void testUpdateDistanceMatrix()
{
PeakListCollection PLC = SamplePeakListCollection();
DistanceMatrix DM = PLC.buildDistanceMatrix_(200., 0.5, 0, 0.);
DistanceMatrix Ref = PLC.buildDistanceMatrix_(200., 0.5, 0, 0.);
DM.addElement(99.); //Add a dummy element, we will delete it again
unsigned int merged_lower = 4; //last two elements
unsigned int merged_upper = 5;
PLC.plContent_.clear();
PLC.plContent_.resize(6,1);
PLC.updateDistanceMatrix_(DM, merged_lower, merged_upper, 200., 0.5, 0, 0.);
//Size should still be 5
shouldEqual((int)DM.size(),5);
//Matrix should be the same as the original matrix Ref
for(unsigned int i = 0; i < 5; i++){
for(unsigned int j = 0; j < i; j++){
shouldEqualTolerance(DM(i,j),Ref(i,j),TOL);
}
}
return;
}
void TreeTools::midpointRooting(Tree& tree)
{
throw Exception("TreeTools::midpointRooting(Tree). This function is deprecated, use TreeTemplateTools::midRoot instead!");
if (tree.isRooted())
tree.unroot();
DistanceMatrix* dist = getDistanceMatrix(tree);
vector<size_t> pos = MatrixTools::whichMax(dist->asMatrix());
double dmid = (*dist)(pos[0], pos[1]) / 2;
int id1 = tree.getLeafId(dist->getName(pos[0]));
int id2 = tree.getLeafId(dist->getName(pos[1]));
int rootId = tree.getRootId();
double d1 = getDistanceBetweenAnyTwoNodes(tree, id1, rootId);
double d2 = getDistanceBetweenAnyTwoNodes(tree, id2, rootId);
int current = d2 > d1 ? id2 : id1;
delete dist;
double l = tree.getDistanceToFather(current);
double c = l;
while (c < dmid)
{
current = tree.getFatherId(current);
l = tree.getDistanceToFather(current);
c += l;
}
tree.newOutGroup(current);
int brother = tree.getSonsId(tree.getRootId())[1];
if (brother == current)
brother = tree.getSonsId(tree.getRootId())[0];
tree.setDistanceToFather(current, l - (c - dmid));
tree.setDistanceToFather(brother, c - dmid);
}
DBL_MATRIX PeakListCollection::mergeAll(double drt, double dmz, double dz, double dint)
{
//preprocessing: delete empty PeakLists from c_
for(unsigned int i = 0; i < c_.size(); i++){
if(c_[i].size() == 0){
c_.erase(c_.begin() + i);
}
}
unsigned int oldSize = c_.size();
// initialization
//fill correspondenceMap with rt-values
correspondenceMap_.clear();
correspondenceMap_.resize(oldSize);
for(unsigned int i = 0; i < oldSize; i++){
correspondenceMap_[i].resize(c_[i].size());
for(unsigned int j = 0; j < c_[i].size(); j++){
//create map_item to write to correspondenceMap
map_item tempItem;
//origin information contains the PeakList index and rt value
originInformation o;
o.rt = c_[i][j].getRt();
o.mz = c_[i][j].getMz();
o.intensity = c_[i][j].getAbundance();
o.originPeakList = i;
o.originPeak = j;
tempItem.push_back( o );
correspondenceMap_[i][j] = tempItem;
}
}
//PeakLists are not merged in the beginning --> fill with 1
plContent_.clear();
plContent_.resize(oldSize,1);
//build distance matrix
DistanceMatrix D = buildDistanceMatrix_(drt,dmz,dz,dint);
//merge, until there is only one PeakList left
while(D.size() > 1){
//find cheapest assignment
unsigned int merge_lower = D.getMin_LowerIndex();
unsigned int merge_upper = D.getMin_HigherIndex();
//merge peaklists into a new one
mergePeakLists_(merge_lower, merge_upper, c_, drt, dmz, dz, dint);
//update distance matrix
updateDistanceMatrix_(D, merge_lower, merge_upper, drt, dmz, dz, dint);
}
return calculateRtCorrespondencesFromCorrespondenceMap_(oldSize);
}
// Find a 1:1 mapping from rows to columns of the specified square matrix such that the total cost is minimized. Returns a
// vector V such that V[i] = j maps rows i to columns j.
static std::vector<long>
findMinimumAssignment(const DistanceMatrix &matrix) {
#ifdef ROSE_HAVE_DLIB
ASSERT_forbid(matrix.size() == 0);
ASSERT_require(matrix.nr() == matrix.nc());
// We can avoid the O(n^3) Kuhn-Munkres algorithm if all values of the matrix are the same.
double minValue, maxValue;
dlib::find_min_and_max(matrix, minValue /*out*/, maxValue /*out*/);
if (minValue == maxValue) {
std::vector<long> ident;
ident.reserve(matrix.nr());
for (long i=0; i<matrix.nr(); ++i)
ident.push_back(i);
return ident;
}
// Dlib's Kuhn-Munkres finds the *maximum* mapping over *integers*, so we negate everything to find the minumum, and we map
// the doubles onto a reasonably large interval of integers. The interval should be large enough to have some precision,
// but not so large that things might overflow.
const int iGreatest = 1000000; // arbitrary upper bound for integer interval
dlib::matrix<long> intMatrix(matrix.nr(), matrix.nc());
for (long i=0; i<matrix.nr(); ++i) {
for (long j=0; j<matrix.nc(); ++j)
intMatrix(i, j) = round(-iGreatest * (matrix(i, j) - minValue) / (maxValue - minValue));
}
return dlib::max_cost_assignment(intMatrix);
#else
throw FunctionSimilarity::Exception("dlib support is necessary for FunctionSimilarity analysis"
"; see ROSE installation instructions");
#endif
}
// Given a square matrix and a 1:1 mapping from rows to columns, return the total cost of the mapping.
