void Classifier2::ClassifyAExample (FeatureVector& example,
MLClassPtr& predClass1,
MLClassPtr& predClass2,
kkint32& predClass1Votes,
kkint32& predClass2Votes,
double& knownClassProb,
double& predClass1Prob,
double& predClass2Prob,
kkint32& numOfWinners,
double& breakTie
)
{
bool knownClassOneOfTheWiners = false;
predClass1 = NULL;
predClass2 = NULL;
knownClassProb = -1.0f;
predClass1Prob = -1.0f;
predClass2Prob = -1.0f;
MLClassPtr origClass = example.MLClass ();
trainedModel->Predict (&example,
origClass,
predClass1,
predClass2,
predClass1Votes,
predClass2Votes,
knownClassProb,
predClass1Prob,
predClass2Prob,
numOfWinners,
knownClassOneOfTheWiners,
breakTie,
log
);
if (!predClass1)
predClass1 = noiseMLClass;
if (subClassifiers)
{
Classifier2Ptr subClassifer = LookUpSubClassifietByClass (predClass1);
if (subClassifer)
{
MLClassPtr subPredClass1 = NULL;
MLClassPtr subPredClass2 = NULL;
kkint32 subPredClass1Votes = 0;
kkint32 subPredClass2Votes = 0;
double subKnownClassProb = 0.0;
double subPredClass1Prob = 0.0;
double subPredClass2Prob = 0.0;
kkint32 subNumOfWinners = 0;
double subBreakTie = 0.0;
subClassifer->ClassifyAExample (example, subPredClass1, subPredClass2,
subPredClass1Votes, subPredClass2Votes, subKnownClassProb,
subPredClass1Prob, subPredClass2Prob, subNumOfWinners,
subBreakTie
);
predClass1 = subPredClass1;
predClass1Votes += subPredClass1Votes;
predClass1Prob *= subPredClass1Prob;
knownClassProb *= subKnownClassProb;
numOfWinners += subNumOfWinners;
breakTie += subBreakTie * (1.0 - breakTie);
}
subClassifer = LookUpSubClassifietByClass (predClass2);
if (subClassifer)
{
MLClassPtr subPredClass1 = NULL;
MLClassPtr subPredClass2 = NULL;
kkint32 subPredClass1Votes = 0;
kkint32 subPredClass2Votes = 0;
double subKnownClassProb = 0.0;
double subPredClass1Prob = 0.0;
double subPredClass2Prob = 0.0;
kkint32 subNumOfWinners = 0;
double subBreakTie = 0.0;
subClassifer->ClassifyAExample (example, subPredClass1, subPredClass2,
subPredClass1Votes, subPredClass2Votes, subKnownClassProb,
subPredClass1Prob, subPredClass2Prob, subNumOfWinners,
subBreakTie
);
predClass2 = subPredClass1;
predClass2Votes += subPredClass1Votes;
predClass2Prob *= subPredClass1Prob;
}
}
example.MLClass (predClass1);
return;
} /* ClassifyAExample */
MLClassPtr Classifier2::ClassifyAImageOneLevel (FeatureVector& example,
double& probability,
kkint32& numOfWinners,
bool& knownClassOneOfTheWinners,
double& breakTie
)
{
probability = 0.0;
double probOfKnownClass = 0.0f;
probability = 0.0;
MLClassPtr origClass = example.MLClass ();
MLClassPtr predictedClass = NULL;
MLClassPtr predictedClass2 = NULL;
kkint32 class1Votes = -1;
kkint32 class2Votes = -1;
double predictedClass2Prob = 0.0f;
trainedModel->Predict (&example,
origClass,
predictedClass,
predictedClass2,
class1Votes,
class2Votes,
probOfKnownClass,
probability,
predictedClass2Prob,
numOfWinners,
knownClassOneOfTheWinners,
breakTie,
log
);
if (predictedClass == NULL)
{
log.Level (-1) << endl << endl
<< "Classifier2::ClassifyAImageOneLevel The trainedModel returned back a NULL pointer for predicted class" << endl
<< endl;
predictedClass = unKnownMLClass;
}
if (subClassifiers)
{
Classifier2Ptr subClassifer = LookUpSubClassifietByClass (predictedClass);
if (subClassifer)
{
double subProbability = 0.0;
kkint32 subNumOfWinners = 0;
double subBreakTie = 0.0;
/**@todo make sure that the following call does not normalize the features. */
MLClassPtr subPrediction
= subClassifer->ClassifyAImageOneLevel (example, subProbability, subNumOfWinners, knownClassOneOfTheWinners, subBreakTie);
if (subPrediction)
{
probability = probability * subProbability;
numOfWinners = numOfWinners + subNumOfWinners;
breakTie += subBreakTie * (1.0 - breakTie);
}
}
}
if (predictedClass->UnDefined ())
predictedClass = noiseMLClass;
example.MLClass (predictedClass);
return predictedClass;
} /* ClassifyAImageOneLevel */
