80 lines
2.3 KiB
Matlab
80 lines
2.3 KiB
Matlab
function Script_HOG_SVM_train_dyn()
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% 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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addpath('../../data extraction/');
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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.^(-9:0.5:1);
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hyperparams.e = 10.^(-3);
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hyperparams.validate_params = {'c', 'e'};
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% Set the training function
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svm_train = @svm_train_linear;
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% Set the test function (the first output will be used for validation)
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svm_test = @svm_test_linear;
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all_recs = cat(2, train_recs, devel_recs);
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%%
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for a=1:numel(all_aus)
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au = all_aus(a);
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rest_aus = setdiff(all_aus, au);
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[train_recs, devel_recs] = get_balanced_fold(FERA2011_dir, all_recs, au, 1/3, 1);
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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, ~, PC, means, scaling] = Prepare_HOG_AU_data_generic_dynamic(train_recs, devel_recs, au, rest_aus, FERA2011_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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%% Cross-validate here
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[ best_params, ~ ] = validate_grid_search_no_par(svm_train, svm_test, false, train_samples, train_labels, valid_samples, valid_labels, hyperparams);
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model = svm_train(train_labels, train_samples, best_params);
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[~, prediction] = svm_test(valid_labels, valid_samples, model);
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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_dyn.dat', au);
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pos_lbl = model.Label(1);
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neg_lbl = model.Label(2);
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write_lin_dyn_svm(name, means, svs, b, pos_lbl, neg_lbl);
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name = sprintf('results_FERA2011_devel/AU_%d_dyn.mat', au);
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tp = sum(valid_labels == 1 & prediction == 1);
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fp = sum(valid_labels == 0 & prediction == 1);
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fn = sum(valid_labels == 1 & prediction == 0);
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tn = sum(valid_labels == 0 & prediction == 0);
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precision = tp/(tp+fp);
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recall = tp/(tp+fn);
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f1 = 2 * precision * recall / (precision + recall);
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save(name, 'model', 'f1', 'precision', 'recall', 'best_params', 'valid_labels', 'prediction');
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end
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end
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