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ourMELONS/matlab/parallel/independent_parallel.m

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Added source Matlab code for reference
2019-12-16 16:47:21 +01:00
function independent_parallel(options)
% INDEPENDENT_PARALLEL is the command line version of the baps partition with
% independent models.
% Input: options is a struct generated by parallel.m
%--------------------------------------------------------------------------
%- Syntax check out
%--------------------------------------------------------------------------
outp = [options.outputMat '.txt'];
inp = options.dataFile;
if strcmp(options.fixedK, 'yes')
fixedK = 1;
else
fixedK = 0;
end
%--------------------------------------------------------------------------
%- Get data file location
%--------------------------------------------------------------------------
switch options.dataType
case 'numeric'
%------------------------------------------------------------------
%- Get name and index file location
%------------------------------------------------------------------
try
data = load(options.dataFile);
catch
disp('*** ERROR: Incorrect numerical text data.');
return
end
ninds = testaaOnkoKunnollinenBapsData(data); %TESTAUS
if (ninds==0)
disp('*** ERROR: Incorrect numerical text data.');
return;
end
if ~isempty(options.nameFile) && ~isempty(options.indexFile)
popnames = initPopNames(options.nameFile{1}, options.indexFile{1});
else
popnames = [];
end
[data, rowsFromInd, alleleCodes, noalle, adjprior, priorTerm] = handleData(data);
[Z,dist] = newGetDistances(data,rowsFromInd);
case 'genepop'
kunnossa = testaaGenePopData(options.dataFile);
if kunnossa == 0
return
end
[data,popnames] = lueGenePopData(options.dataFile);
[data, rowsFromInd, alleleCodes, noalle, adjprior, priorTerm] = handleData(data);
[Z,dist] = newGetDistances(data,rowsFromInd);
case 'matlab'
struct_array = load(options.dataFile);
if isfield(struct_array,'c') %Matlab versio
c = struct_array.c;
if ~isfield(c,'dist')
disp('*** ERROR: Incorrect matlab format');
return
end
elseif isfield(struct_array,'dist') %Mideva versio
c = struct_array;
else
disp('*** ERROR: Incorrect matlab format');
return;
end
data = double(c.data); rowsFromInd = c.rowsFromInd; alleleCodes = c.alleleCodes;
noalle = c.noalle; adjprior = c.adjprior; priorTerm = c.priorTerm;
dist = c.dist; popnames = c.popnames; Z = c.Z;
clear c;
otherwise
error('*** ERROR: data type is not specified or unknown.');
end
% ---------------------------------------------------------
% - Stochastic search algorithm
% ---------------------------------------------------------
global PARTITION; global COUNTS;
global SUMCOUNTS; global POP_LOGML;
clearGlobalVars;
c.data=data;
c.noalle = noalle; c.adjprior = adjprior; c.priorTerm = priorTerm;
c.dist=dist; c.Z=Z; c.rowsFromInd = rowsFromInd;
ninds = length(unique(data(:,end)));
ekat = (1:rowsFromInd:ninds*rowsFromInd)';
c.rows = [ekat ekat+rowsFromInd-1];
npopstext = [];
npopstextExtra = options.initialK;
if length(npopstextExtra)>=255
npopstextExtra = npopstextExtra(1:255);
npopstext = [npopstext ' ' npopstextExtra];
teksti = 'The input field length limit (255 characters) was reached. Input more values: ';
else
% -----------------------------------------------------
% Set the limit of the input value.
% Modified by Jing Tang, 30.12.2005
if max(npopstextExtra) > size(data,1)
error('Initial K larger than the sample size are not accepted. ');
else
npopstext = [npopstext ' ' num2str(npopstextExtra)];
end
end
clear teksti;
if isempty(npopstext) || length(npopstext)==1
return
else
npopsTaulu = str2num(npopstext);
ykkoset = find(npopsTaulu==1);
npopsTaulu(ykkoset) = []; % Mik<EFBFBD>li ykk<EFBFBD>si?annettu yl<EFBFBD>rajaksi, ne poistetaan.
if isempty(npopsTaulu)
return
end
clear ykkoset;
end
if fixedK
% Only the first value of npopsTaulu is used
npops = npopsTaulu(1);
nruns = length(npopsTaulu);
[logml, npops, partitionSummary]=indMix_fixK(c,npops,nruns,1);
else
[logml, npops, partitionSummary]=indMix(c,npopsTaulu,1);
end
if logml==1
return
end
data = noIndex(data,noalle);
changesInLogml = writeMixtureInfo(logml, rowsFromInd, data, adjprior, priorTerm, ...
outp,inp,partitionSummary, popnames, fixedK);
viewMixPartition(PARTITION, popnames);
if exist('baps4_output.baps','file')
copyfile('baps4_output.baps',outp)
delete('baps4_output.baps')
end
% The logml is saved for parallel computing
c.logml = logml;
c.changesInLogml = changesInLogml; % this variable stores the change of likelihoods.
c.PARTITION = PARTITION; c.COUNTS = COUNTS; c.SUMCOUNTS = SUMCOUNTS;
c.alleleCodes = alleleCodes; c.adjprior = adjprior; c.popnames = popnames;
c.rowsFromInd = rowsFromInd; c.data = data; c.npops = npops;
c.noalle = noalle; c.mixtureType = 'mix';
fprintf(1,'Saving the result...')
try
% save(options.outputMat, 'c');
save(options.outputMat, 'c', '-v7.3'); % added by Lu Cheng, 08.06.2012
fprintf(1,'Finished.\n');
catch
display('*** ERROR in saving the result.');
end
% ---------------------------------------------------------
% - Subfunctions
% ---------------------------------------------------------
%-------------------------------------------------------------------------------------
function clearGlobalVars
global COUNTS; COUNTS = [];
global SUMCOUNTS; SUMCOUNTS = [];
global PARTITION; PARTITION = [];
global POP_LOGML; POP_LOGML = [];
%-------------------------------------------------------------------------------------
function rows = computeRows(rowsFromInd, inds, ninds)
% On annettu yksil<EFBFBD>t inds. Funktio palauttaa vektorin, joka
% sis<EFBFBD>lt<EFBFBD><EFBFBD> niiden rivien numerot, jotka sis<EFBFBD>lt<EFBFBD>v<EFBFBD>t yksil<EFBFBD>iden
% dataa.
rows = inds(:, ones(1,rowsFromInd));
rows = rows*rowsFromInd;
miinus = repmat(rowsFromInd-1 : -1 : 0, [ninds 1]);
rows = rows - miinus;
rows = reshape(rows', [1,rowsFromInd*ninds]);
%--------------------------------------------------------------------------
function [partitionSummary, added] = addToSummary(logml, partitionSummary, worstIndex)
% Tiedet<EFBFBD><EFBFBD>n, ett?annettu logml on isompi kuin huonoin arvo
% partitionSummary taulukossa. Jos partitionSummary:ss?ei viel?ole
% annettua logml arvoa, niin lis<EFBFBD>t<EFBFBD><EFBFBD>n worstIndex:in kohtaan uusi logml ja
% nykyist?partitiota vastaava nclusters:in arvo. Muutoin ei tehd?mit<EFBFBD><EFBFBD>n.
global PARTITION;
apu = find(abs(partitionSummary(:,2)-logml)<1e-5);
if isempty(apu)
% Nyt l<EFBFBD>ydetty partitio ei ole viel?kirjattuna summaryyn.
