84 lines
2.8 KiB
Matlab
84 lines
2.8 KiB
Matlab
% Change to your downloaded location
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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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num_test_folds = 5;
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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_shift;
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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_shift;
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test_folds = get_test_folds(num_test_folds, users);
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%%
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for a=1:numel(aus)
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au = aus(a);
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prediction_all = [];
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test_all = [];
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fprintf('Training AU%d ', au);
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for t=1:num_test_folds
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rest_aus = setdiff(all_aus, au);
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% load the training and testing data for the current fold
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[~, ~, test_samples, test_labels, ~, ~, ~, ~, test_ids, test_success] = Prepare_HOG_AU_data_generic_dynamic({}, test_folds{t}, au, rest_aus, DISFA_dir, hog_data_dir);
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% create the training and validation data
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users_train = setdiff(users, unique(test_ids));
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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_train, 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_dynamic(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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test_samples = sparse(test_samples);
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%% Cross-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 = test_success;
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model.success = test_success;
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model.vid_ids = test_ids;
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[~, prediction] = svr_test(test_labels, test_samples, model);
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prediction_all = cat(1, prediction_all, prediction);
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test_all = cat(1, test_all, test_labels);
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fprintf('done fold %d ', t);
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end
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fprintf('\n');
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name = sprintf('5_fold_shift/AU_%d_dyn_shift.mat', au);
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[ accuracies, F1s, corrs, ccc, rms, classes ] = evaluate_regression_results( prediction_all, test_all );
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save(name, 'model', 'accuracies', 'F1s', 'corrs', 'rms', 'ccc', 'prediction_all', 'test_all');
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end
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