1 / 36

Изкуствен интелект Упражнение № 10

Изкуствен интелект Упражнение № 10. спец. Информатика, ФМИ 200 4 /200 5. Машинно самообучение. Формиране на описания на понятия (хипотези): примери положителни отрицателни обучаваща извадка тестова извадка Обучение за: едно понятие много понятия. Машинно самообучение.

flint
Download Presentation

Изкуствен интелект Упражнение № 10

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Изкуствен интелектУпражнение № 10 спец. Информатика, ФМИ 2004/2005

  2. Машинно самообучение • Формиране на описания на понятия (хипотези): • примери • положителни • отрицателни • обучаваща извадка • тестова извадка • Обучение за: • едно понятие • много понятия

  3. Машинно самообучение • Езици за представяне на примерите и хипотезите • атрибут-стойност • релационен език • други

  4. Език атрибут стойност • Представяне на примерите: %example (Id, Class, AVS) example(a4,mammal,[has_covering = hair, milk = t, homeothermic = t, habitat = air, eggs = f, gills = f]). example(a5,fish,[has_covering = scales, milk = f, homeothermic = f, habitat = sea, eggs = t, gills = t]). example(a6,reptile,[has_covering = scales, milk = f, homeothermic = f, habitat = land, eggs = t, gills = f]).

  5. Език атрибут стойност • Представяне на хипотезите: IF Ai=Vij & … & Ak=Vkm THEN Classp … IF As=Vst & … & An=Vnl THEN Classq

  6. Типове атрибути и стойности • Номинални • Линейни • Структурни

  7. learn(H):- findall([Class|AVS], example(_,Class,AVS), Ex), lrn(Ex,H).

  8. lrn(E,H) :- del([Class|A],E,E1), del([Class|B],E1,E2), lgg(A,B,LGG,Class), \+((member([C1|LGG],E2), \+(Class=C1))), lrn([[Class|LGG]|E2],H). lrn(H,H).

  9. lgg([],[],[],Class). lgg([A=V1|Rest1],[A=V2|Rest2],[A=H1|Hyp],Class) :- lgg1(A,V1,V2,H1,Class), lgg(Rest1,Rest2,Hyp,Class).

  10. lgg1(_,X,Y,X,_):- X==Y,!. lgg1(A,X,Y,_,C):- X \== Y, nominal(C,N),member(A,N). lgg1(A,X,Y,H,C):- X \==Y, linear(C,L),member(A,L), cover(X,Y,H). lgg1(A,X,Y,H,C):- X \==Y, struct(C,L),member(A,L), common_parent(X,Y,H,C).

  11. cover(X,X,X):-!. cover(X,Y,[Min,Max]):- atom(X),atom(Y),!, min(X,Y,Min), max(X,Y,Max). cover(X,[Y1,Y2],[Min,Max]):- atom(X),!, min(X,Y1,Min), max(X,Y2,Max). cover([X1,X2],Y,[Min,Max]):- atom(Y),!, min(X1,Y,Min), max(X2,Y,Max). cover([X1,X2],[Y1,Y2],[Min,Max]):- min(X1,Y1,Min), max(X2,Y2,Max).

  12. min(X,X,X):-!. min(X,Y,X):-X<Y,!. min(X,Y,Y):-X>Y. max(X,X,X):-!. max(X,Y,X):-X>Y,!. max(X,Y,Y):-X<Y.

  13. common_parent(X,Y,H,C):- parent(H,X,C), parent(H,Y,C). parent(X,X,C). parent(X,Y,C):-isa(C,Y,X). parent(X,Y,C):-isa(C,Y,Z),parent(X,Z,C).

  14. del(X,[X|T],T). del(X,[Y|T],[Y|V]) :- del(X,T,V). member(X,[X|_]). member(X,[_|T]) :- member(X,T). write_list([]). write_list([H|Rest]):- write(H),nl, write_list(Rest).

  15. Пример example(a1,mammal,[has_covering = hair, milk = t, homeothermic = t, habitat = land, eggs = f, gills = f]). example(a2,mammal,[has_covering = none, milk = t, homeothermic = t, habitat = sea, eggs = f, gills = f]). example(a3,mammal,[has_covering = hair, milk = t, homeothermic = t, habitat = sea, eggs = t, gills = f]). example(a4,mammal,[has_covering = hair, milk = t, homeothermic = t, habitat = air, eggs = f, gills = f]). example(a5,fish,[has_covering = scales, milk = f, homeothermic = f, habitat = sea, eggs = t, gills = t]).