npops = length(unique(PARTITION));
partitionSummary(worstIndex,1) = npops;
partitionSummary(worstIndex,2) = logml;
added = 1;
else
added = 0;
end
%--------------------------------------------------------------------------
function [suurin, i2] = arvoSeuraavaTila(muutokset, logml)
% Suorittaa yksil<EFBFBD>n seuraavan tilan arvonnan
y = logml + muutokset; % siirron j<EFBFBD>lkeiset logml:t
y = y - max(y);
y = exp(y);
summa = sum(y);
y = y/summa;
y = cumsum(y);
i2 = rand_disc(y); % uusi kori
suurin = muutokset(i2);
%--------------------------------------------------------------------------------------
function svar=rand_disc(CDF)
%returns an index of a value from a discrete distribution using inversion method
slump=rand;
har=find(CDF>slump);
svar=har(1);
%-------------------------------------------------------------------------------------
function updateGlobalVariables(ind, i2, rowsFromInd, diffInCounts, ...
adjprior, priorTerm)
% Suorittaa globaalien muuttujien muutokset, kun yksil?ind
% on siirret<EFBFBD><EFBFBD>n koriin i2.
global PARTITION;
global COUNTS;
global SUMCOUNTS;
global POP_LOGML;
i1 = PARTITION(ind);
PARTITION(ind)=i2;
COUNTS(:,:,i1) = COUNTS(:,:,i1) - diffInCounts;
COUNTS(:,:,i2) = COUNTS(:,:,i2) + diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:) - sum(diffInCounts);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:) + sum(diffInCounts);
POP_LOGML([i1 i2]) = computePopulationLogml([i1 i2], adjprior, priorTerm);
%---------------------------------------------------------------------------------
function updateGlobalVariables2( ...
i1, i2, rowsFromInd, diffInCounts, adjprior, priorTerm)
% Suorittaa globaalien muuttujien muutokset, kun kaikki
% korissa i1 olevat yksil<EFBFBD>t siirret<EFBFBD><EFBFBD>n koriin i2.
global PARTITION;
global COUNTS;
global SUMCOUNTS;
global POP_LOGML;
inds = find(PARTITION==i1);
PARTITION(inds) = i2;
COUNTS(:,:,i1) = COUNTS(:,:,i1) - diffInCounts;
COUNTS(:,:,i2) = COUNTS(:,:,i2) + diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:) - sum(diffInCounts);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:) + sum(diffInCounts);
POP_LOGML(i1) = 0;
POP_LOGML(i2) = computePopulationLogml(i2, adjprior, priorTerm);
%------------------------------------------------------------------------------------
function updateGlobalVariables3(muuttuvat, rowsFromInd, diffInCounts, ...
adjprior, priorTerm, i2)
% Suorittaa globaalien muuttujien p<EFBFBD>ivitykset, kun yksil<EFBFBD>t 'muuttuvat'
% siirret<EFBFBD><EFBFBD>n koriin i2. Ennen siirtoa yksil<EFBFBD>iden on kuuluttava samaan
% koriin.
global PARTITION;
global COUNTS;
global SUMCOUNTS;
global POP_LOGML;
i1 = PARTITION(muuttuvat(1));
PARTITION(muuttuvat) = i2;
COUNTS(:,:,i1) = COUNTS(:,:,i1) - diffInCounts;
COUNTS(:,:,i2) = COUNTS(:,:,i2) + diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:) - sum(diffInCounts);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:) + sum(diffInCounts);
POP_LOGML([i1 i2]) = computePopulationLogml([i1 i2], adjprior, priorTerm);
%----------------------------------------------------------------------
function inds = returnInOrder(inds, pop, rowsFromInd, data, adjprior, priorTerm)
% Palauttaa yksil<EFBFBD>t j<EFBFBD>rjestyksess?siten, ett?ensimm<EFBFBD>isen?on
% se, jonka poistaminen populaatiosta pop nostaisi logml:n
% arvoa eniten.
global COUNTS; global SUMCOUNTS;
ninds = length(inds);
apuTaulu = [inds, zeros(ninds,1)];
for i=1:ninds
ind = inds(i);
rows = (ind-1)*rowsFromInd+1 : ind*rowsFromInd;
diffInCounts = computeDiffInCounts(rows, size(COUNTS,1), size(COUNTS,2), data);
diffInSumCounts = sum(diffInCounts);
COUNTS(:,:,pop) = COUNTS(:,:,pop)-diffInCounts;
SUMCOUNTS(pop,:) = SUMCOUNTS(pop,:)-diffInSumCounts;
apuTaulu(i, 2) = computePopulationLogml(pop, adjprior, priorTerm);
COUNTS(:,:,pop) = COUNTS(:,:,pop)+diffInCounts;
SUMCOUNTS(pop,:) = SUMCOUNTS(pop,:)+diffInSumCounts;
end
apuTaulu = sortrows(apuTaulu,2);
inds = apuTaulu(ninds:-1:1,1);
%------------------------------------------------------------------------------------
function [muutokset, diffInCounts] = ...
laskeMuutokset(ind, rowsFromInd, data, adjprior, priorTerm)
% Palauttaa npops*1 taulun, jossa i:s alkio kertoo, mik?olisi
% muutos logml:ss? mik<EFBFBD>li yksil?ind siirret<EFBFBD><EFBFBD>n koriin i.
% diffInCounts on poistettava COUNTS:in siivusta i1 ja lis<EFBFBD>tt<EFBFBD>v?
% COUNTS:in siivuun i2, mik<EFBFBD>li muutos toteutetaan.
global COUNTS; global SUMCOUNTS;
global PARTITION; global POP_LOGML;
npops = size(COUNTS,3);
muutokset = zeros(npops,1);
i1 = PARTITION(ind);
i1_logml = POP_LOGML(i1);
rows = (ind-1)*rowsFromInd+1 : ind*rowsFromInd;
diffInCounts = computeDiffInCounts(rows, size(COUNTS,1), size(COUNTS,2), data);
diffInSumCounts = sum(diffInCounts);
COUNTS(:,:,i1) = COUNTS(:,:,i1)-diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)-diffInSumCounts;
new_i1_logml = computePopulationLogml(i1, adjprior, priorTerm);
COUNTS(:,:,i1) = COUNTS(:,:,i1)+diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)+diffInSumCounts;
i2 = [1:i1-1 , i1+1:npops];
i2_logml = POP_LOGML(i2);
COUNTS(:,:,i2) = COUNTS(:,:,i2)+repmat(diffInCounts, [1 1 npops-1]);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)+repmat(diffInSumCounts,[npops-1 1]);
new_i2_logml = computePopulationLogml(i2, adjprior, priorTerm);
COUNTS(:,:,i2) = COUNTS(:,:,i2)-repmat(diffInCounts, [1 1 npops-1]);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)-repmat(diffInSumCounts,[npops-1 1]);
muutokset(i2) = new_i1_logml - i1_logml ...
