diff --git a/perceptron/perceptron.m b/perceptron/perceptron.m
index 20a0487..348a619 100644
--- a/perceptron/perceptron.m
+++ b/perceptron/perceptron.m
@@ -14,8 +14,8 @@ mnx=-0.1*mxx;%for visualization %mnx = min(X(1,:));
 mxy = max(X(2,:));
 mny=-0.1*mxy; % for visualization %mny = min(X(2,:));
 
-figure;
-ginput(1);
+%%% figure;
+%%% ginput(1);
 err = 1;
 round = 0;
 while err > 0  
@@ -24,17 +24,17 @@ while err > 0
         w = w + rate*X(:,ii) * Y(ii);   %then add (or subtract) this point to w
         x1=mnx:0.01:mxx;
         x2=-(w(1)*x1+w(3))/w(2);
-        %figure;
-        clf;
-        hold on
-        plot(X(1,pos_idx),X(2,pos_idx),'b*','MarkerSize',10);
-        plot(X(1,neg_idx),X(2,neg_idx),'r+','MarkerSize',10);
-        plot(X(1,ii),X(2,ii),'ko','MarkerSize',15);
-        plot(x1,x2);
-        xlim([mnx mxx]);
-        ylim([mny mxy]);
+        %%% figure;
+        %%% clf;
+        %%% hold on
+        %%% plot(X(1,pos_idx),X(2,pos_idx),'b*','MarkerSize',10);
+        %%% plot(X(1,neg_idx),X(2,neg_idx),'r+','MarkerSize',10);
+        %%% plot(X(1,ii),X(2,ii),'ko','MarkerSize',15);
+        %%% plot(x1,x2);
+        %%% xlim([mnx mxx]);
+        %%% ylim([mny mxy]);
         %ginput(1);
-        pause(0.5); %change the delay
+        % pause(0.5); %change the delay
     end
   end
   round = round + 1
diff --git a/zoo/zoo2.m b/zoo/zoo2.m
new file mode 100644
index 0000000..4c98ede
--- /dev/null
+++ b/zoo/zoo2.m
@@ -0,0 +1,26 @@
+% Change K here to any integer < 96
+K = 60;
+
+load zoo; %load matlab data
+sample_idxs = randperm(96, K)
+sample_data = trn_data(sample_idxs, : )
+sample_lab = trn_lab(sample_idxs, : )
+t = fitctree(sample_data,sample_lab,'PredictorNames', fields); %train a decision tree for classification
+view(t,'Mode','graph'); %visualize the tree t
+
+% %for visualization
+% allHandles=findall(groot,'Type','text') 
+% set(allHandles,'FontSize',24)
+% pause;
+% h = findall(groot,'Type','figure');
+% close(h);
+% for i=1:6
+%     clf(gcf);
+%     imshow(strcat(tst_names{i},'.png'));
+%     set(gcf,'Position',[0 0 700 700]+300);
+%     title(tst_names{i},'FontSize',40);
+%     pause;
+%     y=predict(t,tst_data(i,:)); %classify a test sample by tree t
+%     title(sprintf('%s is %s',tst_names{i},y{1}),'FontSize',40,'color', 'r'); 
+%     pause; 
+% end
\ No newline at end of file