static double
totalAssignmentCost(const DistanceMatrix &matrix, const std::vector<long> assignment) {
double sum = 0.0;
ASSERT_require(matrix.nr() == matrix.nc());
ASSERT_require((size_t)matrix.nr() == assignment.size());
for (long i=0; i<matrix.nr(); ++i) {
ASSERT_require(assignment[i] < matrix.nc());
sum += matrix(i, assignment[i]);
}
return sum;
}
int main(int argc, char **argv)
{
welcome();
defineverbose();
filenames.clear();
ClustalWInitializers();
clustalw::ClustalWResources *resources = clustalw::ClustalWResources::Instance();
resources->setPathToExecutable(string(argv[0]));
setUserParameters();
InputFile start;// object reads from an input file and creates a new file
start.run();
string offendingSeq;
Clustal* clustalObj;
clustalObj = new clustalw::Clustal();
int u = clustalObj->sequenceInput(false, &offendingSeq);
string phylipName;
clustalObj->align(&phylipName);
AminoAcidFrequency Table;
Table.openFile("TEST-OutputFile.aln");
vector<ProteinSequence> p1;
p1 = start.getProteinData();
Table.generateAminoAcidTables(p1);
p1.clear();
p1 = Table.getFinalSeqs();
DistanceMatrix dm;
dm.createDistanceTableCodes(p1);
dm.createSimilarityMatrixCodes(p1);
dm.createDistanceTableColours(p1);
dm.createSimilarityMatrixColours(p1);
VerticalPosition vp;
vp.run(p1);
cout<< "\nPROCESS COMPLETED!\nThe following files were created:\n" << endl;
for ( size_t i = 0; i < filenames.size(); i++ )
{
cout << "\t" << i+1 << ". " << filenames[i] << endl;
}
return 0;
}
void testBuildDistanceMatrix()
{
PeakListCollection PLC = SamplePeakListCollection();
DistanceMatrix DM = PLC.buildDistanceMatrix_(200., 0.5, 0, 0.);
//PeakList 4 should have no Peaks in common with the other lists --> Cost == DBL_MAX
for(unsigned int i = 0; i < DM.size()-1; i++){
shouldEqual(DM(i,4),DBL_MAX);
}
//distance PL0 - PL2 should be sqrt(0.1^2/0.5^2 + 30^2/200^2) = 0.25
shouldEqualTolerance(DM(0,2),0.25,TOL);
shouldEqualTolerance(DM(2,0),0.25,TOL);
return;
}
//build a distance matrix out of all PeakLists
DistanceMatrix PeakListCollection::buildDistanceMatrix_(double drt, double dmz, double dz, double dint = 0.)
{
//number of Peaklists
unsigned int numPl = c_.size();
DistanceMatrix DM;
//Add new elements to the Distance Matrix
for(unsigned int i = 0; i<numPl; i++){
DM.addElement();
//Calculate distance to the other elements / the cost of merging them
for(unsigned int j = 0; j<i; j++){
StableMarriage sm(c_[i],c_[j],drt,dmz,dz,dint);
sm.setLimit(1.);
DM(i,j) = sm.getCost();
}
}
return DM;
}
//Update the Distance Matrix
void PeakListCollection::updateDistanceMatrix_(DistanceMatrix& D, unsigned int merged_lower, unsigned int merged_upper,
double drt, double dmz, double dz, double dint = 0.)
{
//remove distances of merged PeakLists from distance matrix
D.deleteElement(merged_upper);
D.deleteElement(merged_lower);
//add a new column for the merged PeakList
D.addElement();
//fill distances
unsigned int sz = D.size();
for(unsigned int i = 0; i<sz-1; i++){
StableMarriage sm(c_[i],c_[sz-1],drt,dmz,dz,dint);
sm.setLimit(1.);
D(i,sz-1) = sm.getCost();
}
return;
}
DBL_MATRIX PeakListCollection::getAlignment(double drt, double dmz, double dz, double dint, AccelerationFlag flag = NORMAL, int param = 0)
{
//Create a copy of c_ to work on
std::vector<PeakList> pls = c_;
unsigned int oldSize = pls.size();
// initialization
//fill correspondenceMap with rt-values
correspondenceMap_.clear();
correspondenceMap_.resize(oldSize);
for(unsigned int i = 0; i < oldSize; i++){
correspondenceMap_[i].resize(pls[i].size());
for(unsigned int j = 0; j < pls[i].size(); j++){
//create map_item to write to correspondenceMap
map_item tempItem;
//origin information contains the PeakList index and rt value
originInformation o;
o.rt = c_[i][j].getRt();
o.mz = c_[i][j].getMz();
o.intensity = c_[i][j].getAbundance();
o.originPeakList = i;
o.originPeak = j;
tempItem.push_back( o );
correspondenceMap_[i][j] = tempItem;
}
}
//PeakLists are not merged in the beginning --> fill with 1
plContent_.clear();
plContent_.resize(oldSize,1);
DistanceMatrix D;
if(flag == NORMAL){
//build distance matrix
D = buildDistanceMatrix_(drt,dmz,dz,dint);
}
if(flag == REFERENCE){
//first, merge PeakList n with one other --> last PeakList contains PeakList n
if(param==0){
//select first two peaklists
unsigned int merge_lower = param;
unsigned int merge_upper = 1;
//merge peaklists into a new one
mergePeakLists_(merge_lower, merge_upper, pls, drt, dmz, dz, dint);
}else{
//select first and n'th PeakList
unsigned int merge_lower = 0;
unsigned int merge_upper = param;
//merge peaklists into a new one
mergePeakLists_(merge_lower, merge_upper, pls, drt, dmz, dz, dint);
}
}
//merge, until there is only one PeakList left
bool exit = false;
while(!exit){
//find cheapest assignment
unsigned int merge_lower = 0;
unsigned int merge_upper = 1;
switch(flag){
case NORMAL:
merge_lower = D.getMin_LowerIndex();
merge_upper = D.getMin_HigherIndex();
break;
case FAST:
//select last two PeakList
merge_lower = pls.size()-2;
merge_upper = pls.size()-1;
break;
case REFERENCE:
//select last two PeakList
merge_lower = pls.size()-2;
merge_upper = pls.size()-1;
break;
}
//merge peaklists into a new one
mergePeakLists_(merge_lower, merge_upper, pls, drt, dmz, dz, dint);
switch(flag){
case NORMAL:
//update distance matrix
updateDistanceMatrix_(D, merge_lower, merge_upper, drt, dmz, dz, dint);
if(D.size()==1){
exit = true;
}
break;
case FAST:
if(pls.size()==1){
exit = true;
}
break;
case REFERENCE:
if(pls.size()==1){
exit = true;
}
break;
}
}
DBL_MATRIX rtc = calculateRtCorrespondencesFromCorrespondenceMap_(oldSize);
return rtc;
}
pair<vector<int>, double> DistanceMatrix::compareAllWindows(DistanceMatrix &_distMat, int choice){
vector<MatrixWindow*> listOther = _distMat.getMatrixWindows();
int length = listMW.size();
int lengthOther = listOther.size();