+ new_i2_logml - i2_logml;
%------------------------------------------------------------------------------------
function [muutokset, diffInCounts] = laskeMuutokset2( ...
i1, rowsFromInd, data, adjprior, priorTerm)
% Palauttaa npops*1 taulun, jossa i:s alkio kertoo, mik?olisi
% muutos logml:ss? mik<EFBFBD>li korin i1 kaikki yksil<EFBFBD>t siirret<EFBFBD><EFBFBD>n
% koriin i.
global COUNTS; global SUMCOUNTS;
global PARTITION; global POP_LOGML;
npops = size(COUNTS,3);
muutokset = zeros(npops,1);
i1_logml = POP_LOGML(i1);
inds = find(PARTITION==i1);
ninds = length(inds);
if ninds==0
diffInCounts = zeros(size(COUNTS,1), size(COUNTS,2));
return;
end
rows = computeRows(rowsFromInd, inds, ninds);
diffInCounts = computeDiffInCounts(rows, size(COUNTS,1), size(COUNTS,2), data);
diffInSumCounts = sum(diffInCounts);
COUNTS(:,:,i1) = COUNTS(:,:,i1)-diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)-diffInSumCounts;
new_i1_logml = computePopulationLogml(i1, adjprior, priorTerm);
COUNTS(:,:,i1) = COUNTS(:,:,i1)+diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)+diffInSumCounts;
i2 = [1:i1-1 , i1+1:npops];
i2_logml = POP_LOGML(i2);
COUNTS(:,:,i2) = COUNTS(:,:,i2)+repmat(diffInCounts, [1 1 npops-1]);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)+repmat(diffInSumCounts,[npops-1 1]);
new_i2_logml = computePopulationLogml(i2, adjprior, priorTerm);
COUNTS(:,:,i2) = COUNTS(:,:,i2)-repmat(diffInCounts, [1 1 npops-1]);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)-repmat(diffInSumCounts,[npops-1 1]);
muutokset(i2) = new_i1_logml - i1_logml ...
+ new_i2_logml - i2_logml;
%------------------------------------------------------------------------------------
function muutokset = laskeMuutokset3(T2, inds2, rowsFromInd, ...
data, adjprior, priorTerm, i1)
% Palauttaa length(unique(T2))*npops taulun, jossa (i,j):s alkio
% kertoo, mik?olisi muutos logml:ss? jos populaation i1 osapopulaatio
% inds2(find(T2==i)) siirret<EFBFBD><EFBFBD>n koriin j.
global COUNTS; global SUMCOUNTS;
global PARTITION; global POP_LOGML;
npops = size(COUNTS,3);
npops2 = length(unique(T2));
muutokset = zeros(npops2, npops);
i1_logml = POP_LOGML(i1);
for pop2 = 1:npops2
inds = inds2(find(T2==pop2));
ninds = length(inds);
if ninds>0
rows = computeRows(rowsFromInd, inds, ninds);
diffInCounts = computeDiffInCounts(rows, size(COUNTS,1), size(COUNTS,2), data);
diffInSumCounts = sum(diffInCounts);
COUNTS(:,:,i1) = COUNTS(:,:,i1)-diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)-diffInSumCounts;
new_i1_logml = computePopulationLogml(i1, adjprior, priorTerm);
COUNTS(:,:,i1) = COUNTS(:,:,i1)+diffInCounts;
SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)+diffInSumCounts;
i2 = [1:i1-1 , i1+1:npops];
i2_logml = POP_LOGML(i2)';
COUNTS(:,:,i2) = COUNTS(:,:,i2)+repmat(diffInCounts, [1 1 npops-1]);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)+repmat(diffInSumCounts,[npops-1 1]);
new_i2_logml = computePopulationLogml(i2, adjprior, priorTerm)';
COUNTS(:,:,i2) = COUNTS(:,:,i2)-repmat(diffInCounts, [1 1 npops-1]);
SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)-repmat(diffInSumCounts,[npops-1 1]);
muutokset(pop2,i2) = new_i1_logml - i1_logml ...
+ new_i2_logml - i2_logml;
end
end
%------------------------------------------------------------------------------------
function muutokset = laskeMuutokset5(inds, rowsFromInd, data, adjprior, ...
priorTerm, i1, i2)
% Palauttaa length(inds)*1 taulun, jossa i:s alkio kertoo, mik?olisi
% muutos logml:ss? mik<EFBFBD>li yksil?i vaihtaisi koria i1:n ja i2:n v<EFBFBD>lill?
global COUNTS; global SUMCOUNTS;
global PARTITION; global POP_LOGML;
ninds = length(inds);
muutokset = zeros(ninds,1);
i1_logml = POP_LOGML(i1);
i2_logml = POP_LOGML(i2);
for i = 1:ninds
ind = inds(i);
if PARTITION(ind)==i1
pop1 = i1; %mist?
pop2 = i2; %mihin
else
pop1 = i2;
pop2 = i1;
end
rows = (ind-1)*rowsFromInd+1 : ind*rowsFromInd;
diffInCounts = computeDiffInCounts(rows, size(COUNTS,1), size(COUNTS,2), data);
diffInSumCounts = sum(diffInCounts);
COUNTS(:,:,pop1) = COUNTS(:,:,pop1)-diffInCounts;
SUMCOUNTS(pop1,:) = SUMCOUNTS(pop1,:)-diffInSumCounts;
COUNTS(:,:,pop2) = COUNTS(:,:,pop2)+diffInCounts;
SUMCOUNTS(pop2,:) = SUMCOUNTS(pop2,:)+diffInSumCounts;
PARTITION(ind) = pop2;
new_logmls = computePopulationLogml([i1 i2], adjprior, priorTerm);
muutokset(i) = sum(new_logmls);
COUNTS(:,:,pop1) = COUNTS(:,:,pop1)+diffInCounts;
SUMCOUNTS(pop1,:) = SUMCOUNTS(pop1,:)+diffInSumCounts;
COUNTS(:,:,pop2) = COUNTS(:,:,pop2)-diffInCounts;
SUMCOUNTS(pop2,:) = SUMCOUNTS(pop2,:)-diffInSumCounts;
PARTITION(ind) = pop1;
end
muutokset = muutokset - i1_logml - i2_logml;
%--------------------------------------------------------------------------
function diffInCounts = computeDiffInCounts(rows, max_noalle, nloci, data)
% Muodostaa max_noalle*nloci taulukon, jossa on niiden alleelien
% lukum<EFBFBD><EFBFBD>r<EFBFBD>t (vastaavasti kuin COUNTS:issa), jotka ovat data:n
% riveill?rows.
diffInCounts = zeros(max_noalle, nloci);
for i=rows
row = data(i,:);
notEmpty = find(row>=0);
if length(notEmpty)>0
diffInCounts(row(notEmpty) + (notEmpty-1)*max_noalle) = ...
diffInCounts(row(notEmpty) + (notEmpty-1)*max_noalle) + 1;
end
end
%------------------------------------------------------------------------------------
function popLogml = computePopulationLogml(pops, adjprior, priorTerm)
% Palauttaa length(pops)*1 taulukon, jossa on laskettu korikohtaiset
% logml:t koreille, jotka on m<EFBFBD><EFBFBD>ritelty pops-muuttujalla.
global COUNTS;
global SUMCOUNTS;
x = size(COUNTS,1);
y = size(COUNTS,2);
z = length(pops);
popLogml = ...
squeeze(sum(sum(reshape(...
gammaln(repmat(adjprior,[1 1 length(pops)]) + COUNTS(:,:,pops)) ...
