/*!
* \brief Calculate statistics of a given deme.
* \param outStats Evaluated statistics.
* \param ioDeme Deme to evalute the statistics.
* \param ioContext Context of the evolution.
*/
void GP::StatsCalcFitnessKozaOp::calculateStatsDeme(Beagle::Stats& outStats,
Beagle::Deme& ioDeme,
Beagle::Context& ioContext) const
{
Beagle_StackTraceBeginM();
outStats.clear();
outStats.clearItems();
outStats.addItem("processed", ioContext.getProcessedDeme());
outStats.addItem("total-processed", ioContext.getTotalProcessedDeme());
if(ioDeme.size() == 0) {
outStats.setGenerationValues(std::string("deme")+uint2str(ioContext.getDemeIndex()),
ioContext.getGeneration(), 0, true);
outStats.resize(7);
outStats[0].mID = "normalized";
outStats[0].mAvg = 0.0;
outStats[0].mStd = 0.0;
outStats[0].mMax = 0.0;
outStats[0].mMin = 0.0;
outStats[1].mID = "adjusted";
outStats[1].mAvg = 0.0;
outStats[1].mStd = 0.0;
outStats[1].mMax = 0.0;
outStats[1].mMin = 0.0;
outStats[2].mID = "standardized";
outStats[2].mAvg = 0.0;
outStats[2].mStd = 0.0;
outStats[2].mMax = 0.0;
outStats[2].mMin = 0.0;
outStats[3].mID = "raw";
outStats[3].mAvg = 0.0;
outStats[3].mStd = 0.0;
outStats[3].mMax = 0.0;
outStats[3].mMin = 0.0;
outStats[4].mID = "hits";
outStats[4].mAvg = 0.0;
outStats[4].mStd = 0.0;
outStats[4].mMax = 0.0;
outStats[4].mMin = 0.0;
outStats[5].mID = "treedepth";
outStats[5].mAvg = 0.0;
outStats[5].mStd = 0.0;
outStats[5].mMax = 0.0;
outStats[5].mMin = 0.0;
outStats[6].mID = "treesize";
outStats[6].mAvg = 0.0;
outStats[6].mStd = 0.0;
outStats[6].mMax = 0.0;
outStats[6].mMin = 0.0;
return;
}
const GP::Individual::Handle lFirstIndiv =
castHandleT<GP::Individual>(ioDeme[0]);
const GP::FitnessKoza::Handle lFirstIndivFitness =
castHandleT<GP::FitnessKoza>(lFirstIndiv->getFitness());
if(ioDeme.size() == 1) {
outStats.setGenerationValues(std::string("deme")+uint2str(ioContext.getDemeIndex()),
ioContext.getGeneration(), 1, true);
outStats.resize(7);
outStats[0].mID = "normalized";
outStats[0].mAvg = lFirstIndivFitness->getNormalizedFitness();
outStats[0].mStd = 0.0;
outStats[0].mMax = lFirstIndivFitness->getNormalizedFitness();
outStats[0].mMin = lFirstIndivFitness->getNormalizedFitness();
outStats[1].mID = "adjusted";
outStats[1].mAvg = lFirstIndivFitness->getAdjustedFitness();
outStats[1].mStd = 0.0;
outStats[1].mMax = lFirstIndivFitness->getAdjustedFitness();
outStats[1].mMin = lFirstIndivFitness->getAdjustedFitness();
outStats[2].mID = "standardized";
outStats[2].mAvg = lFirstIndivFitness->getStandardizedFitness();
outStats[2].mStd = 0.0;
outStats[2].mMax = lFirstIndivFitness->getStandardizedFitness();
outStats[2].mMin = lFirstIndivFitness->getStandardizedFitness();
outStats[3].mID = "raw";
outStats[3].mAvg = lFirstIndivFitness->getRawFitness();
outStats[3].mStd = 0.0;
outStats[3].mMax = lFirstIndivFitness->getRawFitness();
outStats[3].mMin = lFirstIndivFitness->getRawFitness();
outStats[4].mID = "hits";
outStats[4].mAvg = lFirstIndivFitness->getHits();
outStats[4].mStd = 0.0;
outStats[4].mMax = lFirstIndivFitness->getHits();
outStats[4].mMin = lFirstIndivFitness->getHits();
outStats[5].mID = "treedepth";
outStats[5].mAvg = lFirstIndiv->getMaxTreeDepth();
outStats[5].mStd = 0.0;
outStats[5].mMax = outStats[5].mAvg;
outStats[5].mMin = outStats[5].mAvg;
outStats[6].mID = "treesize";
outStats[6].mAvg = lFirstIndiv->getTotalNodes();
outStats[6].mStd = 0.0;
outStats[6].mMax = outStats[6].mAvg;
outStats[6].mMin = outStats[6].mAvg;
return;
}
double lSumNrm = (double)lFirstIndivFitness->getNormalizedFitness();
double lPow2SumNrm = pow2Of<double>(lSumNrm);
float lMaxNrm = lFirstIndivFitness->getNormalizedFitness();
float lMinNrm = lFirstIndivFitness->getNormalizedFitness();
double lSumAdj = (double)lFirstIndivFitness->getAdjustedFitness();
double lPow2SumAdj = pow2Of<double>(lSumAdj);
float lMaxAdj = lFirstIndivFitness->getAdjustedFitness();
float lMinAdj = lFirstIndivFitness->getAdjustedFitness();
double lSumStd = (double)lFirstIndivFitness->getStandardizedFitness();
double lPow2SumStd = pow2Of<double>(lSumStd);
float lMaxStd = (double)lFirstIndivFitness->getStandardizedFitness();
float lMinStd = lFirstIndivFitness->getStandardizedFitness();
