SO

%% LOAD TRAINING DATA %%

load('ocr_names.mat');
A = 26;
pixels = size(TrnData(1).X, 1);

% training data
y_trn = [];
for i = 1:length(TrnData)
    y_trn = strcat(y_trn,TrnData(i).Y);
end

N_trn = length(y_trn);
x_trn = zeros(pixels, N_trn);
i_tmp = 1;


for i = 1:length(TrnData)
    x_trn(:,i_tmp:(i_tmp+length(TrnData(i).Y)-1)) = TrnData(i).X;
    i_tmp = i_tmp+length(TrnData(i).Y);
end
y_trn = uint8(y_trn)-96;

%% LOAD TESTING DATA %%
y_tst = [];
for i = 1:length(TstData)
    y_tst = strcat(y_tst,TstData(i).Y);
end

N_tst = length(y_tst);
x_tst = zeros(pixels, N_tst);
i_tmp = 1;


for i = 1:length(TstData)
    x_tst(:,i_tmp:(i_tmp+length(TstData(i).Y)-1)) = TstData(i).X;
    i_tmp = i_tmp+length(TstData(i).Y);
end
y_tst = uint8(y_tst)-96;

%% #1 - simple perceptron algorithm %%

w1 = zeros(A, pixels+1);    % classifier in matrix form

max_wrong = 0;
last_wrong = 0;
done = 0;

while ~done
    wrong = 0;
    done = 1;
    [i wrong max_wrong last_wrong]
    for i = 1:N_trn
        x = [x_trn(:,i) ; 1];
        [~, y_classified] = max(w1*x);
        if(y_classified ~= y_trn(i))
            done = 0;
            wrong = wrong + 1;
            w1(y_classified,:) = w1(y_classified,:) - x';
            w1(y_trn(i),:) = w1(y_trn(i),:) + x';
        end
    end
    last_wrong = max_wrong;
    max_wrong = wrong;
end


%% clssify and measure label error %%
wrong = 0;
classified = zeros(1, N_tst);
for i = 1:N_tst
    x = [x_tst(:,i) ; 1];
    [~, classified(i)] = max(w1*x);
end

error = mean(classified == y_tst) % 0.7384


%% classify and measure sequence error%%

ok_num = 0;
for i = 1:length(TstData)
    [i ok_num]
    y = uint8(TstData(i).Y) - 96;
    X = [TstData(i).X ; ones(1, length(y))];
    [~, y_class] = max((w1*X))
    ok_num = ok_num + all(y_class == y);
end
error = 1 - (ok_num/length(TstData)) % 0.6660