,[x y z]),1),2)) - sum(gammaln(1+SUMCOUNTS(pops,:)),2) - priorTerm;
%-----------------------------------------------------------------------------------
function npops = poistaTyhjatPopulaatiot(npops)
% Poistaa tyhjentyneet populaatiot COUNTS:ista ja
% SUMCOUNTS:ista. P<EFBFBD>ivitt<EFBFBD><EFBFBD> npops:in ja PARTITION:in.
global COUNTS;
global SUMCOUNTS;
global PARTITION;
notEmpty = find(any(SUMCOUNTS,2));
COUNTS = COUNTS(:,:,notEmpty);
SUMCOUNTS = SUMCOUNTS(notEmpty,:);
for n=1:length(notEmpty)
apu = find(PARTITION==notEmpty(n));
PARTITION(apu)=n;
end
npops = length(notEmpty);
%----------------------------------------------------------------------------------
%Seuraavat kolme funktiota liittyvat alkupartition muodostamiseen.
function initial_partition=admixture_initialization(data_matrix,nclusters,Z)
size_data=size(data_matrix);
nloci=size_data(2)-1;
n=max(data_matrix(:,end));
T=cluster_own(Z,nclusters);
initial_partition=zeros(size_data(1),1);
for i=1:n
kori=T(i);
here=find(data_matrix(:,end)==i);
for j=1:length(here)
initial_partition(here(j),1)=kori;
end
end
function T = cluster_own(Z,nclust)
true=logical(1);
false=logical(0);
maxclust = nclust;
% Start of algorithm
m = size(Z,1)+1;
T = zeros(m,1);
% maximum number of clusters based on inconsistency
if m <= maxclust
T = (1:m)';
elseif maxclust==1
T = ones(m,1);
else
clsnum = 1;
for k = (m-maxclust+1):(m-1)
i = Z(k,1); % left tree
if i <= m % original node, no leafs
T(i) = clsnum;
clsnum = clsnum + 1;
elseif i < (2*m-maxclust+1) % created before cutoff, search down the tree
T = clusternum(Z, T, i-m, clsnum);
clsnum = clsnum + 1;
end
i = Z(k,2); % right tree
if i <= m % original node, no leafs
T(i) = clsnum;
clsnum = clsnum + 1;
elseif i < (2*m-maxclust+1) % created before cutoff, search down the tree
T = clusternum(Z, T, i-m, clsnum);
clsnum = clsnum + 1;
end
end
end
function T = clusternum(X, T, k, c)
m = size(X,1)+1;
while(~isempty(k))
% Get the children of nodes at this level
children = X(k,1:2);
children = children(:);
% Assign this node number to leaf children
t = (children<=m);
T(children(t)) = c;
% Move to next level
k = children(~t) - m;
end
%---------------------------------------------------------------------------------------
function [newData, rowsFromInd, alleleCodes, noalle, adjprior, priorTerm] = ...
handleData(raw_data)
% Alkuper<EFBFBD>isen datan viimeinen sarake kertoo, milt?yksil<EFBFBD>lt?
% kyseinen rivi on per<EFBFBD>isin. Funktio tutkii ensin, ett?montako
% rivi?maksimissaan on per<EFBFBD>isin yhdelt?yksil<EFBFBD>lt? jolloin saadaan
% tiet<EFBFBD><EFBFBD> onko kyseess?haploidi, diploidi jne... T<EFBFBD>m<EFBFBD>n j<EFBFBD>lkeen funktio
% lis<EFBFBD><EFBFBD> tyhji?rivej?niille yksil<EFBFBD>ille, joilta on per<EFBFBD>isin v<EFBFBD>hemm<EFBFBD>n
% rivej?kuin maksimim<EFBFBD><EFBFBD>r?
% Mik<EFBFBD>li jonkin alleelin koodi on =0, funktio muuttaa t<EFBFBD>m<EFBFBD>n alleelin
% koodi pienimm<EFBFBD>ksi koodiksi, joka isompi kuin mik<EFBFBD><EFBFBD>n k<EFBFBD>yt<EFBFBD>ss?oleva koodi.
% T<EFBFBD>m<EFBFBD>n j<EFBFBD>lkeen funktio muuttaa alleelikoodit siten, ett?yhden lokuksen j
% koodit saavat arvoja v<EFBFBD>lill?1,...,noalle(j).
data = raw_data;
nloci=size(raw_data,2)-1;
dataApu = data(:,1:nloci);
nollat = find(dataApu==0);
if ~isempty(nollat)
isoinAlleeli = max(max(dataApu));
dataApu(nollat) = isoinAlleeli+1;
data(:,1:nloci) = dataApu;
end
dataApu = []; nollat = []; isoinAlleeli = [];
noalle=zeros(1,nloci);
alleelitLokuksessa = cell(nloci,1);
for i=1:nloci
alleelitLokuksessaI = unique(data(:,i));
alleelitLokuksessa{i,1} = alleelitLokuksessaI(find(alleelitLokuksessaI>=0));
noalle(i) = length(alleelitLokuksessa{i,1});
end
alleleCodes = zeros(max(noalle),nloci);
for i=1:nloci
alleelitLokuksessaI = alleelitLokuksessa{i,1};
puuttuvia = max(noalle)-length(alleelitLokuksessaI);
alleleCodes(:,i) = [alleelitLokuksessaI; zeros(puuttuvia,1)];
end
for loc = 1:nloci
for all = 1:noalle(loc)
data(find(data(:,loc)==alleleCodes(all,loc)), loc)=all;
end;
end;
nind = max(data(:,end));
nrows = size(data,1);
ncols = size(data,2);
rowsFromInd = zeros(nind,1);
for i=1:nind
rowsFromInd(i) = length(find(data(:,end)==i));
end
maxRowsFromInd = max(rowsFromInd);
a = -999;
emptyRow = repmat(a, 1, ncols);
lessThanMax = find(rowsFromInd < maxRowsFromInd);
missingRows = maxRowsFromInd*nind - nrows;
data = [data; zeros(missingRows, ncols)];
pointer = 1;
for ind=lessThanMax' %K<EFBFBD>y l<EFBFBD>pi ne yksil<EFBFBD>t, joilta puuttuu rivej?
miss = maxRowsFromInd-rowsFromInd(ind); % T<EFBFBD>lt?yksil<EFBFBD>lt?puuttuvien lkm.
for j=1:miss
rowToBeAdded = emptyRow;
rowToBeAdded(end) = ind;
data(nrows+pointer, :) = rowToBeAdded;
pointer = pointer+1;
end
end
data = sortrows(data, ncols); % Sorttaa yksil<EFBFBD>iden mukaisesti
newData = data;
rowsFromInd = maxRowsFromInd;
adjprior = zeros(max(noalle),nloci);
priorTerm = 0;
for j=1:nloci
adjprior(:,j) = [repmat(1/noalle(j), [noalle(j),1]) ; ones(max(noalle)-noalle(j),1)];
priorTerm = priorTerm + noalle(j)*gammaln(1/noalle(j));
end
%----------------------------------------------------------------------------------------
function [Z, dist] = newGetDistances(data, rowsFromInd)
ninds = max(data(:,end));
nloci = size(data,2)-1;
riviLkm = nchoosek(ninds,2);
empties = find(data<0);
data(empties)=0;
data = uint8(data); % max(noalle) oltava <256
pariTaulu = zeros(riviLkm,2);
aPointer=1;
for a=1:ninds-1
pariTaulu(aPointer:aPointer+ninds-1-a,1) = ones(ninds-a,1)*a;
pariTaulu(aPointer:aPointer+ninds-1-a,2) = (a+1:ninds)';
aPointer = aPointer+ninds-a;
end
eka = pariTaulu(:,ones(1,rowsFromInd));
eka = eka * rowsFromInd;
miinus = repmat(rowsFromInd-1 : -1 : 0, [riviLkm 1]);
eka = eka - miinus;
toka = pariTaulu(:,ones(1,rowsFromInd)*2);
toka = toka * rowsFromInd;
toka = toka - miinus;
%eka = uint16(eka);
%toka = uint16(toka);
summa = zeros(riviLkm,1);
vertailuja = zeros(riviLkm,1);
clear pariTaulu; clear miinus;
x = zeros(size(eka)); x = uint8(x);
y = zeros(size(toka)); y = uint8(y);
for j=1:nloci;
for k=1:rowsFromInd
x(:,k) = data(eka(:,k),j);
y(:,k) = data(toka(:,k),j);
end
for a=1:rowsFromInd
for b=1:rowsFromInd
vertailutNyt = double(x(:,a)>0 & y(:,b)>0);
vertailuja = vertailuja + vertailutNyt;
lisays = (x(:,a)~=y(:,b) & vertailutNyt);
summa = summa+double(lisays);
end
end
end
clear x; clear y; clear vertailutNyt;
nollat = find(vertailuja==0);
dist = zeros(length(vertailuja),1);
dist(nollat) = 1;
muut = find(vertailuja>0);
dist(muut) = summa(muut)./vertailuja(muut);
clear summa; clear vertailuja;
Z = linkage(dist');
%----------------------------------------------------------------------------------------
function [Z, distances]= getDistances(data_matrix,nclusters)
%finds initial admixture clustering solution with nclusters clusters, uses simple mean Hamming distance
%gives partition in 8-bit format
%allocates all alleles of a single individual into the same basket
%data_matrix contains #Loci+1 columns, last column indicate whose alleles are placed in each row,
%i.e. ranges from 1 to #individuals. For diploids there are 2 rows per individual, for haploids only a single row
%missing values are indicated by zeros in the partition and by negative integers in the data_matrix.
