void set(DMat<RT>& A, MatrixWindow wA, const DMat<RT>& B, MatrixWindow wB)
{
M2_ASSERT(wA.sameSize(wB));
long rA = wA.begin_row;
long rB = wB.begin_row;
for ( ; rA < wA.end_row; ++rA, ++rB)
{
long cA = wA.begin_column;
long cB = wB.begin_column;
for ( ; cA < wA.end_column; ++cA, ++cB)
A.ring().set(A.entry(rA,cA), B.entry(rB,cB));
}
}
void scalarMultInPlace(DMat<RT>& A, MatrixWindow wA, const typename RT::ElementType& c)
{
M2_ASSERT(wA.sameSize(wB));
long rA = wA.begin_row;
for ( ; rA < wA.end_row; ++rA)
{
long cA = wA.begin_column;
for ( ; cA < wA.end_column; ++cA)
{
auto& a = A.entry(rA,cA);
A.ring().mult(a, a, c);
}
}
}
void scalarMult(DMat<RT>& A, MatrixWindow wA, const typename RT::ElementType& c, const DMat<RT>& B, MatrixWindow wB)
{
M2_ASSERT(wA.sameSize(wB));
long rA = wA.begin_row;
long rB = wB.begin_row;
for ( ; rA < wA.end_row; ++rA, ++rB)
{
long cA = wA.begin_column;
long cB = wB.begin_column;
for ( ; cA < wA.end_column; ++cA, ++cB)
{
A.ring().mult(A.entry(rA,cA), c, B.entry(rB,cB));
}
}
}
void addTo(DMat<RT>& A, MatrixWindow wA, const DMat<RT>& B, MatrixWindow wB)
{
assert(wA.sameSize(wB));
long rA = wA.begin_row;
long rB = wB.begin_row;
for (; rA < wA.end_row; ++rA, ++rB)
{
long cA = wA.begin_column;
long cB = wB.begin_column;
for (; cA < wA.end_column; ++cA, ++cB)
{
auto& a = A.entry(rA, cA);
A.ring().add(a, a, B.entry(rB, cB));
}
}
}
void addMultipleTo(DMat<RT>& A, MatrixWindow wA, const typename RT::ElementType& c, const DMat<RT>& B, MatrixWindow wB)
{
M2_ASSERT(wA.sameSize(wB));
typename RT::ElementType tmp;
A.ring().init(tmp);
long rA = wA.begin_row;
long rB = wB.begin_row;
for ( ; rA < wA.end_row; ++rA, ++rB)
{
long cA = wA.begin_column;
long cB = wB.begin_column;
for ( ; cA < wA.end_column; ++cA, ++cB)
{
A.ring().mult(tmp, c, B.entry(rB,cB));
auto& a = A.entry(rA,cA);
A.ring().add(a, a, tmp);
}
}
A.ring().clear(tmp);
}
void DistanceMatrix::printCompareInfo(vector<double> _inputVec1, vector<double> _inputVec2, pair<int, double> _result, int choice){
//retrieve values from the pair
int index = _result.first;
double minLikeness = _result.second;
//retrieve the winning Matrix Window from listMW
MatrixWindow *mw = listMW[index];
//retrieve and print relevent info
int i = mw->getLeftR();
int j = mw->getLeftC();
string iID = atomVec[i]->getSegID().c_str();
string jID = atomVec[j]->getSegID().c_str();
if(atomVec[i]->getSegID()==""){
iID = atomVec[i]->getChainId().c_str();
jID = atomVec[j]->getChainId().c_str();
}
int ires = atomVec[i]->getResidueNumber();
int jres = atomVec[j]->getResidueNumber();
string PDBname= getFileName(PDBid);
string PDBnameShort = PDBname.substr(0,17);
cout<<"Comparing with PDB "<<PDBnameShort<<endl;
switch(choice){
case minRowWeighted:
fprintf(stdout, "MinRowWeighted compare: \tWindow %3d,%3d (Residues: %1s %3d, %1s %3d)\t%8.3f\n", i, j, iID.c_str(), ires, jID.c_str(), jres, minLikeness);
break;
case minRowAndColWeighted:
fprintf(stdout, "MinRowAndColWeighted compare: \tWindow %3d,%3d (Residues: %1s %3d, %1s %3d)\t%8.3f\n", i, j, iID.c_str(), ires, jID.c_str(), jres, minLikeness);
break;
case correlation:
fprintf(stdout, "Correlation compare: \tWindow %3d,%3d (Residues: %1s %3d, %1s %3d)\t%8.3f\n", i, j, iID.c_str(), ires, jID.c_str(), jres, minLikeness);
break;
case correlationRowCol:
fprintf(stdout, "CorrelationRowCol compare: \tWindow %3d,%3d (Residues: %1s %3d, %1s %3d)\t%8.3f\n", i, j, iID.c_str(), ires, jID.c_str(), jres, minLikeness);
break;
case correlationHeteroRowCol:
fprintf(stdout, "CorrelationHeteroRowCol compare: \tWindow %3d,%3d (Residues: %1s %3d, %1s %3d)\t%8.3f\n", i, j, iID.c_str(), ires, jID.c_str(), jres, minLikeness);
break;
default:
cout<<"you suck! You f****d up printCompareInfo in DistanceMatrix--the one that takes the vector. S******d."<<endl;
exit(3334);
}//end switch
//print out the vectors we compared
string label = "Weights: ";
if(weight.size() != 0){
fprintf(stdout, "%-20s", label.c_str());
for (uint i = 0; i < weight.size();i++){
fprintf(stdout, "%8.3f, ", weight[i]);
}
fprintf(stdout, "\n");
}
label = "DistVectRow: ";
fprintf(stdout, "%-20s", label.c_str());
for (uint i = 0; i < _inputVec1.size();i++){
fprintf(stdout, "%8.3f, ", _inputVec1[i]);
}
fprintf(stdout, "\n");
//print row vector and correlation
label = "RowsVect:";
fprintf(stdout, "%-20s", label.c_str());
vector<double> minRowValues = mw->getMinRowValues();
for (int i=0; i<minRowValues.size(); i++){
fprintf(stdout, "%8.3f, ", minRowValues[i]);
}
fprintf(stdout,"\n");
if(choice != correlation){//if choice is correlation, we already printed out the correlation...
double correlateR = MslTools::correlate(minRowValues, _inputVec1);
fprintf(stdout, "RowCorrelation: \t %8.3f, ", correlateR);
}
cout<<endl;
//print col vector and correlation if choice is MinRowAndColWeighted
if(choice==minRowAndColWeighted || choice == correlationRowCol){
label = "ColsVect: ";
fprintf(stdout, "%-20s", label.c_str());
vector<double> minColValues = mw->getMinColValues();
for (int i=0; i<minColValues.size(); i++){
fprintf(stdout, "%8.3f, ", minColValues[i]);
}
fprintf(stdout,"\n");
double correlateC = MslTools::correlate(minColValues, _inputVec1);
fprintf(stdout, "ColCorrelation: \t %8.3f, ", correlateC);
cout<<endl;
}
if (choice == correlationHeteroRowCol){
label = "DistVectCol: ";
fprintf(stdout, "%-20s", label.c_str());
for (uint i = 0; i < _inputVec2.size();i++){
fprintf(stdout, "%8.3f, ", _inputVec2[i]);
}
fprintf(stdout, "\n");
label = "ColsVect: ";
fprintf(stdout, "%-20s", label.c_str());
vector<double> minColValues = mw->getMinColValues();
for (int i=0; i<minColValues.size(); i++){
fprintf(stdout, "%8.3f, ", minColValues[i]);
}
fprintf(stdout,"\n");
double correlateC = MslTools::correlate(minColValues, _inputVec2);
fprintf(stdout, "ColCorrelation: \t %8.3f, ", correlateC);
cout<<endl;
}
}
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;
}
