visualize_map.m

this file is leverage algorithm written in matlab as m-file and tested in matlab.so anyone can ue th

function varargout = visualize_map(varargin)

% visualize_maps opens a graphical interface for visaulising CPANNs and kohonen maps
% visualize_maps is called by each visualize file (visualize_kohonen and visualize_cpann)
%
% see the HTML HELP files (help.htm) for details 

% Edit the above text to modify the response to help visualize_map

% Last Modified by GUIDE v2.5 02-Jul-2007 13:31:46

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @visualize_map_OpeningFcn, ...
                   'gui_OutputFcn',  @visualize_map_OutputFcn, ...
                   'gui_LayoutFcn',  [] , ...
                   'gui_Callback',   []);
if nargin & isstr(varargin{1})
    gui_State.gui_Callback = str2func(varargin{1});
end

if nargout
    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
    gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT


% --- Executes just before visualize_map is made visible.
function visualize_map_OpeningFcn(hObject, eventdata, handles, varargin)

handles.output = hObject;
movegui(handles.visualize,'center');
handles.model = varargin{1};

% set temp arrays
handles.temp.top_map = handles.model.res.top_map;
handles.temp.W = handles.model.net.W;
if strcmp(handles.model.type,'cpann')
    handles.temp.W_out = handles.model.net.W_out;
    handles.temp.neuron_ass = handles.model.net.neuron_ass;
end

% set variable combo
num_var = size(handles.model.net.W,3);
str_layer{1} = 'none';
for j=1:num_var
    str_layer{j+1}  = ['variable' num2str(j)];
end
if strcmp(handles.model.type,'cpann')
    num_class = size(handles.model.net.W_out,3);
    s = length(str_layer);
    for j=1:num_class
        str_layer{j+s}  = ['weight class ' num2str(j)];
    end
    str_layer{end + 1}  = ['assignations']; 
end
set(handles.pop_layer,'String',str_layer);
set(handles.pop_layer,'Value',1);

% set label combo
str_disp{1} = 'none';
str_disp{2} = 'ID';
handles.sample_label = [];
count = 3;
if size(varargin{2},1) > 0
    str_disp{count} = 'sample labels';
    handles.sample_label = varargin{2};
    count = count + 1;
end
if strcmp(handles.model.type,'cpann')
    str_disp{count} = 'class labels';
    handles.class = handles.model.res.class_true;
end
set(handles.pop_label,'String',str_disp);
set(handles.pop_label,'Value',2);

plot([0 0],'r.')
disp_grid(size(handles.model.net.W,1))
handles = update_plot(handles);
guidata(hObject, handles);


% --- Outputs from this function are returned to the command line.
function varargout = visualize_map_OutputFcn(hObject, eventdata, handles)
varargout{1} = handles.output;


% --- Executes on button press in update_botton.
function update_botton_Callback(hObject, eventdata, handles)
handles = update_plot(handles);
guidata(hObject,handles)


% --- Executes during object creation, after setting all properties.
function pop_layer_CreateFcn(hObject, eventdata, handles)
if ispc
    set(hObject,'BackgroundColor','white');
else
    set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
end
guidata(hObject,handles)


% --- Executes on selection change in popupmenu
function pop_layer_Callback(hObject, eventdata, handles)


% --- Executes during object creation, after setting all properties.
function pop_label_CreateFcn(hObject, eventdata, handles)
if ispc
    set(hObject,'BackgroundColor','white');
else
    set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
end


% --- Executes during object creation, after setting all properties.
function list_labels_CreateFcn(hObject, eventdata, handles)
if ispc
    set(hObject,'BackgroundColor','white');
else
    set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
end


% --- Executes on selection change in pop_label.
function pop_label_Callback(hObject, eventdata, handles)


% --- Executes on selection change in pop_label.
function list_labels_Callback(hObject, eventdata, handles)


% --- Executes on button press in button_up.
function button_up_Callback(hObject, eventdata, handles)
handles = move_map(handles,'up');
guidata(hObject,handles)


