Added code to classify handwritten digits using nearest neighbor with…

… MNIST dataset as the training set

approach1.m

@@ -0,0 +1,189 @@
+% Jervis Muindi
+% Biometrics Final Project
+% 11th December 2011
+
+% Does classification on test data set. 
+% By applying nearest neighbor directly
+% on extracted features. 
+function   approach1()
+% 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); 
+sizeTest = size(test_data,2); 
+sizeTest = 50;
+
+%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; 
+
+    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
+
+%}

approach2.m

@@ -0,0 +1,189 @@
+% Jervis Muindi
+% Does classification on test data set. 
+% By applying nearest neighbor directly
+% on extracted digits that have been bounded to 
+% a box. 
+function   approach2()
+% Does a test for classifying the handwritten images. 
+
+disp('reading data');
+% assume, we have loaded
+[train_data, train_labels ] = readDATA();
+train_data = procTD(train_data);% Process the trainig data to bound image to a bounding box.
+
+[test_data, test_labels] = loadHndDATA();
+
+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); %process the test image so that it's also bound to a box of 28 x 28.
+    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
+
+%}