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testhnd.m
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testhnd.m
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function testhnd()
% Does a test for classifying the handwritten images.
disp('reading data');
% assume, we have loaded
[train_data, train_labels ] = readDATA();
[test_data, test_labels] = loadHndDATA();
train_data = procTD(train_data); % convert pixel to bounded box
sizeTrain = size(train_data,2);
sizeTest = size(test_data,2);
sizeTest = 200;
%Do a quick nearest neighbor test classification.
errors = 0;
for i = 1:sizeTest
fprintf('test %d - ', i);
tv = test_data{i};
tv = proc(tv); % pre process and bind it to box
tv = tv(:);
minIdx = 0;
minDist = Inf;
for j = 1:sizeTrain
av = train_data{j};
av = av(:);
diff = tv - av;
dist = norm(diff,2);
if(dist < minDist)
minDist = dist;
%disp(j);
minIdx = j;
end
%disp('running');
%disp(dist);
end
%error('s');
%save result;
result(i) = train_labels{minIdx};
%do accuracy checking in line
if( test_labels(i) ~= result(i) )
errors = errors + 1;
end
tot = i;
curr_acc = (tot - errors) / tot;
if( test_labels(i) ~= result(i) )
fprintf('Curr Accuracy: %f | %d,%d.\n', curr_acc, test_labels(i),result(i) );
%figure;
im = [test_data{i} train_data{minIdx} ];
%imshow(im);
continue;
end
fprintf('Curr Accuracy: %f.\n', curr_acc);
end
end
%{
K NEaREST NEIGHTBOR
function testhnd()
% Does a test for classifying the handwritten images.
disp('reading data');
% assume, we have loaded
[train_data, train_labels ] = readDATA();
[test_data, test_labels] = loadHndDATA();
sizeTrain = size(train_data,2);
%sizeTrain = 10000;
sizeTest = size(test_data,2);
sizeTest = 200;
%Do a quick nearest neighbor test classification.
errors = 0;
for i = 1:sizeTest
fprintf('test %d - ', i);
tv = test_data{i};
tv = tv(:);
minIdx = 0;
minDist = Inf;
arr(1) = 0;
arr(2) = 0;
arr(3) = 0;
for j = 1:sizeTrain
av = train_data{j};
av = av(:);
diff = tv - av;
dist = norm(diff,2);
if(dist < minDist)
minDist = dist;
%disp(j);
minIdx = j;
prev_first = arr(1);
prev_sec = arr(2);
arr(1) = minIdx;
arr(2) = prev_first;
arr(3) = prev_sec;
end
%disp('running');
%disp(dist);
end
num = [train_labels{arr(1)};train_labels{arr(2)} ;train_labels{arr(3)}];
indices = zeros(10,1);
for ii = 1:3
indices(num(ii)+1) = indices(num(ii)+1) + 1;
end
[x,number] = max(indices);
%error('s');
%save result;
result(i) = number - 1;
%result(i) = train_labels{minIdx};
%do accuracy checking in line
if( test_labels(i) ~= result(i) )
errors = errors + 1;
end
tot = i;
curr_acc = (tot - errors) / tot;
if( test_labels(i) ~= result(i) )
fprintf('Curr Accuracy: %f | %d,%d.\n', curr_acc, test_labels(i),result(i) );
figure;
continue;
end
fprintf('Curr Accuracy: %f.\n', curr_acc );
end
end
%}