vector<int> minCoord(2, 0.0);
MatrixWindow *minWindow1 = NULL;
MatrixWindow *minWindow2 = NULL;
double minLikeness= MslTools::doubleMax;
for (int i =0; i<length; i++){//loops through listMW to get comparee
MatrixWindow *win1 = listMW[i];
for (int j=0; j<lengthOther; j++){//loops through listOther to get comparor
MatrixWindow *win2 = listOther[j];
double likeness;
//decides which compare method from MatrixWindow to call
switch(choice){
case standard:
likeness = (*win1).compare((*win2));
break;
case diag:
likeness = (*win1).compareDiagonal((*win2));
break;
case doubleDiag:
likeness = (*win1).compareDoubleDiagonal((*win2));
break;
case minDist:
likeness = (*win1).compareMinDist((*win2));
break;
case minDistRow:
likeness=(*win1).compareMinRow((*win2));
break;
case minDistCol:
likeness=(*win1).compareMinCol((*win2));
break;
default:
cout<<"Invalid argument in function DistanceMatrix::compareAll()."<<endl;
exit(333);
}//end switch
if (likeness<minLikeness && abs(likeness - minLikeness) > 0.001){
cout << "New likeness: "<<likeness<<endl;
minLikeness=likeness;
minWindow1 = win1;
minWindow2 = win2;
minCoord[0]= i;
minCoord[1]=j;
}//endif
win2=NULL;
}//end for on j
win1= NULL;
}//end for on i
pair<vector<int>, double> coordsAndLikeness(minCoord, minLikeness);
minWindow1=NULL;
minWindow2=NULL;
return coordsAndLikeness;
}
//must add segID
void DistanceMatrix::printCompareInfo(DistanceMatrix &_distMat, pair<vector<int>, double> _result, int choice){
//retrieve values from the pair
vector<int> mwIndex(2, 0.0);
mwIndex= _result.first;
double minLikeness = _result.second;
//retrieve the winning Matrix Windows
vector<MatrixWindow*> listMW2 = _distMat.getMatrixWindows();
MatrixWindow *minWindow1 = listMW[mwIndex[0]];
MatrixWindow *minWindow2 = listMW2[mwIndex[1]];
//print information
int i1 = (*minWindow1).getLeftR();
int j1 = (*minWindow1).getLeftC();
int i2 = (*minWindow2).getLeftR();
int j2 = (*minWindow2).getLeftC();
string i1ID = atomVec[i1]->getSegID().c_str();
string j1ID = atomVec[j1]->getSegID().c_str();
string i2ID = _distMat.getAtomVector()[i2]->getSegID().c_str();
string j2ID = _distMat.getAtomVector()[j2]->getSegID().c_str();
if(i1ID=="" || j1ID=="" || i2ID=="" ||j2ID==""){
i1ID = atomVec[i1]->getChainId().c_str();
j1ID = atomVec[j1]->getChainId().c_str();
i2ID = _distMat.getAtomVector()[i2]->getChainId().c_str();
j2ID = _distMat.getAtomVector()[j2]->getChainId().c_str();
}
int i1res = atomVec[i1]->getResidueNumber();
int j1res = atomVec[j1]->getResidueNumber();
int i2res = _distMat.getAtomVector()[i2]->getResidueNumber();
int j2res = _distMat.getAtomVector()[j2]->getResidueNumber();
string PDBname= getFileName(PDBid);
string PDBnameShort = PDBname.substr(0,17);
string PDBname2 = getFileName(_distMat.getPDBid());
string PDBnameShort2 = PDBname2.substr(0,17);
cout<<"Comparing PDBs "<<PDBnameShort<<", "<<PDBnameShort2<<endl;
switch(choice){
case standard:
fprintf(stdout, "Standard compare:\t\tWindow1 %3d,%3d (Residues: %1s%3d, %1s%3d)\tWindow2 %3d,%3d (Residues: %1s%3d, %1s%3d)\t%8.3f\n", i1, j1, i1ID.c_str(), i1res, j1ID.c_str(), j1res, i2, j2, i2ID.c_str(), i2res, j2ID.c_str(), j2res, minLikeness);
break;
case diag:
fprintf(stdout, "Diagonal compare: \t\tWindow1 %3d,%3d (Residues: %1s%3d, %1s%3d)\tWindow2 %3d,%3d (Residues: %1s%3d, %1s%3d)\t%8.3f\n", i1, j1, i1ID.c_str(), i1res, j1ID.c_str(), j1res, i2, j2, i2ID.c_str(), i2res, j2ID.c_str(), j2res, minLikeness);
break;
case doubleDiag:
fprintf(stdout, "Double Diagonal compare: \tWindow1 %3d,%3d (Residues: %1s%3d, %1s%3d)\tWindow2 %3d,%3d (Residues: %1s%3d, %1s%3d)\t%8.3f\n", i1, j1, i1ID.c_str(), i1res, j1ID.c_str(), j1res, i2, j2, i2ID.c_str(), i2res, j2ID.c_str(), j2res, minLikeness);
break;
case minDist:
fprintf(stdout, "Minimum Distance compare: \tWindow1 %3d,%3d (Residues: %1s%3d, %1s%3d)\tWindow2 %3d,%3d (Residues: %1s%3d, %1s%3d)\t%8.3f\n", i1, j1, i1ID.c_str(), i1res, j1ID.c_str(), j1res, i2, j2, i2ID.c_str(), i2res, j2ID.c_str(), j2res, minLikeness);
break;
case minDistRow:
fprintf(stdout, "Minimum Distance Row compare: \tWindow1 %3d,%3d (Residues: %1s%3d, %1s%3d)\tWindow2 %3d,%3d (Residues: %1s%3d, %1s%3d)\t%8.3f\n", i1, j1, i1ID.c_str(), i1res, j1ID.c_str(), j1res, i2, j2, i2ID.c_str(), i2res, j2ID.c_str(), j2res, minLikeness);
break;
case minDistCol:
fprintf(stdout, "Minimum Distance Column compare: \tWindow1 %3d,%3d (Residues: %1s%3d, %1s%3d)\tWindow2 %3d,%3d (Residues: %1s%3d, %1s%3d)\t%8.3f\n", i1, j1, i1ID.c_str(), i1res, j1ID.c_str(), j1res, i2, j2, i2ID.c_str(), i2res, j2ID.c_str(), j2res, minLikeness);
break;
default:
cout<<"Error. Incorrect int value (choice) in DistanceMatrix::printCompareInfo(...)"<<endl;
exit(334);
}//end switch
}
vector<DistanceMatrixResult> DistanceMatrix::multiCompareAllWindows(DistanceMatrix &_distMat, int choice, int _numCompare){
/*
//which chains to skip:
map<string, bool> forbiddenIDMat1;
map<string, bool> forbiddenIDMat2;
// Maintain the proper spacing between residues of different matrix window pairs.
map<string,int> properRegisterRow;
map<string,int> properRegisterCol;
map<string,int>::iterator findRegistry;
*/
//get list of MWs to compare
vector<MatrixWindow*> listOther = _distMat.getMatrixWindows();
int length = listMW.size();
int lengthOther = listOther.size();
//vector of objects to return
vector<DistanceMatrixResult> returnVec;
//loop over number of times to compare all
for(int k=0; k<_numCompare; k++){
//minimum windows and indices
MatrixWindow *minWindow1 = NULL;
MatrixWindow *minWindow2 = NULL;
double minLikeness = 1000000;
int minIndex1=0;
int minIndex2=0;
//IDs to skip in the future
string i1IDWin;
string j1IDWin;
string i2IDWin;
string j2IDWin;
//which chains to skip:
map<string, bool> forbiddenIDMat1;
map<string, bool> forbiddenIDMat2;
// Maintain the proper spacing between residues of different matrix window pairs.