double lSumRaw = lFirstIndivFitness->getRawFitness();
double lPow2SumRaw = pow2Of<double>(lSumRaw);
float lMaxRaw = (double)lFirstIndivFitness->getRawFitness();
float lMinRaw = lFirstIndivFitness->getRawFitness();
double lSumHit = lFirstIndivFitness->getHits();
double lPow2SumHit = pow2Of<double>(lSumHit);
unsigned int lMaxHit = lFirstIndivFitness->getHits();
unsigned int lMinHit = lFirstIndivFitness->getHits();
unsigned int lMaxDepth = lFirstIndiv->getMaxTreeDepth();
unsigned int lMinDepth = lMaxDepth;
double lSumDepth = (double)lMaxDepth;
double lPow2SumDepth = pow2Of<double>(lSumDepth);
unsigned int lMaxSize = lFirstIndiv->getTotalNodes();
unsigned int lMinSize = lMaxSize;
double lSumSize = (double)lMaxSize;
double lPow2SumSize = pow2Of<double>(lSumSize);
for(unsigned int i=1; i<ioDeme.size(); i++) {
const GP::Individual::Handle lIndiv =
castHandleT<GP::Individual>(ioDeme[i]);
const GP::FitnessKoza::Handle lIndivFitness =
castHandleT<GP::FitnessKoza>(lFirstIndiv->getFitness());
lSumNrm += (double)lIndivFitness->getNormalizedFitness();
lPow2SumNrm += pow2Of<double>((double)lIndivFitness->getNormalizedFitness());
lSumAdj += (double)lIndivFitness->getAdjustedFitness();
lPow2SumAdj += pow2Of<double>((double)lIndivFitness->getAdjustedFitness());
lSumStd += (double)lIndivFitness->getStandardizedFitness();
lPow2SumStd += pow2Of<double>(lIndivFitness->getStandardizedFitness());
lSumRaw += (double)lIndivFitness->getRawFitness();
lPow2SumRaw += pow2Of<double>((double)lIndivFitness->getRawFitness());
lSumHit += (double)lIndivFitness->getHits();
lPow2SumHit += pow2Of<double>((double)lIndivFitness->getHits());
if(lIndivFitness->getNormalizedFitness() > lMaxNrm) {
lMaxNrm = lIndivFitness->getNormalizedFitness();
lMaxAdj = lIndivFitness->getAdjustedFitness();
lMaxStd = lIndivFitness->getStandardizedFitness();
lMaxRaw = lIndivFitness->getRawFitness();
lMaxHit = lIndivFitness->getHits();
}
if(lIndivFitness->getNormalizedFitness() < lMinNrm) {
lMinNrm = lIndivFitness->getNormalizedFitness();
lMinAdj = lIndivFitness->getAdjustedFitness();
lMinStd = lIndivFitness->getStandardizedFitness();
lMinRaw = lIndivFitness->getRawFitness();
lMinHit = lIndivFitness->getHits();
}
unsigned int lTmpDepth = lIndiv->getMaxTreeDepth();
lSumDepth += (double)lTmpDepth;
lPow2SumDepth += pow2Of<double>((double)lTmpDepth);
lMaxDepth = maxOf(lMaxDepth, lTmpDepth);
lMinDepth = minOf(lMinDepth, lTmpDepth);
unsigned int lTmpSize = lIndiv->getTotalNodes();
lSumSize += (double)lTmpSize;
lPow2SumSize += pow2Of<double>((double)lTmpSize);
lMaxSize = maxOf(lMaxSize, lTmpSize);
lMinSize = minOf(lMinSize, lTmpSize);
}
float lNrmAverage = (float)(lSumNrm / ioDeme.size());
float lNrmStdError =
(float)(lPow2SumNrm - (pow2Of<double>(lSumNrm) / ioDeme.size())) / (ioDeme.size() - 1);
lNrmStdError = sqrt(lNrmStdError);
float lAdjAverage = (float)(lSumAdj / ioDeme.size());
float lAdjStdError =
(float)(lPow2SumAdj - (pow2Of<double>(lSumAdj) / ioDeme.size())) / (ioDeme.size() - 1);
lAdjStdError = sqrt(lAdjStdError);
float lStdAverage = (float)(lSumStd / ioDeme.size());
float lStdStdError =
(float)(lPow2SumStd - (pow2Of<double>(lSumStd) / ioDeme.size())) / (ioDeme.size() - 1);
lStdStdError = sqrt(lStdStdError);
float lRawAverage = (float)(lSumRaw / ioDeme.size());
float lRawStdError =
(float)(lPow2SumRaw - (pow2Of<double>(lSumRaw) / ioDeme.size())) / (ioDeme.size() - 1);
lRawStdError = sqrt(lRawStdError);
float lHitAverage = (float)(lSumHit / ioDeme.size());
float lHitStdError =
(float)(lPow2SumHit - (pow2Of<double>(lSumHit) / ioDeme.size())) / (ioDeme.size() - 1);
lHitStdError = sqrt(lHitStdError);
float lDepthAverage = (float)(lSumDepth / ioDeme.size());
float lDepthStdError =
(float)(lPow2SumDepth - (pow2Of<double>(lSumDepth) / ioDeme.size())) / (ioDeme.size() - 1);
lDepthStdError = sqrt(lDepthStdError);
float lSizeAverage = (float)(lSumSize / ioDeme.size());
float lSizeStdError =
(float)(lPow2SumSize - (pow2Of<double>(lSumSize) / ioDeme.size())) / (ioDeme.size() - 1);
lSizeStdError = sqrt(lSizeStdError);
outStats.setGenerationValues(std::string("deme")+uint2str(ioContext.getDemeIndex()),
ioContext.getGeneration(), ioDeme.size(), true);