  16. Пример example(a6,reptile,[has_covering = scales, milk = f, homeothermic = f, habitat = land, eggs = t, gills = f]). example(a7,reptile,[has_covering = scales, milk = f, homeothermic = f, habitat = sea, eggs = t, gills = f]). example(a8,bird,[has_covering = feathers, milk = f, homeothermic = t, habitat = air, eggs = t, gills = f]). example(a9,bird,[has_covering = feathers, milk = f, homeothermic = t, habitat = land, eggs = t, gills = f]). example(a10,amphibian,[has_covering = none, milk = f, homeothermic = f, habitat = land, eggs = t, gills = f]).

  17. Пример struct(_,[]). linear(_,[]). nominal(_,[has_covering,milk,homeothermic,habitat,eggs,gills])

  18. Пример | ?- learn(H),write_list(H). [bird,has_covering=feathers,milk=f,homeothermic=t, habitat=_3699,eggs=t,gills=f] [reptile,has_covering=scales,milk=f, homeothermic=f,habitat=_3039,eggs=t,gills=f] [mammal,has_covering=_2095,milk=t,homeothermic=t, habitat=_2324,eggs=_2464,gills=f] [fish,has_covering=scales,milk=f,homeothermic=f, habitat=sea,eggs=t,gills=t] [amphibian,has_covering=none,milk=f,homeothermic=f,habitat=land,eggs=t,gills=f]

  19. Пример example(1,pos,[hs = octagon, bs = octagon, sm = no, ho = sword, jc = red, ti = yes]). example(2,pos,[hs = octagon, bs = octagon, sm = yes, ho = sword, jc = red, ti = yes]). example(3,neg,[hs = round, bs = square, sm = yes, ho = sword, jc = blue, ti = no]). example(4,neg,[hs = round, bs = square, sm = yes, ho = flag, jc = green, ti = yes]). example(5,pos,[hs = octagon, bs = round, sm = yes, ho = sword, jc = red, ti = yes]). example(6,pos,[hs = square, bs = octagon, sm = yes, ho = sword, jc = red, ti = yes]). example(7,pos,[hs = square, bs = square, sm = no, ho = sword, jc = red, ti = yes]).

  20. Машинно самообучение | ?- learn(H),write_list(H). [neg,hs=octagon,bs=square,sm=yes,ho=_125218,jc=blue, ti=no] [neg,hs=square,bs=round,sm=_123819,ho=_123946,jc=green, ti=_124150] [neg,hs=octagon,bs=round,sm=_120834,ho=_120961,jc=yellow,ti=no] [neg,hs=square,bs=octagon,sm=_116815,ho=_116942,jc=green,ti=_117146] [neg,hs=square,bs=round,sm=_113808,ho=_113935,jc=yellow, ti=no] [neg,hs=square,bs=round,sm=_111648,ho=_111775,jc=blue, ti=_111979] [neg,hs=round,bs=octagon,sm=_106498,ho=_106625,jc=yellow,ti=_106829] [neg,hs=round,bs=octagon,sm=_101722,ho=_101849,jc=green, ti=_102053]

  21. [neg,hs=round,bs=octagon,sm=_100472,ho=_100599,jc=blue, ti=_100803] [pos,hs=round,bs=round,sm=_93315,ho=_93442,jc=_93582, ti=_93735] [pos,hs=square,bs=square,sm=_84522,ho=_84649,jc=_84789, ti=_84942] [pos,hs=octagon,bs=octagon,sm=_70090,ho=_70217,jc=_70357, ti=_70510] [neg,hs=round,bs=square,sm=_55652,ho=_55779,jc=yellow, ti=_55983] [neg,hs=round,bs=square,sm=_51710,ho=_51837,jc=green, ti=_52041] [neg,hs=round,bs=square,sm=_45417,ho=_45544,jc=blue, ti=_45748] [pos,hs=_41557,bs=_41658,sm=_41772,ho=_41899,jc=red, ti=_42103] [neg,hs=octagon,bs=round,sm=no,ho=flag,jc=green,ti=no] [neg,hs=square,bs=octagon,sm=yes,ho=balloon,jc=blue,ti=no] [neg,hs=square,bs=octagon,sm=no,ho=flag,jc=yellow,ti=yes] [neg,hs=octagon,bs=square,sm=no,ho=balloon,jc=yellow,ti=yes] [neg,hs=octagon,bs=square,sm=no,ho=sword,jc=green,ti=no]

  22. Език атрибут стойност attribute(size,[small,large]). attribute(shape,[long,compact,other]). attribute(holes,[none,1,2,3,many]).

