diff toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/hme_class_plot.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/examples/static/HME/hme_class_plot.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,142 @@
+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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