map<string,int> properRegisterRow;
map<string,int> properRegisterCol;
map<string,int>::iterator findRegistry;
for (int i =0; i<length; i++){//loops through listMW to get compare
MatrixWindow *win1 = listMW[i];
for (int j=0; j<lengthOther; j++){//loops through listOther to get comparor
MatrixWindow *win2 = listOther[j];
//get the ID (seg or chain) so we can filter ones we want to skip
int i1 = win1->getLeftR();
int j1 = win1->getLeftC();
int i2 = win2->getLeftR();
int j2 = win2->getLeftC();
string i1ID = atomVec[i1]->getSegID();
string j1ID = atomVec[j1]->getSegID();
string i2ID = _distMat.getAtomVector()[i2]->getSegID();
string j2ID = _distMat.getAtomVector()[j2]->getSegID();
if(i1ID=="" || j1ID=="" || i2ID=="" ||j2ID==""){
i1ID = atomVec[i1]->getChainId();
j1ID = atomVec[j1]->getChainId();
i2ID = _distMat.getAtomVector()[i2]->getChainId();
j2ID = _distMat.getAtomVector()[j2]->getChainId();
}//end if
// Skip if both chains are forbidden within a matrix
if (forbiddenIDMat1.find(i1ID+":"+j1ID)!=forbiddenIDMat1.end() || forbiddenIDMat2.find(i2ID+":"+j2ID)!=forbiddenIDMat2.end()) continue;
// Skip if both inter-matrix segids found and if difference in residue number is not the same.
findRegistry = properRegisterRow.find(i1ID+":"+i2ID);
double diffInResidueNumber = atomVec[i1]->getResidueNumber() - _distMat.getAtomVector()[i2]->getResidueNumber();
if (findRegistry != properRegisterRow.end() && findRegistry->second != diffInResidueNumber) continue;
findRegistry = properRegisterCol.find(j1ID+":"+j2ID);
diffInResidueNumber = atomVec[j1]->getResidueNumber() - _distMat.getAtomVector()[j2]->getResidueNumber();
if (findRegistry != properRegisterCol.end() && findRegistry->second != diffInResidueNumber) continue;
double likeness;
//decides which compare method from MatrixWindow to call
switch(choice){
case standard:
likeness = (*win1).compare((*win2));
break;
case diag:
likeness = (*win1).compareDiagonal((*win2));
break;
case doubleDiag:
likeness = (*win1).compareDoubleDiagonal((*win2));
break;
case minDist:
likeness = (*win1).compareMinDist((*win2));
break;
case minDistRow:
likeness=(*win1).compareMinRow((*win2));
break;
case minDistCol:
likeness=(*win1).compareMinCol((*win2));
break;
default:
cout<<"Invalid argument in function DistanceMatrix::compareAll()."<<endl;
exit(333);
}//end switch
if (likeness<minLikeness && abs(likeness - minLikeness) > 0.001){
minLikeness = likeness;
minWindow1 = win1;
minWindow2 = win2;
minIndex1 = i;
minIndex2 = j;
//set the ID to avoid in the future
i1IDWin = i1ID;
j1IDWin = j1ID;
i2IDWin = i2ID;
j2IDWin = j2ID;
}//endif
win2=NULL;
}//end for on j
win1= NULL;
}//end for on i
// Allowed inter matrix identifier[SEGID] difference in residue numbers
vector<int> residueNumbers1 = minWindow1->getUpLeftResidueNumbers();
vector<int> residueNumbers2 = minWindow2->getUpLeftResidueNumbers();
properRegisterRow[i1IDWin+":"+i2IDWin] = (residueNumbers1[0] - residueNumbers2[0]);
properRegisterRow[i2IDWin+":"+i1IDWin] = - (residueNumbers1[0] - residueNumbers2[0]);
properRegisterCol[j1IDWin+":"+j2IDWin] = (residueNumbers1[1] - residueNumbers2[1]);
properRegisterCol[j2IDWin+":"+j1IDWin] = - (residueNumbers1[1] - residueNumbers2[1]);
// Forbidden intra matrix identifier[SEGID] pairs
forbiddenIDMat1[i1IDWin+":"+j1IDWin] = false;
forbiddenIDMat1[j1IDWin+":"+i1IDWin] = false;
forbiddenIDMat2[i2IDWin+":"+j2IDWin] = false;
forbiddenIDMat2[j2IDWin+":"+i2IDWin] = false;
DistanceMatrixResult currentResult(*this, *minWindow1, _distMat, *minWindow2, minLikeness);
returnVec.push_back(currentResult);
minWindow1=NULL;
minWindow2=NULL;
}//end for on k
return returnVec;
}
TreeTemplate<Node>* OptimizationTools::buildDistanceTree(
DistanceEstimation& estimationMethod,
AgglomerativeDistanceMethod& reconstructionMethod,
const ParameterList& parametersToIgnore,
bool optimizeBrLen,
const std::string& param,
double tolerance,
unsigned int tlEvalMax,
OutputStream* profiler,
OutputStream* messenger,
unsigned int verbose) throw (Exception)
{
estimationMethod.resetAdditionalParameters();
estimationMethod.setVerbose(verbose);
if (param == DISTANCEMETHOD_PAIRWISE)
{
ParameterList tmp = estimationMethod.getSubstitutionModel().getIndependentParameters();
tmp.addParameters(estimationMethod.getRateDistribution().getIndependentParameters());
tmp.deleteParameters(parametersToIgnore.getParameterNames());
estimationMethod.setAdditionalParameters(tmp);
}
TreeTemplate<Node>* tree = NULL;
TreeTemplate<Node>* previousTree = NULL;
bool test = true;
while (test)
{
// Compute matrice:
if (verbose > 0)
ApplicationTools::displayTask("Estimating distance matrix", true);
estimationMethod.computeMatrix();
DistanceMatrix* matrix = estimationMethod.getMatrix();
if (verbose > 0)
ApplicationTools::displayTaskDone();
// Compute tree:
if (matrix->size() == 2) {
//Special case, there is only one possible tree:
Node* n1 = new Node(0);
Node* n2 = new Node(1, matrix->getName(0));
n2->setDistanceToFather((*matrix)(0,0) / 2.);
Node* n3 = new Node(2, matrix->getName(1));
n3->setDistanceToFather((*matrix)(0,0) / 2.);
n1->addSon(n2);
n1->addSon(n3);
tree = new TreeTemplate<Node>(n1);
break;
}
if (verbose > 0)
ApplicationTools::displayTask("Building tree");
reconstructionMethod.setDistanceMatrix(*matrix);
reconstructionMethod.computeTree();
previousTree = tree;
delete matrix;
tree = dynamic_cast<TreeTemplate<Node>*>(reconstructionMethod.getTree());
if (verbose > 0)
ApplicationTools::displayTaskDone();
if (previousTree && verbose > 0)
{
int rf = TreeTools::robinsonFouldsDistance(*previousTree, *tree, false);
ApplicationTools::displayResult("Topo. distance with previous iteration", TextTools::toString(rf));
test = (rf == 0);
delete previousTree;
}
if (param != DISTANCEMETHOD_ITERATIONS)
break; // Ends here.