outStats.resize(7);
outStats[0].mID = "normalized";
outStats[0].mAvg = lNrmAverage;
outStats[0].mStd = lNrmStdError;
outStats[0].mMax = lMaxNrm;
outStats[0].mMin = lMinNrm;
outStats[1].mID = "adjusted";
outStats[1].mAvg = lAdjAverage;
outStats[1].mStd = lAdjStdError;
outStats[1].mMax = lMaxAdj;
outStats[1].mMin = lMinAdj;
outStats[2].mID = "standardized";
outStats[2].mAvg = lStdAverage;
outStats[2].mStd = lStdStdError;
outStats[2].mMax = lMaxStd;
outStats[2].mMin = lMinStd;
outStats[3].mID = "raw";
outStats[3].mAvg = lRawAverage;
outStats[3].mStd = lRawStdError;
outStats[3].mMax = lMaxRaw;
outStats[3].mMin = lMinRaw;
outStats[4].mID = "hits";
outStats[4].mAvg = lHitAverage;
outStats[4].mStd = lHitStdError;
outStats[4].mMax = (float)lMaxHit;
outStats[4].mMin = (float)lMinHit;
outStats[5].mID = "treedepth";
outStats[5].mAvg = lDepthAverage;
outStats[5].mStd = lDepthStdError;
outStats[5].mMax = (float)lMaxDepth;
outStats[5].mMin = (float)lMinDepth;
outStats[6].mID = "treesize";
outStats[6].mAvg = lSizeAverage;
outStats[6].mStd = lSizeStdError;
outStats[6].mMax = (float)lMaxSize;
outStats[6].mMin = (float)lMinSize;
Beagle_StackTraceEndM("void GP::StatsCalcFitnessKozaOp::calculateStatsDeme(Beagle::Stats& outStats, Beagle::Deme& ioDeme, Beagle::Context& ioContext) const");
}
/*!
* \brief Calculate MCC statistics of a given deme.
* \param outStats Evaluated statistics.
* \param ioDeme Deme to evalute.
* \param ioContext Context of the evolution.
*
* The following statistics are calculated and made available in outStats:
* + mcc (as returned by FitnessMCC::getValue)
* + true-positives
* + false-positives
* + true-negatives
* + false-negatives
* + true-positives-relative
* + false-positives-relative
* + true-negatives-relative
* + false-negatives-relative
* + treedepth
* + treesize
*
*/
void GP::StatsCalcFitnessMCCOp::calculateStatsDeme(Beagle::Stats& outStats,
Beagle::Deme& ioDeme,
Beagle::Context& ioContext) const
{
Beagle_StackTraceBeginM();
outStats.clear();
outStats.clearItems();
outStats.addItem("processed", ioContext.getProcessedDeme());
outStats.addItem("total-processed", ioContext.getTotalProcessedDeme());
//std::cerr << ioDeme.size() << std::endl;
if(ioDeme.size() == 0) {
/*
* The deme does not contain any individuals.
* Set all statistics to 0.0.
*/
outStats.setGenerationValues(std::string("deme")+uint2str(ioContext.getDemeIndex()),
ioContext.getGeneration(), 0, true);
outStats.resize(11);
outStats[0].mID = "mcc";
outStats[0].mAvg = 0.0;
outStats[0].mStd = 0.0;
outStats[0].mMax = 0.0;
outStats[0].mMin = 0.0;
outStats[1].mID = "true-positives";
outStats[1].mAvg = 0.0;
outStats[1].mStd = 0.0;
outStats[1].mMax = 0.0;
outStats[1].mMin = 0.0;
outStats[2].mID = "false-positives";
outStats[2].mAvg = 0.0;
outStats[2].mStd = 0.0;
outStats[2].mMax = 0.0;
outStats[2].mMin = 0.0;
outStats[3].mID = "true-negatives";
outStats[3].mAvg = 0.0;
outStats[3].mStd = 0.0;
outStats[3].mMax = 0.0;
outStats[3].mMin = 0.0;
outStats[4].mID = "false-negatives";
outStats[4].mAvg = 0.0;
outStats[4].mStd = 0.0;
outStats[4].mMax = 0.0;
outStats[4].mMin = 0.0;
outStats[5].mID = "true-positives-relative";
outStats[5].mAvg = 0.0;
outStats[5].mStd = 0.0;
outStats[5].mMax = 0.0;
outStats[5].mMin = 0.0;
outStats[6].mID = "false-positives-relative";
outStats[6].mAvg = 0.0;
outStats[6].mStd = 0.0;
outStats[6].mMax = 0.0;
outStats[6].mMin = 0.0;
outStats[7].mID = "true-negatives-relative";
outStats[7].mAvg = 0.0;
outStats[7].mStd = 0.0;
outStats[7].mMax = 0.0;
outStats[7].mMin = 0.0;
outStats[8].mID = "false-negatives-relative";
outStats[8].mAvg = 0.0;
outStats[8].mStd = 0.0;
outStats[8].mMax = 0.0;
outStats[8].mMin = 0.0;
outStats[9].mID = "treedepth";
outStats[9].mAvg = 0.0;
outStats[9].mStd = 0.0;
outStats[9].mMax = 0.0;
outStats[9].mMin = 0.0;
outStats[10].mID = "treesize";
outStats[10].mAvg = 0.0;
outStats[10].mStd = 0.0;
outStats[10].mMax = 0.0;
outStats[10].mMin = 0.0;
return;
}
const GP::Individual::Handle lFirstIndiv= castHandleT<GP::Individual>(ioDeme[0]);
const GP::FitnessMCC::Handle lFirstIndivFitness= castHandleT<GP::FitnessMCC>(lFirstIndiv->getFitness());
if(ioDeme.size() == 1) {
/*
* The deme contains a single individual.
