int main (int argc,
char** argv
)
{
time_t long_time;
SRand48 ((KKU::uint)time (&long_time));
RandomNND neraestNeighborReport (argc, argv);
if (!neraestNeighborReport.Abort ())
neraestNeighborReport.GenerateReport ();
}
void OurNeighbors::RandomReport (ImageFeaturesList& images)
{
double allClassesMeanNNDAnyClass = 0.0f;
double allClassesMeanStdDevAnyClass = 0.0f;
ClassSummaryList classSummaries (log);
MLClassList::iterator classIdx;
VectorKKStr zScoreSummaryLines;
for (classIdx = mlClasses->begin (); classIdx != mlClasses->end (); classIdx++)
{
MLClassPtr mlClass = *classIdx;
if (fromPlankton && (fromPlankton != mlClass))
continue;
double randomMeanNND = 0.0f;
double randomStdDevNND = 0.0f;
double realDataU2Stat = 0.0f;
double sampleMeanNND = 0.0f;
double sampleStdDevNND = 0.0f;
double sampleMaxDist = 0.0f;
double sampleMinDist = 0.0f;
ImageFeaturesListPtr imagesInClass = images.ExtractExamplesForAGivenClass (mlClass);
if (imagesInClass->QueueSize () > 0)
{
// We are going to make a list of images that has duplicate instances of 'ImageFeatures' objects
// This way when we Randomize the locations of each 'sfCentroidRow' and 'sfCentroidCol' we do not
// imapct on the original data.
ImageFeaturesListPtr imagesToRandomize = new ImageFeaturesList (*imagesInClass,
true // 'true means we want to own the data so new instances will be created.
);
LLoydsEntryListPtr lloydsEntries = DeriveAllLLoydsBins (*imagesToRandomize);
{
// We will now get mean and stdDev of nnd for this class
NeighborList neighbors (*imagesToRandomize, log);
neighbors.FindNearestNeighbors (neighborType, mlClass);
neighbors.CalcStatistics (sampleMeanNND, sampleStdDevNND, sampleMaxDist, sampleMinDist);
}
KKStr randomReportFileName;
if (reportFileName.Empty ())
randomReportFileName = "RandomDistanceReport";
else
randomReportFileName = osRemoveExtension (reportFileName) + "_Random";
randomReportFileName << "_" << mlClass->Name () << ".txt";
ofstream randomReport (randomReportFileName.Str ());
randomReport << "Random Distribution Report" << endl
<< endl;
randomReport << "Source Directory [" << sourceRootDirPath << "]" << endl;
randomReport << "Class [" << mlClass->Name () << "]" << endl;
RandomNND randomizeLocations (lastScanLine,
*imagesToRandomize,
numOfIterations,
numOfBuckets,
bucketSize,
randomReport,
log
);
randomizeLocations.GenerateReport ();
randomMeanNND = randomizeLocations.NND_Mean ();
randomStdDevNND = randomizeLocations.NND_StdDev ();
realDataU2Stat = randomizeLocations.RealDataU2Stat ();
//double sampleMeanNND = 0.0f;
//double sampleStdDevNND = 0.0f;
double z_Score = Z_Score (sampleMeanNND, randomMeanNND, randomStdDevNND, imagesToRandomize->QueueSize ());
randomReport << endl << endl << endl
<< "Z-Score" << endl
<< "=======" << endl
<< endl
<< "SampleMeanNND " << "\t" << sampleMeanNND << endl
<< "SampleStdDevNND " << "\t" << sampleStdDevNND << endl
<< "RandomMeanNND " << "\t" << randomMeanNND << endl
<< "RandomStdDevNND " << "\t" << randomStdDevNND << endl
<< "------- Z-Score " << "\t" << z_Score << endl
<< endl;
KKStr zScoreSummaryLine = mlClass->Name () + "\t" +
StrFormatDouble (sampleMeanNND, "###,##0.00") + "\t" +
StrFormatDouble (sampleStdDevNND, "###,##0.00") + "\t" +
StrFormatDouble (randomMeanNND, "###,##0.00") + "\t" +
StrFormatDouble (randomStdDevNND, "###,##0.00") + "\t" +
StrFormatDouble (z_Score, "###,##0.000");
zScoreSummaryLines.push_back (zScoreSummaryLine);
// The new instance on 'ClassSummary' that is aboiut to be created will take ownership
// of lloydsBins.
