mlp_iris_class.m

classification using MLP with GUi

% hObject    handle to Etah (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



function Etay_Callback(hObject, eventdata, handles)
% hObject    handle to Etay (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of Etay as text
%        str2double(get(hObject,'String')) returns contents of Etay as a double
input = str2num(get(hObject,'String'));
 
%checks to see if input is empty. if so, default input1_editText to zero
if (isempty(input))
     set(hObject,'String','0')
end
guidata(hObject, handles);

% --- Executes during object creation, after setting all properties.
function Etay_CreateFcn(hObject, eventdata, handles)
% hObject    handle to Etay (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end

function MSE_lim_Callback(hObject, eventdata, handles)
% hObject    handle to MSE_lim (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Hints: get(hObject,'String') returns contents of MSE_lim as text
%        str2double(get(hObject,'String')) returns contents of MSE_lim as a double
input = str2num(get(hObject,'String'));iris_data.dat
 
%checks to see if input is empty. if so, default input1_editText to zero
if (isempty(input))iris_data.dat
     set(hObject,'String','0')
end
guidata(hObject, handles);

% --- Executes during object creation, after setting all properties.
function MSE_lim_CreateFcn(hObject, eventdata, handles)
% hObject    handle to MSE_lim (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background on Windows.
%       See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
    set(hObject,'BackgroundColor','white');
end



%% --- Executes on button press in Classify.
function Classify_Callback(hObject, eventdata, handles)
% hObject    handle to Classify (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
set1_ir = str2num(get(handles.set1_LR,'String')) ;
set1_ur = str2num(get(handles.set1_UR,'String'));
set2_ir = str2num(get(handles.set2_LR,'String'));
set2_ur = str2num(get(handles.set2_UR,'String'));
set3_ir = str2num(get(handles.set3_LR,'String'));
set3_ur = str2num(get(handles.set3_UR,'String'));
set1_ir2 = str2num(get(handles.set1_LR_td,'String'));
set1_ur2 = str2num(get(handles.set1_UR_td,'String'));
set2_ir2 = str2num(get(handles.set2_LR_td,'String'));
set2_ur2 = str2num(get(handles.set2_UR_td,'String'));
set3_ir2 = str2num(get(handles.set3_LR_td,'String'));
set3_ur2 = str2num(get(handles.set3_UR_td,'String'));
in_ir = str2num(get(handles.Input_LR,'String'));
in_ur = str2num(get(handles.Input_UR,'String'));
out_ir = str2num(get(handles.Output_LR,'String'));
out_ur = str2num(get(handles.Output_UR,'String'));
L = str2num(get(handles.Hidden_Nodes,'String'));
etay = str2num(get(handles.Etay,'String'));
etah = str2num(get(handles.Etah,'String'));
MSE_thr = str2num(get(handles.MSE_lim,'String'));


%load iris_data.dat;    %Incorporate din UI
%a= iris_data;
global a
Dim = size(a);

p = (in_ur-in_ir) + 2 ; %number of input + bias
m = (out_ur-out_ir) + 1 ;  % no. of output

% Training data set
D11=a(set1_ir:set1_ur,in_ir:in_ur);
D12=a(set2_ir:set2_ur,in_ir:in_ur);
D13=a(set3_ir:set3_ur,in_ir:in_ur);

Np = (in_ur-in_ir) + 1 ; % No. of inputs excluding bias

D1=[D11; D12 ;D13] ;     % Training data set (INPUT)
Dim2 = size(D1);
X11=a(set1_ir:set1_ur,out_ir:out_ur);
X12=a(set2_ir:set2_ur,out_ir:out_ur);
X13=a(set3_ir:set3_ur,out_ir:out_ur);
X1=[X11; X12 ;X13]/10;  % Training data set (Normalised OUTPUT) 
Dim3 = size(X1);
% Normalising Input
pmin=min(D1);
pmax=max(D1);
for i=1:Dim2(1)         
    d1(i,:)=D1(i,:)-pmin;
end;
x=(0.9-0.1)./(pmax-pmin);
for i=1:Np
    pin(:,i)=x(i)*d1(:,i)+0.1;
end
%

N=Dim2(1);
X= [pin'; ones(1,N)];   % add bias input 1

Wh = 0.25*randn(L-1,p)/p;
Wy = 0.25*randn(m,L)/L;

MSE=10.0;       %Initialise some random value but greater than test aim value

while(MSE>=MSE_thr)
    H = ones(L-1,N)./(1+exp(-Wh*X));    % f(net)
    Hp = H.*(1-H);                      % f_prime(net) hidden
    
    H=[H;ones(1,N)];                    % add bias 1 
    Y = tanh(Wy*H);                     % output = fj(netj)(semilinear activation function)
    Yp=1-Y.^2;                          % f_prime(net) output
    