// Now, re-estimate parameters:
auto_ptr<SubstitutionModel> model(estimationMethod.getSubstitutionModel().clone());
auto_ptr<DiscreteDistribution> rdist(estimationMethod.getRateDistribution().clone());
DRHomogeneousTreeLikelihood tl(*tree,
*estimationMethod.getData(),
model.get(),
rdist.get(),
true, verbose > 1);
tl.initialize();
ParameterList parameters = tl.getParameters();
if (!optimizeBrLen)
{
vector<string> vs = tl.getBranchLengthsParameters().getParameterNames();
parameters.deleteParameters(vs);
}
parameters.deleteParameters(parametersToIgnore.getParameterNames());
optimizeNumericalParameters(&tl, parameters, NULL, 0, tolerance, tlEvalMax, messenger, profiler, verbose > 0 ? verbose - 1 : 0);
if (verbose > 0)
{
ParameterList tmp = tl.getSubstitutionModelParameters();
for (unsigned int i = 0; i < tmp.size(); i++)
{
ApplicationTools::displayResult(tmp[i].getName(), TextTools::toString(tmp[i].getValue()));
}
tmp = tl.getRateDistributionParameters();
for (unsigned int i = 0; i < tmp.size(); i++)
{
ApplicationTools::displayResult(tmp[i].getName(), TextTools::toString(tmp[i].getValue()));
}
}
}
return tree;
}
int main(int argc, char *argv[]){
// Option Parser
Options opt = setupOptions(argc,argv);
ifstream fs2;
//create system and dm for first PDB
PDBReader reader;
reader.open(opt.inputPDB);
reader.read();
reader.close();
System *constSys = new System(reader.getAtoms());
DistanceMatrix constDM;
//add CA atoms to the atom vectors
for (int j=0; j<constSys->residueSize(); j++){
Residue &tempRes=constSys->getResidue(j);
if (tempRes.exists("CA")){
constDM.addAtom(tempRes("CA"));
}
}//end for on j
//fill the DistanceMatrix and set window size
constDM.setGeneralWinSize(opt.windowSize);
constDM.createDistanceMatrix();
constDM.setIntraChain(opt.intraChainCompare);
constDM.setPDBid(opt.inputPDB);
constDM.setDebug(opt.debug);
//create matrix windows
constDM.createMatrixWindows();
delete(constSys);
if (constDM.getMatrixWindows().size()==0){
cout<<"Uh-oh.All the windows got filtered in the PDB you wanted to compare against."<<endl;
exit(111);
}
// COMMMENT OUT BEGINS
/*
//read in list of PDBs to compare to first PDB
vector<string> list;
ifstream fs;
fs.open(opt.pdbList.c_str());
if (fs.fail()){
cerr<<"Cannot open file "<<opt.pdbList<<endl;
exit(1);
}
while(true){
string line;
getline(fs, line);
if(fs.fail()){
//no more lines to read, quite the while.
break;
}
if(line==""){
continue;
}
list.push_back(line);
}
fs.close();
// List of distance matrices, one for each PDB
vector<DistanceMatrix> DMVec(list.size());
// A system object for each PDB
vector<System*> sysVec(list.size(), NULL);
// Create DistanceMatrix and System Objects for list of PDBs
for(int i=0; i<list.size(); i++){
cout<<i<<"create sys and dm."<<endl;
PDBReader rAv;
rAv.open(list[i]);
rAv.read();
rAv.close();
sysVec[i] =new System(rAv.getAtoms());
//add CA atoms to the atom vectors
for (int j=0; j<sysVec[i]->residueSize(); j++){
Residue &tempRes=sysVec[i]->getResidue(j);
if (tempRes.exists("CA")){
//only add CA if it is on a helix
string segID = tempRes("CA").getSegID();
//if(segID == "" || segID.at(0) == 'H'){
DMVec[i].addAtom(tempRes("CA"));
// }
}
}//end for on j
//fill the DistanceMatrix and set window size
DMVec[i].setGeneralWinSize(opt.windowSize);
DMVec[i].createDistanceMatrix();
DMVec[i].setIntraChain(opt.intraChainCompare);
DMVec[i].setPDBid(list[i]);
//create matrix windows
DMVec[i].createMatrixWindows();
delete(sysVec[i]);
}//end for on i
*/
// COMMMENT OUT ENDS
// NEW BEGINS
// load distance matrix database from an external binary file
DistanceMatrixDatabase dmd;
dmd.load_checkpoint("try.bin");
// List of distance matrices, one for each PDB
//************* from now on, all DMVec become pointers ****************//
vector<DistanceMatrix *> &DMVec = dmd.getDistanceMatrixList();
/*
for (uint i = 0;i < dms.size();i++){
cout << "DM["<<i<<"]: "<<dms[i]->getPDBid()<<endl;
}
cout << "Done"<<endl;
*/
//ManageResults to take care of printing/sorting at end
ManageDistanceMatrixResults resultManager;
for(int i=0; i<DMVec.size(); i++){
cout<< "Trying "<<DMVec[i]->getPDBid()<<" ("<<i<<") # Residues: "<<DMVec.size()<<" Number of MatrixWindows to compare: "<<DMVec[i]->getMatrixWindows().size();
//don't compare if all of the windows got filtered out
if (DMVec[i]->getMatrixWindows().size() == 0){
cout << " Sorry Zero Matrix Windows !"<<endl;
continue;
}
cout <<endl;
vector<DistanceMatrixResult> resultsToAdd;
if(opt.searchCriteria=="standard"){
resultsToAdd = constDM.multiCompareAllWindows(*DMVec[i], DistanceMatrix::standard, opt.numberOfIterations);
}//end if
if(opt.searchCriteria=="diagonal"){
resultsToAdd = constDM.multiCompareAllWindows(*DMVec[i], DistanceMatrix::diag, opt.numberOfIterations);
}
if(opt.searchCriteria=="doubleDiagonal"){
resultsToAdd = constDM.multiCompareAllWindows(*DMVec[i], DistanceMatrix::doubleDiag, opt.numberOfIterations);
}
if(opt.searchCriteria=="minDistance"){
resultsToAdd = constDM.multiCompareAllWindows(*DMVec[i], DistanceMatrix::minDist, opt.numberOfIterations);
}
if(opt.searchCriteria=="minDistanceRow"){
resultsToAdd = constDM.multiCompareAllWindows(*DMVec[i], DistanceMatrix::minDistRow, opt.numberOfIterations);
}
