/* Do inference ------------------------------------------------------------ */
int NaiveBayes::doInference(const MultidimArray<double> &newFeatures, double &cost,
Matrix1D<double> &classesProbs, Matrix1D<double> &allCosts)
{
classesProbs=__priorProbsLog10;
for(int f=0; f<Nfeatures; f++)
{
const LeafNode &leaf_f=*(__leafs[f]);
double newFeatures_f=DIRECT_A1D_ELEM(newFeatures,f);
for (int k=0; k<K; k++)
{
double p = leaf_f.assignProbability(newFeatures_f, k);
if (fabs(p) < 1e-2)
VEC_ELEM(classesProbs,k) += -2*DIRECT_A1D_ELEM(__weights,f);
else
VEC_ELEM(classesProbs,k) += DIRECT_A1D_ELEM(__weights,f)*std::log10(p);
#ifdef DEBUG_FINE_CLASSIFICATION
if(debugging == true)
{
std::cout << "Feature " << f
<< " Probability for class " << k << " = "
<< classesProbs(k) << " increase= " << p
<< std::endl;
char c;
// COSS std::cin >> c;
// if (c=='q') debugging = false;
}
#endif
}
}
classesProbs-=classesProbs.computeMax();
// std::cout << "classesProbs " << classesProbs.transpose() << std::endl;
for (int k=0; k<K; k++)
VEC_ELEM(classesProbs,k)=pow(10.0,VEC_ELEM(classesProbs,k));
classesProbs*=1.0/classesProbs.sum();
// std::cout << "classesProbs norm " << classesProbs.transpose() << std::endl;
allCosts=__cost*classesProbs;
// std::cout << "allCosts " << allCosts.transpose() << std::endl;
int bestk=0;
cost=VEC_ELEM(allCosts,0)=std::log10(VEC_ELEM(allCosts,0));
for (int k=1; k<K; k++)
{
VEC_ELEM(allCosts,k)=std::log10(VEC_ELEM(allCosts,k));
if (VEC_ELEM(allCosts,k)<cost)
{
cost=VEC_ELEM(allCosts,k);
bestk=k;
}
}
#ifdef DEBUG_CLASSIFICATION
if(debugging == true)
{
for (int k=0; k<K; k++)
classesProbs(k)=log10(classesProbs(k));
std::cout << "Class probababilities=" << classesProbs.transpose()
<< "\n costs=" << allCosts.transpose()
<< " best class=" << bestk << " cost=" << cost << std::endl;
char c;
// COSS std::cin >> c;
// if (c=='q') debugging = false;
}
#endif
return bestk;
}