% --- Executes on button press in botton_down.
function button_down_Callback(hObject, eventdata, handles)
handles = move_map(handles,'down');
guidata(hObject,handles)


% --- Executes on button press in botton_right.
function button_right_Callback(hObject, eventdata, handles)
handles = move_map(handles,'right');
guidata(hObject,handles)


% --- Executes on button press in button_left.
function button_left_Callback(hObject, eventdata, handles)
handles = move_map(handles,'left');
guidata(hObject,handles)


% --- Executes on button press in get_lab_neuron.
function get_lab_neuron_Callback(hObject, eventdata, handles)
handles = set_list_labels(handles);
guidata(hObject,handles)


% --- Executes on button press in get_weight_neuron.
function get_weight_neuron_Callback(hObject, eventdata, handles)
handles = show_weight_neuron(handles);
guidata(hObject,handles)


% --- Executes on button press in save_figure.
function save_figure_Callback(hObject, eventdata, handles)
handles = update_plot(handles);
figure
plot([0 0],'r.')
handles = update_plot(handles);
guidata(hObject,handles)


% ---------------------------------------------------------
function disp_grid(n)
axis([0.5 (n + 0.4999) 0.5 (n + 0.5)])
tick_pos = 0.5:1:(n + 0.5);
set(gca,'YTick',tick_pos)
set(gca,'XTick',tick_pos)
grid on
set(gca,'YTickLabel','')
set(gca,'XTickLabel','')


% ---------------------------------------------------------
function handles = update_plot(handles)
cla;
pop_layer_index = get(handles.pop_layer, 'Value');
pop_label_index = get(handles.pop_label, 'Value');

% display weights
if pop_layer_index > 1
    var_in = pop_layer_index - 1;
    disp_weights(handles,var_in);
end

% display samples
if pop_label_index > 1
    handles = disp_samples(handles,pop_label_index);
else
    handles.disp_label = [];
end

% display grid
disp_grid(size(handles.model.net.W,1))

% ---------------------------------------------------------
function handles = disp_samples(handles,pop_label_index)
pos = handles.temp.top_map;
col_in = get(handles.pop_layer, 'Value');
if col_in > 1
    text_color = 'red';
else
    text_color = 'black';
end
disp_label = [];
if pop_label_index == 2
    for j=1:size(pos,1)
        store_label(j) = j;
    end
elseif strcmp(handles.model.type,'cpann')
    if pop_label_index == 3 & length(handles.sample_label) > 0
        store_label = handles.sample_label;  
    elseif pop_label_index == 3 & length(handles.sample_label) == 0
        store_label = handles.class;
    else
        store_label = handles.class;
    end
elseif strcmp(handles.model.type,'kohonen_map')
    store_label = handles.sample_label;
end

for j=1:length(store_label)
    if iscell(store_label(j))
        disp_label{j} = store_label(j);
    else
        disp_label{j} = num2str(store_label(j));
    end
end   

for i=1:size(pos,1)
    y_pos = size(handles.model.net.W,1) + 1 - pos(i,1) + 0.3 - rand*0.6;
    x_pos = pos(i,2) + 0.3 - rand*0.6;
    text(x_pos,y_pos,disp_label{i},'color',text_color,'FontSize',8);
end

handles.disp_label = disp_label;

% ---------------------------------------------------------
function disp_weights(handles,var_in)
if strcmp(handles.model.type,'cpann')
    num_var = size(handles.temp.W,3);
    num_cla = size(handles.temp.W_out,3);
    if var_in <= num_var
        W = squeeze(handles.temp.W(:,:,var_in));
        type = 'var';
    elseif var_in <= num_cla + num_var
        W = squeeze(handles.temp.W_out(:,:,var_in - num_var));
        type = 'cla';
    else
        W = handles.temp.neuron_ass;
        type = 'ass';
        col_ass(1,:)  = [1 1 1];   % white, not assigned
        col_ass(2,:)  = [1 1 0];   % yellow
        col_ass(3,:)  = [0 1 0];   % green
        col_ass(4,:)  = [0 0 1];   % blue
        col_ass(5,:)  = [0 1 1];   % cyan
        col_ass(6,:)  = [1 0 1];   % magenta
        col_ass(7,:)  = [0.5 0.5 0.5];
        col_ass(8,:)  = [1 0.5 0.5];   
        col_ass(9,:)  = [0.5 0.5 1];   
        col_ass(10,:) = [0.5 1 0.5];    
        col_ass(11,:) = [0 0.5 1];
    end
elseif strcmp(handles.model.type,'kohonen_map')
    W = squeeze(handles.temp.W(:,:,var_in));
    type = 'var';
end