bool addFlag = false;
for (uint j = 0; j< resultsToAdd.size();j++){
if (opt.likenessTolerance == MslTools::doubleMax || resultsToAdd[j].getLikeness() <= opt.likenessTolerance){
addFlag = true;
break;
}
}
if (addFlag && resultsToAdd.size() > 0){
resultManager.addResults(resultsToAdd);
}
}//end for on i
// NEW ENDS
cout << "Printing"<<endl;
resultManager.setAlignPdbs(opt.alignPdbs);
// cout << "hello"<<endl;
resultManager.setRmsdTol(opt.rmsdTol);
// cout << "hello again"<<endl;
resultManager.printResults();
cout << "Done."<<endl;
return 0;
}
string Alignment::_computeTree(DistanceMatrix dists, DistanceMatrix vars) throw (Exception) {
// Initialization:
std::map<size_t, Node*> currentNodes_;
std::vector<double> sumDist_(dists.size());
double lambda_;
for (size_t i = 0; i < dists.size(); i++) {
currentNodes_[i] = new Node(static_cast<int>(i), dists.getName(i));
}
int idNextNode = dists.size();
vector<double> newDist(dists.size());
vector<double> newVar(dists.size());
// Build tree:
while (currentNodes_.size() > 3) {
// get best pair
for (std::map<size_t, Node*>::iterator i = currentNodes_.begin(); i != currentNodes_.end(); i++) {
size_t id = i->first;
sumDist_[id] = 0;
for (map<size_t, Node*>::iterator j = currentNodes_.begin(); j != currentNodes_.end(); j++) {
size_t jd = j->first;
sumDist_[id] += dists(id, jd);
}
}
vector<size_t> bestPair(2);
double critMax = std::log(0.);
for (map<size_t, Node*>::iterator i = currentNodes_.begin(); i != currentNodes_.end(); i++) {
size_t id = i->first;
map<size_t, Node*>::iterator j = i;
j++;
for ( ; j != currentNodes_.end(); j++) {
size_t jd = j->first;
double crit = sumDist_[id] + sumDist_[jd] - static_cast<double>(currentNodes_.size() - 2) * dists(id, jd);
// cout << "\t" << id << "\t" << jd << "\t" << crit << endl;
if (crit > critMax) {
critMax = crit;
bestPair[0] = id;
bestPair[1] = jd;
}
}
}
if (critMax == std::log(0.)) throw Exception("Unexpected error: no maximum criterium found.");
// get branch lengths for pair
double ratio = (sumDist_[bestPair[0]] - sumDist_[bestPair[1]]) / static_cast<double>(currentNodes_.size() - 2);
vector<double> d(2);
d[0] = std::max(.5 * (dists(bestPair[0], bestPair[1]) + ratio), MIN_BRANCH_LENGTH);
d[1] = std::max(.5 * (dists(bestPair[0], bestPair[1]) - ratio), MIN_BRANCH_LENGTH);
Node* best1 = currentNodes_[bestPair[0]];
Node* best2 = currentNodes_[bestPair[1]];
// Distances may be used by getParentNodes (PGMA for instance).
best1->setDistanceToFather(d[0]);
best2->setDistanceToFather(d[1]);
Node* parent = new Node(idNextNode++);
parent->addSon(best1);
parent->addSon(best2);
// compute lambda
lambda_ = 0;
if (vars(bestPair[0], bestPair[1]) == 0) lambda_ = .5;
else {
for (map<size_t, Node*>::iterator i = currentNodes_.begin(); i != currentNodes_.end(); i++) {
size_t id = i->first;
if (id != bestPair[0] && id != bestPair[1]) lambda_ += (vars(bestPair[1], id) - vars(bestPair[0], id));
}
double div = 2 * static_cast<double>(currentNodes_.size() - 2) * vars(bestPair[0], bestPair[1]);
lambda_ /= div;
lambda_ += .5;
}
if (lambda_ < 0.) lambda_ = 0.;
if (lambda_ > 1.) lambda_ = 1.;
for (map<size_t, Node*>::iterator i = currentNodes_.begin(); i != currentNodes_.end(); i++) {
size_t id = i->first;
if (id != bestPair[0] && id != bestPair[1]) {
newDist[id] = std::max(lambda_ * (dists(bestPair[0], id) - d[0]) + (1 - lambda_) * (dists(bestPair[1], id) - d[1]), 0.);
newVar[id] = lambda_ * vars(bestPair[0], id) + (1 - lambda_) * vars(bestPair[1], id) - lambda_ * (1 - lambda_) * vars(bestPair[0], bestPair[1]);
}
else newDist[id] = 0;
}
// Actualize currentNodes_:
currentNodes_[bestPair[0]] = parent;
currentNodes_.erase(bestPair[1]);
for (map<size_t, Node*>::iterator i = currentNodes_.begin(); i != currentNodes_.end(); i++) {
size_t id = i->first;
dists(bestPair[0], id) = dists(id, bestPair[0]) = newDist[id];
vars(bestPair[0], id) = vars(id, bestPair[0]) = newVar[id];
}
}
// final step
Node* root = new Node(idNextNode);
map<size_t, Node* >::iterator it = currentNodes_.begin();
size_t i1 = it->first;
Node* n1 = it->second;
it++;
size_t i2 = it->first;
Node* n2 = it->second;
if (currentNodes_.size() == 2) {
// Rooted
double d = dists(i1, i2) / 2;
root->addSon(n1);
root->addSon(n2);
n1->setDistanceToFather(d);
n2->setDistanceToFather(d);
}
else {
// Unrooted
it++;
size_t i3 = it->first;
Node* n3 = it->second;
double d1 = std::max(dists(i1, i2) + dists(i1, i3) - dists(i2, i3), MIN_BRANCH_LENGTH);
double d2 = std::max(dists(i2, i1) + dists(i2, i3) - dists(i1, i3), MIN_BRANCH_LENGTH);
double d3 = std::max(dists(i3, i1) + dists(i3, i2) - dists(i1, i2), MIN_BRANCH_LENGTH);
root->addSon(n1);
root->addSon(n2);
root->addSon(n3);
n1->setDistanceToFather(d1 / 2.);
n2->setDistanceToFather(d2 / 2.);
n3->setDistanceToFather(d3 / 2.);
}
Tree *tree_ = new TreeTemplate<Node>(root);
stringstream ss;
Newick treeWriter;
if (!tree_) throw Exception("The tree is empty");
treeWriter.write(*tree_, ss);
delete tree_;
string s{ss.str()};
s.erase(s.find_last_not_of(" \n\r\t")+1);
return s;
}
void SingleLinkage::operator()(DistanceMatrix<float> & original_distance, std::vector<BinaryTreeNode> & cluster_tree, const float threshold /*=1*/) const
{
// input MUST have >= 2 elements!