* No need to compute average values.
*/
outStats.setGenerationValues(std::string("deme")+uint2str(ioContext.getDemeIndex()),
ioContext.getGeneration(), 1, true);
std::cerr << lFirstIndivFitness->getTruePositives() << "/" << lFirstIndivFitness->getFalsePositives() << "/";
std::cerr << lFirstIndivFitness->getFalseNegatives() << "/" << lFirstIndivFitness->getTrueNegatives() << std::endl;
std::cerr << lFirstIndivFitness->getTruePositivesRel() << "/" << lFirstIndivFitness->getFalsePositivesRel() << "/";
std::cerr << lFirstIndivFitness->getFalseNegativesRel() << "/" << lFirstIndivFitness->getTrueNegativesRel() << std::endl;
std::cerr << std::endl;
outStats.resize(11);
outStats[0].mID = "mcc";
outStats[0].mAvg = lFirstIndivFitness->getValue();
outStats[0].mStd = 0.0;
outStats[0].mMax = lFirstIndivFitness->getValue();
outStats[0].mMin = lFirstIndivFitness->getValue();
outStats[1].mID = "true-positives";
outStats[1].mAvg = lFirstIndivFitness->getTruePositives();
outStats[1].mStd = 0.0;
outStats[1].mMax = lFirstIndivFitness->getTruePositives();
outStats[1].mMin = lFirstIndivFitness->getTruePositives();
outStats[2].mID = "false-positives";
outStats[2].mAvg = lFirstIndivFitness->getFalsePositives();
outStats[2].mStd = 0.0;
outStats[2].mMax = lFirstIndivFitness->getFalsePositives();
outStats[2].mMin = lFirstIndivFitness->getFalsePositives();
outStats[3].mID = "true-negatives";
outStats[3].mAvg = lFirstIndivFitness->getTrueNegatives();
outStats[3].mStd = 0.0;
outStats[3].mMax = lFirstIndivFitness->getTrueNegatives();
outStats[3].mMin = lFirstIndivFitness->getTrueNegatives();
outStats[4].mID = "false-negatives";
outStats[4].mAvg = lFirstIndivFitness->getFalseNegatives();
outStats[4].mStd = 0.0;
outStats[4].mMax = lFirstIndivFitness->getFalseNegatives();
outStats[4].mMin = lFirstIndivFitness->getFalseNegatives();
outStats[5].mID = "true-positives-relative";
outStats[5].mAvg = lFirstIndivFitness->getTruePositivesRel();
outStats[5].mStd = 0.0;
outStats[5].mMax = lFirstIndivFitness->getTruePositivesRel();
outStats[5].mMin = lFirstIndivFitness->getTruePositivesRel();
outStats[6].mID = "false-positives-relative";
outStats[6].mAvg = lFirstIndivFitness->getFalsePositivesRel();
outStats[6].mStd = 0.0;
outStats[6].mMax = lFirstIndivFitness->getFalsePositivesRel();
outStats[6].mMin = lFirstIndivFitness->getFalsePositivesRel();
outStats[7].mID = "true-negatives-relative";
outStats[7].mAvg = lFirstIndivFitness->getTrueNegativesRel();
outStats[7].mStd = 0.0;
outStats[7].mMax = lFirstIndivFitness->getTrueNegativesRel();
outStats[7].mMin = lFirstIndivFitness->getTrueNegativesRel();
outStats[8].mID = "false-negatives-relative";
outStats[8].mAvg = lFirstIndivFitness->getFalseNegativesRel();
outStats[8].mStd = 0.0;
outStats[8].mMax = lFirstIndivFitness->getFalseNegativesRel();
outStats[8].mMin = lFirstIndivFitness->getFalseNegativesRel();
outStats[9].mID = "treedepth";
outStats[9].mAvg = lFirstIndiv->getMaxTreeDepth();
outStats[9].mStd = 0.0;
outStats[9].mMax = outStats[5].mAvg;
outStats[9].mMin = outStats[5].mAvg;
outStats[10].mID = "treesize";
outStats[10].mAvg = lFirstIndiv->getTotalNodes();
outStats[10].mStd = 0.0;
outStats[10].mMax = outStats[6].mAvg;
outStats[10].mMin = outStats[6].mAvg;
return;
}
/*
* Compute AVG, STD, MIN, and MAX over all individuals in the deme
* for each of the 11 measures (mcc, tp, fp, tn, fn, tpr, fpr,
* tnr, fnr, treesize, treedepth).
