64 lines
2.0 KiB
Matlab
64 lines
2.0 KiB
Matlab
% Change to your downloaded location
|
|
clear
|
|
addpath('C:\liblinear\matlab')
|
|
addpath('../training_code/');
|
|
addpath('../utilities/');
|
|
addpath('../../data extraction/');
|
|
%% load shared definitions and AU data
|
|
shared_defs;
|
|
|
|
% Set up the hyperparameters to be validated
|
|
hyperparams.c = 10.^(-7:0.5:1);
|
|
hyperparams.e = 10.^(-3);
|
|
|
|
hyperparams.validate_params = {'c', 'e'};
|
|
|
|
% Set the training function
|
|
svm_train = @svm_train_linear;
|
|
|
|
% Set the test function (the first output will be used for validation)
|
|
svm_test = @svm_test_linear;
|
|
|
|
pca_loc = '../../pca_generation/generic_face_rigid.mat';
|
|
|
|
hog_data_dir_BP4D = hog_data_dir;
|
|
|
|
aus = [1, 2, 4, 6, 7, 10, 12, 14, 15, 17, 23];
|
|
%%
|
|
for a=1:numel(aus)
|
|
|
|
au = aus(a);
|
|
|
|
rest_aus = setdiff(all_aus, au);
|
|
|
|
% load the training and testing data for the current fold
|
|
[train_samples, train_labels, valid_samples, valid_labels, ~, PC, means, scaling] = Prepare_HOG_AU_data_generic_dynamic(train_recs, devel_recs, au, BP4D_dir, hog_data_dir_BP4D);
|
|
|
|
train_samples = sparse(train_samples);
|
|
valid_samples = sparse(valid_samples);
|
|
|
|
%% Cross-validate here
|
|
[ best_params, ~ ] = validate_grid_search_no_par(svm_train, svm_test, false, train_samples, train_labels, valid_samples, valid_labels, hyperparams);
|
|
model = svm_train(train_labels, train_samples, best_params);
|
|
|
|
[~, predictions_all] = svm_test(valid_labels, valid_samples, model);
|
|
|
|
name = sprintf('results_BP4D_devel/AU_%d_dynamic.mat', au);
|
|
|
|
[ accuracies, F1s, corrs, ccc, rms, classes ] = evaluate_regression_results( predictions_all, valid_labels );
|
|
|
|
save(name, 'model', 'F1s', 'accuracies', 'predictions_all', 'valid_labels');
|
|
|
|
% Write out the model
|
|
name = sprintf('models/AU_%d_dynamic.dat', au);
|
|
|
|
pos_lbl = model.Label(1);
|
|
neg_lbl = model.Label(2);
|
|
|
|
w = model.w(1:end-1)';
|
|
b = model.w(end);
|
|
|
|
svs = bsxfun(@times, PC, 1./scaling') * w;
|
|
|
|
write_lin_dyn_svm(name, means, svs, b, pos_lbl, neg_lbl);
|
|
end |