if (original_distance.dimensionsize() < 2)
{
throw ClusterFunctor::InsufficientInput(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION, "Distance matrix to start from only contains one element");
}
cluster_tree.clear();
if (threshold < 1)
{
LOG_ERROR << "You tried to use Single Linkage clustering with a threshold. This is currently not supported!" << std::endl;
throw Exception::NotImplemented(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION);
}
//SLINK
std::vector<Size> pi;
pi.reserve(original_distance.dimensionsize());
std::vector<float> lambda;
lambda.reserve(original_distance.dimensionsize());
startProgress(0, original_distance.dimensionsize(), "clustering data");
//initialize first pointer values
pi.push_back(0);
lambda.push_back(std::numeric_limits<float>::max());
for (Size k = 1; k < original_distance.dimensionsize(); ++k)
{
std::vector<float> row_k;
row_k.reserve(k);
//initialize pointer values for element to cluster
pi.push_back(k);
lambda.push_back(std::numeric_limits<float>::max());
// get the right distances
for (Size i = 0; i < k; ++i)
{
row_k.push_back(original_distance.getValue(i, k));
}
//calculate pointer values for element k
for (Size i = 0; i < k; ++i)
{
if (lambda[i] >= row_k[i])
{
row_k[pi[i]] = std::min(row_k[pi[i]], lambda[i]);
lambda[i] = row_k[i];
pi[i] = k;
}
else
{
row_k[pi[i]] = std::min(row_k[pi[i]], row_k[i]);
}
}
//update clustering if necessary
for (Size i = 0; i < k; ++i)
{
if (lambda[i] >= lambda[pi[i]])
{
pi[i] = k;
}
}
setProgress(k);
}
for (Size i = 0; i < pi.size() - 1; ++i)
{
//strict order is always kept in algorithm: i < pi[i]
cluster_tree.push_back(BinaryTreeNode(i, pi[i], lambda[i]));
//~ std::cout << i << '\n' << pi[i] << '\n' << lambda[i] << std::endl;
}
//sort pre-tree
std::sort(cluster_tree.begin(), cluster_tree.end(), compareBinaryTreeNode);
// convert -pre-tree to correct format
for (Size i = 0; i < cluster_tree.size(); ++i)
{
if (cluster_tree[i].right_child < cluster_tree[i].left_child)
{
std::swap(cluster_tree[i].left_child, cluster_tree[i].right_child);
}
for (Size k = i + 1; k < cluster_tree.size(); ++k)
{
if (cluster_tree[k].left_child == cluster_tree[i].right_child)
{
cluster_tree[k].left_child = cluster_tree[i].left_child;
}
else if (cluster_tree[k].left_child > cluster_tree[i].right_child)
{
--cluster_tree[k].left_child;
}
if (cluster_tree[k].right_child == cluster_tree[i].right_child)
{
cluster_tree[k].right_child = cluster_tree[i].left_child;
}
else if (cluster_tree[k].right_child > cluster_tree[i].right_child)
{
--cluster_tree[k].right_child;
}
}
}
//~ prepare to redo clustering to get all indices for binarytree in min index element representation
std::vector<std::set<Size> > clusters(original_distance.dimensionsize());
for (Size i = 0; i < original_distance.dimensionsize(); ++i)
{
clusters[i].insert(i);
}
for (Size cluster_step = 0; cluster_step < cluster_tree.size(); ++cluster_step)
{
Size new_left_child = *(clusters[cluster_tree[cluster_step].left_child].begin());
Size new_right_child = *(clusters[cluster_tree[cluster_step].right_child].begin());
clusters[cluster_tree[cluster_step].left_child].insert(clusters[cluster_tree[cluster_step].right_child].begin(), clusters[cluster_tree[cluster_step].right_child].end());
clusters.erase(clusters.begin() + cluster_tree[cluster_step].right_child);
std::swap(cluster_tree[cluster_step].left_child, new_left_child);
std::swap(cluster_tree[cluster_step].right_child, new_right_child);
if (cluster_tree[cluster_step].left_child > cluster_tree[cluster_step].right_child)
{
std::swap(cluster_tree[cluster_step].left_child, cluster_tree[cluster_step].right_child);
}
}
endProgress();
}
void AverageLinkage::operator()(DistanceMatrix<float> & original_distance, std::vector<BinaryTreeNode> & cluster_tree, const float threshold /*=1*/) const
{
// input MUST have >= 2 elements!
if (original_distance.dimensionsize() < 2)
{
throw ClusterFunctor::InsufficientInput(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION, "Distance matrix to start from only contains one element");
}
std::vector<std::set<Size> > clusters(original_distance.dimensionsize());
for (Size i = 0; i < original_distance.dimensionsize(); ++i)
{
clusters[i].insert(i);
}
cluster_tree.clear();
cluster_tree.reserve(original_distance.dimensionsize() - 1);
// Initial minimum-distance pair
original_distance.updateMinElement();
std::pair<Size, Size> min = original_distance.getMinElementCoordinates();
Size overall_cluster_steps(original_distance.dimensionsize());
startProgress(0, original_distance.dimensionsize(), "clustering data");
while (original_distance(min.second, min.first) < threshold)
{
//grow the tree
cluster_tree.push_back(BinaryTreeNode(*(clusters[min.second].begin()), *(clusters[min.first].begin()), original_distance(min.first, min.second)));
if (cluster_tree.back().left_child > cluster_tree.back().right_child)
{
std::swap(cluster_tree.back().left_child, cluster_tree.back().right_child);
}
if (original_distance.dimensionsize() > 2)
{
//pick minimum-distance pair i,j and merge them
//calculate parameter for lance-williams formula
float alpha_i = (float)(clusters[min.first].size() / (float)(clusters[min.first].size() + clusters[min.second].size()));
float alpha_j = (float)(clusters[min.second].size() / (float)(clusters[min.first].size() + clusters[min.second].size()));
//~ std::cout << alpha_i << '\t' << alpha_j << std::endl;
//pushback elements of second to first (and then erase second)
clusters[min.second].insert(clusters[min.first].begin(), clusters[min.first].end());
// erase first one
clusters.erase(clusters.begin() + min.first);
//update original_distance matrix
//average linkage: new distance between clusters is the minimum distance between elements of each cluster
//lance-williams update for d((i,j),k): (m_i/m_i+m_j)* d(i,k) + (m_j/m_i+m_j)* d(j,k) ; m_x is the number of elements in cluster x
for (Size k = 0; k < min.second; ++k)
{
float dik = original_distance.getValue(min.first, k);
float djk = original_distance.getValue(min.second, k);
original_distance.setValueQuick(min.second, k, (alpha_i * dik + alpha_j * djk));
}
for (Size k = min.second + 1; k < original_distance.dimensionsize(); ++k)
{
float dik = original_distance.getValue(min.first, k);
float djk = original_distance.getValue(min.second, k);
original_distance.setValueQuick(k, min.second, (alpha_i * dik + alpha_j * djk));
}
//reduce
original_distance.reduce(min.first);
//update minimum-distance pair
original_distance.updateMinElement();
//get min-pair from triangular matrix
min = original_distance.getMinElementCoordinates();
}
else
{
break;
}
setProgress(overall_cluster_steps - original_distance.dimensionsize());
//repeat until only two cluster remains, last step skips matrix operations
}
//fill tree with dummy nodes
Size sad(*clusters.front().begin());
for (Size i = 1; (i < clusters.size()) && (cluster_tree.size() < cluster_tree.capacity()); ++i)
{
cluster_tree.push_back(BinaryTreeNode(sad, *clusters[i].begin(), -1.0));
}
endProgress();
}
DistanceMatrix *Johnson(dgraph *g)
{
assert(g);
unsigned int v_id = g->add_vertex();
unsigned int it = 0;
for (it = 0; it < g->vsize(); it++)
{
if (it == v_id)
continue;
// Adding edge from newly created vertex to all
// existing vertices with edge cost 0.