size_data=size(data_matrix);
nloci=size_data(2)-1;
n=max(data_matrix(:,end));
distances=zeros(nchoosek(n,2),1);
pointer=1;
for i=1:n-1
i_data=data_matrix(find(data_matrix(:,end)==i),1:nloci);
for j=i+1:n
d_ij=0;
j_data=data_matrix(find(data_matrix(:,end)==j),1:nloci);
vertailuja = 0;
for k=1:size(i_data,1)
for l=1:size(j_data,1)
here_i=find(i_data(k,:)>=0);
here_j=find(j_data(l,:)>=0);
here_joint=intersect(here_i,here_j);
vertailuja = vertailuja + length(here_joint);
d_ij = d_ij + length(find(i_data(k,here_joint)~=j_data(l,here_joint)));
end
end
d_ij = d_ij / vertailuja;
distances(pointer)=d_ij;
pointer=pointer+1;
end
end
Z=linkage(distances');
%----------------------------------------------------------------------------------------
function Z = linkage(Y, method)
[k, n] = size(Y);
m = (1+sqrt(1+8*n))/2;
if k ~= 1 || m ~= fix(m)
error('The first input has to match the output of the PDIST function in size.');
end
if nargin == 1 % set default switch to be 'co'
method = 'co';
end
method = lower(method(1:2)); % simplify the switch string.
monotonic = 1;
Z = zeros(m-1,3); % allocate the output matrix.
N = zeros(1,2*m-1);
N(1:m) = 1;
n = m; % since m is changing, we need to save m in n.
R = 1:n;
for s = 1:(n-1)
X = Y;
[v, k] = min(X);
i = floor(m+1/2-sqrt(m^2-m+1/4-2*(k-1)));
j = k - (i-1)*(m-i/2)+i;
Z(s,:) = [R(i) R(j) v]; % update one more row to the output matrix A
I1 = 1:(i-1); I2 = (i+1):(j-1); I3 = (j+1):m; % these are temp variables.
U = [I1 I2 I3];
I = [I1.*(m-(I1+1)/2)-m+i i*(m-(i+1)/2)-m+I2 i*(m-(i+1)/2)-m+I3];
J = [I1.*(m-(I1+1)/2)-m+j I2.*(m-(I2+1)/2)-m+j j*(m-(j+1)/2)-m+I3];
switch method
case 'si' %single linkage
Y(I) = min(Y(I),Y(J));
case 'av' % average linkage
Y(I) = Y(I) + Y(J);
case 'co' %complete linkage
Y(I) = max(Y(I),Y(J));
case 'ce' % centroid linkage
K = N(R(i))+N(R(j));
Y(I) = (N(R(i)).*Y(I)+N(R(j)).*Y(J)-(N(R(i)).*N(R(j))*v^2)./K)./K;
case 'wa'
Y(I) = ((N(R(U))+N(R(i))).*Y(I) + (N(R(U))+N(R(j))).*Y(J) - ...
N(R(U))*v)./(N(R(i))+N(R(j))+N(R(U)));
end
J = [J i*(m-(i+1)/2)-m+j];
Y(J) = []; % no need for the cluster information about j.
% update m, N, R
m = m-1;
N(n+s) = N(R(i)) + N(R(j));
R(i) = n+s;
R(j:(n-1))=R((j+1):n);
end
%-----------------------------------------------------------------------------------
function popnames = initPopNames(nameFile, indexFile)
%Palauttaa tyhj<EFBFBD>n, mik<EFBFBD>li nimitiedosto ja indeksitiedosto
% eiv<EFBFBD>t olleet yht?pitki?
popnames = [];
indices = load(indexFile);
fid = fopen(nameFile);
if fid == -1
%File didn't exist
msgbox('Loading of the population names was unsuccessful', ...
'Error', 'error');
return;
end;
line = fgetl(fid);
counter = 1;
while (line ~= -1) && ~isempty(line)
names{counter} = line;
line = fgetl(fid);
counter = counter + 1;
end;
fclose(fid);
if length(names) ~= length(indices)
disp('The number of population names must be equal to the number of ');
disp('entries in the file specifying indices of the first individuals of ');
disp('each population.');
return;
end
popnames = cell(length(names), 2);
for i = 1:length(names)
popnames{i,1} = names(i);
popnames{i,2} = indices(i);
end
%-----------------------------------------------------------------------------------
function [sumcounts, counts, logml] = ...
initialCounts(partition, data, npops, rowsFromInd, noalle)
nloci=size(data,2);
ninds = size(data,1)/rowsFromInd;
counts = zeros(max(noalle),nloci,npops);
sumcounts = zeros(npops,nloci);
for i=1:npops
for j=1:nloci
havainnotLokuksessa = find(partition==i & data(:,j)>=0);
sumcounts(i,j) = length(havainnotLokuksessa);
for k=1:noalle(j)
alleleCode = k;
N_ijk = length(find(data(havainnotLokuksessa,j)==alleleCode));
counts(k,j,i) = N_ijk;
end
end
end
initializeGammaln(ninds, rowsFromInd, max(noalle));
logml = computeLogml(counts, sumcounts, noalle, data, rowsFromInd);
%-----------------------------------------------------------------------
function logml=computeLogml(counts, sumcounts, noalle, data, rowsFromInd)
nloci = size(counts,2);
npops = size(counts,3);
adjnoalle = zeros(max(noalle),nloci);
for j=1:nloci
adjnoalle(1:noalle(j),j)=noalle(j);
if noalle(j)<max(noalle)
adjnoalle(noalle(j)+1:end,j)=1;
end
end
%logml2 = sum(sum(sum(gammaln(counts+repmat(adjprior,[1 1 npops]))))) ...
% - npops*sum(sum(gammaln(adjprior))) - ...
% sum(sum(gammaln(1+sumcounts)));
%logml = logml2;
global GAMMA_LN;
rowsInG = size(data,1)+rowsFromInd;
logml = sum(sum(sum(GAMMA_LN(counts+1 + repmat(rowsInG*(adjnoalle-1),[1 1 npops]))))) ...
- npops*sum(sum(GAMMA_LN(1, adjnoalle))) ...