  23. Език атрибут стойност • Представяне на примерите %example (Class, AVS) example(nut,[size=small,shape=compact,holes=1]). example(screw,[size=small,shape=long,holes=none]). example(key,[size=small,shape=long,holes=1]). example(nut,[size=small,shape=compact,holes=1]). example(key,[size=large,shape=long,holes=1]). example(screw,[size=small,shape=compact,holes=none]). example(nut,[size=small,shape=compact,holes=1]). example(pen,[size=large,shape=long,holes=none]). example(scissors,[size=large,shape=long,holes=2]). example(pen,[size=large,shape=long,holes=none]). example(scissors,[size=large,shape=other,holes=2]). example(key,[size=small,shape=other,holes=2]).

  24. match(Object,Description):- member(Conjunction,Description), satisfy(Object,Conjunction). satisfy(Object,Conjunction):- not ( member(Att=Val,Conjunction), member(Att=ValX,Object), ValX \== Val ).

  25. :-op(300,xfx,<==). learn(Class):- bagof(example(CalssX,Obj), example(CalssX,Obj), Examples), learn(Examples,Class,Description), nl,write(Class),write(‘<==‘),nl, writelist(Description), assert(Class<==Description).

  26. learn(Examples,Class,[]):- not member(example(Calss,_),Examples). learn(Examples,Class,[Conj|Conjs]):- learn_conj(Examples,Class,Conj), remove(Examples,Conj,RestExamples), learn(REstExamples,Class,Conjs).

  27. learn_conj(Examples,Class,[]):- not (member(example(CalsX,_),Examples), ClasX \== Class). learn_conj(Examples,Class,[Cond|Conds]):- choose_cond(Examples,Class,Cond), filter(Examples,[Cond],Examples1), learn_conj(Examples1,Class,Conds). learn_conj(Examples,_,[]).

  28. choose_cond(Examples,Class,AttVal):- findall(AV/Score, score(Examples,Class,AV,Score), AVs), best(AVs,AttVal). best([AttVal/_],AttVal). best([AV0/S0, AV1/S1 |AVSlist],AttVal):- S1 > S0, !, best([AV1/S1 |AVSlist],AttVal) ; best([AV0/S0 |AVSlist],AttVal).

  29. filter(Examples,Cond,Examples1):- findall(example(Calss,Obj), (member(example(Calss,Obj),Examples), satisfy(Obj,Cond)), Examples1).

  30. remove([],_,[]). remove([example(Class,Obj)|Es],Conj,Es1):- satisfy(Obj,Conj),!, remove(Es,Conj,Es1). remove([E|Es],Conj,[E|Es1]):- remove(Es,Conj,Es1).

  31. satisfy(Object,Conjunction):- not ( member(Att=Val,Conjunction), member(Att=ValX,Object), ValX \== Val ). score(Examples,Class,AttVal,Score):- candidate(Examples,Class,AttVal), filter(Examples,[AttVal],Examples1), length(Examples1,N1), count_pos(Examples1,Class,NPos1), NPos1 >0, Score is 2*NPos1 – N1.

  32. candidate(Examples,Class,Att=Val):- attribute(Att,Values), member(Val,Values), suitable(Att=Val,Examples,Class). suitable(AttVal,Examples,Class):- member(example(ClassX,ObjX),Examples), ClassX \== Class, not satisfy(ObjX, [AttVal]),!.

  33. count_pos([],_,0). count_pos([example(ClassX,_)|Ex],Class,N):- count_pos(Ex,Class,N1), (ClassX=Class,!, N is N1+1 ; N = N1). writelist([]). writelist([X|L]):- tab(2), write(X), nl, writelist(L).

  34. | ?- learn(pen). pen<== [holes=none,size=large] [] yes | ?- learn(key). key<== [shape=other,size=small] [holes=1,shape=long] [] yes | ?- learn(nut). nut<== [shape=compact,holes=1] [] yes

  35. | ?- learn(screw). screw<== [holes=none,size=small] [] yes | ?- learn(scissors). scissors<== [holes=2,size=large] [] yes

More Related