int RandomSampleJobList::DetermineCompressedImageCount (FeatureVectorListPtr trainData,
TrainingConfigurationPtr config
)
{
FileDescPtr fileDesc = trainData->FileDesc ();
FeatureVectorListPtr srcImages = trainData->DuplicateListAndContents ();
FeatureVectorListPtr imagesToTrain = new FeatureVectorList (fileDesc, false, log, 10000);
MLClassListPtr mlClasses = srcImages->ExtractListOfClasses ();
{
MLClassList::const_iterator idx;
for (idx = mlClasses->begin (); idx != mlClasses->end (); idx++)
{
MLClassPtr mlClass = *idx;
FeatureVectorListPtr imagesThisClass = srcImages->ExtractImagesForAGivenClass (mlClass);
imagesToTrain->AddQueue (*imagesThisClass);
delete imagesThisClass;
}
}
NormalizationParms normParms (config, *imagesToTrain, log);
normParms.NormalizeImages (imagesToTrain);
ClassAssignments classAssignments (*mlClasses, log);
FeatureVectorListPtr compressedImageList = new FeatureVectorList (fileDesc, true, log, 10000);
BitReduction br (config->SVMparamREF (), fileDesc, trainData->AllFeatures ());
CompressionStats compressionStats
= br.compress (*imagesToTrain,
compressedImageList,
classAssignments
);
int compressedImageCount = compressionStats.num_images_after;
log.Level (10) << "DetermineCompressedImageCount compressedImageCount[" << compressedImageCount << "]" << endl;
delete compressedImageList; compressedImageList = NULL;
delete mlClasses; mlClasses = NULL;
delete imagesToTrain; imagesToTrain = NULL;
delete srcImages; srcImages = NULL;
return compressedImageCount;
} /* DetermineCompressedImageCount */
void RandomSampleJob::EvaluteNode (FeatureVectorListPtr validationData,
MLClassListPtr classes
)
{
log.Level (9) << " " << endl;
log.Level (9) << " " << endl;
log.Level (9) << "RandomSampleJob::EvaluteNode JobId[" << jobId << "] Ordering[" << orderingNum << "]" << endl;
status = rjStarted;
config->CompressionMethod (BRnoCompression);
config->KernalType (kernelType);
config->EncodingMethod (encodingMethod);
config->C_Param (c);
config->Gamma (gamma);
FileDescPtr fileDesc = config->FileDesc ();
const FeatureVectorListPtr srcExamples = orderings->Ordering (orderingNum);
if (numExamplesToKeep > srcExamples->QueueSize ())
{
log.Level (-1) << endl << endl << endl
<< "RandomSampleJob::EvaluteNode *** ERROR *** RandomExamples to large" << endl
<< endl
<< " RandomExamples > num in Training set." << endl
<< endl;
osWaitForEnter ();
exit (-1);
}
FeatureVectorListPtr trainingData = new FeatureVectorList (srcExamples->FileDesc (), false, log, 10000);
for (int x = 0; x < numExamplesToKeep; x++)
{
trainingData->PushOnBack (srcExamples->IdxToPtr (x));
}
bool allClassesRepresented = true;
{
MLClassListPtr classesInRandomSample = trainingData->ExtractListOfClasses ();
if (*classesInRandomSample != (*classes))
{
log.Level (-1) << endl << endl
<< "RandomSampling *** ERROR ***" << endl
<< endl
<< " Missing Classes From Random Sample." << endl
<< endl
<< "MLClasses[" << classes->ToCommaDelimitedStr () << "]" << endl
<< "Found [" << classesInRandomSample->ToCommaDelimitedStr () << "]" << endl
<< endl;
allClassesRepresented = false;
}
delete classesInRandomSample; classesInRandomSample = NULL;
}
//if (!allClassesRepresented)
//{
// accuracy = 0.0;
// trainTime = 0.0;
// testTime = 0.0;
//}
//else
{
delete crossValidation; crossValidation = NULL;
compMethod = config->CompressionMethod ();
bool cancelFlag = false;
crossValidation = new CrossValidation
(config,
trainingData,
classes,
10,
false, // False = Features are not normalized already.
trainingData->FileDesc (),
log,
cancelFlag
);
crossValidation->RunValidationOnly (validationData, NULL);
accuracy = crossValidation->Accuracy ();
trainTime = crossValidation->TrainTimeMean ();
testTime = crossValidation->TestTimeMean ();
supportVectors = crossValidation->SupportPointsMean ();
}
delete trainingData;
status = rjDone;
} /* EvaluteNode */
