void GI_libDAI::copyLabels_MSRC(vector<std::size_t>& labels,
labelType* nodeLabels,
BP& bp,
FactorGraph& fg)
{
labelType voidLabel = classIdxToLabel[0];
labelType moutainLabel = classIdxToLabel[4161600];
labelType horseLabel = classIdxToLabel[8323328];
INFERENCE_PRINT("[gi_libDAI] copyLabels_MSRC void=%d, moutain=%d, horse=%d\n",
(int)voidLabel, (int)moutainLabel, (int)horseLabel);
int label;
int n = slice->getNbSupernodes();
for(int sid = 0; sid < n; sid++) {
label = labels[sid];
//if(lossPerLabel == 0 && label > 20) // void label
if(label == voidLabel || label == moutainLabel || label == horseLabel) {
//Factor f = bp.beliefV(sid);
Factor f = bp.belief(fg.var(sid));
double maxProb = -1;
for(int i = 0; i < (int)f.states(); i++) {
if(i != voidLabel && i != moutainLabel && i != horseLabel)
if(maxProb < f[i]) {
maxProb = f[i];
label = i;
}
}
//INFERENCE_PRINT("label=%d\n", label);
}
nodeLabels[sid] = label;
}
}
void BruteForceOptMatching::getMaxProbAssignments(
const BP& ia, const FactorGraph& fg, const ConnectedFactorGraph& graph,
McDArray<McVec2i>& pairs) {
for (int i = 0; i < graph.variables.size(); i++) {
McDArray<int> possibleAssignments;
getAssignmentsForVariable(graph.variables[i], possibleAssignments);
Factor belief =
ia.belief(Var(graph.variables[i], possibleAssignments.size() + 1));
float maxVal = -1 * FLT_MAX;
int maxIdx = -1;
for (int j = 0; j < possibleAssignments.size() + 1; j++) {
if (belief.get(j) > maxVal) {
maxVal = belief.get(j);
maxIdx = j;
}
}
int indexOfAssignmentInVertexList =
mapVariableAssignmentToIndexInVertexList(graph.variables[i],
maxIdx);
McVec2i pair =
McVec2i(graph.variables[i], indexOfAssignmentInVertexList);
pairs.append(pair);
}
outputSingleFactorValues(graph);
// std::vector<std::size_t> maxes= ia.findMaximum();
// vector<std::size_t>::iterator it=maxes.begin();
}
// use bp.findmaximum instead
void GI_libDAI::getLabels(BP& bp,
FactorGraph& fg,
vector<std::size_t>& labels)
{
float maxProb;
int label;
ofstream ofs("beliefs2");
ofs << "------------------------------- UNARY -------------------------------\n";
for(long sid = 0; sid < slice->getNbSupernodes(); sid++) {
Factor f = bp.belief(fg.var(sid));
maxProb = f[0];
label = 0;
ofs << sid;
for(int i=1; i < (int)param->nClasses; i++) {
ofs << " " << i << ":" << f[i];
if(f[i] > maxProb) {
maxProb = f[i];
label = i;
}
}
ofs << endl;
labels[sid] = label;
}
ofs.close();
}
void BruteForceOptMatching::checkAmbiguities(const BP& ia,
const FactorGraph& fg,
const ConnectedFactorGraph& graph,
McDArray<int>& ambiguities) {
for (int h = 0; h < graph.variables.size(); h++) {
McDArray<int> possibleAssignments;
getAssignmentsForVariable(graph.variables[h], possibleAssignments);
Factor belief =
ia.belief(Var(graph.variables[h], possibleAssignments.size() + 1));
float maxProb = belief.max();
int countSame = 0;
for (int k = 0; k < possibleAssignments.size() + 1; k++) {
float curProb = belief.get(k);
if (fabs(curProb - maxProb) < 0.1)
countSame++;
}
/////
cout << "\n Belief for var " << graph.variables[h] << "\n";
for (int k = 0; k < possibleAssignments.size() + 1; k++) {
float curProb = belief.get(k);
cout << curProb << " ";
}
cout << "\n";
////
if (countSame > 1) {
// oh no! We found an ambiguos assignment!
ambiguities.append(graph.variables[h]);
// print it out:
cout << "Found an ambiguous assignemnt to variable "
<< graph.variables[h] << "\n";
for (int k = 0; k < possibleAssignments.size() + 1; k++) {
float curProb = belief.get(k);
cout << curProb << " ";
}
cout << "\n";
}
}
}