Minor changes adding some display if tracking>0 and >1

Code-2S-SOM/2s_som/som_batchtrainRTOM.m

@@ -536,7 +536,12 @@ radius(find(radius==0)) = eps;
          H=diag(ones(1,blen));
      
          %% Apprentissage
+         if tracking>1,
+           fprintf(1,' ... tlen=%s with %d radius values varying between %s and %s\n', ...
+                   num2str(trainlen),length(radius),num2str(radius(1)),num2str(radius(end)));
+         end
          for t = 1:trainlen,
+           if tracking>1, fprintf(1,' ... rad=%s           ',num2str(radius(t))); end
              % t=1 initialisation
              if t==1,
                  %% Affectation des observations aux cellules
@@ -766,7 +771,7 @@ function [] = trackplot(M,D,tracking,start,n,qe)
   l = length(qe);
   elap_t = etime(clock,start); 
   tot_t = elap_t*l/n;
-  fprintf(1,'\rTraining: %3.0f/ %3.0f s',elap_t,tot_t)  
+  fprintf(1,'\r Training: %3.0f/ %3.0f s',elap_t,tot_t)  
   switch tracking
    case 1, 
    case 2,   

Code-2S-SOM/learn_2s_som.m

@@ -250,17 +250,19 @@ function [sMap sMap_denorm Result] = learn_2s_som(A,nb_neurone,varargin)
   %   end
 
   if bool_pre_training
-    tracking_ini = tracking;
-    %tracking_ini = 1;
+    pretrain_tracking = tracking;
+    %pretrain_tracking = 1;
     
     % batchtrain avec radius ...
     if (bool_rad && ~bool_trainlen)
       fprintf(1,'\n-- BATCHTRAIN initial avec radius ... ')
-      if tracking_ini, fprintf(1,'\n'); end
+      if pretrain_tracking, fprintf(1,'\n'); end
       j=1;
       while j<length(rad)
         
-        sMap=som_batchtrain(sMap,sD_norm.data,'radius',[rad(j) rad(j+1)],'tracking',tracking_ini);
+        sMap=som_batchtrain(sMap, sD_norm.data, ...
+                            'radius',[rad(j) rad(j+1)], ...
+                            'tracking',pretrain_tracking);
         j=j+1;
         
       end
@@ -268,11 +270,13 @@ function [sMap sMap_denorm Result] = learn_2s_som(A,nb_neurone,varargin)
     % batchtrain avec trainlen ...
     if (~bool_rad && bool_trainlen) 
       fprintf(1,'\n-- BATCHTRAIN initial avec trainlen ... ')
-      if tracking_ini, fprintf(1,'\n'); end
+      if pretrain_tracking, fprintf(1,'\n'); end
       j=1;
       while j<=length(trlen)
         
-        sMap=som_batchtrain(sMap,sD_norm.data,'trainlen',trlen(j),'tracking',tracking_ini);
+        sMap=som_batchtrain(sMap, sD_norm.data, ...
+                            'trainlen',trlen(j), ...
+                            'tracking',pretrain_tracking);
         j=j+1;
         
       end
@@ -280,13 +284,16 @@ function [sMap sMap_denorm Result] = learn_2s_som(A,nb_neurone,varargin)
     % batchtrain avec radius et trainlen             
     if (bool_rad && bool_trainlen)
       fprintf(1,'\n-- BATCHTRAIN initial avec radius et trainlen ... \n')
-      if tracking_ini, fprintf(1,'\n'); end
+      if pretrain_tracking, fprintf(1,'\n'); end
       if length(rad)==length(trlen)+1
         
         j=1;
         while j<length(rad)
           
-          sMap=som_batchtrain(sMap,sD_norm.data,'radius',[rad(j) rad(j+1)],'trainlen',trlen(j),'tracking',tracking_ini);
+          sMap=som_batchtrain(sMap, sD_norm.data, ...
+                              'radius',[rad(j) rad(j+1)], ...
+                              'trainlen',trlen(j), ...
+                              'tracking',pretrain_tracking);
           j=j+1;
           
         end
@@ -358,9 +365,18 @@ function [sMap sMap_denorm Result] = learn_2s_som(A,nb_neurone,varargin)
       
       i_train = 1;
       n_train = length(lambda)*length(eta);
+      
+      radius_2s_som =  [rad(round(length(rad)/2)) ...
+                        rad((round(length(rad)/2))+1)];
+      trainlen_2s_som = trlen(round(length(trlen)/2));
+
       fprintf(1,[ '\n-- batchtrainRTOM loop for %d lambda and %d eta values:\n', ... 
                   '-- ------------------------------------------------------------------\n' ], ...
               length(lambda), length(eta));
+      if tracking > 1,
+        fprintf(1,'   ... trainlen_2s_som ... %s\n', num2str(trainlen_2s_som))
+        fprintf(1,'   ... radius_2s_som ..... [%s]\n', join(string(radius_2s_som),', '))
+      end
       for i=1:length(lambda)
         for j=1:length(eta)
           fprintf(1,'-- batchtrainRTOM (%d/%d) with lambda=%s and eta=%s ... ',i_train, ...
@@ -374,9 +390,8 @@ function [sMap sMap_denorm Result] = learn_2s_som(A,nb_neurone,varargin)
               'DimBloc',DimBloc, ...
               'lambda', lambda(i), ...
               'eta',eta(j), ...
-              'radius',[rad(round(length(rad)/2)) ...
-                        rad((round(length(rad)/2))+1)], ...
-              'trainlen',trlen(round(length(trlen)/2)), ...
+              'radius',radius_2s_som, ...
+              'trainlen',trainlen_2s_som, ...
               'tracking',tracking);
           
           current_perf = som_distortion(Result(i,j).sMap,sD_norm);

Code-2S-SOM/som_for_2s_som/som_batchtrain.m

@@ -455,8 +455,8 @@ for t = 1:trainlen,
    case 'cutgauss', H = exp(-Ud/(2*radius(t))) .* (Ud<=radius(t));
    case 'ep',       H = (1-Ud/radius(t)) .* (Ud<=radius(t));
   end  
-  if tracking > 0,
-    disp(sprintf('radius(%d)=%s',t,num2str(radiuss(1))))
+  if tracking > 1,
+    disp(sprintf(' radius(%d)=%s',t,num2str(radius(t))))
   end
   % update 
 

Code-2S-SOM/som_for_2s_som/som_make.m

@@ -302,6 +302,7 @@ else
    case 'long',  sTrain.trainlen = sTrain.trainlen*4;
   end
 end
+if tracking>1, fprintf(1,' ... trainlen=%d iterations.\n',sTrain.trainlen); end
 sTrain.neigh=neigh;  %OM
 switch func,
  case 'seq',    sMap = som_seqtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);
@@ -320,6 +321,7 @@ else
    case 'long',  sTrain.trainlen = sTrain.trainlen*4;
   end
 end
+if tracking>1, fprintf(1,' ... trainlen=%d iterations.\n',sTrain.trainlen); end
 sTrain.neigh=neigh;  %OM
 switch func,
  case 'seq',    sMap = som_seqtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);