本文整理汇总了C++中LabelledClassificationData::getClassLabels方法的典型用法代码示例。如果您正苦于以下问题：C++ LabelledClassificationData::getClassLabels方法的具体用法？C++ LabelledClassificationData::getClassLabels怎么用？C++ LabelledClassificationData::getClassLabels使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类LabelledClassificationData的用法示例。
在下文中一共展示了LabelledClassificationData::getClassLabels方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: train
bool RandomForests::train(LabelledClassificationData trainingData){
//Clear any previous model
clear();
const unsigned int M = trainingData.getNumSamples();
const unsigned int N = trainingData.getNumDimensions();
const unsigned int K = trainingData.getNumClasses();
if( M == 0 ){
errorLog << "train(LabelledClassificationData labelledTrainingData) - Training data has zero samples!" << endl;
return false;
}
numInputDimensions = N;
numClasses = K;
classLabels = trainingData.getClassLabels();
ranges = trainingData.getRanges();
//Scale the training data if needed
if( useScaling ){
//Scale the training data between 0 and 1
trainingData.scale(0, 1);
}
//Train the random forest
forestSize = 10;
Random random;
DecisionTree tree;
tree.enableScaling( false ); //We have already scaled the training data so we do not need to scale it again
tree.setTrainingMode( DecisionTree::BEST_RANDOM_SPLIT );
tree.setNumSplittingSteps( numRandomSplits );
tree.setMinNumSamplesPerNode( minNumSamplesPerNode );
tree.setMaxDepth( maxDepth );
for(UINT i=0; i<forestSize; i++){
LabelledClassificationData data = trainingData.getBootstrappedDataset();
if( !tree.train( data ) ){
errorLog << "train(LabelledClassificationData labelledTrainingData) - Failed to train tree at forest index: " << i << endl;
return false;
}
//Deep copy the tree into the forest
forest.push_back( tree.deepCopyTree() );
}
//Flag that the algorithm has been trained
trained = true;
return trained;
}示例2: train
bool BAG::train(LabelledClassificationData trainingData){
const unsigned int M = trainingData.getNumSamples();
const unsigned int N = trainingData.getNumDimensions();
const unsigned int K = trainingData.getNumClasses();
trained = false;
classLabels.clear();
if( M == 0 ){
errorLog << "train(LabelledClassificationData trainingData) - Training data has zero samples!" << endl;
return false;
}
numFeatures = N;
numClasses = K;
classLabels.resize(K);
ranges = trainingData.getRanges();
UINT ensembleSize = (UINT)ensemble.size();
if( ensembleSize == 0 ){
errorLog << "train(LabelledClassificationData trainingData) - The ensemble size is zero! You need to add some classifiers to the ensemble first." << endl;
return false;
}
for(UINT i=0; i<ensembleSize; i++){
if( ensemble[i] == NULL ){
errorLog << "train(LabelledClassificationData trainingData) - The classifier at ensemble index " << i << " has not been set!" << endl;
return false;
}
}
//Train the ensemble
for(UINT i=0; i<ensembleSize; i++){
LabelledClassificationData boostedDataset = trainingData.getBootstrappedDataset();
//Train the classifier with the bootstrapped dataset
if( !ensemble[i]->train( boostedDataset ) ){
errorLog << "train(LabelledClassificationData trainingData) - The classifier at ensemble index " << i << " failed training!" << endl;
return false;
}
}
//Set the class labels
classLabels = trainingData.getClassLabels();
//Flag that the algorithm has been trained
trained = true;
return trained;
}