% M=3 scattering, cv parameters

run_name = 'DSS_Table2_TIMIT_m3';

src = phone_src('/path/to/timit');

[train_set,test_set,valid_set] = phone_partition(src);

N = 2^13;
T_s = 2560;

filt1_opt.filter_type = {'gabor_1d','morlet_1d'};
filt1_opt.Q = [8 1];
filt1_opt.J = T_to_J(512,filt1_opt);

sc1_opt.M = 3;

Wop = wavelet_factory_1d(N, filt1_opt, sc1_opt);

scatt_fun = @(x)(format_scat(log_scat(renorm_scat(scat(x,Wop)))));

duration_fun = @(x,obj)(32*duration_feature(x,obj));

features = {scatt_fun, duration_fun};

for k = 1:length(features)
	fprintf('testing feature #%d...',k);
	tic;
	if nargin(features{k}) == 1
		sz = size(features{k}(randn(N,1)));
	else
		sz = size(features{k}(randn(N,1),struct('u1',1,'u2',N)));
	end
	aa = toc;
	fprintf('OK (%.2fs) (size [%d,%d])\n',aa,sz(1),sz(2));
end

database_opt.input_sz = N;
database_opt.output_sz = T_s;
database_opt.obj_normalize = 2;
database_opt.collapse = 1;

db = prepare_database(src,features,database_opt);
db.features = single(db.features);
db = svm_calc_kernel(db,'gaussian','triangle',[db.indices{train_set}]);

optt.kernel_type = 'gaussian';
optt.gamma = 2.^[-14:2:-10];
optt.C = 2.^[2:2:6];
optt.search_depth = 2;
optt.full_test_kernel = 1;

[dev_err_grid,C_grid,gamma_grid] = ...
	svm_adaptive_param_search(db,train_set,valid_set,optt);

[dev_err,ind] = min(dev_err_grid{end});
C = C_grid{end}(ind);
gamma = gamma_grid{end}(ind);

optt1 = optt;
optt1.C = C;
optt1.gamma = gamma;

model = svm_train(db,train_set,optt1);
labels = svm_test(db,model,test_set);
err = classif_err(labels,test_set,db.src);
			
save([run_name '.mat'],'labels','err','C','gamma');

