RegressionData ClassificationData::reformatAsRegressionData() 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!
RegressionData 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;
}
bool MultidimensionalRegression::train_(RegressionData &trainingData){
const unsigned int M = trainingData.getNumSamples();
const unsigned int N = trainingData.getNumInputDimensions();
const unsigned int K = trainingData.getNumTargetDimensions();
trained = false;
trainingResults.clear();
deleteRegressionModules();
if( !getIsRegressionModuleSet() ){
errorLog << "train_(RegressionData &trainingData) - The regression module has not been set!" << std::endl;
return false;
}
if( M == 0 ){
errorLog << "train_(RegressionData &trainingData) - Training data has zero samples!" << std::endl;
return false;
}
numInputDimensions = N;
numOutputDimensions = K;
inputVectorRanges.clear();
targetVectorRanges.clear();
//Scale the training and validation data, if needed
if( useScaling ){
//Find the ranges for the input data
inputVectorRanges = trainingData.getInputRanges();
//Find the ranges for the target data
targetVectorRanges = trainingData.getTargetRanges();
//Scale the training data
trainingData.scale(inputVectorRanges,targetVectorRanges,0.0,1.0);
}
//Setup the regression modules
regressionModules.resize( K, NULL );
//Any scaling will happpen at the meta level, not the regression module letter, so ensure scaling is turned off for the modules
regressifier->enableScaling( false );
for(UINT k=0; k<K; k++){
regressionModules[k] = regressifier->deepCopy();
if( regressionModules[k] == NULL ){
errorLog << "train(LabelledRegressionData &trainingData) - Failed to deep copy module " << k << std::endl;
return false;
}
}
//Train each regression module
for(UINT k=0; k<K; k++){
trainingLog << "Training regression module: " << k << std::endl;
//We need to create a 1 dimensional training dataset for the k'th target dimension
RegressionData data;
data.setInputAndTargetDimensions(N, 1);
for(UINT i=0; i<M; i++){
if( !data.addSample(trainingData[i].getInputVector(), VectorFloat(1,trainingData[i].getTargetVector()[k]) ) ){
errorLog << "train_(RegressionData &trainingData) - Failed to add sample to dataset for regression module " << k << std::endl;
return false;
}
}
if( !regressionModules[k]->train( data ) ){
errorLog << "train_(RegressionData &trainingData) - Failed to train regression module " << k << std::endl;
return false;
}
}
//Flag that the algorithm has been trained
regressionData.resize(K,0);
trained = true;
return trained;
}
