float UCBVHaarSingleStumpLearner::run()
{
if ( UCBVHaarSingleStumpLearner::_numOfCalling == 0 ) {
init();
}
UCBVHaarSingleStumpLearner::_numOfCalling++;
//cout << "Num of iter:\t" << UCBVHaarSingleStumpLearner::_numOfCalling << " " << this->getTthSeriesElement( UCBVHaarSingleStumpLearner::_numOfCalling ) << flush << endl;
const int numClasses = _pTrainingData->getNumClasses();
// set the smoothing value to avoid numerical problem
// when theta=0.
setSmoothingVal( 1.0 / (float)_pTrainingData->getNumExamples() * 0.01 );
vector<sRates> mu(numClasses); // The class-wise rates. See BaseLearner::sRates for more info.
vector<float> tmpV(numClasses); // The class-wise votes/abstentions
float tmpThreshold;
float tmpAlpha;
float bestEnergy = numeric_limits<float>::max();
float tmpEnergy;
HaarData* pHaarData = static_cast<HaarData*>(_pTrainingData);
// get the whole data matrix
//const vector<int*>& intImages = pHaarData->getIntImageVector();
// The data matrix transformed into the feature's space
vector< pair<int, float> > processedHaarData(_pTrainingData->getNumExamples());
// I need to prepare both type of sampling
StumpAlgorithm<float> sAlgo(numClasses);
sAlgo.initSearchLoop(_pTrainingData);
float halfTheta;
if ( _abstention == ABST_REAL || _abstention == ABST_CLASSWISE )
halfTheta = _theta/2.0;
else
halfTheta = 0;
// The declared features types
vector<HaarFeature*>& loadedFeatures = pHaarData->getLoadedFeatures();
// for every feature type
vector<HaarFeature*>::iterator ftIt;
//vector<HaarFeature*>::iterator maxftIt;
vector<float> maxV( loadedFeatures.size() );
vector<int> maxKey( loadedFeatures.size() );
vector<int> maxNum( loadedFeatures.size() );
//claculate the Bk,s,t of the randomly chosen features
int key = getKeyOfMaximalElement();
int featureIdx = (int) (key / 10);
int featureType = (key % 10);
//for (i = 0, ftIt = loadedFeatures.begin(); ftIt != loadedFeatures.end(); i++ ++ftIt)
//*ftIt = loadedFeatures[ featureType ];
// just for readability
//HaarFeature* pCurrFeature = *ftIt;
HaarFeature* pCurrFeature = loadedFeatures[ featureType ];
if (_samplingType != ST_NO_SAMPLING)
pCurrFeature->setAccessType(AT_RANDOM_SAMPLING);
// Reset the iterator on the configurations. For random sampling
// this clear the visited list
pCurrFeature->loadConfigByNum( featureIdx );
if (_verbose > 1)
cout << "Learning type " << pCurrFeature->getName() << ".." << flush;
// transform the data from intImages to the feature's space
pCurrFeature->fillHaarData( _pTrainingData->getExamples(), processedHaarData );
//pCurrFeature->fillHaarData(intImages, processedHaarData);
// sort the examples in the new space by their coordinate
sort( processedHaarData.begin(), processedHaarData.end(),
nor_utils::comparePair<2, int, float, less<float> >() );
// find the optimal threshold
tmpThreshold = sAlgo.findSingleThresholdWithInit(processedHaarData.begin(),
processedHaarData.end(),
_pTrainingData, halfTheta, &mu, &tmpV);
tmpEnergy = getEnergy(mu, tmpAlpha, tmpV);
// Store it in the current weak hypothesis.
// note: I don't really like having so many temp variables
// but the alternative would be a structure, which would need
// to be inheritable to make things more consistent. But this would
// make it less flexible. Therefore, I am still undecided. This
// might change!
_alpha = tmpAlpha;
_v = tmpV;
// I need to save the configuration because it changes within the object
_selectedConfig = pCurrFeature->getCurrentConfig();
// I save the object because it contains the informations about the type,
// the name, etc..
