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; }