*/
// MCC
double lMCCSum= (double)lFirstIndivFitness->getValue();
double lMCCPow2Sum= pow2Of<double>(lMCCSum);
double lMCCMax= lMCCSum;
double lMCCMin= lMCCSum;
// True positives
double lTPSum= (double)lFirstIndivFitness->getTruePositives();
double lTPPow2Sum= pow2Of<double>(lTPSum);
double lTPMax= lTPSum;
double lTPMin= lTPSum;
// False positives
double lFPSum= (double)lFirstIndivFitness->getFalsePositives();
double lFPPow2Sum= pow2Of<double>(lFPSum);
double lFPMax= lFPSum;
double lFPMin= lFPSum;
// True negatives
double lTNSum= (double)lFirstIndivFitness->getTrueNegatives();
double lTNPow2Sum= pow2Of<double>(lTNSum);
double lTNMax= lTNSum;
double lTNMin= lTNSum;
// False negatives
double lFNSum= (double)lFirstIndivFitness->getFalseNegatives();
double lFNPow2Sum= pow2Of<double>(lFNSum);
double lFNMax= lFNSum;
double lFNMin= lFNSum;
// True positives, relative
double lTPRSum= (double)lFirstIndivFitness->getTruePositivesRel();
double lTPRPow2Sum= pow2Of<double>(lTPRSum);
double lTPRMax= lTPRSum;
double lTPRMin= lTPRSum;
// False positives, relative
double lFPRSum= (double)lFirstIndivFitness->getFalsePositivesRel();
double lFPRPow2Sum= pow2Of<double>(lFPRSum);
double lFPRMax= lFPRSum;
double lFPRMin= lFPRSum;
// True negatives, relative
double lTNRSum= (double)lFirstIndivFitness->getTrueNegativesRel();
double lTNRPow2Sum= pow2Of<double>(lTNRSum);
double lTNRMax= lTNRSum;
double lTNRMin= lTNRSum;
// False negatives, relative
double lFNRSum= (double)lFirstIndivFitness->getFalseNegativesRel();
double lFNRPow2Sum= pow2Of<double>(lFNRSum);
double lFNRMax= lFNRSum;
double lFNRMin= lFNRSum;
// Tree depth
double lDepthMax= lFirstIndiv->getMaxTreeDepth();
double lDepthMin= lDepthMax;
double lDepthSum= (double)lDepthMax;
double lDepthPow2Sum= pow2Of<double>(lDepthSum);
// Tree size
double lSizeMax= lFirstIndiv->getTotalNodes();
double lSizeMin= lSizeMax;
double lSizeSum= (double)lSizeMax;
double lSizePow2Sum= pow2Of<double>(lSizeSum);
// Cummulate values.
std::cerr << ioDeme.size() << " individuals in deme." << std::endl;
for(unsigned int i=1; i<ioDeme.size(); i++)
{
const GP::Individual::Handle lIndiv= castHandleT<GP::Individual>(ioDeme[i]);
const GP::FitnessMCC::Handle lIndivFitness= castHandleT<GP::FitnessMCC>(lIndiv->getFitness());
double lValue= 0.0;
//if (lIndivFitness->getTruePositives()+ lIndivFitness->getFalsePositives()+ lIndivFitness->getFalseNegatives()+ lIndivFitness->getTrueNegativesRel() == 0)
//{
//std::cerr << i << " / " << ioDeme.size() << ", ";
//std::cerr << lIndivFitness->getTruePositives() << "/" << lIndivFitness->getFalsePositives() << "/";
//std::cerr << lIndivFitness->getFalseNegatives() << "/" << lIndivFitness->getTrueNegatives() << std::endl;
//std::cerr << lIndivFitness->getTruePositivesRel() << "/" << lIndivFitness->getFalsePositivesRel() << "/";
//std::cerr << lIndivFitness->getFalseNegativesRel() << "/" << lIndivFitness->getTrueNegativesRel() << std::endl;
//std::cerr << std::endl;
//}
// MCC
lValue= lIndivFitness->getValue();
lMCCSum+= lValue;
lMCCPow2Sum+= pow2Of<double>(lValue);
lMCCMax= maxOf(lMCCMax, lValue);
lMCCMin= minOf(lMCCMin, lValue);
// True positives
lValue= lIndivFitness->getTruePositives();
lTPSum+= lValue;
lTPPow2Sum+= pow2Of<double>(lValue);
lTPMax= maxOf(lTPMax, lValue);
lTPMin= minOf(lTPMin, lValue);
// False positives
lValue= lIndivFitness->getFalsePositives();
lFPSum+= lValue;
lFPPow2Sum+= pow2Of<double>(lValue);
lFPMax= maxOf(lFPMax, lValue);
lFPMin= minOf(lFPMin, lValue);