hold on
for k=1:size(W,1)
    for j=1:size(W,2)
        if strcmp(type,'ass')
            color_in = col_ass(handles.temp.neuron_ass(j,k) + 1,:);
        else
            color_in = [(1-W(j,k)) (1-W(j,k)) (1-W(j,k))];
        end
        pos_x = k;
        pos_y = size(W,2) + 1 - j;
        area([(pos_x - 0.5) (pos_x + 0.5)],[(pos_y + 0.5) (pos_y + 0.5)],pos_y - 0.5,'FaceColor',color_in);
    end
end
hold off


% ---------------------------------------------------------
function handles = move_map(handles,where)
if strcmp(where,'up')
    for i=1:size(handles.temp.top_map,1)
        handles.temp.top_map(i,1) = handles.temp.top_map(i,1) - 1;
        if handles.temp.top_map(i,1) == 0
            handles.temp.top_map(i,1) = size(handles.model.net.W,1);
        end
    end
    for i=1:size(handles.temp.W,1) - 1
        temp_W(i,:,:) = handles.temp.W(i + 1,:,:);
    end
    temp_W(size(handles.temp.W,1),:,:) = handles.temp.W(1,:,:);
    handles.temp.W = temp_W;
elseif strcmp(where,'down')
    for i=1:size(handles.temp.top_map,1)
        handles.temp.top_map(i,1) = handles.temp.top_map(i,1) + 1;
        if handles.temp.top_map(i,1) > size(handles.model.net.W,1)
            handles.temp.top_map(i,1) = 1;
        end
    end
    for i=1:size(handles.temp.W,1) - 1
        temp_W(i+1,:,:) = handles.temp.W(i,:,:);
    end
    temp_W(1,:,:) = handles.temp.W(size(handles.temp.W,1),:,:);
    handles.temp.W = temp_W;
elseif strcmp(where,'right')
    for i=1:size(handles.temp.top_map,1)
        handles.temp.top_map(i,2) = handles.temp.top_map(i,2) + 1;
        if handles.temp.top_map(i,2) > size(handles.model.net.W,2)
            handles.temp.top_map(i,2) = 1;
        end
    end
    for i=1:size(handles.temp.W,2) - 1
        temp_W(:,i+1,:) = handles.temp.W(:,i,:);
    end
    temp_W(:,1,:) = handles.temp.W(:,size(handles.temp.W,2),:);
    handles.temp.W = temp_W;
elseif strcmp(where,'left')
    for i=1:size(handles.temp.top_map,1)
        handles.temp.top_map(i,2) = handles.temp.top_map(i,2) - 1;
        if handles.temp.top_map(i,2) == 0
            handles.temp.top_map(i,2) = size(handles.model.net.W,2);
        end
    end
    for i=1:size(handles.temp.W,2) - 1
        temp_W(:,i,:) = handles.temp.W(:,i+1,:);
    end
    temp_W(:,size(handles.temp.W,2),:) = handles.temp.W(:,1,:);
    handles.temp.W = temp_W;
end