g->add_edge(v_id, it, 0);
}
if (!BellmanFord(g, g->get_vertex(v_id)))
{
// Return an invalid matrix if Bellman Ford algorithm returns false.
// This means there exits a negetive weight cycle.
return (new DistanceMatrix(0));
}
// If Bellman Ford returns true then d-values of all the vertices contain
// shortest distances to all vertices from newly created vertex (v_id).
int *h = (int*) calloc(g->vsize(), sizeof(int));
assert(h);
for (it = 0; it < g->vsize(); it++)
{
assert(g->get_vertex(it));
h[it] = g->get_vertex(it)->d;
}
// Reassign the weights of the edges so that they are non negetive.
// w'(u,v) = w(u,v) + h(u) - h(v)
for (it = 0; it < g->vsize(); it++)
{
vertex *u = g->get_vertex(it);
assert(u);
unsigned int is = 0;
for (is = 0; is < u->edges.size(); is++)
{
assert(u->edges[is]);
unsigned int v_index = u->edges[is]->ends[DESTINATION];
vertex *v = g->get_vertex(v_index);
assert(v);
int new_cost = u->edges[is]->cost + h[it] - h[v_index];
u->edges[is]->cost = new_cost;
//g->set_edge_cost(it, u->edges[is]->ends[DESTINATION], new_cost);
}
}
DistanceMatrix *D = new DistanceMatrix(g->vsize());
for (it = 0; it < g->vsize(); it++)
{
vertex *u = g->get_vertex(it);
Dijkstra(g, g->get_vertex(it));
unsigned int is = 0;
for (is = 0; is < g->vsize(); is++)
{
vertex *v = g->get_vertex(is);
assert(v);
D->SetDistance(it, is, v->d + h[is] - h[it]);
}
}
// Revert back the original edge weights.
for (it = 0; it < g->vsize(); it++)
{
vertex *u = g->get_vertex(it);
assert(u);
unsigned int is = 0;
for (is = 0; is < u->edges.size(); is++)
{
assert(u->edges[is]);
unsigned int v_index = u->edges[is]->ends[DESTINATION];
vertex *v = g->get_vertex(v_index);
assert(v);
int old_cost = u->edges[is]->cost + h[v_index] - h[it];
u->edges[is]->cost = old_cost;
}
}
//Remove the extra vertex.
return D;
}
void CompleteLinkage::operator()(DistanceMatrix<float> & original_distance, std::vector<BinaryTreeNode> & cluster_tree, const float threshold /*=1*/) const
{
// attention: clustering process is done by clustering the indices
// pointing to elements in inputvector and distances in inputmatrix
// input MUST have >= 2 elements!
if (original_distance.dimensionsize() < 2)
{
throw ClusterFunctor::InsufficientInput(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION, "Distance matrix to start from only contains one element");
}
std::vector<std::set<Size> > clusters(original_distance.dimensionsize());
for (Size i = 0; i < original_distance.dimensionsize(); ++i)
{
clusters[i].insert(i);
}
cluster_tree.clear();
cluster_tree.reserve(original_distance.dimensionsize() - 1);
// Initial minimum-distance pair
original_distance.updateMinElement();
std::pair<Size, Size> min = original_distance.getMinElementCoordinates();
Size overall_cluster_steps(original_distance.dimensionsize());
startProgress(0, original_distance.dimensionsize(), "clustering data");
while (original_distance(min.first, min.second) < threshold)
{
//grow the tree
cluster_tree.push_back(BinaryTreeNode(*(clusters[min.second].begin()), *(clusters[min.first].begin()), original_distance(min.first, min.second)));
if (cluster_tree.back().left_child > cluster_tree.back().right_child)
{
std::swap(cluster_tree.back().left_child, cluster_tree.back().right_child);
}
if (original_distance.dimensionsize() > 2)
{
//pick minimum-distance pair i,j and merge them
//pushback elements of second to first (and then erase second)
clusters[min.second].insert(clusters[min.first].begin(), clusters[min.first].end());
// erase first one
clusters.erase(clusters.begin() + min.first);
//update original_distance matrix
//complete linkage: new distance between clusters is the minimum distance between elements of each cluster
//lance-williams update for d((i,j),k): 0.5* d(i,k) + 0.5* d(j,k) + 0.5* |d(i,k)-d(j,k)|
for (Size k = 0; k < min.second; ++k)
{
float dik = original_distance.getValue(min.first, k);
float djk = original_distance.getValue(min.second, k);
original_distance.setValueQuick(min.second, k, (0.5f * dik + 0.5f * djk + 0.5f * std::fabs(dik - djk)));
}
for (Size k = min.second + 1; k < original_distance.dimensionsize(); ++k)
{
float dik = original_distance.getValue(min.first, k);
float djk = original_distance.getValue(min.second, k);
original_distance.setValueQuick(k, min.second, (0.5f * dik + 0.5f * djk + 0.5f * std::fabs(dik - djk)));
}
//reduce
original_distance.reduce(min.first);
//update minimum-distance pair
original_distance.updateMinElement();
//get new min-pair
min = original_distance.getMinElementCoordinates();
}
else
{
break;
}
setProgress(overall_cluster_steps - original_distance.dimensionsize());
//repeat until only two cluster remains or threshold exceeded, last step skips matrix operations
}
//fill tree with dummy nodes
Size sad(*clusters.front().begin());
for (Size i = 1; i < clusters.size() && (cluster_tree.size() < cluster_tree.capacity()); ++i)
{
cluster_tree.push_back(BinaryTreeNode(sad, *clusters[i].begin(), -1.0));
}
//~ while(cluster_tree.size() < cluster_tree.capacity())
//~ {
//~ cluster_tree.push_back(BinaryTreeNode(0,1,-1.0));
//~ }
endProgress();
}
