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2 changed files with 38 additions and 12 deletions
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@ -14,8 +14,8 @@ mnx=-0.1*mxx;%for visualization %mnx = min(X(1,:));
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mxy = max(X(2,:));
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mny=-0.1*mxy; % for visualization %mny = min(X(2,:));
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figure;
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ginput(1);
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%%% figure;
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%%% ginput(1);
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err = 1;
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round = 0;
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while err > 0
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@ -24,17 +24,17 @@ while err > 0
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w = w + rate*X(:,ii) * Y(ii); %then add (or subtract) this point to w
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x1=mnx:0.01:mxx;
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x2=-(w(1)*x1+w(3))/w(2);
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%figure;
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clf;
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hold on
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plot(X(1,pos_idx),X(2,pos_idx),'b*','MarkerSize',10);
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plot(X(1,neg_idx),X(2,neg_idx),'r+','MarkerSize',10);
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plot(X(1,ii),X(2,ii),'ko','MarkerSize',15);
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plot(x1,x2);
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xlim([mnx mxx]);
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ylim([mny mxy]);
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%%% figure;
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%%% clf;
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%%% hold on
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%%% plot(X(1,pos_idx),X(2,pos_idx),'b*','MarkerSize',10);
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%%% plot(X(1,neg_idx),X(2,neg_idx),'r+','MarkerSize',10);
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%%% plot(X(1,ii),X(2,ii),'ko','MarkerSize',15);
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%%% plot(x1,x2);
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%%% xlim([mnx mxx]);
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%%% ylim([mny mxy]);
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%ginput(1);
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pause(0.5); %change the delay
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% pause(0.5); %change the delay
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end
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end
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round = round + 1
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26
zoo/zoo2.m
Normal file
26
zoo/zoo2.m
Normal file
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@ -0,0 +1,26 @@
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% Change K here to any integer < 96
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K = 60;
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load zoo; %load matlab data
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sample_idxs = randperm(96, K)
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sample_data = trn_data(sample_idxs, : )
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sample_lab = trn_lab(sample_idxs, : )
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t = fitctree(sample_data,sample_lab,'PredictorNames', fields); %train a decision tree for classification
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view(t,'Mode','graph'); %visualize the tree t
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% %for visualization
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% allHandles=findall(groot,'Type','text')
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% set(allHandles,'FontSize',24)
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% pause;
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% h = findall(groot,'Type','figure');
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% close(h);
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% for i=1:6
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% clf(gcf);
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% imshow(strcat(tst_names{i},'.png'));
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% set(gcf,'Position',[0 0 700 700]+300);
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% title(tst_names{i},'FontSize',40);
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% pause;
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% y=predict(t,tst_data(i,:)); %classify a test sample by tree t
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% title(sprintf('%s is %s',tst_names{i},y{1}),'FontSize',40,'color', 'r');
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% pause;
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% end
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