/*!
* \brief Evaluate the individual fitness for the spambase problem.
* \param inIndividual Individual to evaluate.
* \param ioContext Evolutionary context.
* \return Handle to the fitness measure,
*/
Fitness::Handle SpambaseEvalOp::evaluate(GP::Individual& inIndividual, GP::Context& ioContext)
{
// Get reference to data set
DataSetClassification::Handle lDataSet =
castHandleT<DataSetClassification>(ioContext.getSystem().getComponent("DataSet"));
if(lDataSet == NULL) {
throw Beagle_RunTimeExceptionM("Data set is not present in the system, could not proceed further!");
}
// Generate indices used as data subset for fitness evaluation
std::vector<unsigned int> lSubSet(lDataSet->size());
for(unsigned int i=0; i<lSubSet.size(); ++i) lSubSet[i] = i;
std::random_shuffle(lSubSet.begin(), lSubSet.end(),
ioContext.getSystem().getRandomizer());
if(Spambase_TestSize < lSubSet.size()) {
lSubSet.resize(Spambase_TestSize);
}
// Evaluate sampled test cases
unsigned int lCorrectCount = 0;
for(unsigned int i=0; i<Spambase_TestSize; ++i) {
const bool lPositiveID = ((*lDataSet)[lSubSet[i]].first == 1);
const Beagle::Vector& lData = (*lDataSet)[lSubSet[i]].second;
for(unsigned int j=0; j<lData.size(); ++j) {
std::ostringstream lOSS;
lOSS << "IN" << j;
setValue(lOSS.str(), Double(lData[j]), ioContext);
}
Bool lResult;
inIndividual.run(lResult, ioContext);
if(lResult.getWrappedValue() == lPositiveID) ++lCorrectCount;
}
double lFitness = double(lCorrectCount) / Spambase_TestSize;
return new EC::FitnessSimple(lFitness);
}
/*!
* \brief Evaluate the individual fitness for the even-6 parity problem.
* \param inIndividual Individual to evaluate.
* \param ioContext Evolutionary context.
* \return Handle to the fitness measure,
*/
Fitness::Handle ParityEvalOp::evaluate(GP::Individual& inIndividual, GP::Context& ioContext)
{
unsigned int lGood = 0;
for(unsigned int i=0; i<ParitySizeM; i++) {
for(unsigned int j=0; j<ParityFanInM; j++) {
std::string lName = "IN";
lName += uint2str(j);
setValue(lName, mInputs[i][j], ioContext);
}
Bool lResult;
inIndividual.run(lResult, ioContext);
if(lResult.getWrappedValue() == mOutputs[i].getWrappedValue()) lGood++;
}
double lFitness = double(lGood) / ParitySizeM;
return new FitnessSimple(lFitness);
}
/*!
* \brief Evaluate the individual fitness for the symbolic regression problem.
* \param inIndividual Individual to evaluate.
* \param ioContext Evolutionary context.
* \return Handle to the fitness measure,
*/
Fitness::Handle SymbRegEvalOp::evaluate(GP::Individual& inIndividual, GP::Context& ioContext)
{
double lSquareError = 0.;
for(unsigned int i=0; i<mDataSet->size(); i++) {
Beagle_AssertM((*mDataSet)[i].second.size() == 1);
const Double lX((*mDataSet)[i].second[0]);
setValue("X", lX, ioContext);
const Double lY((*mDataSet)[i].first);
Double lResult;
inIndividual.run(lResult, ioContext);
const double lError = lY-lResult;
lSquareError += (lError*lError);
}
const double lMSE = lSquareError / mDataSet->size();
const double lRMSE = sqrt(lMSE);
const double lFitness = 1. / (1. + lRMSE);
return new FitnessSimple(lFitness);
}
/*!
* \brief Evaluate the individual fitness for the boolean 11-multiplexer problem.
* \param inIndividual Individual to evaluate.
* \param ioContext Evolutionary context.
* \return Handle to the fitness measure,
*/
Fitness::Handle MultiplexerEvalOp::evaluate(GP::Individual& inIndividual, GP::Context& ioContext)
{
unsigned int lNbGood = 0;
for(unsigned int i=0; i<2048; ++i) {
setValue("A0", mInputs[i][0], ioContext);
setValue("A1", mInputs[i][1], ioContext);
setValue("A2", mInputs[i][2], ioContext);
setValue("D0", mInputs[i][3], ioContext);
setValue("D1", mInputs[i][4], ioContext);
setValue("D2", mInputs[i][5], ioContext);
setValue("D3", mInputs[i][6], ioContext);
setValue("D4", mInputs[i][7], ioContext);
setValue("D5", mInputs[i][8], ioContext);
setValue("D6", mInputs[i][9], ioContext);
setValue("D7", mInputs[i][10], ioContext);
Bool lResult;
inIndividual.run(lResult, ioContext);
if(lResult.getWrappedValue() == mOutputs[i].getWrappedValue()) ++lNbGood;
}
double lFitness = double(lNbGood) / 2048.;
return new EC::FitnessSimple(lFitness);
}
/*!
* \brief Select a node for mating in the given individual, following the constraints penalties.
* \param outSelectTreeIndex Tree index of the selected node.
* \param outSelectNodeIndex Index of the selected node.
* \param inSelectABranch True if node to select must be a branch, false if it must a leaf.
* \param inNodeReturnType Desired return type for the nodes to be selected.
* \param inPrimitSetIndex Primitive set index to which the tree must be associated.
* \param inMaxSubTreeDepth Maximum sub tree depth allowed of the node to be selected.
* \param inMaxSubTreeSize Maximum sub tree size allowed of the node to be selected.
* \param inIndividual Individual to select the node from.
* \param ioContext Evolutionary context.
* \return True if there was node to select, false if no node respected all constraints.
*/
bool STGP::CrossoverConstrainedOp::selectNodeToMateWithType(unsigned int& outSelectTreeIndex,
unsigned int& outSelectNodeIndex,
bool inSelectABranch,
const std::type_info* inNodeReturnType,
unsigned int inPrimitSetIndex,
unsigned int inMaxSubTreeDepth,
unsigned int inMaxSubTreeSize,
GP::Individual& inIndividual,
GP::Context& ioContext) const
{
Beagle_StackTraceBeginM();
RouletteT< std::pair<unsigned int,unsigned int> > lRoulette;
GP::Tree::Handle lOldTreeHandle = ioContext.getGenotypeHandle();
const unsigned int lOldTreeIndex = ioContext.getGenotypeIndex();
ioContext.emptyCallStack();
for(unsigned int i=0; i<inIndividual.size(); ++i) {
if(inIndividual[i]->getPrimitiveSetIndex() != inPrimitSetIndex) continue;
ioContext.setGenotypeHandle(inIndividual[i]);
ioContext.setGenotypeIndex(i);
buildRouletteWithType(lRoulette,
inSelectABranch,
inNodeReturnType,
inMaxSubTreeDepth,
inMaxSubTreeSize,
0,
*inIndividual[i],
ioContext);
}
ioContext.setGenotypeIndex(lOldTreeIndex);
ioContext.setGenotypeHandle(lOldTreeHandle);
if(lRoulette.size() == 0) return false;
std::pair<unsigned int,unsigned int> lSelectedNode =
lRoulette.select(ioContext.getSystem().getRandomizer());
outSelectTreeIndex = lSelectedNode.first;
outSelectNodeIndex = lSelectedNode.second;
return true;
Beagle_StackTraceEndM();
}