-sum(sum(GAMMA_LN(sumcounts+1,1)));
%--------------------------------------------------------------------------
function initializeGammaln(ninds, rowsFromInd, maxAlleles)
%Alustaa GAMMALN muuttujan s.e. GAMMALN(i,j)=gammaln((i-1) + 1/j)
global GAMMA_LN;
GAMMA_LN = zeros((1+ninds)*rowsFromInd, maxAlleles);
for i=1:(ninds+1)*rowsFromInd
for j=1:maxAlleles
GAMMA_LN(i,j)=gammaln((i-1) + 1/j);
end
end
%-------------------------------------------------------------------
function changesInLogml = writeMixtureInfo(logml, rowsFromInd, data, adjprior, ...
priorTerm, outPutFile, inputFile, partitionSummary, popnames, fixedK)
global PARTITION;
global COUNTS;
global SUMCOUNTS;
global LOGDIFF;
ninds = size(data,1)/rowsFromInd;
npops = size(COUNTS,3);
names = (size(popnames,1) == ninds); %Tarkistetaan ett?nimet viittaavat yksil<EFBFBD>ihin
if length(outPutFile)>0
fid = fopen(outPutFile,'w');
else
fid = -1;
diary('baps4_output.baps'); % save in text anyway.
end
dispLine;
disp('RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:');
disp(['Data file: ' inputFile]);
disp(['Model: independent']);
disp(['Number of clustered individuals: ' ownNum2Str(ninds)]);
disp(['Number of groups in optimal partition: ' ownNum2Str(npops)]);
disp(['Log(marginal likelihood) of optimal partition: ' ownNum2Str(logml)]);
disp(' ');
if (fid ~= -1)
fprintf(fid,'%s \n', ['RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:']); fprintf(fid,'\n');
fprintf(fid,'%s \n', ['Data file: ' inputFile]); fprintf(fid,'\n');
fprintf(fid,'%s \n', ['Number of clustered individuals: ' ownNum2Str(ninds)]); fprintf(fid,'\n');
fprintf(fid,'%s \n', ['Number of groups in optimal partition: ' ownNum2Str(npops)]); fprintf(fid,'\n');
fprintf(fid,'%s \n', ['Log(marginal likelihood) of optimal partition: ' ownNum2Str(logml)]); fprintf(fid,'\n');
end
cluster_count = length(unique(PARTITION));
disp(['Best Partition: ']);
if (fid ~= -1)
fprintf(fid,'%s \n',['Best Partition: ']); fprintf(fid,'\n');
end
for m=1:cluster_count
indsInM = find(PARTITION==m);
length_of_beginning = 11 + floor(log10(m));
cluster_size = length(indsInM);
if names
text = ['Cluster ' num2str(m) ': {' char(popnames{indsInM(1)})];
for k = 2:cluster_size
text = [text ', ' char(popnames{indsInM(k)})];
end;
else
text = ['Cluster ' num2str(m) ': {' num2str(indsInM(1))];
for k = 2:cluster_size
text = [text ', ' num2str(indsInM(k))];
end;
end
text = [text '}'];
while length(text)>58
%Take one line and display it.
new_line = takeLine(text,58);
text = text(length(new_line)+1:end);
disp(new_line);
if (fid ~= -1)
fprintf(fid,'%s \n',[new_line]);
fprintf(fid,'\n');
end
if length(text)>0
text = [blanks(length_of_beginning) text];
else
text = [];
end;
end;
if ~isempty(text)
disp(text);
if (fid ~= -1)
fprintf(fid,'%s \n',[text]);
fprintf(fid,'\n');
end
end;
end
if npops > 1
disp(' ');
disp(' ');
disp('Changes in log(marginal likelihood) if indvidual i is moved to group j:');
if (fid ~= -1)
fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
fprintf(fid, '%s \n', ['Changes in log(marginal likelihood) if indvidual i is moved to group j:']); fprintf(fid, '\n');
end
if names
nameSizes = zeros(ninds,1);
for i = 1:ninds
nimi = char(popnames{i});
nameSizes(i) = length(nimi);
end
maxSize = max(nameSizes);
maxSize = max(maxSize, 5);
erotus = maxSize - 5;
alku = blanks(erotus);
ekarivi = [alku ' ind' blanks(6+erotus)];
else
ekarivi = ' ind ';
end
for i = 1:cluster_count
ekarivi = [ekarivi ownNum2Str(i) blanks(8-floor(log10(i)))];
end
disp(ekarivi);
if (fid ~= -1)
fprintf(fid, '%s \n', [ekarivi]); fprintf(fid, '\n');
end
%ninds = size(data,1)/rowsFromInd;
changesInLogml = LOGDIFF';
for ind = 1:ninds
%[muutokset, diffInCounts] = laskeMuutokset(ind, rowsFromInd, data, ...
% adjprior, priorTerm);
%changesInLogml(:,ind) = muutokset;
muutokset = changesInLogml(:,ind);
if names
nimi = char(popnames{ind});
rivi = [blanks(maxSize - length(nimi)) nimi ':'];
else
rivi = [blanks(4-floor(log10(ind))) ownNum2Str(ind) ':'];
end
for j = 1:npops
rivi = [rivi ' ' logml2String(omaRound(muutokset(j)))];
end
disp(rivi);
if (fid ~= -1)
fprintf(fid, '%s \n', [rivi]); fprintf(fid, '\n');
end
end
disp(' '); disp(' ');
disp('KL-divergence matrix in PHYLIP format:');
dist_mat = zeros(npops, npops);
if (fid ~= -1)
fprintf(fid, '%s \n', [' ']); %fprintf(fid, '\n');
fprintf(fid, '%s \n', [' ']); %fprintf(fid, '\n');
fprintf(fid, '%s \n', ['KL-divergence matrix in PHYLIP format:']); %fprintf(fid, '\n');
end
maxnoalle = size(COUNTS,1);
nloci = size(COUNTS,2);
d = zeros(maxnoalle, nloci, npops);
prior = adjprior;
prior(find(prior==1))=0;
nollia = find(all(prior==0)); %Lokukset, joissa oli havaittu vain yht?alleelia.
