258 lines
7.8 KiB
R
258 lines
7.8 KiB
R
function changesInLogml = writeMixtureInfoPop(logml, rows, data, adjprior, ...
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priorTerm, outPutFile, inputFile, partitionSummary, popnames, fixedK)
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global PARTITION;
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global COUNTS;
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global SUMCOUNTS;
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global LOGDIFF;
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ninds = size(rows,1);
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npops = size(COUNTS,3);
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names = (size(popnames,1) == ninds); %Tarkistetaan ett?nimet viittaavat yksilöihin
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changesInLogml = [];
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if length(outPutFile)>0
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fid = fopen(outPutFile,'a');
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else
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fid = -1;
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diary('baps4_output.baps'); % save in text anyway.
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end
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dispLine;
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disp('RESULTS OF GROUP LEVEL MIXTURE ANALYSIS:');
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disp(['Data file: ' inputFile]);
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disp(['Number of clustered groups: ' ownNum2Str(ninds)]);
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disp(['Number of clusters in optimal partition: ' ownNum2Str(npops)]);
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disp(['Log(marginal likelihood) of optimal partition: ' ownNum2Str(logml)]);
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disp(' ');
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if (fid ~= -1)
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fprintf(fid,'%s \n', ['RESULTS OF GROUP LEVEL MIXTURE ANALYSIS:']); fprintf(fid,'\n');
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fprintf(fid,'%s \n', ['Data file: ' inputFile]); fprintf(fid,'\n');
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fprintf(fid,'%s \n', ['Number of clustered groups: ' ownNum2Str(ninds)]); fprintf(fid,'\n');
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fprintf(fid,'%s \n', ['Number of clusters in optimal partition: ' ownNum2Str(npops)]); fprintf(fid,'\n');
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fprintf(fid,'%s \n', ['Log(marginal likelihood) of optimal partition: ' ownNum2Str(logml)]); fprintf(fid,'\n');
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fprintf(fid,'\n');
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end
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cluster_count = length(unique(PARTITION));
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disp(['Best Partition: ']);
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if (fid ~= -1)
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fprintf(fid,'%s \n',['Best Partition: ']); fprintf(fid,'\n');
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end
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for m=1:cluster_count
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indsInM = find(PARTITION==m);
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length_of_beginning = 11 + floor(log10(m));
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cluster_size = length(indsInM);
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if names
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text = ['Cluster ' num2str(m) ': {' char(popnames{indsInM(1)})];
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for k = 2:cluster_size
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text = [text ', ' char(popnames{indsInM(k)})];
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end;
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else
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text = ['Cluster ' num2str(m) ': {' num2str(indsInM(1))];
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for k = 2:cluster_size
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text = [text ', ' num2str(indsInM(k))];
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end;
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end
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text = [text '}'];
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while length(text)>58
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%Take one line and display it.
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new_line = takeLine(text,58);
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text = text(length(new_line)+1:end);
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disp(new_line);
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if (fid ~= -1)
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fprintf(fid,'%s \n',[new_line]);
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fprintf(fid,'\n');
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end
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if length(text)>0
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text = [blanks(length_of_beginning) text];
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else
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text = [];
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end;
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end;
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if ~isempty(text)
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disp(text);
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if (fid ~= -1)
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fprintf(fid,'%s \n',[text]);
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fprintf(fid,'\n');
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end
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end;
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end
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if npops > 1
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disp(' ');
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disp(' ');
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disp('Changes in log(marginal likelihood) if group i is moved to cluster j:');
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if (fid ~= -1)
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fprintf(fid, '%s \n', [' ']); %fprintf(fid, '\n');
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fprintf(fid, '%s \n', [' ']); %fprintf(fid, '\n');
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fprintf(fid, '%s \n', ['Changes in log(marginal likelihood) if group i is moved to cluster j:']); %fprintf(fid, '\n');
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end
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if names
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nameSizes = zeros(ninds,1);
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for i = 1:ninds
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nimi = char(popnames{i});
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nameSizes(i) = length(nimi);
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end
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maxSize = max(nameSizes);
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maxSize = max(maxSize, 5);
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erotus = maxSize - 5;
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alku = blanks(erotus);
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ekarivi = [alku 'group' blanks(6+erotus)];
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else
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ekarivi = 'group ';
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end
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for i = 1:cluster_count
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ekarivi = [ekarivi ownNum2Str(i) blanks(8-floor(log10(i)))];
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end
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disp(ekarivi);
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if (fid ~= -1)
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fprintf(fid, '%s \n', [ekarivi]); %fprintf(fid, '\n');
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end
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changesInLogml = LOGDIFF';
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for ind = 1:ninds
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%[muutokset, diffInCounts] = laskeMuutokset(ind, rows, data, ...