// True negatives
lValue= lIndivFitness->getTrueNegatives();
lTNSum+= lValue;
lTNPow2Sum+= pow2Of<double>(lValue);
lTNMax= maxOf(lTNMax, lValue);
lTNMin= minOf(lTNMin, lValue);
// False negatives
lValue= lIndivFitness->getFalseNegatives();
lFNSum+= lValue;
lFNPow2Sum+= pow2Of<double>(lValue);
lFNMax= maxOf(lFNMax, lValue);
lFNMin= minOf(lFNMin, lValue);
// True positives, relative
lValue= lIndivFitness->getTruePositivesRel();
lTPRSum+= lValue;
lTPRPow2Sum+= pow2Of<double>(lValue);
lTPRMax= maxOf(lTPRMax, lValue);
lTPRMin= minOf(lTPRMin, lValue);
// False positives, relative
lValue= (double)lIndivFitness->getFalsePositivesRel();
lFPRSum+= lValue;
lFPRPow2Sum+= pow2Of<double>(lValue);
lFPRMax= maxOf(lFPRMax, lValue);
lFPRMin= minOf(lFPRMin, lValue);
// True negatives, relative
lValue+= (double)lIndivFitness->getTrueNegativesRel();
lTNRSum+= lValue;
lTNRPow2Sum+= pow2Of<double>(lValue);
lTNRMax= maxOf(lTNRMax, lValue);
lTNRMin= minOf(lTNRMin, lValue);
// False negatives, relative
lValue+= (double)lIndivFitness->getFalseNegativesRel();
lFNRSum+= lValue;
lFNRPow2Sum+= pow2Of<double>(lValue);
lFNRMax= maxOf(lFNRMax, lValue);
lFNRMin= minOf(lFNRMin, lValue);
// Tree depth
lValue= lIndiv->getMaxTreeDepth();
lDepthSum+= lValue;
lDepthPow2Sum+= pow2Of<double>(lValue);
lDepthMax= maxOf(lDepthMax, lValue);
lDepthMin= minOf(lDepthMin, lValue);
// Tree size
lValue= lIndiv->getTotalNodes();
lSizeSum+= lValue;
lSizePow2Sum+= pow2Of<double>(lValue);
lSizeMax= maxOf(lSizeMax, lValue);
lSizeMin= minOf(lSizeMin, lValue);
}
// Compute average and standard error.
double lDemeSize= ioDeme.size();
// MCC
float lMCCAverage= lMCCSum/ lDemeSize;
float lMCCStdError= sqrt((lMCCPow2Sum- (pow2Of<double>(lMCCSum)/ lDemeSize))/ (lDemeSize- 1));
// True positives
float lTPAverage= lTPSum/ lDemeSize;
float lTPStdError= sqrt((lTPPow2Sum- (pow2Of<double>(lTPSum)/ lDemeSize))/ (lDemeSize- 1));
// False positives
float lFPAverage= lFPSum/ lDemeSize;
float lFPStdError= sqrt((lFPPow2Sum- (pow2Of<double>(lFPSum)/ lDemeSize))/ (lDemeSize- 1));
// True negatives
float lTNAverage= lTNSum/ lDemeSize;
float lTNStdError= sqrt((lTNPow2Sum- (pow2Of<double>(lTNSum)/ lDemeSize))/ (lDemeSize- 1));
// False negatives
float lFNAverage= lFNSum/ lDemeSize;
float lFNStdError= sqrt((lFNPow2Sum- (pow2Of<double>(lFNSum)/ lDemeSize))/ (lDemeSize- 1));
// True positives, relative
float lTPRAverage= lTPRSum/ lDemeSize;
float lTPRStdError= sqrt((lTPRPow2Sum- (pow2Of<double>(lTPRSum)/ lDemeSize))/ (lDemeSize- 1));
// False positives, relative
float lFPRAverage= lFPRSum/ lDemeSize;
float lFPRStdError= sqrt((lFPRPow2Sum- (pow2Of<double>(lFPRSum)/ lDemeSize))/ (lDemeSize- 1));
// True negatives, relative
float lTNRAverage= lTNRSum/ lDemeSize;
float lTNRStdError= sqrt((lTNRPow2Sum- (pow2Of<double>(lTNRSum)/ lDemeSize))/ (lDemeSize- 1));
// False negatives, relative
float lFNRAverage= lFNRSum/ lDemeSize;
float lFNRStdError= sqrt((lFNRPow2Sum- (pow2Of<double>(lFNRSum)/ lDemeSize))/ (lDemeSize- 1));
// Tree size
float lSizeAverage= lSizeSum/ lDemeSize;
float lSizeStdError= sqrt((lSizePow2Sum- (pow2Of<double>(lSizeSum)/ lDemeSize))/ (lDemeSize- 1));
// Tree depth
float lDepthAverage= lDepthSum/ lDemeSize;
float lDepthStdError= sqrt((lDepthPow2Sum- (pow2Of<double>(lDepthSum)/ lDemeSize))/ (lDemeSize- 1));
outStats.setGenerationValues(std::string("deme")+uint2str(ioContext.getDemeIndex()),
ioContext.getGeneration(), ioDeme.size(), true);
outStats.resize(11);
outStats[0].mID = "mcc";
outStats[0].mAvg = lMCCAverage;