prior(1,nollia)=1;
for pop1 = 1:npops
d(:,:,pop1) = (squeeze(COUNTS(:,:,pop1))+prior) ./ repmat(sum(squeeze(COUNTS(:,:,pop1))+prior),maxnoalle,1);
%dist1(pop1) = (squeeze(COUNTS(:,:,pop1))+adjprior) ./ repmat((SUMCOUNTS(pop1,:)+adjprior), maxnoalle, 1);
end
% ekarivi = blanks(7);
% for pop = 1:npops
% ekarivi = [ekarivi num2str(pop) blanks(7-floor(log10(pop)))];
% end
ekarivi = num2str(npops);
disp(ekarivi);
if (fid ~= -1)
fprintf(fid, '%s \n', [ekarivi]); %fprintf(fid, '\n');
end
for pop1 = 1:npops
% rivi = [blanks(2-floor(log10(pop1))) num2str(pop1) ' '];
for pop2 = 1:pop1-1
dist1 = d(:,:,pop1); dist2 = d(:,:,pop2);
div12 = sum(sum(dist1.*log2((dist1+10^-10) ./ (dist2+10^-10))))/nloci;
div21 = sum(sum(dist2.*log2((dist2+10^-10) ./ (dist1+10^-10))))/nloci;
div = (div12+div21)/2;
% rivi = [rivi kldiv2str(div) ' '];
dist_mat(pop1,pop2) = div;
end
% disp(rivi);
% if (fid ~= -1)
% fprintf(fid, '%s \n', [rivi]); fprintf(fid, '\n');
% end
end
end
dist_mat = dist_mat + dist_mat'; % make it symmetric
for pop1 = 1:npops
rivi = ['Cluster_' num2str(pop1) ' '];
for pop2 = 1:npops
rivi = [rivi kldiv2str(dist_mat(pop1,pop2)) ' '];
end
disp(rivi);
if (fid ~= -1)
fprintf(fid, '%s \n', [rivi]); %fprintf(fid, '\n');
end
end
disp(' ');
disp(' ');
disp('List of sizes of 10 best visited partitions and corresponding log(ml) values');
if (fid ~= -1)
fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
fprintf(fid, '%s \n', ['List of sizes of 10 best visited partitions and corresponding log(ml) values']); fprintf(fid, '\n');
end
partitionSummary = sortrows(partitionSummary,2);
partitionSummary = partitionSummary(size(partitionSummary,1):-1:1 , :);
partitionSummary = partitionSummary(find(partitionSummary(:,2)>-1e49),:);
if size(partitionSummary,1)>10
vikaPartitio = 10;
else
vikaPartitio = size(partitionSummary,1);
end
for part = 1:vikaPartitio
line = [num2str(partitionSummary(part,1)) ' ' num2str(partitionSummary(part,2))];
disp(line);
if (fid ~= -1)
fprintf(fid, '%s \n', [line]); fprintf(fid, '\n');
end
end
if ~fixedK
disp(' ');
disp(' ');
disp('Probabilities for number of clusters');
if (fid ~= -1)
fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
fprintf(fid, '%s \n', [' ']); fprintf(fid, '\n');
fprintf(fid, '%s \n', ['Probabilities for number of clusters']); fprintf(fid, '\n');
end
npopsTaulu = unique(partitionSummary(:,1));
len = length(npopsTaulu);
probs = zeros(len,1);
partitionSummary(:,2) = partitionSummary(:,2)-max(partitionSummary(:,2));
sumtn = sum(exp(partitionSummary(:,2)));
for i=1:len
npopstn = sum(exp(partitionSummary(find(partitionSummary(:,1)==npopsTaulu(i)),2)));
probs(i) = npopstn / sumtn;
end
for i=1:len
if probs(i)>1e-5
line = [num2str(npopsTaulu(i)) ' ' num2str(probs(i))];
disp(line);
if (fid ~= -1)
fprintf(fid, '%s \n', [line]); fprintf(fid, '\n');
end
end
end
end
if (fid ~= -1)
fclose(fid);
else
diary off
end
%---------------------------------------------------------------
function dispLine
disp('---------------------------------------------------');
%--------------------------------------------------------------
function num2 = omaRound(num)
% Py<EFBFBD>rist<EFBFBD><EFBFBD> luvun num 1 desimaalin tarkkuuteen
num = num*10;
num = round(num);
num2 = num/10;
%---------------------------------------------------------
function digit = palautaYks(num,yks)
% palauttaa luvun num 10^yks termin kertoimen
% string:in?
% yks t<EFBFBD>ytyy olla kokonaisluku, joka on
% v<EFBFBD>hint<EFBFBD><EFBFBD>n -1:n suuruinen. Pienemmill?
% luvuilla tapahtuu jokin py<EFBFBD>ristysvirhe.
if yks>=0
digit = rem(num, 10^(yks+1));
digit = floor(digit/(10^yks));
else
digit = num*10;
digit = floor(rem(digit,10));
end
digit = num2str(digit);
function mjono = kldiv2str(div)
mjono = ' ';
if abs(div)<100
%Ei tarvita e-muotoa
mjono(6) = num2str(rem(floor(div*1000),10));
mjono(5) = num2str(rem(floor(div*100),10));
mjono(4) = num2str(rem(floor(div*10),10));
mjono(3) = '.';
mjono(2) = num2str(rem(floor(div),10));
arvo = rem(floor(div/10),10);
if arvo>0
mjono(1) = num2str(arvo);
end
else
suurinYks = floor(log10(div));
mjono(6) = num2str(suurinYks);
mjono(5) = 'e';
mjono(4) = palautaYks(abs(div),suurinYks-1);
mjono(3) = '.';
mjono(2) = palautaYks(abs(div),suurinYks);
end
%--------------------------------------------------------------------
function newline = takeLine(description,width)
%Returns one line from the description: line ends to the first
%space after width:th mark.
newLine = description(1:width);
n = width+1;
while ~isspace(description(n)) && n<length(description)
n = n+1;
end;
newline = description(1:n);
%-------------------------------------------------------
function pal = testaaPop(rivi)
% pal=1, mik<EFBFBD>li rivi alkaa jollain seuraavista
% kirjainyhdistelmist? Pop, pop, POP. Kaikissa muissa
% tapauksissa pal=0.
if length(rivi)<3
pal = 0;
return
end
if (all(rivi(1:3)=='Pop') || ...
all(rivi(1:3)=='pop') || ...
all(rivi(1:3)=='POP'))
pal = 1;
return
else
pal = 0;
return
end
%----------------------------------------------------------------------------
function dist2 = laskeOsaDist(inds2, dist, ninds)
% Muodostaa dist vektorista osavektorin, joka sis<EFBFBD>lt<EFBFBD><EFBFBD> yksil<EFBFBD>iden inds2
% v<EFBFBD>liset et<EFBFBD>isyydet. ninds=kaikkien yksil<EFBFBD>iden lukum<EFBFBD><EFBFBD>r?
ninds2 = length(inds2);
apu = zeros(nchoosek(ninds2,2),2);
rivi = 1;
for i=1:ninds2-1
for j=i+1:ninds2
apu(rivi, 1) = inds2(i);
apu(rivi, 2) = inds2(j);
rivi = rivi+1;
end
end
apu = (apu(:,1)-1).*ninds - apu(:,1) ./ 2 .* (apu(:,1)-1) + (apu(:,2)-apu(:,1));
dist2 = dist(apu);
%--------------------------------------------------------
function ninds = testaaOnkoKunnollinenBapsData(data)
%Tarkastaa onko viimeisess?sarakkeessa kaikki
%luvut 1,2,...,n johonkin n:<EFBFBD><EFBFBD>n asti.
%Tarkastaa lis<EFBFBD>ksi, ett?on v<EFBFBD>hint<EFBFBD><EFBFBD>n 2 saraketta.
if size(data,1)<2
ninds = 0; return;
end
lastCol = data(:,end);
ninds = max(lastCol);
if ~isequal((1:ninds)',unique(lastCol))
ninds = 0; return;
end
%--------------------------------------------------------------------------
function [emptyPop, pops] = findEmptyPop(npops)
% Palauttaa ensimm<EFBFBD>isen tyhj<EFBFBD>n populaation indeksin. Jos tyhji?
% populaatioita ei ole, palauttaa -1:n.
global PARTITION;
pops = unique(PARTITION)';
if (length(pops) ==npops)
emptyPop = -1;
else
popDiff = diff([0 pops npops+1]);
emptyPop = min(find(popDiff > 1));
end
%--------------------------------------------------------------------
function kunnossa = testaaGenePopData(tiedostonNimi)
% kunnossa == 0, jos data ei ole kelvollinen genePop data.
