void Classifier2::ProbabilitiesByClass (const MLClassList& classes,
FeatureVectorPtr example,
kkint32* votes,
double* probabilities
)
{
ClassProbListPtr predictions = ProbabilitiesByClass (example);
kkuint32 numClasses = (kkuint32)classes.size ();
for (kkuint32 x = 0; x < numClasses; ++x)
{
votes[x] = 0;
probabilities[x] = 0.0;
MLClassPtr c = classes.IdxToPtr (x);
ClassProbConstPtr cp = predictions->LookUp (c);
if (cp)
{
votes[x] = (kkint32)(0.5f + cp->votes);
probabilities[x] = cp->probability;
}
}
delete predictions;
predictions= NULL;
} /* ProbabilitiesByClass */
void ModelUsfCasCor::ProbabilitiesByClass (FeatureVectorPtr _example,
const MLClassList& _mlClasses,
double* _probabilities,
RunLog& _log
)
{
if (!usfCasCorClassifier)
{
KKStr errMsg = "ModelUsfCasCor::ProbabilitiesByClass ***ERROR*** (usfCasCorClassifier == NULL)";
_log.Level (-1) << endl << endl << errMsg << endl << endl;
throw KKException (errMsg);
}
VectorFloat probabilities;
MLClassPtr pc1 = NULL;
MLClassPtr pc2 = NULL;
MLClassPtr kc = NULL;
float pc1p = 0.0f;
float pc2p = 0.0f;
float kcp = 0.0f;
bool newExampleCreated = false;
FeatureVectorPtr encodedExample = PrepExampleForPrediction (_example, newExampleCreated);
usfCasCorClassifier->PredictConfidences (encodedExample,
kc,
pc1, pc1p,
pc2, pc2p,
kcp,
_mlClasses,
probabilities
);
if (newExampleCreated)
{
delete encodedExample;
encodedExample = NULL;
}
if (_mlClasses.size () != probabilities.size ())
{
_log.Level (-1) << endl << "ModelUsfCasCor::ProbabilitiesByClass ***ERROR***" << endl
<< "\"_mlClasses.size () != probabilities.size ()\" This should not ever be able to happen." << endl
<< endl;
for (int x = 0; x < _mlClasses.QueueSize (); ++x)
{
_probabilities[x] = 0.0;
}
}
else
{
for (kkuint32 x = 0; x < probabilities.size (); ++x)
_probabilities[x] = probabilities[x];
}
return;
} /* ProbabilitiesByClass */