bool RegressionTree::computeBestSpilt( const RegressionData &trainingData, const Vector< UINT > &features, UINT &featureIndex, Float &threshold, Float &minError ){
switch( trainingMode ){
case BEST_ITERATIVE_SPILT:
return computeBestSpiltBestIterativeSpilt( trainingData, features, featureIndex, threshold, minError );
break;
case BEST_RANDOM_SPLIT:
//return computeBestSpiltBestRandomSpilt( trainingData, features, featureIndex, threshold, minError );
break;
default:
Regressifier::errorLog << "Uknown trainingMode!" << std::endl;
return false;
break;
}
return false;
}
bool DecisionTreeNode::computeBestSpilt( const UINT &trainingMode, const UINT &numSplittingSteps,const ClassificationData &trainingData, const vector< UINT > &features, const vector< UINT > &classLabels, UINT &featureIndex, double &minError ){
switch( trainingMode ){
case Tree::BEST_ITERATIVE_SPILT:
return computeBestSpiltBestIterativeSpilt( numSplittingSteps, trainingData, features, classLabels, featureIndex, minError );
break;
case Tree::BEST_RANDOM_SPLIT:
return computeBestSpiltBestRandomSpilt( numSplittingSteps, trainingData, features, classLabels, featureIndex, minError );
break;
default:
errorLog << "computeBestSpilt(...) - Uknown trainingMode!" << endl;
return false;
break;
}
return false;
}
