LabelledClassificationData LabelledClassificationData::getTrainingFoldData(UINT foldIndex){
LabelledClassificationData trainingData;
trainingData.setNumDimensions( numDimensions );
trainingData.setAllowNullGestureClass( allowNullGestureClass );
if( !crossValidationSetup ){
errorLog << "getTrainingFoldData(UINT foldIndex) - Cross Validation has not been setup! You need to call the spiltDataIntoKFolds(UINT K,bool useStratifiedSampling) function first before calling this function!" << endl;
return trainingData;
}
if( foldIndex >= kFoldValue ) return trainingData;
//Add the data to the training set, this will consist of all the data that is NOT in the foldIndex
UINT index = 0;
for(UINT k=0; k<kFoldValue; k++){
if( k != foldIndex ){
for(UINT i=0; i<crossValidationIndexs[k].size(); i++){
index = crossValidationIndexs[k][i];
trainingData.addSample( data[ index ].getClassLabel(), data[ index ].getSample() );
}
}
}
trainingData.sortClassLabels();
return trainingData;
}
LabelledClassificationData LabelledClassificationData::getBootstrappedDataset(UINT numSamples) const{
Random rand;
LabelledClassificationData newDataset;
newDataset.setNumDimensions( getNumDimensions() );
newDataset.setAllowNullGestureClass( allowNullGestureClass );
if( numSamples == 0 ) numSamples = totalNumSamples;
for(UINT i=0; i<numSamples; i++){
UINT randomIndex = rand.getRandomNumberInt(0, totalNumSamples);
newDataset.addSample(data[randomIndex].getClassLabel(), data[randomIndex].getSample());
}
newDataset.sortClassLabels();
return newDataset;
}
LabelledClassificationData LabelledClassificationData::getTestFoldData(UINT foldIndex){
LabelledClassificationData testData;
testData.setNumDimensions( numDimensions );
testData.setAllowNullGestureClass( allowNullGestureClass );
if( !crossValidationSetup ) return testData;
if( foldIndex >= kFoldValue ) return testData;
//Add the data to the training
UINT index = 0;
for(UINT i=0; i<crossValidationIndexs[ foldIndex ].size(); i++){
index = crossValidationIndexs[ foldIndex ][i];
testData.addSample( data[ index ].getClassLabel(), data[ index ].getSample() );
}
testData.sortClassLabels();
return testData;
}