// 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
}
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;
}
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);
}
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;
}
//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;
}
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;
}
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;
}