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dgirardeau merged 1 commit into from
Aug 29, 2026
Merged

Syntax cleanup #135

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110 changes: 75 additions & 35 deletions src/StatisticalTestingTools.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -17,20 +17,20 @@

using namespace CCCoreLib;

//! Max computable Chi2 distance
static double CHI2_MAX = 1e7;
static const double CHI2_MAX = 1e7; //!< Max computable Chi2 distance

//! An element of a double-chained-list structure (used by computeAdaptativeChi2Dist)
struct Chi2Class
{

double pi; /**< Probability Pi **/
int n; /**< Number of elements for the class **/
double pi; //!< Probability Pi
int n; //!< Number of elements for the class

//! Default constructor
Chi2Class() : pi(0.0) , n(0) {}
//! Constructor from parameters
Chi2Class(double _pi, int _n) : pi(_pi) , n(_n) {}
Chi2Class(double _pi = 0.0, int _n = 0)
: pi(_pi)
, n(_n)
{
}

};

Expand All @@ -50,8 +50,10 @@ double StatisticalTestingTools::computeAdaptativeChi2Dist( const GenericDistribu
assert(distrib && cloud);
unsigned n = cloud->size();

if (n==0 || !distrib->isValid())
if (n == 0 || !distrib->isValid())
{
return -1.0;
}

//compute min and max (valid) values
ScalarType minV = 0;
Expand All @@ -72,22 +74,32 @@ double StatisticalTestingTools::computeAdaptativeChi2Dist( const GenericDistribu
else
{
if (V > maxV)
{
maxV = V;
}
else if (V < minV)
{
minV = V;
}
}
++numberOfValidValues;
}
}
}

if (numberOfValidValues == 0)
{
return -1.0;
}

if (histoMin)
{
minV = *histoMin;
}
if (histoMax)
{
maxV = *histoMax;
}

//shall we automatically compute the number of classes?
if (numberOfClasses == 0)
Expand All @@ -96,7 +108,7 @@ double StatisticalTestingTools::computeAdaptativeChi2Dist( const GenericDistribu
}
if (numberOfClasses < 2)
{
return -2.0; //not enough points/classes
return -2.0; // not enough points/classes
}

//try to allocate the histogram values array (if necessary)
Expand All @@ -106,13 +118,13 @@ double StatisticalTestingTools::computeAdaptativeChi2Dist( const GenericDistribu
//not enough memory
return -1.0;
}
memset(histo, 0, sizeof(unsigned)*numberOfClasses);
memset(histo, 0, sizeof(unsigned) * numberOfClasses);

//accumulate histogram
ScalarType dV = maxV - minV;
unsigned histoBefore = 0;
unsigned histoAfter = 0;
if ( GreaterThanEpsilon( dV ) )
if (GreaterThanEpsilon(dV))
{
for (unsigned i = 0; i < n; ++i)
{
Expand All @@ -127,9 +139,13 @@ double StatisticalTestingTools::computeAdaptativeChi2Dist( const GenericDistribu
else if (bin >= static_cast<int>(numberOfClasses))
{
if (V > maxV)
{
histoAfter++;
}
else
{
histo[numberOfClasses - 1]++;
}
}
else
{
Expand Down Expand Up @@ -169,7 +185,9 @@ double StatisticalTestingTools::computeAdaptativeChi2Dist( const GenericDistribu
currentClass.n = histo[k - 1];
currentClass.pi = p2 - p1;
if (npis)
{
npis[k - 1] = currentClass.pi * numberOfValidValues;
}

try
{
Expand Down Expand Up @@ -209,11 +227,17 @@ double StatisticalTestingTools::computeAdaptativeChi2Dist( const GenericDistribu
Chi2ClassList::iterator it = classes.begin();
Chi2ClassList::iterator minIt = it;
for (; it != classes.end(); ++it)
{
if (it->pi < minIt->pi)
{
minIt = it;
}
}

if (minIt->pi >= minPi) //all classes are bigger than the minimum requirement
if (minIt->pi >= minPi) // all classes are bigger than the minimum requirement
{
break;
}

//otherwise we must merge the smallest class with its neighbor (to make the classes repartition more equilibrated)
Chi2ClassList::iterator smallestIt;
Expand Down Expand Up @@ -263,7 +287,9 @@ double StatisticalTestingTools::computeAdaptativeChi2Dist( const GenericDistribu
}

if (!histoValues)
{
delete[] histo;
}

finalNumberOfClasses = static_cast<unsigned>(classes.size());

Expand All @@ -272,12 +298,12 @@ double StatisticalTestingTools::computeAdaptativeChi2Dist( const GenericDistribu

double StatisticalTestingTools::computeChi2Fractile(double p, int d)
{
return Chi2Helper::critchi(p,d);
return Chi2Helper::critchi(p, d);
}

double StatisticalTestingTools::computeChi2Probability(double chi2result, int d)
{
return Chi2Helper::pochisq(chi2result,d);
return Chi2Helper::pochisq(chi2result, d);
}

double StatisticalTestingTools::testCloudWithStatisticalModel(const GenericDistribution* distrib,
Expand All @@ -290,7 +316,9 @@ double StatisticalTestingTools::testCloudWithStatisticalModel(const GenericDistr
assert(theCloud);

if (!distrib->isValid())
{
return -1.0;
}

DgmOctree* theOctree = inputOctree;
if (!theOctree)
Expand All @@ -307,7 +335,9 @@ double StatisticalTestingTools::testCloudWithStatisticalModel(const GenericDistr
if (!theCloud->enableScalarField())
{
if (!inputOctree)
{
delete theOctree;
}
return -3.0;
}

