This repository has been archived on 2023-08-20. You can view files and clone it, but cannot push or open issues or pull requests.
yap-6.3/packages/prism/exs/bloodABO.psm
2011-11-10 12:24:47 +00:00

112 lines
4.1 KiB
Plaintext

%%%%
%%%% ABO blood type --- bloodABO.psm
%%%%
%%%% Copyright (C) 2004,2006,2008
%%%% Sato Laboratory, Dept. of Computer Science,
%%%% Tokyo Institute of Technology
%% ABO blood type consists of A, B, O and AB. They are observable
%% (phenotypes) and determined by a pair of blood type genes (geneotypes).
%% There are three ABO genes, namely a, b and o located on the 9th
%% chromosome of a human being. There are 6 geneotypes ({a,a},{a,b},{a,o},
%% {b,b},{b,o},{o,o}) and each determines a blood type. For example {a,b}
%% gives blood type AB etc. Our task is to estimate frequencies of ABO
%% genes from a random sample of ABO blood type, assuming random mate.
%%-------------------------------------
%% Quick start : sample session
%%
%% ?- prism(bloodABO),go,print_blood.
%% % Learn parameters from randomly generated
%% % 100 samples with A:B:O:AB = 38:22:31:9
%%
%% ?- sample(bloodtype(X)).
%% % Pick up a person with blood type X randomly
%% % acccording to the currrent parameter settings
%%
%% ?- get_samples(100,bloodtype(X),_Gs),countlist(_Gs,Cs).
%% % Pick up 100 persons and get the frequencies
%% % of their blood types
%%
%% ?- probf(bloodtype(ab),E),print_graph(E).
%% % Print all explanations for blooodtype(ab) in
%% % a compressed form
%%
%% ?- prob(bloodtype(ab),P).
%% % P is the probability of bloodtype(ab) being true
%%
%% ?- viterbif(bloodtype(ab)).
%% ?- viterbif(bloodtype(ab),P,E),print_graph(E).
%% ?- viterbi(bloodtype(ab),P).
%% % P is the probability of a most likely
%% % explanation E for bloodtype(ab).
%%
%% ?- viterbit(bloodtype(ab)).
%% % Print the most likely explanation for
%% % bloodtype(ab) in a tree form.
go:- learn_bloodtype(100).
%%-------------------------------------
%% Declarations:
:- set_prism_flag(data_source,file('bloodtype.dat')).
% When we run learn/0, the data are supplied
% by `bloodtype.dat'.
values(gene,[a,b,o],[0.5,0.2,0.3]).
% We declare msw(gene,V) s.t. V takes on
% one of the genes {a,b,o} when executed,
% with the freq.: a 50%, b 20%, o 30%.
%%------------------------------------
%% Modeling part:
bloodtype(P) :-
genotype(X,Y),
( X=Y -> P=X
; X=o -> P=Y
; Y=o -> P=X
; P=ab
).
genotype(X,Y) :- msw(gene,X),msw(gene,Y).
% We assume random mate. Note that msw(gene,X)
% and msw(gene,Y) are i.i.d. (independent and
% identically distributed) random variables
% in Prism because they have the same id but
% different subgoals.
%%------------------------------------
%% Utility part:
learn_bloodtype(N) :- % Learn parameters from N observations
random_set_seed(214857), % Set seed of the random number generator
gen_bloodtype(N,Gs),!, % Sample bloodtype/1 of size N
learn(Gs). % Perform search and graphical EM learning
% learn. % <= when using the file `bloodtype.dat'
gen_bloodtype(N,Gs) :-
N > 0,
random_select([a,b,o,ab],[0.38,0.22,0.31,0.09],X),
Gs = [bloodtype(X)|Gs1], % Sample a blood type with an empirical
N1 is N-1,!, % ratio for Japanese people.
gen_bloodtype(N1,Gs1).
gen_bloodtype(0,[]).
print_blood :-
prob(bloodtype(a),PA),prob(bloodtype(b),PB),
prob(bloodtype(o),PO),prob(bloodtype(ab),PAB),
nl,
format("P(A) = ~6f~n",[PA]),
format("P(B) = ~6f~n",[PB]),
format("P(O) = ~6f~n",[PO]),
format("P(AB) = ~6f~n",[PAB]).
print_gene :-
get_sw(gene,[_,[a,b,o],[GA,GB,GO]]),
nl,
format("P(a) = ~6f~n",[GA]),
format("P(b) = ~6f~n",[GB]),
format("P(o) = ~6f~n",[GO]).