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function fh=hme_class_plot(net, nodes_info, train_data, test_data)
%
% Use this function ONLY when the input dimension is 2
% and the problem is a classification one.
% We assume that each row of 'train_data' & 'test_data' is an example.
%
%------Line Spec------------------------------------------------------------------------
%
% LineWidth - specifies the width (in points) of the line
% MarkerEdgeColor - specifies the color of the marker or the edge color
% forfilled markers (circle, square, diamond, pentagram, hexagram, and the
% four triangles).
% MarkerFaceColor - specifies the color of the face of filled markers.
% MarkerSize - specifies the size of the marker in points.
%
% Example
% -------
% plot(t,sin(2*t),'-mo',...
% 'LineWidth',2,...
% 'MarkerEdgeColor','k',... % 'k'=black
% 'MarkerFaceColor',[.49 1 .63],... % RGB color
% 'MarkerSize',12)
%----------------------------------------------------------------------------------------
class_num=nodes_info(2,end);
mn_x = round(min(train_data(:,1))); mx_x = round(max(train_data(:,1)));
mn_y = round(min(train_data(:,2))); mx_y = round(max(train_data(:,2)));
if nargin==4,
mn_x = round(min([train_data(:,1); test_data(:,1)]));
mx_x = round(max([train_data(:,1); test_data(:,1)]));
mn_y = round(min([train_data(:,2); test_data(:,2)]));
mx_y = round(max([train_data(:,1); test_data(:,2)]));
end
x = mn_x(1)-1:0.2:mx_x(1)+1;
y = mn_y(1)-1:0.2:mx_y(1)+1;
[X, Y] = meshgrid(x,y);
X = X(:);
Y = Y(:);
num_g=size(X,1);
griglia = [X Y];
rand('state',1);
if class_num<=6,
colors=['r'; 'g'; 'b'; 'c'; 'm'; 'y'];
else
colors=rand(class_num, 3); % each row is an RGB color
end
fh = figure('Name','Data & decision boundaries', 'MenuBar', 'none', 'NumberTitle', 'off');
ms=5; % Marker Size
if nargin==4,
% ms=4; % Marker Size
subplot(1,2,1);
end
% Plot of train_set -------------------------------------------------------------------------
axis([mn_x-1 mx_x+1 mn_y-1 mx_y+1]);
set(gca, 'Box', 'on');
c_max_train = max(train_data(:,3));
hold on
for m=1:c_max_train,
app_x=train_data(:,1);
app_y=train_data(:,2);
thisX=app_x(train_data(:,3)==m);
thisY=app_y(train_data(:,3)==m);
if class_num<=6,
str_col=[];
str_col=['o', colors(m,:)];
plot(thisX, thisY, str_col, 'MarkerSize', ms);
else
plot(thisX, thisY, 'o',...
'LineWidth', 1,...
'MarkerEdgeColor', colors(m,:), 'MarkerSize', ms)
end
end
%---hmefwd_generale(net,data,ndata)-----------------------------------------------------------
Z=fhme(net, nodes_info, griglia, num_g); % forward propagation trougth the HME
%---------------------------------------------------------------------------------------------
[foo , class] = max(Z'); % 0/1 loss function => we assume that the true class is the one with the
% maximum posterior prob.
class = class';
for m = 1:class_num,
thisX=[]; thisY=[];
thisX = X(class == m);
thisY = Y(class == m);
if class_num<=6,
str_col=[];
str_col=['d', colors(m,:)];
h=plot(thisX, thisY, str_col);
else
h = plot(thisX, thisY, 'd',...
'MarkerEdgeColor',colors(m,:),...
'MarkerFaceColor','w');
end
set(h, 'MarkerSize', 4);
end
title('Training set and Decision Boundaries (0/1 loss)')
hold off
% Plot of test_set --------------------------------------------------------------------------
if nargin==4,
subplot(1,2,2);
axis([mn_x-1 mx_x+1 mn_y-1 mx_y+1]);
set(gca, 'Box', 'on');
hold on
if size(test_data,2)==3, % we know the classification of the test set examples
c_max_test = max(test_data(:,3));
for m=1:c_max_test,
app_x=test_data(:,1);
app_y=test_data(:,2);
thisX=app_x(test_data(:,3)==m);
thisY=app_y(test_data(:,3)==m);
if class_num<=6,
str_col=[];
str_col=['o', colors(m,:)];
plot(thisX, thisY, str_col, 'MarkerSize', ms);
else
plot(thisX, thisY, 'o',...
'LineWidth', 1,...
'MarkerEdgeColor', colors(m,:),...
'MarkerSize',ms);
end
end
else
plot(test_data(:,1), test_data(:,2), 'ko',...
'MarkerSize', ms);
end
for m = 1:class_num,
thisX=[]; thisY=[];
thisX = X(class == m);
thisY = Y(class == m);
if class_num<=6,
str_col=[];
str_col=['d', colors(m,:)];
h=plot(thisX, thisY, str_col);
else
h = plot(thisX, thisY, 'd',...
'MarkerEdgeColor', colors(m,:),...
'MarkerFaceColor','w');
end
set(h, 'MarkerSize', 4);
end
title('Test set and Decision Boundaries (0/1 loss)')
hold off
end
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