classSummaries.PushOnBack (new ClassSummary (mlClass, lloydsEntries, (float)realDataU2Stat, (float)z_Score));
delete imagesToRandomize; imagesToRandomize = NULL;
}
delete imagesInClass; imagesInClass = NULL;
}
if (!fromPlankton)
{
LLoydsEntryListPtr allClassesLLoydsEntries = DeriveAllLLoydsBins (images);
// Create a report for all classes
KKStr randomReportFileName;
if (reportFileName.Empty ())
randomReportFileName = "RandomDistanceReport_All.txt";
else
randomReportFileName = osRemoveExtension (reportFileName) + "_Random_All.txt";
ofstream randomReport (randomReportFileName.Str ());
randomReport << "Source Directory [" << sourceRootDirPath << "]" << endl;
randomReport << "Class [" << "All" << "]" << endl;
{
// Find the mean and stddev of Nearest Neighbor regardless of class.
NeighborList allClassesNeighbors (images, log);
allClassesNeighbors.FindNearestNeighbors (NeighborType::AnyPlankton, fromPlankton);
double allClassesMinDistAnyClass = 0.0f;
double allClassesMaxDistAnyClass = 0.0f;
allClassesNeighbors.CalcStatistics (allClassesMeanNNDAnyClass,
allClassesMeanStdDevAnyClass,
allClassesMinDistAnyClass,
allClassesMaxDistAnyClass
);
}
RandomNND randomizeLocations (lastScanLine,
images,
numOfIterations,
numOfBuckets,
bucketSize,
randomReport,
log
);
randomizeLocations.GenerateReport ();
// All classes Z-Score
double allClassesRandomMeanNND = randomizeLocations.NND_Mean ();
double allClassesRandomStdDevNND = randomizeLocations.NND_StdDev ();
double allClassesRealDataU2Stat = randomizeLocations.RealDataU2Stat ();
double z_Score = Z_Score (allClassesMeanNNDAnyClass, allClassesRandomMeanNND, allClassesRandomStdDevNND, images.QueueSize ());
KKStr zScoreSummaryLine = KKStr ("All-Classes") + "\t" +
StrFormatDouble (allClassesMeanNNDAnyClass, "###,##0.00") + "\t" +
StrFormatDouble (allClassesMeanStdDevAnyClass, "###,##0.00") + "\t" +
StrFormatDouble (allClassesRandomMeanNND, "###,##0.00") + "\t" +
StrFormatDouble (allClassesRandomStdDevNND, "###,##0.00") + "\t" +
StrFormatDouble (z_Score, "###,##0.00");
zScoreSummaryLines.push_back (zScoreSummaryLine);
randomReport << endl << endl << endl
<< "Z-Score" << endl
<< "=======" << endl
<< endl
<< "SampleMeanNND " << "\t" << allClassesMeanNNDAnyClass << endl
<< "SampleStdDevNND " << "\t" << allClassesMeanStdDevAnyClass << endl
<< "RandomMeanNND " << "\t" << allClassesRandomMeanNND << endl
<< "RandomStdDevNND " << "\t" << allClassesRandomStdDevNND << endl
<< "------- Z-Score " << "\t" << z_Score << endl
<< endl;
classSummaries.PushOnBack (new ClassSummary (MLClass::CreateNewMLClass (KKStr ("AllClasses")),
allClassesLLoydsEntries,
(float)allClassesRealDataU2Stat,
(float)z_Score
)
);
}
{
// Z-Score Summary Report
KKB::kkuint32 x;
*report << std::endl << std::endl
<< "Z-Score Summary By Class" << std::endl
<< std::endl
<< "ClassName" << "\t" << "SampleMean" << "\t" << "SampleStdDev" << "\t" << "RandomMean" << "\t" << "RandomStdDev" << "\t" << "Z-Score" << std::endl
<< "=========" << "\t" << "==========" << "\t" << "============" << "\t" << "==========" << "\t" << "============" << "\t" << "=======" << std::endl;
for (x = 0; x < zScoreSummaryLines.size (); x++)
*report << zScoreSummaryLines[x] << std::endl;
}
*report << endl << endl << endl;
classSummaries.SummaryReport (*report);
*report << endl << endl << endl;
classSummaries.SpatialOverlapReport (*report);
classSummaries.SaveLLoydsBinsData (lloydsBinsFileName, sourceRootDirPath, lastScanLine, baseLLoydsBinSize);
} /* RandomReport */
