pair<int, double> DistanceMatrix::compareAllWindows(vector<double> minDistVec1, vector<double> minDistVec2, vector<string> allowedAminoAcids1,vector<string> allowedAminoAcids2, int choice){
int length = listMW.size();
int bestIndex;
MatrixWindow *bestWindow = NULL;
double minLikeness = 1000000;//for choice == minRowWeighted or choice==minRowAndColWeighted
double maxLikeness = -1.0;//for choice == correlation
double deviationFromMaxLikeness = 0.5;
vector<pair<double,int> > results;
for (int i=0; i<length; i++){
MatrixWindow *win = listMW[i];
//cout << "Testing window: "<<endl<<win->toString()<<endl;
double likeness;
switch(choice){
case minRowWeighted:
likeness = win->compareMinRowWeighted(minDistVec1);
if(likeness<(minLikeness+deviationFromMaxLikeness) && abs(likeness - minLikeness) > 0.001){
minLikeness=likeness;
bestWindow=win;
bestIndex = i;
results.push_back(pair<double,int>(minLikeness,bestIndex));
}//end if
break;
case minRowAndColWeighted:
likeness = win-> compareMinRCWeighted(minDistVec1);
if(likeness<(minLikeness+deviationFromMaxLikeness) && abs(likeness - minLikeness) > 0.001){
minLikeness=likeness;
bestWindow=win;
bestIndex = i;
results.push_back(pair<double,int>(minLikeness,bestIndex));
}//end if
break;
case correlation:
likeness = win->compareCorrelation(minDistVec1);
if(likeness>(maxLikeness-deviationFromMaxLikeness) && abs(likeness-maxLikeness) > 0.001){
maxLikeness = likeness;
bestWindow = win;
bestIndex = i;
results.push_back(pair<double,int>(maxLikeness,bestIndex));
}
break;
case correlationRowCol:
if (win->getUpLeftResidueNumbers()[0] == win->getUpLeftResidueNumbers()[1]){
likeness = win -> compareCorrelationRowColAverage(minDistVec1);
if(likeness>(maxLikeness-deviationFromMaxLikeness) && abs(likeness-maxLikeness) > 0.001){
maxLikeness = likeness;
bestWindow = win;
bestIndex = i;
results.push_back(pair<double,int>(maxLikeness,bestIndex));
}
}
break;
case correlationHeteroRowCol:
likeness = win->compareCorrelationHeteroRowCol(minDistVec1, minDistVec2);
if(likeness>(maxLikeness-deviationFromMaxLikeness) && abs(likeness-maxLikeness) > 0.001){
maxLikeness = likeness;
bestWindow = win;
bestIndex = i;
results.push_back(pair<double,int>(maxLikeness,bestIndex));
}
break;
default:
cout<< "Invalid int (choice) in function compareAllMinRowWeighted in DistanceMatrix."<<endl;
exit(336);
}//end switch
//fprintf(stdout, "\t%8.3f\n", likeness);
win=NULL;
}//end for on i
pair<int, double> indexAndLikeness (0, MslTools::doubleMax);
if (results.size() == 0){
return indexAndLikeness;
}
// Sort the results
if(choice==minRowWeighted || choice==minRowAndColWeighted){
sort(results.begin(), results.end());
} else{
sort(results.begin(), results.end(),greater<pair<double,int> >());
}
// Apply Sequence Filtering to results.
// Instead of results.size(), do results[0].first - 0.1 or something.
int bestResultIndex = 0;
if (allowedAminoAcids1.size() == listMW[0]->getWinSize() && allowedAminoAcids2.size() == listMW[0]->getWinSize()) {
for (uint i = 0; i < results.size();i++){
if (listMW[results[i].second]->doesSequenceMatchRow(allowedAminoAcids1) && listMW[results[i].second]->doesSequenceMatchCol(allowedAminoAcids2)){
bestResultIndex = i;
break;
}
}
} else if (allowedAminoAcids1.size() == listMW[0]->getWinSize()){
for (uint i = 0; i < results.size();i++){
if (listMW[results[i].second]->doesSequenceMatchRow(allowedAminoAcids1)){
bestResultIndex = i;
break;
}
}
} else if (allowedAminoAcids2.size() == listMW[0]->getWinSize()){
for (uint i = 0; i < results.size();i++){
if (listMW[results[i].second]->doesSequenceMatchCol(allowedAminoAcids2)){
bestResultIndex = i;
break;
}
}
}
indexAndLikeness.first = results[bestResultIndex].second;
indexAndLikeness.second = results[bestResultIndex].first;
bestWindow=NULL;
return indexAndLikeness;
}
