp(hyper.vid_ids, users(i))) = preds_user - shift;
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end
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prediction_curr(prediction_curr<0)=0;
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prediction_curr(prediction_curr>5)=5;
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[ ~, ~, ~, ccc, ~, ~ ] = evaluate_regression_results( prediction_curr, hyper.valid_labels );
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result = ccc;
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results(c) = result;
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end
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% option of no cutoff as well
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cutoffs = cat(2,cutoffs, -1);
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prediction(prediction<0)=0;
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prediction(prediction>5)=5;
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[ ~, ~, ~, ccc, ~, ~ ] = evaluate_regression_results( prediction, hyper.valid_labels );
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results(end) = ccc;
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[best, best_id] = max(results);
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result = results(best_id);
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model.cutoff = cutoffs(best_id);
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model.vid_ids = hyper.vid_ids;
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model.success = hyper.success;
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if(isfield(hyper, 'eval_ids'))
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model.eval_ids = hyper.eval_ids;
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end
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end |