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  1. %obsluga elementow nietypowych
  2. %klasteryzacja
  3. %klasyfikacja
  4. %metoda bayesa iris from uci maschine learning
  5. clear;clc;
  6. load fisheriris;
  7. lab=findgroups(species);
  8. d1=3;
  9. d2=4;
  10. gscatter(meas(:,d1),meas(:,d2),lab);
  11. n=length(lab);
  12. ratio=.2;
  13. an=ratio*n;
  14. bn=n-an;
  15. aidx=randperm(n,an);
  16. bidx=setdiff(1:n,aidx);
  17. adata=meas(aidx,:);
  18. bdata=meas(bidx,:);%etykiety
  19. alab=lab(aidx);
  20. blab=lab(bidx);
  21. lab2=lab;
  22. lab2(bidx)=lab2(bidx)+3;
  23. gscatter(meas(:,d1),meas(:,d2),lab2);
  24. %korzystamy  z klasyfikatora bayes w celu klasyfikacji
  25. for j=1:3
  26.     mu(j,:)=mean(adata(alab==j,:));
  27.     sigma(j,:)=std(adata(alab==j,:));
  28.     pc(j)=sum(alab==j)/an;
  29. end
  30. for j=1:3
  31.     for i=1:bn
  32.         P(i,j)=pc(j)*mvnpdf(bdata(i,:),mu(j,:),sigma(j,:));
  33.     end
  34. end
  35. [~,reslab]=max(P,[],2);
  36. skutecznosc=sum(blab==reslab)/bn;
  37. %klasyfikacja
  38. X=linspace(min(meas(:,d1)),max(meas(:,d1)),100);
  39. Y=linspace(min(meas(:,d2)),max(meas(:,d2)),100);
  40. [gx,gy]=meshgrid(X,Y);
  41. for j=1:3
  42.     gp(:,j)=pc(j)*mvnpdf([gx(:),gy(:)],mu(j,[d1,d2]),sigma(j,[d1,d2]));
  43. end
  44. [~,glab]=max(gp,[],2);
  45. gscatter(gx(:),gy(:),glab);
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