if strcmp(handles.model.type,'cpann')
    if strcmp(where,'up')
        for i=1:size(handles.temp.W_out,1) - 1
            temp_W_out(i,:,:) = handles.temp.W_out(i + 1,:,:);
            temp_neuron_ass(i,:) = handles.temp.neuron_ass(i + 1,:);
        end
        temp_W_out(size(handles.temp.W_out,1),:,:) = handles.temp.W_out(1,:,:);
        temp_neuron_ass(size(handles.temp.W_out,1),:) = handles.temp.neuron_ass(1,:);
        handles.temp.W_out = temp_W_out;
        handles.temp.neuron_ass = temp_neuron_ass;
    elseif strcmp(where,'down')
        for i=1:size(handles.temp.W,1) - 1
            temp_W_out(i+1,:,:) = handles.temp.W_out(i,:,:);
            temp_neuron_ass(i+1,:) = handles.temp.neuron_ass(i,:);
        end
        temp_W_out(1,:,:) = handles.temp.W_out(size(handles.temp.W_out,1),:,:);
        temp_neuron_ass(1,:) = handles.temp.neuron_ass(size(handles.temp.W_out,1),:);
        handles.temp.W_out = temp_W_out;
        handles.temp.neuron_ass = temp_neuron_ass;
    elseif strcmp(where,'right')
        for i=1:size(handles.temp.W_out,2) - 1
            temp_W_out(:,i+1,:) = handles.temp.W_out(:,i,:);
            temp_neuron_ass(:,i+1) = handles.temp.neuron_ass(:,i);
        end
        temp_W_out(:,1,:) = handles.temp.W_out(:,size(handles.temp.W_out,2),:);
        temp_neuron_ass(:,1) = handles.temp.neuron_ass(:,size(handles.temp.W_out,1));
        handles.temp.W_out = temp_W_out;
        handles.temp.neuron_ass = temp_neuron_ass;
    elseif strcmp(where,'left')
        for i=1:size(handles.temp.W_out,2) - 1
            temp_W_out(:,i,:) = handles.temp.W_out(:,i+1,:);
            temp_neuron_ass(:,i) = handles.temp.neuron_ass(:,i+1);
        end
        temp_W_out(:,size(handles.temp.W_out,2),:) = handles.temp.W_out(:,1,:);
        temp_neuron_ass(:,size(handles.temp.W_out,2)) = handles.temp.neuron_ass(:,1);
        handles.temp.W_out = temp_W_out;
        handles.temp.neuron_ass = temp_neuron_ass;
    end
end

handles = update_plot(handles);


% ---------------------------------------------------------
function handles = set_list_labels(handles)
[y,x] = ginput(1);
[cord_y,cord_x] = find_neuron(y,x,handles);
in = find(handles.temp.top_map(:,1) == cord_x & handles.temp.top_map(:,2) == cord_y);
if length(in > 0) & length(handles.disp_label) > 0
    cur_labs = handles.disp_label(in);
    visualize_labels(cur_labs);
end


% ---------------------------------------------------------
function handles = show_weight_neuron(handles)
[y,x] = ginput(1);
[cord_y,cord_x] = find_neuron(y,x,handles);
if cord_y > 0 & cord_y <= size(handles.temp.W,1) & cord_x > 0 & cord_x <= size(handles.temp.W,1)
    w = squeeze(handles.temp.W(cord_x,cord_y,:));
    figure
    if length(w) < 20
        bar(w,'r')
    else
        plot(w,'r')
    end
    ind = som_which_col(cord_x,cord_y,size(handles.model.net.W,1)^2);
    title(['kohonen weights of neuron ' num2str(ind) ' (row ' num2str(cord_x) ' - column ' num2str(cord_y) ')'])
    axis([0.5 length(w)+0.5 0 1])
    xlabel('variables')
    ylabel('kohonen weight')
    % plot of class weight (output weight)
    if strcmp(handles.model.type,'cpann')
        w_out = squeeze(handles.temp.W_out(cord_x,cord_y,:));
        figure
        bar(w_out,'b')
        title(['output weights of neuron ' num2str(ind) ' (row ' num2str(cord_x) ' - column ' num2str(cord_y) ')'])
        axis([0.5 length(w_out)+0.5 0 1])
        xlabel('classes')
        ylabel('output weight')
    end
end


% ---------------------------------------------------------
function [cord_y,cord_x] = find_neuron(y,x,handles)
x = abs(x - size(handles.model.net.W,1)) + 1;
cord_x = round(x);
cord_y = round(y);