outStats[0].mStd = lMCCStdError;
outStats[0].mMax = lMCCMax;
outStats[0].mMin = lMCCMin;
outStats[1].mID = "true-positives";
outStats[1].mAvg = lTPAverage;
outStats[1].mStd = lTPStdError;
outStats[1].mMax = (unsigned int)lTPMax;
outStats[1].mMin = (unsigned int)lTPMin;
outStats[2].mID = "false-positives";
outStats[2].mAvg = lFPAverage;
outStats[2].mStd = lFPStdError;
outStats[2].mMax = (unsigned int)lFPMax;
outStats[2].mMin = (unsigned int)lFPMin;
outStats[3].mID = "true-negatives";
outStats[3].mAvg = lTNAverage;
outStats[3].mStd = lTNStdError;
outStats[3].mMax = (unsigned int)lTNMax;
outStats[3].mMin = (unsigned int)lTNMin;
outStats[4].mID = "false-negatives";
outStats[4].mAvg = lFNAverage;
outStats[4].mStd = lFNStdError;
outStats[4].mMax = (unsigned int)lFNMax;
outStats[4].mMin = (unsigned int)lFNMin;
outStats[5].mID = "true-positives-relative";
outStats[5].mAvg = lTPRAverage;
outStats[5].mStd = lTPRStdError;
outStats[5].mMax = lTPRMax;
outStats[5].mMin = lTPRMin;
outStats[6].mID = "false-positives-relative";
outStats[6].mAvg = lFPRAverage;
outStats[6].mStd = lFPRStdError;
outStats[6].mMax = lFPRMax;
outStats[6].mMin = lFPRMin;
outStats[7].mID = "true-negatives-relative";
outStats[7].mAvg = lTNRAverage;
outStats[7].mStd = lTNRStdError;
outStats[7].mMax = lTNRMax;
outStats[7].mMin = lTNRMin;
outStats[8].mID = "false-negatives-relative";
outStats[8].mAvg = lFNRAverage;
outStats[8].mStd = lFNRStdError;
outStats[8].mMax = lFNRMax;
outStats[8].mMin = lFNRMin;
outStats[9].mID = "treedepth";
outStats[9].mAvg = lDepthAverage;
outStats[9].mStd = lDepthStdError;
outStats[9].mMax = (unsigned int)lDepthMax;
outStats[9].mMin = (unsigned int)lDepthMin;
outStats[10].mID = "treesize";
outStats[10].mAvg = lSizeAverage;
outStats[10].mStd = lSizeStdError;
outStats[10].mMax =(unsigned int) lSizeMax;
outStats[10].mMin =(unsigned int) lSizeMin;
Beagle_StackTraceEndM("void GP::StatsCalcFitnessMCCOp::calculateStatsDeme(Beagle::Stats& outStats, Beagle::Deme& ioDeme, Beagle::Context& ioContext) const");
}
/*!
* \brief Evaluate the fitness of the given individual.
* \param inIndividual Current individual to evaluate.
* \param ioContext Evolutionary context.
* \return Handle to the fitness value of the individual.
*/
Beagle::Fitness::Handle AnalogFilterParameterEvalOp::evaluate(Beagle::Individual& inIndividual, Beagle::Context& ioContext) {
Beagle_AssertM(inIndividual.size() == 1);
Beagle_LogVerboseM(
ioContext.getSystem().getLogger(),
"evaluation", "AnalogFilterParameterEvalOp",
std::string("Evaluating individual: ")+
inIndividual.serialize()
);
BGFitness *lFitness = new BGFitness(0);
GrowingBondGraph::Handle lBondGraph;
GA::FloatVector::Handle lParametersVector = castHandleT<GA::FloatVector>(inIndividual[0]);
BGContext& lBGContext = castObjectT<BGContext&>(ioContext);
SpeciesGA& lSpecies = castObjectT<SpeciesGA&>(ioContext.getDeme());
RepresentantGP::Handle lIndividual = castHandleT<RepresentantGP>(lSpecies.getRepresentant());
TreeSTag::Handle lTree = castHandleT<TreeSTag>((*lIndividual)[0]);
// lTree->assignNewParameterVector(lParametersVector, lBGContext);
lBondGraph = lTree->getBondGraph();
lBondGraph->assignParameters(*lParametersVector);
lIndividual->getFitness()->setInvalid();
Beagle_LogDebugM(
ioContext.getSystem().getLogger(),
"evaluation", "AnalogFilterParameterEvalOp",
std::string("Individual after parameter assignment: ")+
lIndividual->serialize()
);
try {
vector<double> lInitial;
//Run the individual to create the bond graph.