    Ey = X1' - Y;                       % error in output
    JJ = (sum((Ey.*Ey)'))';
    MSE = sum(JJ)/(N*Dim3(2))           %#ok<NOPTS> % Mean square error in output 
    
    delY = Ey.*Yp;                      % error*f_prime(net)
    dWy = delY*H';                      % Gradient output layer weight
    
    Eh = Wy(:,1:L-1)'*delY;             % error in output from hidden layer          
    delH=Eh.*Hp;
    dWh = delH*X';                      % Gradient output layer weight
    
    Wy= Wy + etay*dWy;                    % Output layer weight correction    
    Wh = Wh + etah*dWh;                   % Hidden layer weight correction
    
end
 
dataTrain(3,3) = 0;
 % classify
 for p = 1:N
     for i = 1:3
         if X1(p)*10 == i
             for j = 1:3
                 if round(Y(p)*10) == j
                     dataTrain(i,j) = dataTrain(i,j)+1;
                     break
                 end
             end   
         end
     end
 end

 
% Testing of Model
D21=a(set1_ir2:set1_ur2,in_ir:in_ur);        % Test Input
D22=a(set2_ir2:set2_ur2,in_ir:in_ur);
D23=a(set3_ir2:set3_ur2,in_ir:in_ur);

D2=[D21; D22 ;D23];

X21=a(set1_ir2:set1_ur2,out_ir:out_ur);          % Test output
X22=a(set2_ir2:set2_ur2,out_ir:out_ur);
X23=a(set3_ir2:set3_ur2,out_ir:out_ur);
X2=[X21; X22 ;X23];

Dim = size(D2);
N=Dim(1);
% Normalising Test Input
pmin=min(D2);
pmax=max(D2);
for i=1:N         
    d2(i,:)=D2(i,:)-pmin;
end;
x=(0.9-0.1)./(pmax-pmin);
for i=1:Np
    pin2(:,i)=x(i)*d2(:,i)+0.1;
end

X= [pin2'; ones(1,N)];   % add bias input 1
H = ones(L-1,N)./(1+exp(-Wh*X));
H=[H;ones(1,N)];                    % add bias 1 
Y2 = tanh(Wy*H);

 
 dataTest(3,3) = 0;
 % classify
 for p = 1:N
     for i = 1:3
         if X2(p) == i
             for j = 1:3
                 if round(Y2(p)*10) == j
                     dataTest(i,j) = dataTest(i,j)+1;
                     break
                 end
             end   
         end
     end
 end





c = num2str(dataTrain(1,1));
set(handles.tr11,'String',c);
guidata(hObject, handles);

c = num2str(dataTrain(1,2));
set(handles.tr12,'String',c);
guidata(hObject, handles);

c = num2str(dataTrain(1,3));
set(handles.tr13,'String',c);
guidata(hObject, handles);

c = num2str(dataTrain(2,1));
set(handles.tr21,'String',c);
guidata(hObject, handles);

c = num2str(dataTrain(2,2));
set(handles.tr22,'String',c);
guidata(hObject, handles);

c = num2str(dataTrain(2,3));
set(handles.tr23,'String',c);
guidata(hObject, handles);

c = num2str(dataTrain(3,1));
set(handles.tr31,'String',c);
guidata(hObject, handles);

c = num2str(dataTrain(3,2));
set(handles.tr32,'String',c);
guidata(hObject, handles);

c = num2str(dataTrain(3,3));
set(handles.tr33,'String',c);
guidata(hObject, handles);

c = num2str(dataTest(1,1));
set(handles.tt11,'String',c);
guidata(hObject, handles);

c = num2str(dataTest(1,2));
set(handles.tt12,'String',c);
guidata(hObject, handles);

c = num2str(dataTest(1,3));
set(handles.tt13,'String',c);
guidata(hObject, handles);

c = num2str(dataTest(2,1));
set(handles.tt21,'String',c);
guidata(hObject, handles);

c = num2str(dataTest(2,2));
set(handles.tt22,'String',c);
guidata(hObject, handles);

c = num2str(dataTest(2,3));
set(handles.tt23,'String',c);
guidata(hObject, handles);

c = num2str(dataTest(3,1));
set(handles.tr31,'String',c);
guidata(hObject, handles);

c = num2str(dataTest(3,2));
set(handles.tt32,'String',c);
guidata(hObject, handles);

c = num2str(dataTest(3,3));
set(handles.tt33,'String',c);
guidata(hObject, handles);


% --- If Enable == 'on', executes on mouse press in 5 pixel border.
% --- Otherwise, executes on mouse press in 5 pixel border or over text4.
function text4_ButtonDownFcn(hObject, eventdata, handles)
% hObject    handle to text4 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)