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% adjprior, priorTerm);
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%changesInLogml(:,ind) = muutokset;
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muutokset = changesInLogml(:,ind);
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if names
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nimi = char(popnames{ind});
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rivi = [blanks(maxSize - length(nimi)) nimi ':'];
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else
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rivi = [blanks(4-floor(log10(ind))) ownNum2Str(ind) ':'];
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end
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for j = 1:npops
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rivi = [rivi ' ' logml2String(omaRound(muutokset(j)))];
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end
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disp(rivi);
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if (fid ~= -1)
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fprintf(fid, '%s \n', [rivi]); fprintf(fid, '\n');
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end
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end
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disp(' '); disp(' ');
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disp('KL-divergence matrix in PHYLIP format:');
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dist_mat = zeros(npops, npops);
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if (fid ~= -1)
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fprintf(fid, '%s \n', [' ']); %fprintf(fid, '\n');
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fprintf(fid, '%s \n', [' ']); %fprintf(fid, '\n');
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fprintf(fid, '%s \n', ['KL-divergence matrix in PHYLIP format:']); %fprintf(fid, '\n');
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end
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maxnoalle = size(COUNTS,1);
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nloci = size(COUNTS,2);
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d = zeros(maxnoalle, nloci, npops);
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prior = adjprior;
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prior(find(prior==1))=0;
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nollia = find(all(prior==0)); %Lokukset, joissa oli havaittu vain yht?alleelia.
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prior(1,nollia)=1;
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for pop1 = 1:npops
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d(:,:,pop1) = (squeeze(COUNTS(:,:,pop1))+prior) ./ repmat(sum(squeeze(COUNTS(:,:,pop1))+prior),maxnoalle,1);
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%dist1(pop1) = (squeeze(COUNTS(:,:,pop1))+adjprior) ./ repmat((SUMCOUNTS(pop1,:)+adjprior), maxnoalle, 1);
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end
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% ekarivi = blanks(7);
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% for pop = 1:npops
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% ekarivi = [ekarivi num2str(pop) blanks(7-floor(log10(pop)))];
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% end
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ekarivi = num2str(npops);
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disp(ekarivi);
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if (fid ~= -1)
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fprintf(fid, '%s \n', [ekarivi]); %fprintf(fid, '\n');
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end
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for pop1 = 1:npops
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rivi = [blanks(2-floor(log10(pop1))) num2str(pop1) ' '];
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for pop2 = 1:pop1-1
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dist1 = d(:,:,pop1); dist2 = d(:,:,pop2);
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div12 = sum(sum(dist1.*log2((dist1+10^-10) ./ (dist2+10^-10))))/nloci;
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div21 = sum(sum(dist2.*log2((dist2+10^-10) ./ (dist1+10^-10))))/nloci;
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div = (div12+div21)/2;
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% rivi = [rivi kldiv2str(div) ' '];
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dist_mat(pop1,pop2) = div;
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end
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% disp(rivi);
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% if (fid ~= -1)
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% fprintf(fid, '%s \n', [rivi]); fprintf(fid, '\n');
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% end
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end
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dist_mat = dist_mat + dist_mat'; % make it symmetric
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for pop1 = 1:npops
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rivi = ['Cluster_' num2str(pop1) ' '];
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for pop2 = 1:npops
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rivi = [rivi kldiv2str(dist_mat(pop1,pop2)) ' '];
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end
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disp(rivi);
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if (fid ~= -1)
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fprintf(fid, '%s \n', [rivi]); %fprintf(fid, '\n');
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end
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end
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end
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disp(' ');
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disp(' ');
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disp('List of sizes of 10 best visited partitions and corresponding log(ml) values');
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if (fid ~= -1)
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fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
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fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
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fprintf(fid, '%s \n', ['List of sizes of 10 best visited partitions and corresponding log(ml) values']); fprintf(fid, '\n');
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end
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partitionSummary = sortrows(partitionSummary,2);
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partitionSummary = partitionSummary(size(partitionSummary,1):-1:1 , :);
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partitionSummary = partitionSummary(find(partitionSummary(:,2)>-1e49),:);
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if size(partitionSummary,1)>10
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vikaPartitio = 10;
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else
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vikaPartitio = size(partitionSummary,1);
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end
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for part = 1:vikaPartitio
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line = [num2str(partitionSummary(part,1)) ' ' num2str(partitionSummary(part,2))];
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disp(line);
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if (fid ~= -1)
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fprintf(fid, '%s \n', [line]); fprintf(fid, '\n');
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end
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end
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if ~fixedK
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disp(' ');
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disp(' ');
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disp('Probabilities for number of clusters');
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if (fid ~= -1)
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fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
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fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
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fprintf(fid, '%s \n', ['Probabilities for number of clusters']); fprintf(fid, '\n');
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end
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npopsTaulu = unique(partitionSummary(:,1));
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len = length(npopsTaulu);
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probs = zeros(len,1);
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partitionSummary(:,2) = partitionSummary(:,2)-max(partitionSummary(:,2));
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sumtn = sum(exp(partitionSummary(:,2)));
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for i=1:len
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npopstn = sum(exp(partitionSummary(find(partitionSummary(:,1)==npopsTaulu(i)),2)));
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probs(i) = npopstn / sumtn;
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end
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for i=1:len
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if probs(i)>1e-5
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line = [num2str(npopsTaulu(i)) ' ' num2str(probs(i))];
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disp(line);
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if (fid ~= -1)
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fprintf(fid, '%s \n', [line]); fprintf(fid, '\n');
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end
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end
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end
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end
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if (fid ~= -1)
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fclose(fid);
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else
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diary off
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end
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