% Muussa tapauksessa kunnossa == 1.
kunnossa = 0;
fid = fopen(tiedostonNimi);
line1 = fgetl(fid); %ensimm<EFBFBD>inen rivi
line2 = fgetl(fid); %toinen rivi
line3 = fgetl(fid); %kolmas
if (isequal(line1,-1) || isequal(line2,-1) || isequal(line3,-1))
disp('Incorrect file format 1168'); fclose(fid);
return
end
if (testaaPop(line1)==1 || testaaPop(line2)==1)
disp('Incorrect file format 1172'); fclose(fid);
return
end
if testaaPop(line3)==1
%2 rivi t<EFBFBD>ll<EFBFBD>in lokusrivi
nloci = rivinSisaltamienMjonojenLkm(line2);
line4 = fgetl(fid);
if isequal(line4,-1)
disp('Incorrect file format 1180'); fclose(fid);
return
end
if ~any(line4==',')
% Rivin nelj?t<EFBFBD>ytyy sis<EFBFBD>lt<EFBFBD><EFBFBD> pilkku.
disp('Incorrect file format 1185'); fclose(fid);
return
end
pointer = 1;
while ~isequal(line4(pointer),',') %Tiedet<EFBFBD><EFBFBD>n, ett?pys<EFBFBD>htyy
pointer = pointer+1;
end
line4 = line4(pointer+1:end); %pilkun j<EFBFBD>lkeinen osa
nloci2 = rivinSisaltamienMjonojenLkm(line4);
if (nloci2~=nloci)
disp('Incorrect file format 1195'); fclose(fid);
return
end
else
line = fgetl(fid);
lineNumb = 4;
while (testaaPop(line)~=1 && ~isequal(line,-1))
line = fgetl(fid);
lineNumb = lineNumb+1;
end
if isequal(line,-1)
disp('Incorrect file format 1206'); fclose(fid);
return
end
nloci = lineNumb-2;
line4 = fgetl(fid); %Eka rivi pop sanan j<EFBFBD>lkeen
if isequal(line4,-1)
disp('Incorrect file format 1212'); fclose(fid);
return
end
if ~any(line4==',')
% Rivin t<EFBFBD>ytyy sis<EFBFBD>lt<EFBFBD><EFBFBD> pilkku.
disp('Incorrect file format 1217'); fclose(fid);
return
end
pointer = 1;
while ~isequal(line4(pointer),',') %Tiedet<EFBFBD><EFBFBD>n, ett?pys<EFBFBD>htyy.
pointer = pointer+1;
end
line4 = line4(pointer+1:end); %pilkun j<EFBFBD>lkeinen osa
nloci2 = rivinSisaltamienMjonojenLkm(line4);
if (nloci2~=nloci)
disp('Incorrect file format 1228'); fclose(fid);
return
end
end
kunnossa = 1;
fclose(fid);
%------------------------------------------------------
function [data, popnames] = lueGenePopData(tiedostonNimi)
fid = fopen(tiedostonNimi);
line = fgetl(fid); %ensimm<EFBFBD>inen rivi
line = fgetl(fid); %toinen rivi
count = rivinSisaltamienMjonojenLkm(line);
line = fgetl(fid);
lokusRiveja = 1;
while (testaaPop(line)==0)
lokusRiveja = lokusRiveja+1;
line = fgetl(fid);
end
if lokusRiveja>1
nloci = lokusRiveja;
else
nloci = count;
end
popnames = cell(10,2);
data = zeros(100, nloci+1);
nimienLkm=0;
ninds=0;
poimiNimi=1;
digitFormat = -1;
while line ~= -1
line = fgetl(fid);
if poimiNimi==1
%Edellinen rivi oli 'pop'
nimienLkm = nimienLkm+1;
ninds = ninds+1;
if nimienLkm>size(popnames,1);
popnames = [popnames; cell(10,2)];
end
nimi = lueNimi(line);
if digitFormat == -1
digitFormat = selvitaDigitFormat(line);
divider = 10^digitFormat;
end
popnames{nimienLkm, 1} = {nimi}; %N<EFBFBD>in se on greedyMix:iss<EFBFBD>kin?!?
popnames{nimienLkm, 2} = ninds;
poimiNimi=0;
data = addAlleles(data, ninds, line, divider);
elseif testaaPop(line)
poimiNimi = 1;
elseif line ~= -1
ninds = ninds+1;
data = addAlleles(data, ninds, line, divider);
end
end
data = data(1:ninds*2,:);
popnames = popnames(1:nimienLkm,:);
fclose(fid);
%--------------------------------------------------------
function data = addAlleles(data, ind, line, divider)
% Lisaa BAPS-formaatissa olevaan datataulukkoon
% yksil<EFBFBD><EFBFBD> ind vastaavat rivit. Yksil<EFBFBD>n alleelit
% luetaan genepop-formaatissa olevasta rivist?
% line. Jos data on 3 digit formaatissa on divider=1000.
% Jos data on 2 digit formaatissa on divider=100.
nloci = size(data,2)-1;
if size(data,1) < 2*ind
data = [data; zeros(100,nloci+1)];
end
k=1;
merkki=line(k);
while ~isequal(merkki,',')
k=k+1;
merkki=line(k);
end
line = line(k+1:end);
clear k; clear merkki;
alleeliTaulu = sscanf(line,'%d');
if length(alleeliTaulu)~=nloci
disp('Incorrect data format.');
end
for j=1:nloci
ekaAlleeli = floor(alleeliTaulu(j)/divider);
if ekaAlleeli==0
ekaAlleeli=-999;
end
tokaAlleeli = rem(alleeliTaulu(j),divider);
if tokaAlleeli==0
tokaAlleeli=-999;
end
data(2*ind-1,j) = ekaAlleeli;
data(2*ind,j) = tokaAlleeli;
end
data(2*ind-1,end) = ind;
data(2*ind,end) = ind;
%------------------------------------------------------
function count = rivinSisaltamienMjonojenLkm(line)
% Palauttaa line:n sis<EFBFBD>lt<EFBFBD>mien mjonojen lukum<EFBFBD><EFBFBD>r<EFBFBD>n.
% Mjonojen v<EFBFBD>liss?t<EFBFBD>ytyy olla v<EFBFBD>lily<EFBFBD>nti.
count = 0;
pit = length(line);
tila = 0; %0, jos odotetaan v<EFBFBD>lily<EFBFBD>ntej? 1 jos odotetaan muita merkkej?
for i=1:pit
merkki = line(i);
if (isspace(merkki) && tila==0)
%Ei tehd?mit<EFBFBD><EFBFBD>n.
elseif (isspace(merkki) && tila==1)
tila = 0;
elseif (~isspace(merkki) && tila==0)
tila = 1;
count = count+1;
elseif (~isspace(merkki) && tila==1)
%Ei tehd?mit<EFBFBD><EFBFBD>n
end
end
%-------------------------------------------------------
function nimi = lueNimi(line)
%Palauttaa line:n alusta sen osan, joka on ennen pilkkua.
n = 1;
merkki = line(n);
nimi = '';
while ~isequal(merkki,',')
nimi = [nimi merkki];
n = n+1;
merkki = line(n);
end
%-------------------------------------------------------
function df = selvitaDigitFormat(line)
% line on ensimm<EFBFBD>inen pop-sanan j<EFBFBD>lkeinen rivi
% Genepop-formaatissa olevasta datasta. funktio selvitt<EFBFBD><EFBFBD>
% rivin muodon perusteella, ovatko datan alleelit annettu
% 2 vai 3 numeron avulla.
n = 1;
merkki = line(n);
while ~isequal(merkki,',')
n = n+1;
merkki = line(n);
end
while ~any(merkki == '0123456789');
n = n+1;
merkki = line(n);
end
numeroja = 0;
while any(merkki == '0123456789');
numeroja = numeroja+1;
n = n+1;
merkki = line(n);
end
df = numeroja/2;
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