Expand All @@ -325,7 +355,9 @@ double StatisticalTestingTools::testCloudWithStatisticalModel(const GenericDistr
{
//not enough memory
if (!inputOctree)
{
delete theOctree;
}
return -3.0;
}

Expand All @@ -351,12 +383,12 @@ double StatisticalTestingTools::testCloudWithStatisticalModel(const GenericDistr
}

//additional parameters for local process
void* additionalParameters[] = { reinterpret_cast<void*>(const_cast<GenericDistribution*>(distrib)),
reinterpret_cast<void*>(&numberOfNeighbours),
reinterpret_cast<void*>(&numberOfChi2Classes),
reinterpret_cast<void*>(histoValues.data()),
reinterpret_cast<void*>(histoMin),
reinterpret_cast<void*>(histoMax) };
void* additionalParameters[] { reinterpret_cast<void*>(const_cast<GenericDistribution*>(distrib)),
reinterpret_cast<void*>(&numberOfNeighbours),
reinterpret_cast<void*>(&numberOfChi2Classes),
reinterpret_cast<void*>(histoValues.data()),
reinterpret_cast<void*>(histoMin),
reinterpret_cast<void*>(histoMax) };

double maxChi2 = -1.0;

Expand All @@ -373,13 +405,15 @@ double StatisticalTestingTools::testCloudWithStatisticalModel(const GenericDistr
if (!progressCb || !progressCb->isCancelRequested())
{
//theoretical Chi2 fractile
maxChi2 = computeChi2Fractile(pTrust, numberOfChi2Classes-1);
maxChi2 = computeChi2Fractile(pTrust, numberOfChi2Classes - 1);
maxChi2 = sqrt(maxChi2); //on travaille avec les racines carrees des distances du Chi2
}
}

if (!inputOctree)
{
delete theOctree;
}

return maxChi2;
}
Expand All @@ -388,22 +422,22 @@ bool StatisticalTestingTools::computeLocalChi2DistAtLevel( const DgmOctree::octr
void** additionalParameters,
NormalizedProgress* nProgress/*=nullptr*/)
{
//variables additionnelles
GenericDistribution* statModel = reinterpret_cast<GenericDistribution*>(additionalParameters[0]);
unsigned numberOfNeighbours = *reinterpret_cast<unsigned*>(additionalParameters[1]);
unsigned numberOfChi2Classes = *reinterpret_cast<unsigned*>(additionalParameters[2]);
unsigned* histoValues = reinterpret_cast<unsigned*>(additionalParameters[3]);
ScalarType* histoMin = reinterpret_cast<ScalarType*>(additionalParameters[4]);
ScalarType* histoMax = reinterpret_cast<ScalarType*>(additionalParameters[5]);
// variables additionnelles
GenericDistribution* statModel = reinterpret_cast<GenericDistribution*>(additionalParameters[0]);
unsigned numberOfNeighbours = *reinterpret_cast<unsigned*>(additionalParameters[1]);
unsigned numberOfChi2Classes = *reinterpret_cast<unsigned*>(additionalParameters[2]);
unsigned* histoValues = reinterpret_cast<unsigned*>(additionalParameters[3]);
ScalarType* histoMin = reinterpret_cast<ScalarType*>(additionalParameters[4]);
ScalarType* histoMax = reinterpret_cast<ScalarType*>(additionalParameters[5]);

//number of points in the current cell
unsigned n = cell.points->size();

DgmOctree::NearestNeighboursSearchStruct nNSS;
nNSS.level = cell.level;
nNSS.minNumberOfNeighbors = numberOfNeighbours;
cell.parentOctree->getCellPos(cell.truncatedCode,cell.level,nNSS.cellPos,true);
cell.parentOctree->computeCellCenter(nNSS.cellPos,cell.level,nNSS.cellCenter);
nNSS.level = cell.level;
nNSS.minNumberOfNeighbors = numberOfNeighbours;
cell.parentOctree->getCellPos(cell.truncatedCode, cell.level, nNSS.cellPos, true);
cell.parentOctree->computeCellCenter(nNSS.cellPos, cell.level, nNSS.cellCenter);

//we already know the points of the first cell (this is the one we are currently processing!)
{
Expand All @@ -417,9 +451,9 @@ bool StatisticalTestingTools::computeLocalChi2DistAtLevel( const DgmOctree::octr
}

DgmOctree::NeighboursSet::iterator it = nNSS.pointsInNeighbourhood.begin();
for (unsigned j=0;j<n;++j,++it)
for (unsigned j = 0; j < n; ++j, ++it)
{
it->point = cell.points->getPointPersistentPtr(j);
it->point = cell.points->getPointPersistentPtr(j);
it->pointIndex = cell.points->getPointGlobalIndex(j);
}
nNSS.alreadyVisitedNeighbourhoodSize = 1;
Expand All @@ -443,11 +477,15 @@ bool StatisticalTestingTools::computeLocalChi2DistAtLevel( const DgmOctree::octr

unsigned k = cell.parentOctree->findNearestNeighborsStartingFromCell(nNSS, true);
if (k > numberOfNeighbours)
{
k = numberOfNeighbours;
}

neighboursCloud.clear();
for (unsigned j = 0; j < k; ++j)
{
neighboursCloud.addPointIndex(nNSS.pointsInNeighbourhood[j].pointIndex);
}

unsigned finalNumberOfChi2Classes = 0;
//LAZY VERSION (approximate test)
Expand All @@ -462,7 +500,9 @@ bool StatisticalTestingTools::computeLocalChi2DistAtLevel( const DgmOctree::octr
cell.points->setPointScalarValue(i, D);

if (nProgress && !nProgress->oneStep())
{
return false;
}
}

return true;
Expand Down
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