84 lines
2.5 KiB
Matlab
84 lines
2.5 KiB
Matlab
%% load shared definitions and AU data
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clear
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addpath('../../data extraction/');
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addpath('../utilities/');
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addpath('../training_code/');
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shared_defs;
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% Set up the hyperparameters to be validated
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hyperparams.c = 10.^(-7:1:4);
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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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pca_loc = '../../pca_generation/generic_face_rigid.mat';
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hog_data_dir_BP4D = hog_data_dir;
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aus = [6, 10, 12, 14, 17];
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%%
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for a=1:numel(aus)
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predictions_all = [];
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test_labels_all = [];
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au = aus(a);
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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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[train_samples, train_labels, ~, valid_samples, valid_labels, vid_ids_devel, ~, PC, means, scaling, success_devel] = Prepare_HOG_AU_data_generic_intensity(train_recs, devel_recs, au, BP4D_dir_int, hog_data_dir_BP4D);
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ignore = valid_labels == 9;
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valid_samples = valid_samples(~ignore, :);
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valid_labels = valid_labels(~ignore);
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vid_ids_devel = vid_ids_devel(~ignore);
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success_devel = success_devel(~ignore);
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train_samples = sparse(train_samples);
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valid_samples = sparse(valid_samples);
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hyperparams.success = success_devel;
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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 = vid_ids_devel;
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%% Cross-validate here
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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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clear 'train_samples'
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%% Now test the model
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model.vid_ids = vid_ids_devel;
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[~, prediction] = svr_test(valid_labels, valid_samples, model);
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name = sprintf('results_BP4D_devel/AU_%d_static_intensity_shift.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', 'F1s', 'corrs', 'accuracies', 'ccc', 'rms', 'prediction', 'valid_labels');
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% Go from raw data to the prediction
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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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name = sprintf('models/AU_%d_static_intensity_shift.dat', au);
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write_lin_svr(name, means, svs, b);
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end
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