Code cleanup
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Waldir Leoncio
parent
c2bbe701be
commit
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1 changed files with 0 additions and 323 deletions
323
R/greedyMix.R
323
R/greedyMix.R
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@ -366,22 +366,6 @@ greedyMix <- function(
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# global PARTITION; PARTITION = [];
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# global POP_LOGML; POP_LOGML = [];
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# %-------------------------------------------------------------------------------------
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# function rows = computeRows(rowsFromInd, inds, ninds)
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# % On annettu yksil<69>t inds. Funktio palauttaa vektorin, joka
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# % sis<69>lt<6C><74> niiden rivien numerot, jotka sis<69>lt<6C>v<EFBFBD>t yksil<69>iden
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# % dataa.
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# rows = inds(:, ones(1,rowsFromInd));
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# rows = rows*rowsFromInd;
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# miinus = repmat(rowsFromInd-1 : -1 : 0, [ninds 1]);
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# rows = rows - miinus;
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# rows = reshape(rows', [1,rowsFromInd*ninds]);
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# %--------------------------------------------------------------------------
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@ -1210,270 +1194,6 @@ greedyMix <- function(
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# end
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# end
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# %-------------------------------------------------------------------
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# function changesInLogml = writeMixtureInfo(logml, rowsFromInd, 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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# changesInLogml = [];
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# ninds = size(data,1)/rowsFromInd;
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# npops = size(COUNTS,3);
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# names = (size(popnames,1) == ninds); %Tarkistetaan ett?nimet viittaavat yksil<69>ihin
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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 INDIVIDUAL LEVEL MIXTURE ANALYSIS:');
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# disp(['Data file: ' inputFile]);
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# disp(['Model: independent']);
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# disp(['Number of clustered individuals: ' ownNum2Str(ninds)]);
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# disp(['Number of groups 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 INDIVIDUAL 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 individuals: ' ownNum2Str(ninds)]); fprintf(fid,'\n');
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# fprintf(fid,'%s \n', ['Number of groups 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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# 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 indvidual i is moved to group 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 indvidual i is moved to group 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 ' ind' blanks(6+erotus)];
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# else
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# ekarivi = ' ind ';
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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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# %ninds = size(data,1)/rowsFromInd;
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# changesInLogml = LOGDIFF';
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# for ind = 1:ninds
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# %[muutokset, diffInCounts] = laskeMuutokset(ind, rowsFromInd, 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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# %---------------------------------------------------------------
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@ -1488,49 +1208,6 @@ greedyMix <- function(
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# num = round(num);
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# num2 = num/10;
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# %---------------------------------------------------------
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# function digit = palautaYks(num,yks)
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# % palauttaa luvun num 10^yks termin kertoimen
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# % string:in?
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# % yks t<>ytyy olla kokonaisluku, joka on
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# % v<>hint<6E><74>n -1:n suuruinen. Pienemmill?
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# % luvuilla tapahtuu jokin py<70>ristysvirhe.
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# if yks>=0
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# digit = rem(num, 10^(yks+1));
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# digit = floor(digit/(10^yks));
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# else
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# digit = num*10;
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# digit = floor(rem(digit,10));
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# end
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# digit = num2str(digit);
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# function mjono = kldiv2str(div)
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# mjono = ' ';
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# if abs(div)<100
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# %Ei tarvita e-muotoa
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# mjono(6) = num2str(rem(floor(div*1000),10));
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# mjono(5) = num2str(rem(floor(div*100),10));
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# mjono(4) = num2str(rem(floor(div*10),10));
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# mjono(3) = '.';
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# mjono(2) = num2str(rem(floor(div),10));
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# arvo = rem(floor(div/10),10);
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# if arvo>0
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# mjono(1) = num2str(arvo);
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# end
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# else
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# suurinYks = floor(log10(div));
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# mjono(6) = num2str(suurinYks);
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# mjono(5) = 'e';
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# mjono(4) = palautaYks(abs(div),suurinYks-1);
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# mjono(3) = '.';
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# mjono(2) = palautaYks(abs(div),suurinYks);
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# end
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# %--------------------------------------------------------------------
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# function dist2 = laskeOsaDist(inds2, dist, ninds)
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