bool KfoldTimeSeriesData::spiltDataIntoKFolds(const GRT::UINT K) {
kFoldValue = K;
//K can not be zero
if( K == 0 ){
std::cout << "spiltDataIntoKFolds(UINT K) - K can not be zero!" << std::endl;
return false;
}
//K can not be larger than the number of examples
if( K > inputDataset.getNumSamples()){
std::cout << "spiltDataIntoKFolds(UINT K,bool useStratifiedSampling) - K can not be 0!" << std::endl;
return false;
}
//K can not be larger than the number of examples in a specific class if the stratified sampling option is true
for(UINT c=0; c < inputDataset.getNumClasses(); c++) {
if( K > classTracker[c].counter ){
cout << "spiltDataIntoKFolds(UINT K,bool useStratifiedSampling) - K can not be larger than the number of samples in any given class!" << std::endl;
return false;
}
}
//Setup the dataset for k-fold cross validation
kFoldValue = K;
vector< UINT > indexs( inputDataset.getNumSamples() );
//Work out how many samples are in each fold, the last fold might have more samples than the others
UINT numSamplesPerFold = (UINT) floor( inputDataset.getNumSamples() / double(K) );
//Create the random partion indexs
Random random;
UINT randomIndex = 0;
//Break the data into seperate classes
vector< vector< UINT > > classData( inputDataset.getNumClasses() );
//Add the indexs to their respective classes
for(UINT i = 0; i < inputDataset.getNumSamples(); i++) {
classData[ inputDataset.getClassLabelIndexValue(
inputDataset[i].getClassLabel() ) ].push_back( i );
}
//Randomize the order of the indexs in each of the class index buffers
for(UINT c = 0; c < inputDataset.getNumClasses(); c++) {
UINT numSamples = (UINT)classData[c].size();
for(UINT x = 0; x < numSamples; x++) {
//Pick a random index
randomIndex = random.getRandomNumberInt(0, numSamples);
//Swap the indexs
SWAP( classData[c][ x ] , classData[c][ randomIndex ] );
}
}
//Resize the cross validation indexs buffer
crossValidationIndexs.resize( K );
for (UINT k = 0; k < K; k++) {
crossValidationIndexs[k].resize(inputDataset.getNumClasses());
}
//Loop over each of the classes and add the data equally to each of the k folds until there is no data left
vector< UINT >::iterator iter;
for(UINT c = 0; c < inputDataset.getNumClasses(); c++){
iter = classData[ c ].begin();
UINT k = 0;
while( iter != classData[c].end() ){
crossValidationIndexs[ k ][c].push_back( *iter );
iter++;
k = ++k % K;
}
}
crossValidationSetup = true;
return true;
}
