LabelledRegressionData LabelledRegressionData::getTrainingFoldData(const UINT foldIndex) const{
LabelledRegressionData trainingData;
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;
trainingData.setInputAndTargetDimensions(numInputDimensions, numTargetDimensions);
//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 ].getInputVector(), data[ index ].getTargetVector() );
}
}
}
return trainingData;
}
LabelledRegressionData LabelledClassificationData::reformatAsLabelledRegressionData() const{
//Turns the classification into a regression data to enable regression algorithms like the MLP to be used as a classifier
//This sets the number of targets in the regression data equal to the number of classes in the classification data
//The output of each regression training sample will then be all 0's, except for the index matching the classLabel, which will be 1
//For this to work, the labelled classification data cannot have any samples with a classLabel of 0!
LabelledRegressionData regressionData;
if( totalNumSamples == 0 ){
return regressionData;
}
const UINT numInputDimensions = numDimensions;
const UINT numTargetDimensions = getNumClasses();
regressionData.setInputAndTargetDimensions(numInputDimensions, numTargetDimensions);
for(UINT i=0; i<totalNumSamples; i++){
VectorDouble targetVector(numTargetDimensions,0);
//Set the class index in the target vector to 1 and all other values in the target vector to 0
UINT classLabel = data[i].getClassLabel();
if( classLabel > 0 ){
targetVector[ classLabel-1 ] = 1;
}else{
regressionData.clear();
return regressionData;
}
regressionData.addSample(data[i].getSample(),targetVector);
}
return regressionData;
}
LabelledRegressionData LabelledRegressionData::getTestFoldData(const UINT foldIndex) const{
LabelledRegressionData testData;
if( !crossValidationSetup ) return testData;
if( foldIndex >= kFoldValue ) return testData;
//Add the data to the training
testData.setInputAndTargetDimensions(numInputDimensions, numTargetDimensions);
UINT index = 0;
for(UINT i=0; i<crossValidationIndexs[ foldIndex ].size(); i++){
index = crossValidationIndexs[ foldIndex ][i];
testData.addSample( data[ index ].getInputVector(), data[ index ].getTargetVector() );
}
return testData;
}