// RootReturn lResult;
//
// lBGContext.setIndividualHandle(lIndividual);
// lIndividual->run(lResult, lBGContext);
// lBondGraph = lBGContext.getBondGraph();
// lTree->setBondGraph(lBondGraph);
//
// //Set output bond
// Bond* lOutputBond = lBondGraph->getComponents()[lResult.getValue()-1]->getPorts()[0]->getBond();
// lBondGraph->setOutputBonds(lOutputBond, 0);
Beagle_LogDebugM(
ioContext.getSystem().getLogger(),
"evaluation", "AnalogFilterParameterEvalOp",
std::string("Evaluating bondgrap: ")+
lBondGraph->BondGraph::serialize()
);
//lBondGraph->simplify();
//Get state equations
lBondGraph->assignCausality();
lBondGraph->computeStateEquation();
PACC::Matrix lA,lB,lB2,lC,lD,lD2;
lBondGraph->getStateMatrix(lA,lB,lB2);
lBondGraph->getOutputMatrix(lC,lD,lD2);
lFitness->addStateMatrix(lA);
lFitness->addStateMatrix(lB);
lFitness->addStateMatrix(lC);
lFitness->addStateMatrix(lD);
//Check to see if the system is LTI
if(lBondGraph->hasDeferentialCausality()) {
//lFitness->setValue(ioContext.getSystem().getRandomizer().getFloat());
lFitness->setValue(0);
//delete lBondGraph;
return lFitness;
} else {
#ifndef DEBUG_NOMATLAB
//Evalute the response in Matlab.
// Cast the state matrix as input data
// The mwArray::SetData is copying in column major order and the PACC::Matrix is a row major order
// Therefore, the matrix need to be transposed.
PACC::Matrix lAt,lBt,lCt,lDt;
if(!lA.empty())
lAt = lA.transpose();
if(!lB.empty())
lBt = lB.transpose();
if(!lC.empty())
lCt = lC.transpose();
if(!lD.empty())
lDt = lD.transpose();
double *lValueA = new double[lAt.size()];
std::copy(lAt.begin(), lAt.end(), lValueA);
mwArray lArrayA(lAt.getCols(),lAt.getRows(), mxDOUBLE_CLASS, mxREAL);
lArrayA.SetData(lValueA,lAt.size());
double *lValueB = new double[lBt.size()];
std::copy(lBt.begin(), lBt.end(), lValueB);
mwArray lArrayB(lBt.getCols(),lBt.getRows(), mxDOUBLE_CLASS, mxREAL);
lArrayB.SetData(lValueB,lBt.size());
double *lValueC = new double[lCt.size()];
std::copy(lCt.begin(), lCt.end(), lValueC);
mwArray lArrayC(lCt.getCols(),lCt.getRows(), mxDOUBLE_CLASS, mxREAL);
lArrayC.SetData(lValueC,lCt.size());
double *lValueD = new double[lDt.size()];
std::copy(lDt.begin(), lDt.end(), lValueD);
mwArray lArrayD(lDt.getCols(),lDt.getRows(), mxDOUBLE_CLASS, mxREAL);
lArrayD.SetData(lValueD,lDt.size());
// Create output array
mwArray loutArray;
// Call the library function
AnalogFilterEval(1, loutArray, lArrayA, lArrayB, lArrayC,lArrayD);
// Extract the output
int lNbOutput = loutArray.NumberOfElements();
double* loutValues = new double[lNbOutput];
loutArray.GetData(loutValues, lNbOutput);
// Bundle the fitness
lFitness->setValue(loutValues[0]);
delete [] lValueA;
delete [] lValueB;
delete [] lValueC;
delete [] lValueD;
delete [] loutValues;
#else
lFitness->setValue(ioContext.getSystem().getRandomizer().getFloat());
#endif
}
}
catch (const mwException& inException) {
std::cerr << inException.what() << std::endl;
PACC::Matrix lA,lB,lB2,lC,lD,lD2;
lBondGraph->getStateMatrix(lA,lB,lB2);
lBondGraph->getOutputMatrix(lC,lD,lD2);
PACC::XML::Streamer lStream(cerr);
lA.write(lStream);
cerr << endl;
lB.write(lStream);
cerr << endl;
lC.write(lStream);
cerr << endl;
lD.write(lStream);
cerr << endl;
//Save bond graph for debuging
std::ostringstream lFilename;
lFilename << "bug/bondgraph_bug_" << ioContext.getGeneration() << "_" << ioContext.getIndividualIndex();
#ifndef WITHOUT_GRAPHVIZ
lBondGraph->plotGraph(lFilename.str()+std::string(".svg"));
#endif
ofstream lFileStream((lFilename.str()+std::string(".xml")).c_str());
PACC::XML::Streamer lStreamer(lFileStream);
lBondGraph->write(lStreamer);
#ifdef STOP_ON_ERROR
exit(EXIT_FAILURE);
#endif
}
catch(std::runtime_error inError) {
std::cerr << "Error catched while evaluating the bond graph: " << inError.what() << std::endl;
//Save bond graph for debuging
std::ostringstream lFilename;
lFilename << "bug/bondgraph_bug_" << ioContext.getGeneration() << "_" << ioContext.getIndividualIndex();
#ifndef WITHOUT_GRAPHVIZ
lBondGraph->plotGraph(lFilename.str()+std::string(".svg"));
#endif
ofstream lFileStream((lFilename.str()+std::string(".xml")).c_str());
PACC::XML::Streamer lStreamer(lFileStream);
lBondGraph->write(lStreamer);
//Assign null fitness
lFitness->setValue(0);
#ifdef XMLBEAGLE
XMLStreamer lStreamer2(std::cout);
inIndividual.write(lStreamer2);
#else
inIndividual.write(lStreamer);
#endif
#ifdef STOP_ON_ERROR
exit(EXIT_FAILURE);
#endif
}
//delete lBondGraph;
return lFitness;
}