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run_softSVM.m
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run_softSVM.m
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% -- runs the SGD for SoftSVM
t = [-1* ones(762,1); ones(610,1)];
x = [dataset(:,1:4)];
[D,n] = size(x);
%runs softSVM
lambda = [100, 10, 1, 0.1, 0.01, 0.001];
for i=1:6
[w, binary_cell, hinge_cell] = softSVM(x, t,lambda(i));
wi{i,1} = w;
binary_cell_i{i,1} = binary_cell;
hinge_cell_i{i,1} = hinge_cell;
end
% plot for lambda = 100
[value_l_100, position_l_100] = min(cell2mat(binary_cell_i{1,1}))
figure
plot(cell2mat(binary_cell_i{1,1}),'linewidth',3)
%ylim([0 :])
title('Binary loss for λ = 100');
ylabel('Binary loss');
xlabel('T');
print -depsc bigB
figure
plot(cell2mat(hinge_cell_i{1,1}),'linewidth',3,'color','red')
%ylim([0 a])
title('Hinge loss for λ = 100');
ylabel('Hinge loss');
xlabel('T');
print -depsc bigH
% plot for lambda = 1
[value_l_1, position_l_1] = min(cell2mat(binary_cell_i{3,1}))
figure
plot(cell2mat(binary_cell_i{3,1}),'linewidth',3)
%ylim([0 :])
title('Binary loss for λ = 1');
ylabel('Binary loss');
xlabel('T');
print -depsc midB
figure
plot(cell2mat(hinge_cell_i{3,1}),'linewidth',3,'color','red')
%ylim([0 1])
title('Hinge losses for λ = 1');
ylabel('Hinge loss');
xlabel('T');
print -depsc midH
% plot for lambda = 0.1
[value_l_01, position_l_01] = min(cell2mat(binary_cell_i{6,1}))
figure
plot(cell2mat(binary_cell_i{4,1}),'linewidth',3)
%ylim([0 1])
title('Binary loss for λ = 0.01');
ylabel('Binary loss');
xlabel('T');
print -depsc smallB
figure
plot(cell2mat(hinge_cell_i{4,1}),'linewidth',3,'color','red')
%ylim([0 1])
title('Hinge loss for λ = 0.01');
ylabel('Hinge loss');
xlabel('T');
print -depsc smallH