_pSelectedFeature = pCurrFeature;
_threshold = tmpThreshold;
bestEnergy = tmpEnergy;
float edge = 0.0;
for( vector<sRates>::iterator itR = mu.begin(); itR != mu.end(); itR++ ) edge += ( itR->rPls - itR->rMin );
//need to set the X value
updateKeys( key, edge * edge );
if (!_pSelectedFeature)
{
cerr << "ERROR: No Haar Feature found. Something must be wrong!" << endl;
exit(1);
}
else
{
if (_verbose > 1)
cout << "Selected type: " << _pSelectedFeature->getName() << endl;
}
return bestEnergy;
}
AlphaReal HaarMultiStumpLearner::run()
{
const int numClasses = _pTrainingData->getNumClasses();
// set the smoothing value to avoid numerical problem
// when theta=0.
setSmoothingVal( 1.0 / (AlphaReal)_pTrainingData->getNumExamples() * 0.01 );
vector<sRates> mu(numClasses); // The class-wise rates. See BaseLearner::sRates for more info.
vector<AlphaReal> tmpV(numClasses); // The class-wise votes/abstentions
vector<FeatureReal> tmpThresholds(numClasses);
AlphaReal tmpAlpha;
AlphaReal bestEnergy = numeric_limits<AlphaReal>::max();
AlphaReal tmpEnergy;
HaarData* pHaarData = static_cast<HaarData*>(_pTrainingData);
// get the whole data matrix
// const vector<int*>& intImages = pHaarData->getIntImageVector();
// The data matrix transformed into the feature's space
vector< pair<int, FeatureReal> > processedHaarData(_pTrainingData->getNumExamples());
// I need to prepare both type of sampling
int numConf; // for ST_NUM
time_t startTime, currentTime; // for ST_TIME
long numProcessed;
bool quitConfiguration;
StumpAlgorithm<FeatureReal> sAlgo(numClasses);
sAlgo.initSearchLoop(_pTrainingData);
// The declared features types
vector<HaarFeature*>& loadedFeatures = pHaarData->getLoadedFeatures();
// for every feature type
vector<HaarFeature*>::iterator ftIt;
for (ftIt = loadedFeatures.begin(); ftIt != loadedFeatures.end(); ++ftIt)
{
// just for readability
HaarFeature* pCurrFeature = *ftIt;
if (_samplingType != ST_NO_SAMPLING)
pCurrFeature->setAccessType(AT_RANDOM_SAMPLING);
// Reset the iterator on the configurations. For random sampling
// this shuffles the configurations.
pCurrFeature->resetConfigIterator();
quitConfiguration = false;
numProcessed = 0;
numConf = 0;
time( &startTime );
if (_verbose > 1)
cout << "Learning type " << pCurrFeature->getName() << ".." << flush;
// While there is a configuration available
while ( pCurrFeature->hasConfigs() )
{
// transform the data from intImages to the feature's space
pCurrFeature->fillHaarData(_pTrainingData->getExamples(), processedHaarData);
// sort the examples in the new space by their coordinate
sort( processedHaarData.begin(), processedHaarData.end(),
nor_utils::comparePair<2, int, float, less<float> >() );
// find the optimal threshold
sAlgo.findMultiThresholdsWithInit(processedHaarData.begin(), processedHaarData.end(),
_pTrainingData, tmpThresholds, &mu, &tmpV);
tmpEnergy = getEnergy(mu, tmpAlpha, tmpV);
++numProcessed;
if (tmpEnergy < bestEnergy)
{
// Store it in the current weak hypothesis.
// note: I don't really like having so many temp variables
// but the alternative would be a structure, which would need
// to be inheritable to make things more consistent. But this would
// make it less flexible. Therefore, I am still undecided. This
// might change!
_alpha = tmpAlpha;
_v = tmpV;
// I need to save the configuration because it changes within the object
_selectedConfig = pCurrFeature->getCurrentConfig();
// I save the object because it contains the informations about the type,
// the name, etc..
_pSelectedFeature = pCurrFeature;
_thresholds = tmpThresholds;
bestEnergy = tmpEnergy;
}
// Move to the next configuration
pCurrFeature->moveToNextConfig();
// check stopping criterion for random configurations
switch (_samplingType)
{
case ST_NUM:
++numConf;
if (numConf >= _samplingVal)
quitConfiguration = true;
break;
case ST_TIME:
{
time( ¤tTime );
float diff = difftime(currentTime, startTime); // difftime is in seconds
if (diff >= _samplingVal)
quitConfiguration = true;
}
break;
case ST_NO_SAMPLING:
perror("ERROR: st no sampling... not sure what this means");
break;
} // end switch
if (quitConfiguration)
break;
} // end while
if (_verbose > 1)
{
time( ¤tTime );
float diff = difftime(currentTime, startTime); // difftime is in seconds
cout << "done! "
<< "(processed: " << numProcessed
<< " - elapsed: " << diff << " sec)"
<< endl;
}
}
if (!_pSelectedFeature)
{
cerr << "ERROR: No Haar Feature found. Something must be wrong!" << endl;
exit(1);
}
else
{
if (_verbose > 1)
cout << "Selected type: " << _pSelectedFeature->getName() << endl;
}
return bestEnergy;
}