68 lines
2.1 KiB
Matlab
68 lines
2.1 KiB
Matlab
% Change to your downloaded location
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clear
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addpath('C:\liblinear\matlab')
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addpath('../training_code')
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addpath('../utilities')
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%% load shared definitions and AU data
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shared_defs;
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% Set up the hyperparameters to be validated
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hyperparams.c = 10.^(-7:1:3);
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hyperparams.p = 10.^(-2);
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hyperparams.validate_params = {'c', 'p'};
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% Set the training function
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svr_train = @svr_train_linear;
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% Set the test function (the first output will be used for validation)
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svr_test = @svr_test_linear;
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%%
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for a=1:numel(aus)
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au = aus(a);
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rest_aus = setdiff(all_aus, au);
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% make sure validation data's labels are balanced
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[users_train, users_valid] = get_balanced_fold(DISFA_dir, users, au, 1/4, 1);
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% need to split the rest
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[train_samples, train_labels, valid_samples, valid_labels, ~, PC, means, scaling, valid_ids, valid_success] = Prepare_HOG_AU_data_generic(users_train, users_valid, au, rest_aus, DISFA_dir, hog_data_dir);
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train_samples = sparse(train_samples);
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valid_samples = sparse(valid_samples);
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%% Validate here
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hyperparams.success = valid_success;
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hyperparams.valid_samples = valid_samples;
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hyperparams.valid_labels = valid_labels;
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hyperparams.vid_ids = valid_ids;
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[ best_params, ~ ] = validate_grid_search_no_par(svr_train, svr_test, false, train_samples, train_labels, valid_samples, valid_labels, hyperparams);
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model = svr_train(train_labels, train_samples, best_params);
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model.success = valid_success;
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model.vid_ids = valid_ids;
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[~, prediction] = svr_test(valid_labels, valid_samples, model);
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name = sprintf('regressors/AU_%d_static_intensity.mat', au);
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[ accuracies, F1s, corrs, ccc, rms, classes ] = evaluate_regression_results( prediction, valid_labels );
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save(name, 'model', 'accuracies', 'F1s', 'corrs', 'rms', 'ccc', 'prediction', 'valid_labels');
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% Write out the model
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name = sprintf('regressors/AU_%d_static_intensity.dat', au);
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w = model.w(1:end-1)';
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b = model.w(end);
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svs = bsxfun(@times, PC, 1./scaling') * w;
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write_lin_svr(name, means, svs, b);
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end |